RADIO FREQUENCY HEATING FOR DEHYDRATION AND PEST CONTROL OF 
IN-SHELL PEANUTS 
 
 
Except when reference is made to the work of others, the work described in this  
thesis is my own or was done in collaboration with my advisory committee.   
This thesis does not included proprietary or classified information. 
 
 
 
_____________________________ 
Hatem Harraz 
 
 
 
Certificate of Approval: 
 
 
 
________________________   ________________________ 
Christopher B. Roberts        Yifen Wang, Chair   
Professor      Assistant Professor   
Chemical Engineering    Biosystems Engineering 
 
 
 
________________________   ________________________ 
Steven Taylor      Oladiran Fasina 
Professor      Assistant Professor 
Biosystems Engineering    Biosystems Engineering 
 
 
________________________ 
George T. Flowers  
Interim Dean 
Graduate School 
  
RADIO FREQUENCY HEATING FOR DEHYDRATION AND PEST CONTROL OF 
IN-SHELL PEANUTS 
 
 
Hatem Harraz 
 
A Thesis 
Submitted to 
the Graduate Faculty of 
Auburn University 
in Partial Fulfillment of the 
Requirements for the 
Degree of 
Master of Sciences 
 
 
Auburn, Alabama 
May 10, 2007 
iii  
RADIO FREQUENCY HEATING FOR DEHYDRATION AND PEST CONTROL OF 
IN-SHELL PEANUTS 
 
 
 
Hatem Harraz 
 
 
Permission is granted to Auburn University to make copies of this thesis at its 
discretion, upon request of individuals or institutions and at their expense.  
The author reserves all publication rights. 
 
 
 
 
________________________ 
      Signature of Author 
 
 
________________________ 
          Date of Graduation 
iv  
THESIS ABSTRACT 
RADIO FREQUENCY HEATING FOR DEHYDRATION AND PEST CONTROL OF 
IN-SHELL PEANUTS 
Hatem Harraz 
Master of Science, May 10, 2007 
(M.S., Salford University, Salford, England, 1995) 
(B.S., Cairo University, Cairo, Egypt, 1992) 
105 typed pages 
Directed by Yifen Wang 
For many decades conventional wagon drying (curing) systems or 
batch/continuous flow drying systems have been used for peanut dehydration. Both are 
time and energy consuming. Radio Frequency (RF) dielectric heating has been applied 
to many types of food processing including tree nut. In this study, we would like to 
apply RF energy to dehydration and pest control of in-shell peanuts. RF is a fast and 
effective alternative drying method because heat is generated by the interaction between 
RF energy and moisture inside in-shell peanuts. In RF heating applications, the drying 
energy is evolved from inside the products. This will not only save time and energy, but 
will also eliminate or reduce thermal abuse to the products compared to the 
conventional processes. 
Despite the effectiveness of this method, it also presents some difficulties in its 
application.  Primarily, RF heating or dehydration of peanuts process is non-uniform.
v  
Non-uniformity of RF heating of peanuts is due to many circumstances that have 
been discussed and taken into consideration in the RF treatment protocol. In this 
treatment protocol we overcome non-uniformity using multiple numbers of RF heating 
cycles with intermittent stirring and cooling. A set of 6 runs (each run consisting  of a 
RF heating cycle followed by intermittent stirring and cooling cycle) were designed to 
dry peanuts at three different targeting temperatures of 40, 50 and 60 ?C. The quality of 
peanuts after RF treatments at 40 or 50 ?C are acceptable while the quality at 60 ?C is 
not acceptable due to the percentage of broken and shelled peanuts. For the three 
targeting temperatures, the total RF heating time was in the range of 10 to 20 minutes. 
We found that the relation between peanut kernels? temperature and the time of RF 
heating could be fitted in a straight line.  
In the second part of this work we studied the mortality of Red Flour Beetles 
(RFB), which are the most heat resistant insects that live in peanuts.  We used a custom 
built heating block system manufactured by the Washington State University (WSU). 
We found different results from those in the literature on RFB. In our results RFB 
showed resistance to similar time-temperature combinations used before in others? 
research. These results indicate that RFB in the southeastern area of the United States 
are more thermal resistant than those in the west coast. Further research with increased 
heating temperature, increased heating time or both are needed.  In addition, it  is  
necessary  to review  the  nature  of  heat  transfer  differences  between  RF heating and 
conduction heating in the WSU heating block system. 
vi  
ACKNOWLEDGEMENTS 
 
During the preparation of this thesis, many people contributed to this project and 
have given me endless support. I am extremely grateful to each and every one of them. 
Especially I would like to thank: 
 Processor Yifen Wang, I owe my sincerest thanks to him for his endless 
amounts of patience, guidance, and numerous supports. He made my time in this 
research very memorable. I want to thank him for his mentorship and his words of 
encouragement. Professor Christopher Roberts, I owe thanks to him for his assistance 
during my research in chemical engineering and Biosystems engineering departments 
and for his patience and guidance as well. I would also like to thank Professor Steven 
Taylor and Oladiran Fasina in Biosystems engineering for their patience, guidance and 
endless support. 
 I would also like to thank my family and friends for their help and 
encouragement.  Thanks are also due to all of my colleagues at Auburn, especially those 
in chemical engineering department and biosystems engineering department. Special 
thanks for Dr. Tamar Awad and Hecham Hady who helped printing this thesis and with 
all of whom I have had a great pleasure of working.  Finally, I have to sincerely thank 
Auburn University for providing me with an unbelievable education and a phenomenal 
experience. The time I spent here will be cherished for many years to come.  
vii  
Style Manual or Journal used          Journal of Postharvest Biology and Technology 
 
 
 
Computer software used____Microsoft Office Word 2003
viii  
VITA 
 
 
Hatem Harraz, son of Mohamed Azmy Harraz and Hekmat Attia, was born in 
March 27th, 1970 in Cairo, Egypt. In 1992 he graduated with a Bachelor degree in 
chemical engineering from Faculty of Engineering, Cairo University, Cairo, Egypt. He 
finished his first Master?s degree in Chemical Engineering from Chemical and 
Biochemical Engineering Department, Salford University, Salford, England in May 
1995, and then he worked in the Oil & Gas Industry for about eight years in 
multinational companies as a process and senior process engineer. In August, 2002 he 
joined Auburn University and worked in Chemical Engineering Department as a 
Graduate Research Assistant in the area of biomass-to-ethanol. He framed his work 
with his advisor in a US Patent for producing an innovative form of cellulose. In August 
2005 he joined the Biosystems Engineering Department at Auburn University to pursue 
his second master?s degree with research on Radio Frequency applications for 
dehydration and pest control of peanuts under supervision of Dr. Yifen Wang. He 
started his work with Harris Group Inc. as a Senior Process Engineer in Seattle, 
Washington in June 2006. 
ix  
TABLE OF CONTENTS 
 
LIST OF TABLES ..........................................................................................................x 
LIST OF FIGURES.......................................................................................................xii 
RADIO FREQUENCY (RF) HEATING FOR DEHYDRATION AND PEST 
CONTROL OF IN-SHELL PEANUTS ...........................................................................1 
INTRODUCTION ...............................................................................................1 
LITERATURE SURVEY ....................................................................................7 
MATERIALS AND METHODS........................................................................19 
RESULTS AND DISCUSSION.........................................................................25 
THERMAL DEATH KINETICS OF RED FLOUR BEETLE .......................................40 
INTRODUCTION .............................................................................................40 
MATERIALS AND METHODS........................................................................44 
RESULTS AND DISCUSSION.........................................................................47 
CONCLUSIONS...........................................................................................................57 
REFERENCES..............................................................................................................59 
APPENDIXES ..............................................................................................................64 
x  
LIST OF TABLES 
 
Table 1.  Planted and Harvested Acres of Peanuts in Alabama and Production in 2005-
2006 ................................................................................................................................2 
Table 2.  Temperature characteristics of conventional heating methods..........................13 
Table 3. Reported radio frequency and microwave heat treatments for different products 
and insects at various temperatures ................................................................................16 
Table 4.  Average RF dehydration results ......................................................................33 
Table 5. Total RF heating/drying time for 8 experiments at the three RF heating 
temperatures ..................................................................................................................36 
Table 6.  Percentage total evaporated water after the six RF cycles ................................37 
Table 7.  Average percentage water evaporated and times for RF treatment ...................37 
Table 8.  Number of live RFB older larvae after heating at different times.....................47 
Table 9. Percentage of mortality of RFB older larvae at different temperature-time 
combinations       ????????.???................................................................49 
Table 10. Number of live RFB adults after heating at different temperature-time 
combinations .................................................................................................................51 
Table 11. Percentage of mortality of RFB adults at different temperature-time                 
combinations .................................................................................................................52
xi  
Table 12. Average percentage of mortality of RFB older larvae and adults different 
temperature-time combinations......................................................................................54
xii  
LIST OF FIGURES 
 
Figure 1.  Top Five World Producers and Exporters of Peanuts, 2004..............................2 
Figure 2.  Photo of the actual RF heating machine .........................................................20 
Figure 3.  Schematic Diagram of the RF heating System................................................20 
Figure 4.  Locations of the four fiber optic temperature sensors in the plastic container .21 
Figure 5(a).  The RF Heating Container filled with 3.5 Kg peanuts................................23 
Figure 5(b).  The fixed position for upper and lower electrodes and the plastic container23 
Figure 6(a).  The four fiber optic probe temperature readings at 50 ?C RF heating .........25 
Figure 6(b).  RF heating curves at 50 ?C for six consecutive cycles ...............................25 
Figure 7.  Thermal image of typical peanut shells temperature distribution immediately 
after RF heating cycle at 40 ?C ......................................................................................27 
Figure 8.  Thermal image present handful of peanut shells in middle and bottom of the 
plastic container for RF heating cycle at 50 ?C...............................................................28 
Figure 9.  Thermal image of peanut shells temperature distribution at the end of RF 
heating cycle at 60 ?C....................................................................................................29  
Figure 10(a). Statistical analysis results for the circle (Ar1) in Figure 9 .........................30 
Figure 10(b). Statistical analysis results for the line (Li1) in Figure 9.............................30 
Figure 11(a). Decrease in sample weight through the six RF heating cycles ...................32 
Figure 11(b). Sample weight decrease after RF treatment ..............................................32 
Figure 12. Percentage water evaporated after RF 6 treatment cycles and average values 36 
xiii  
Figure 13. Natural decrease in peanut kernels temperature immediately after the end of 
a RF heating cycle at 50 ?C............................................................................................38 
Figure 14. Heating block system....................................................................................44
1  
RADIO FREQUENCY (RF) HEATING FOR DEHYDRATION AND PEST 
CONTROL OF IN-SHELL PEANUTS 
 
 
INTRODUCTION  
 
The United States is the largest producer of nuts in the world, representing 
approximately two-thirds of the world nut trade. Figure 1. shows the top five world 
producers and exporters of peanuts in 2004. California is the leading state in nut 
production in the US with the major crops of almonds, walnuts and pecans. Georgia 
peanut production accounted for 40% of the U.S. crop in 2002. Alabama comes the 
second in planting and harvesting peanuts after Georgia, which commonly ranks first in 
the United States in peanut acreage planted and acreage harvested. Planted acreage from 
2000-2002 ranged from 494,000 (2000) to 515,000 (2001) acres. Acreage harvested 
ranged from 492,000 (2000) to 515,000 (2001) acres. More information concerning 
yield/acreage/price/etc. is available at these web sites, (USDA, National Agricultural 
Statistics Service, 2007). Table 1 represents the acres planted and harvested in the last 
three years in Alabama as well as the production pounds per one harvested acre and the 
total peanuts produced. 
2  
 
Figure 1. Top Five World Producers and Exporters of Peanuts, 2004 
 
Table 1. Planted and Harvested Acres of Peanuts in Alabama and Production in 2005-
2006. (USDA, National Agricultural Statistics Service, last update February 20, 2007) 
  2004 2005 2006 
Planted Acres 200,000 225,000 165,000 
Harvested Acres 199,000 223,000 163,000 
Yield per Acre, lbs 2,800 2,750 2,500 
Production, lbs 557,200,000 613,250,000 407,500,000 
 
  In 2005 the total planted acres of peanuts were 1,657,000. Of these planted 
acres, 98.3% have been harvested with an average 2,989 lb/acres. The average selling 
price was $0.174/lb with total revenue of $845,873,000. In 2006 the total planted acres 
were 1,243,000 and 1,209,000 harvested (97.3%) with an average 2,874 lb/acres 
3  
harvested. This means the total produced peanuts in the United States last year was 
3,474,450 thousands pounds. 
 Every 100 grams of fresh or roasted peanuts contain 24 grams protein, 50 
grams fat, and 9 grams carbohydrates. The energy to be obtained from 100 grams of 
peanuts is 570 Kcal.   
Americans consume large quantities of peanuts.  The peanut is not actually a nut 
at all, but rather a legume.  It is not clearly associated with some of the heart protective 
benefits of true nuts, but is considered to be a relatively inexpensive form of high 
quality protein.  
In the past ten years, peanut harvest capacity in the US has increased 
dramatically with no appreciable change in commercial drying (curing) methods or 
equipment (Sanders, et. al., 2001).  The conventional wagon drying method is still the 
predominant method of drying. This method is based on loading peanuts during harvest 
into perforated wagons for transport and curing (or drying).  Drying occurs when hot air 
is forced through the perforated flax of the wagon. The disadvantages of this method are 
high energy consumption, long drying times (typically more than one day) and 
problems relating to non-uniformity of airflow and variable temperature distribution 
over the peanut load.  
 In the continuous airflow dryer system, the problems of non-uniformity and 
variable distribution in temperature are improved but the process still consumes a 
tremendous amount of heat and time. 
4  
An example of the conventional wagon drying method can be found in the 
southeastern U.S. Peanuts are usually allowed to partially cure in the windrow for three 
to seven days; they are then harvested, and placed in drying wagons with a perforated 
plenum floor.  Each four to six loads are mechanically cured by forcing heated air up 
through a 1.5 m deep bed of peanuts until the moisture content of the peanut kernel is 
less than 0.11 kg kg?1.  
Young et al. (1982) developed optimum plenum conditions for curing peanuts. 
Temperatures in excess of 35 ?C were shown to increase the risk of the development of 
off-flavors (Whitaker and Dickens, 1964) and removing moisture too rapidly increased 
the incidence of split kernels and loose skins (Beasley and Dickens, 1963; Troeger, 
1989). The current recommendation consists of heating air 8?11 ?C above ambient so as 
not to exceed 35 ?C (Samples, 1984; Cundiff et al., 1991). 
There are two additional problems with the use of hot air in peanut treatment: 
the low thermal conductivity of the peanut shell and the voids within the shell. These 
interfere with the transfer of thermal energy from the hot air outside to the inside of the 
shell where the kernel exists. Peanut kernels are meant to be dried, and by using the 
conventional method most of the energy will be lost before it transfers from outside the 
shell to the kernel. 
Steele (1982) developed and implemented a microprocessor temperature 
controlled system for curing peanuts in Virginia.  This process increased peanut drying 
time 10% while reducing consumption of liquefied petroleum gas (LPG) and electricity 
by 49% and 33%, respectively, compared to conventional dryer controls. However, the 
5  
complexity of the hardware and software prevented commercial adaptation of this 
technology.  
Baker et al. (1993) used regression analysis to fit three separate line segments to 
the upper limit of the preferred curing zone specified by Young et al. (1982). Using the 
drying rate control (DRC) in Virginia, reduced percent skin slippage approximately 
30% compared with similar curing times and fuel costs.  
Butts (1996) manually controlled plenum temperatures, to maintain a plenum 
relative humidity between 40 and 60%. Drying time increased 56% and energy 
consumption decreased 30% compared to conventional constant set point controls. 
However, labor availability for commercial peanut drying facilities prohibits manual 
manipulation of individual thermostat settings.  
Butts, C. L., et al. (1998) reduced the DRC used by Baker et al. (1993) to a 
single equation and used a microprocessor to control a commercial peanut dryer. Under 
typical weather conditions experienced in South Georgia during the peanut harvest, the 
plenum temperature remained fairly constant (? 2 ?C) throughout the 24-h drying cycle. 
The purpose of this research was to determine the optimum peanut curing temperature 
control algorithm in order to minimize drying time and minimize detrimental effects on 
resulting peanut quality. 
 There is considerable interest in the use of other alternatives to replace the 
conventional drying/curing system. One of these alternatives is the radio frequency (RF) 
energy as we will research and discuss that later in details.  The RF method can be used 
for both drying and disinfestations of fruits and nuts. 
6  
 The principle upon which RF treatment is based, is that the energy of RF 
interacts directly with a dielectric material, such as a nut, to generate heat by converting 
electromagnetic energy into thermal energy, thereby tremendously reducing the heating 
time (Tang et al., 2000). 
7  
LITERATURE SURVEY 
 
DIFFERENT TREATMENT PROCESSES USED IN INFESTATION OF 
INSECT PESTS IN NUTS 
 
Presently, there are five well known potential quarantine treatment methods 
used to combat infestation of insect pests such as red flour beetle (Tribolium 
castaneum), codling moth (Cydia pomonella), navel orangeworm (Amyelois transitella), 
and Indian meal moth (Plodia interpunctella). These quarantine methods, used for, both 
the domestic and international nut markets, are: 
 
1- Chemical fumigation,  
2- Ionizing radiation, controlled atmosphere, 
3- Conventional hot air or water heating, 
4- Cold treatment,  
5- Dielectric heating using radio frequency (RF) and microwave (MW) energy.  
 
Infestation with insect pests is generally a major problem in producing, storing 
and marketing nuts.  Postharvest control of insects in nuts is essential for quarantine 
regulations in many countries.  Larvae of codling moths are targeted by quarantine 
regulations in Japan and South Korea, and navel orangeworm is of phytosanitary 
concern for Australia (S. Wang  and  J. Tang, 2001).   
8  
The traditional treatment is chemical fumigation with low cost, fast processing 
and easy application.  However, due to concerns about health hazards of chemical 
pesticides and their resulting environmental pollution, other treatment methods have 
been conducted for controlling nut insects.  These include ionizing radiation, controlled 
atmosphere, cold treatment, conventional hot air or water heating and novel radio 
frequency (RF) and microwave (MW) dielectric heating. Currently, the use of chemical 
fumigation remains widespread and the efficient use of RF and MW methods for nut 
insect control is still in research stage. 
Comparison of the properties of these five methods has been reported as a 
review (S. Wang and J. Tang, 2001) where the last one method, dielectric heating using 
RF and MW energy, was proposed as an alternate, innovative quarantine treatment for 
nuts.  Great effort and research are needed to overcome the problems associated with 
this new method, such as the high cost, the non-uniformity of heating and the damage to 
quality.
9  
Chemical Fumigation: 
Alternative control processes need to be developed to replace current treatment 
practices for both the domestic and international markets.  It is a complex task to 
develop an effective thermal treatment that provides required quarantine security and 
simultaneously ensures minimum adverse effect on product quality.  
Chemical fumigation has two distinct advantages for postharvest pest control in 
nuts: ease of use and low cost.   Most postharvest pest management programs, therefore, 
rely heavily on fumigants, and most processing systems are designed to allow for 
fumigant treatments. Methyl bromide (MeBr) fumigation exposure of three hours 
considered short exposure time compared to hydrogen phosphide. MeBr used to control 
codling moths in cherries, nectarines, watermelons and in unshelled walnuts. 
When used in commodity fumigation, MeBr gas is injected into a chamber or 
under a tarp containing the commodities. About 80 to 95% of the MeBr used for a 
typical commodity treatment eventually enters the atmosphere (USEPA, 1998). 
MeBr has been identified by the U.S. Environmental Protection Agency (EPA) 
as having high ozone depletion potential (Anonymous, 1995). The U.S. has signed an 
international accord, known as the Montreal Protocol, to ban the use of MeBr in order 
to protect the Earth?s atmosphere. According to recent report from the congress 
(September 26th, 2007) MeBr is regulated for its potential ozone-depleting effects in 
the Earth?s stratosphere. Controls on production, emissions, and trade are mandated 
internationally under the 1987 Montreal Protocol on Substances that Deplete the Ozone 
Layer (the ?Protocol?) and domestically under Title VI of the U.S. Clean Air Act 
(CAA). 
10  
 However, the Protocol still regulates post-2004 production for critical uses. But 
U.S. agribusinesses have sought Critical Use Exemptions (CUE) from the EPA to treat 
commodities with MeBr after the ban. Production allowances for MeBr for 2005-2007 
were approved under the Protocol and the EPA has approved allocation for registered 
users. Therefore, an alternative quarantine treatment is urgently required to replace this 
chemical fumigation. 
 
Ionizing Radiation  
Irradiation treatment is a process which exposes infested commodities to 
ionizing radiation so as to sterilize, kill, or prevent emergence of insect pests by 
damaging their DNA. This method includes three types of ionizing radiation used on 
foods: gamma rays from radioactive cobalt-60 and cesium-137, high energy electrons, 
and x-rays. The ability of gamma rays to deeply penetrate pallet loads of food makes it 
one of the most commonly used in post-harvest pest control. Some research showed that 
irradiation levels as low as 0.30 kGy were effective in controlling plum curculio, 
blueberry maggot, cherry fruit flies and codling moths, without altering overall fruit 
quality. Because these doses do not cause immediate kill of treated insects, a particular 
concern for radiation treatments is the possibility of inspectors or consumers finding 
live insects in the treated product.  
Another issue with irradiation is the substantial initial investment per site to 
establish the facility, including a radiation shield control system and other auxiliary 
equipment. In order to be economically feasible, the facility must remain in continuous 
operation. However, the seasonal nature of nut and produce harvest prevents efficient 
11  
use of facilities. Consumers also have concerns over the disposal of radioactive wastes, 
the safety of the irradiation technology and its effect on food (Wang and Tang 2001). 
 
Controlled Atmosphere Treatments  
Controlled atmosphere (CA) has been used for many years to extend commodity 
shelf life. CA has been used for the control of insects in stored grain and nut crops, and 
research has demonstrated its efficacy for fresh commodities. In general, O2 
concentrations must be below 1% and CO2 concentrations must be above 20% for insect 
control. For most applications, however, CA treatments require long exposures. For 
example, to disinfest walnuts of navel orange worm two days of purging is required, 
followed by six days exposure to 0.5% O2. This long treatment time may not be 
acceptable for some markets. To be able to certify and ship the quantities needed for the 
vital European market, optimal treatment time is 24 hours. In addition, prolonged 
exposure to low O2 has detrimental effect on some fresh fruits (Wang et. al. 2000). 
 
Cold Treatments   
Chilled aeration has been used as a means to slow the development of insect pest 
populations within stored grains, and given sufficient exposure times, may effectively 
disinfest the product. Cold storage treatments have also been developed for quarantine 
purposes and for use against exotic fruit flies and other insects. Cold treatments may 
take several weeks to be effective, and thus work best when incorporated into existing 
storage or shipping regimes. Cold storage is an important component in existing 
12  
quarantine treatments for codling moth on apple and has been combined with other 
treatments such as hot air and water heating.  
While effective in some situations, the use of cold treatment is limited because 
of the lengthy treatment times required to kill insects, and the high costs associated with 
building and maintaining refrigerated storage. (Wang and Tang 2001). 
 
Conventional Heating  
Conventional heating methods are increasingly being used to provide an 
alternative treatment for chemical fumigation; these include forced hot air and hot water 
treatments. Since the heating mechanism is simple and the process can be easily 
controlled, many studies on various fruit types and insect species have been carried out 
using different thermal treatments alone or in combination with cold or controlled 
storage conditions. The fruit core must reach certain temperatures so that the treatment 
is effective even in the most insulated areas (such as inside nuts or into the center flesh, 
seeds and kernels). Slow heating rates by forced hot air or water results in a long 
treatment time.  
Table 2 shows the heating time required to reach the core temperature, obtained 
by experiments with different medium temperatures, heating methods, and fruit types. 
Forced hot air is usually used to treat nuts because hot water heating results in 
unacceptable moisture content for storage, greater than 6% (Wang and Tang 2001). The 
core temperature is lower than the medium temperature and only reaches the target 
value after a long heating time. The heating duration required for the core to reach the 
medium temperature reported in Table 2 varies from 23 min to 360 min, which is 
13  
mainly dependent on the fruit size (Wang et al., 2001b). Such heating methods are 
limited due to heat convection from the medium to the surface and heat conduction 
from the surface to the fruit core. Increasing the air speed and using small fruits can 
slightly decrease the heating time. 
Table 2. Temperature characteristics of conventional heating methods (Wang and Tang 
2001). 
 
Medium Heating Fruit Speed, Core Time Sources
temp., methods types ms-1 temp., required
?C ?C min
40 Hot air Apple 1 40 360 Whiting et al. (1999)
44 Moist air Apple 2 42 97 Neven et al. (1996)
45 Hot air Tangerine 2 44 60 Shellie & Mangan (1996)
45 Hot air Cherry 2 44 23 Neven & Mitcham (1996)
48 Hot water Small Potato 2 48 140 Hansen (1992)
48 Hot water Large Potato 2 48 220 Hansen (1992)
48 Hot water Grapefruit 2 48 155 Hansen (1992)
50 Hot air Mango 2 48 150 Mangan & Ingle (1992)
52 Hot air Mango 2.5 39 75 Sharp et al. (1991)
52 Hot air Grapefruit 2 48 90 Shellie & Mangan (1996)
 
The slow heating process requires long treatment times in order to kill the 
insects. This heating method is limited due to the time required to heat the medium 
around the fruits then heat will be transferred by convection from medium to the surface 
and finally from surface to the fruit core by conduction. Furthermore, external and 
internal damage caused by heat over long exposure times included peel browning, 
pitting, poor color development and abnormal softening and prolonged heating may not 
be practical in industry applications. Therefore, RF and microwave (MW) heat 
treatments have been proposed to reach the same level of insect mortality in a shorter 
time (Wang and Tang 2001). 
 
14  
Dielectric Heating  
Dielectric materials are those containing relatively few charge carriers like water 
molecules, so when the material is placed in an electrical field there is a displacement of 
charge and the material becomes polarized. If the electrical field is alternating, the 
displacement will follow the charges in the field direction. The material will therefore 
absorb energy from carrying out these displacements. This energy will dissipate out as 
heat, and the faster these displacements are, the larger energy will be dissipated. This 
technique is practical for industrial heating processes (Barber, H, 1983)  
Dielectric heating occurs with both RF and microwave (MW) exposure. These 
are high frequency electromagnetic waves generated by magnetrons and klystrons. 
When a material containing water molecules is subjected to an electromagnetic field 
that rapidly changes direction, the water molecules rotate into alignment with the 
direction of the electrical field. The water molecular friction causes the internal heating 
of the material. A frequency in the range of 12 MHz-2450 MHz is usually used in food 
engineering. Dielectric materials, such as most agricultural products, can store electric 
energy and convert that electric energy into heat. The increase in temperature of a 
material by absorbed electromagnetic energy can be expressed by (Nelson, 1996): 
                   "1063.55 212 ?? fEtTC p ??=??                       Eq. (1) 
Where Cp is the specific heat of the material (J.kg-1.?C-1), ? is the density of the material 
(kg.m-3), E is the electric field intensity (V.m-1), f is the frequency (Hz), ?? is the 
dielectric loss factor (s) of the material, ?t is the time duration (s) and ?T is the 
temperature rise in the material (?C). From Eq. (1), the increase in temperature depends 
15  
on the power, frequency, heating time and the material?s dielectric loss factor. Higher 
temperatures in commodities can be achieved by long heating duration and high power 
input. If the dielectric loss factor is relatively constant, rapid dielectric heating using 
higher frequencies can be achieved with much lower field intensities. However, the 
frequency interacts with the dielectric loss factor where the dielectric loss factor 
variable is a function of the frequency, temperature and water content of the material.  
Electromagnetic energy has been studied to control insects in commodities for 
many years. Initial investigations using RF heating to control pests of grain and nuts 
were conducted by Frings (1952), Thomas (1952) and Nelson (1966; 1973). Hirose et 
al. (1970) studied the use of dielectric heating (2450 MHz) to control tobacco moth 
larvae.  
A recent study demonstrated the possibility of using 2450 MHz MW to destroy 
woodworms by heating the larvae to 52-53 ?C for less than 3 minutes (Andreuccetti et 
al., 1994). Hallman and Sharp (1994) summarized RF and MW treatments which 
destroyed selected pests in many postharvest food crops. Nelson (1996) summarized 
more than five decades of research on the susceptibility of various stored grain insect 
species to RF and MW treatments. 
Table 3 briefly presents a selection of RF and MW treatments targeting various 
insects under different conditions and temperatures.  Since the congested bands of RF 
and MW are already in use for communication purposes, the Federal Communications 
Commission (FCC) has allocated five frequencies for industrial, scientific and medical 
(ISM) applications: 13.56, 27.12 and 40.68 MHz for RF, 915 and 2450 MHz for MWs.  
16  
Higher temperatures were used for stored grain than for fruits. The product 
quality after RF and MW treatments was rarely examined. More recently, Ikediala et al. 
(1999) and Wang et al. (2001a) reported that MW and RF treatments might have 
particular advantages over conventional heating methods in treating cherries and 
walnuts because the desired level of insect mortality was achieved without quality 
damage. 
Table 3: Reported RF and MW heat treatments for different products and insects at 
various temperatures. (Wang and Tang (2001)) 
 
Frequency, Temp.,
MHz ?C
27 56 Wheat (weevil) No Anglade et al. (1979)
53 Walnut (codling moth) Yes Wang et al. (2001a)
40 80 Pecan (weevil) No Nelson and Payne (1982)
915 55 Cherry (codling moth) Yes Ikediala et al. (1999)
915 & 2450 50-60 Cheese (microorganism) Yes Herve et al. (1998)
2450 45 Papaya (D. dorsalis) No Hayes et al. (1984)
50 Fruit (Fruit fly) Yes Sharp et al. (1999)
57 Wood (woodworm) No Andreuccetti et al. (1994)
80 Cereal (weevil) No Shayesteh & Barthakur (1996)
12000-55000 43-61 Wheat (weevil) No Halverson et al. (1996)
QualityProduct (Insect) Sources
 
 
DIELECTRIC PROPERTIES AND PERMITTIVITY 
 
 The dielectric properties of biological materials are important in the research of 
microwave processing of foods and agricultural materials, and the destruction of insect 
pests in postharvest and stored products.  Dielectric properties, among other parameters, 
are required to provide insight into the interaction between materials and microwave 
and RF energy during microwave and RF heating.   
17  
It?s essential to learn about the dielectric properties of any material before to be 
processed in the microwave regime (Metaxas, A. C. and Meredith, R. J., 1983). The 
property which describes the behavior of a dielectric under the influence of a high 
frequency field is the relative or complex permittivity, ?*, which is defined by the 
following equation (Eq. 2): 
        
"'*
effj??? ?=         Eq. (2) 
where "eff?  is the effective loss factor and includes the effects of conductivity. In other 
words permittivity is a complex quantity commonly used to describe the electrical 
properties that influence reflection of electromagnetic waves at interfaces and the 
attenuation of the wave energy within materials.  The real part is expressed in terms of 
the dielectric constant (energy stored), which influences the electric field distribution 
and the phase of waves traveling through the material. Dielectric loss factor, which is 
the imaginary part, mainly influences energy absorption. 
The relative permittivity values of agricultural and biological materials are 
generally influenced by frequency, temperature, density, salt content, moisture content 
and the state of moisture (frozen, free or bound), as well as the size and arrangement of 
the cell structure. 
A dielectric could be any material that is an electrical insulator and becomes 
polarized when placed in electric fields. As mentioned before, for example it may be 
between the electrodes of a capacitor. When the electric field is alternating, successive 
distortion of the molecules causes heating. This thermal heating effect is known as 
dielectric hysteresis heating, dielectric loss heating, or dielectric heating for simplicity 
18  
(Roussy, G. and Pearce, J. A., 1995). The heat dissipation mechanism is extremely 
complex, and can be explained by the movement of electric charges due to the electric 
field within a given atom and at the limits between two heterogeneous environments. 
When the electrodes polarity is inverted, the charges of the atom or molecules (electrons 
and protons) are drawn in the opposite direction; these successive changes of direction 
cause heating. The higher the frequency of the electric field, the more intense the 
friction and the higher the heat dissipated.  
In general, a distinction is made between RF dielectric hysteresis heating (in 
which the frequency is between 10 and 300 MHz) and MW heating (in which the 
frequency is between 300 to 30,000 MHz). Frequency range is the not the only 
difference between RF and MW, their respective characteristics are not identical either 
(Orfeuil, M. and Robin, A., 1987). 
To develop a suitable quarantine treatment protocol we need information on the 
dielectric properties of both the host nuts or kernels and the insects to be controlled. 
However limited literature reports exist on the permittivities and/or dielectric properties 
of insects.
19  
MATERIALS AND METHODS 
 
Industrial radio frequency (RF) heating operating between 10 and 100 MHz has 
been successfully used in the food processing and textile industries. It involves direct 
interactions between dielectric materials, such as fruits and nuts, with electromagnetic 
waves to generate heat. Unlike conventional surface heating with air or water, this 
avoids heating limitations caused by airspaces or bulkiness of the product. Because of 
their different dielectric properties, RF may also heat insects faster than the surrounding 
nut (Wang et al., 2003). RF is classified as ?non-ionizing? radiation because these 
frequencies produce insufficient energy to ionize water molecules, unlike higher levels 
of energy such as X-rays and gamma rays that can alter molecular structures. It is 
therefore regarded as a safe treatment that will be acceptable for consumers. RF 
treatments also meet organic labeling standards. The challenge with RF treatments has 
been lack of heating uniformity, which is particularly difficult for products with limited 
heat tolerance. 
Peanuts in this research were obtained from the USDA-ARS, National Peanut 
Research Laboratory in Dawson, GA and kept at temperatures around 5 ?C. Peanuts 
were then cleaned from just before RF experiments took place. A 200 g sample was 
taken before each heating run to measure the moisture content according to ASAE 
standard procedures (S410.1 FEB03). 
20  
RF System: 
 
Figure 2. Photo of the actual RF heating machine. 
 The RF heating machine system used in this research is manufactured by 
Strayfield International Limited, Workingham, UK.  It is A 6 kW, 27 MHz pilot-scale 
RF system (COMBI 6-S). Photo of the actual RF system is illustrated in Figure 2.  
 
 
Figure 3: Schematic diagram of the RF heating system 
 
21  
# 2 # 1 
# 3 
# 4 
Figure 3 shows a simple schematic diagram of the RF heating system. This 
system consists of a transformer, rectifier, oscillator, an inductance-capacitance pair 
commonly referred to as the ?tank circuit,? and the ?work circuit.? The transformer 
raised the voltage to about 9 kV, and the rectifier changed the alternating current to 
direct current. Direct current was then converted by the oscillator into RF energy at 27 
MHz. This frequency was determined by the values of the inductance and the capacitor 
in the tank circuit. The parallel plate electrodes, with the sample placed in-between, 
acted as the capacitor in the work circuit. The gap of the electrode plates can be changed 
to adjust RF power, coupled to the sample between the two plates.  
3500 g (3.5 Kg) of in-shell peanuts were placed in every RF run in a closed 
plastic container (30 X 20 X 15 cm) made of polyvinyl chloride (PVC). Air in the gap 
between the electrodes, and the plastic container did not heat during the RF treatment. 
The gap of the electrodes was adjusted to 6.12 cm to provide 0.4 kW of power. When 
RF waves are directed at peanut kernels infested with codling moth larvae, the 
absorption of RF energy depends on their dielectric loss factors. The difference between 
the dielectric properties of insects and the host material is important when considering 
the possibilities of preferential differential heating of insects. 
 
 
 
Figure 4. Locations of the four fiber optic temperature sensors in the plastic container. 
 
22  
Four fiber optic thermal sensors were used. Without cracking the peanut?s shell, 
a small hole was drilled into the kernel through the shell, and sensors were inserted into 
the kernels. During each cycle, the four sensors were placed in the same positions in the 
plastic container. Figure 4 shows the fixed positions for each fiber optic sensor in the 
plastic container during each RF heating cycle. 
Each plastic container held a 3500 g of in-shell peanut sample. The container is 
perforated on all sides to ensure the flow of heat out of the container; thus the peanuts 
around the walls have similar exposure as those in the middle. 
Taking into consideration the uniformity of the dielectric properties of the 
products, the peanuts were placed into the container. During the experiments, the gap 
between upper and lower electrodes and the RF power source was unchanged. All the 
experiments were identical and repeated in the exact procedures in order to minimize 
any change in the dielectric properties during the RF heating runs/cycles and during the 
stirring/cooling cycles. Figure 5 (a) shows a picture of peanuts in the plastic container 
almost filled with 3500 g of peanuts with the 4 fiber optic probes inserted in their fixed 
positions.  Figure 5 (b) shows a picture of the plastic container put in its fixed position 
between the upper and lower electrodes. 
23  
Figure 5 (a)       Figure 5 (b) 
         
Figure 5(a). The RF Heating Container filled with 3.5 Kg peanuts. 
Figure 5(b). The fixed position for upper and lower electrodes and the plastic container. 
 
During RF heating, the kernel temperature of the peanuts was monitored using 4 
fiber-optic probes/sensors. A small hole was drilled into the kernel through the shell 
without cracking the peanut?s shell. In every RF heating run the four probes were 
placed in the container in same positions; two probes in the middle (one in the bottom 
and one close to the surface), third probe close to one side, and each entered from a 
different side of the container. Experiments stopped when the coldest of the 4 monitored 
peanuts reached the target temperatures (40 ?C, 50 ?C or 60 ?C). However, occasionally 
if the coldest sensor/probe indicated a very low increase in temperature, we ignored this 
reading and took the reading of the next coldest sensor into consideration. 
 Three treatment heating minimum temperatures of 40, 50 or 60 ?C was applied 
during RF drying process. Each experiment consisted of 6 cycles of the RF 
heating/drying process and each cycle was followed by intermittent stirring and cooling 
24  
using a laboratory fan at medium high speed for 20 minutes. The weight of the samples 
was taken just at the end of the stirring/cooling process. 
The moisture measurement procedure is according to ASAE standards (S410.1 
FEB03), where 200 g portions of peanuts were weighed, shelled, reweighed, and then 
put in a 130 ?C oven for 6 hrs.  The combined moisture calculation was completed 
immediately after removing each portion from the oven. Each heating/drying cycle lasts 
from 1.5 to 4 minutes according to the minimum heating temperature (40, 50 or 60 ?C) 
and other factors. The heating cycles are followed by a 20 minute stirring and cooling 
cycle. The average air flow rate used in cooling cycle was around 3000 CFM. The dry 
air temperature was in the range of 22.0 to 25.6 ?C and the wet air temperature was in 
the range of 17.3 to 21.4 ?C. According to the wet and dry heat temperatures readings 
during all the experiments the relative humidity was in the range of 47% to 67% with 
the average relative humidity percentage of 60%. 
The set of 6 cycles of RF heating (variable time) and 5 intermittent stirrings and 
cooling for 20 minutes was sufficient to overcome the non-uniformity due to differences 
in orientation, size and shape of shell, as well as the location of the shells in the drying 
container and to achieve the required peanut storage moisture content (less than 10.5% 
wet basis). In general, we found that the total average RF drying time is 10-11, 16-17 
and 18-19 minutes for RF drying temperatures of 40, 50 and 60 ?C respectively. 
The average values of the RF treatment are stated in the table. The average 
heating time is for one complete RF cycle, i.e. 6 RF heating cycles with 5 intermittent 
stirring cycles. 
25  
RESULTS AND DISCUSSION 
 
Non-uniformity of RF heating of peanuts: 
During RF heating we observed a non-uniform heating for peanuts due to the 
difference in orientation, size and shape of the shells, whether the shell is opened or not, 
as well as the location of shells in the drying plastic container.  
According to Wang, S. et al., (2004) 6 cycles of RF heating followed by 
intermittent stirring and cooling was sufficient to overcome the non-uniformity of 
peanuts. According to our results, for higher peanut internal drying temperatures, longer 
times were required to reach the temperature during RF treatments. 
 
 
 
 
 
 
 
 
Figure 6(a). The four fiber optic probe temperature readings at 50 ?C RF heating. 
Figure 6(b). RF heating curves at 50 ?C for six consecutive cycles.
RF Heating Curves
0
20
40
60
80
100
0 50 100 150 200 250
Time, Sec
Te
mp
er
atu
re
, ?C
F.O. Probe 1 F.O. Probe 2 F.O. Probe 3 F.O. Probe 4
Heating curves for 6 Cycles to 50 ?C
0
10
20
30
40
50
60
0 50 100 150 200
Time, Sec
Te
mp
er
atu
re
, ?C
Cycle 1
Cycle 2
Cycle 3
Cycle 4
Cycle 5
Cycle 6
  
Figure 6(a) Figure 6(b) 
26  
Figure 6(a) shows the heating curves for one RF cycle of four peanut shells at 
different positions in the plastic container, those curves are simply the readings of the 
four fiber optic temperature sensors. As we can notice from this Figure (6(a)), the 
greatest difference of two probe temperature readings was around 30 ?C. 
Figure 6(b) shows the RF heating curves at average temperature of 50 ?C for the 
six consecutive cycles. The probe position was fixed in all these six cycles. Comparing 
the differences between the highest and lowest temperature readings in this figure to 
those in Figure 6(a) we will find that differences are lower here. This means the 
difference in peanut temperatures in the same cycle due to the different positions of 
shells in the plastic container are higher than the differences of temperature reading for 
shells in the same positions even at different RF cycles. In other words, non-uniformity 
of RF heating due to the position of shells in the plastic container is the main reason for 
non-uniformity. 
27  
Ar1
Ar2
9.0
59.4 ?C
10
20
30
40
50
FLIR Systems
 
Figure 7. Thermal image of typical peanut shells temperature distribution immediately 
after RF heating cycle at 40 ?C. 
 
Thermal images have been taken immediately at the end of RF heating cycle at 
40 ?C for 2-3 minutes.  Figure 7 shows infrared image of typical peanuts shells 
temperature distribution of peanuts right after the ending of RF. The shells around the 
corners and edges are easy to be cooled and dissipate their heat out than those in the 
middle or deep close to the bottom of the plastic container. Temperatures of those shells 
are less than those inside or to the bottom of the plastic container as it appears in Figure 
7. In another experiment where the targeting heating temperature was 50 ?C we took a 
thermal image directly at the end of the RF heating cycle for those shells in the middle 
and bottom of the plastic container and we measured the highest temperature in the 
28  
media between these shells. This temperature was 65.5 ?C as shown in Figure 8 and it 
represents the cursor on line Li1. 
 
Li1
18.8
69.3 ?C
20
30
40
50
60
FLIR Systems
 
Figure 8. Thermal image present handful of peanut shells in middle and bottom of the 
plastic container for RF heating cycle at 50 ?C. 
 
The blue color represents the plastic container and two hands. The purple color 
represents the media outside the plastic container, and other colors represent the shells 
and media inside the plastic container.  These temperatures measured by infrared 
imaging camera characterize the media, or hot air, in between the shells or the shells? 
temperatures, but not the temperature inside the shells or kernels. The difference is 15.5 
?C between the media temperature and the targeted dehydration temperature (50.0 ?C).  
Apparently, in all the infra red pictures, the temperature of the media outside the 
shell is higher than the shells due to the fact that water vapor or steam is capable to 
29  
further respond with RF and evolve heat while transferring from the kernels and shells 
to the media outside.  This will lead us to expect temperature profile differences 
between the media, on the shells, inside the shells, and inside the kernels. 
It was observed from these thermal images that overheating and less heating 
existed in many spots in the plastic container, hence the repeated stirrings and cooling 
cycles is needed to overcome heating non-uniformity. 
 
Ar1
Li1
Sp1:temp 46.3
25.4
77.0 ?C
30
40
50
60
70
FLIR Systems
 
Figure 9. Thermal image of peanut shells temperature distribution at the end of RF 
heating cycle at 60 ?C. 
 
30  
%
40 45 50 55 60 65 70 75 80
?C0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
Label Peak Min Max Avg
Ar1 5.4 42.9 76.3 65.8
Li1 9.9 45.7 69.2 59.5  
Figure 10(a). Statistical analysis results for the circle (Ar1) in Figure 9. 
 
?C
30
35
40
45
50
55
60
65
70
75
Label Cursor Min Max
Li1 59.0 45.7 69.2  
Figure 10(b). Statistical analysis results for the line (Li1) in Figure 9. 
 
Figure 9 represents the temperature distribution for peanuts shells just at the end 
of RF heating cycle with targeting dehydration temperature at 60 ?C. The hot spots 
represent temperature above 65 ?C in the media between the shells and the average 
temperature of the shells is around 60 ?C. The single point temperature reading in this 
Figure is 46.3 and represents the temperature of one single shell in the spot circle with 
highest temperature; maximum temperature in this circle is 76.3 ?C as shown later.  
31  
Statistical analysis has been carried out using these infrared images. In Figure 9, 
we selected one circle spot area (Ar1) and one line (Li1) to extract some statistical 
numbers. Those statistical results are shown in Figure 10 (a & b). The average 
temperature in the circle (Ar1), minimum temperature and maximum temperature are 
65.8, 42.9 and 76.3 ?C respectively as shown in Figure 10 (a). The peak for this circle 
(Ar1) was 5.4%. For the line (Li1) the average temperature, minimum temperature, and 
maximum temperature are 59.0, 45.7 and 69.2 ?C respectively as shown in Figure 10 
(b). Figure 10 (a) shows skewed temperature profile toward the higher temperatures; in 
the range of 65 ? 75 ?C, with average peaks in the range of 2.5-3% per one RF Heating 
cycle, while the percentage of spots with temperature lower than 60 ?C are less than 
10% and those spots higher than are less than 25%. However the spot temperatures do 
not represents the shells? or kernels? temperatures. 
 Generally, the non-uniformity of peanuts or nuts  is due to many factors, 
including but not limited to the following: 
? Difference in peanut orientation in the heating container 
? Differences of size and shape of shells 
? The location of shells in the drying container  
? The wet and dry bulb temperature during the treatment 
? Difference in the initial moisture content of peanuts 
? The variety of peanut type, different harvesting time and planting locations 
The proposed idea to overcome this non-uniformity problem is to use multiple 
numbers of RF treatments and intermittent stirrings. A set of cycles of RF treatment 
32  
with intermittent stirring was used to dry peanuts to different final temperatures as will 
be described later in this research. 
 Applying RF treatment for a large or industrial scale will be applicable once the 
non-uniformity problem is resolved. The non-uniformity problem was also found in 
treating walnuts using a pilot-scale RF treatment protocol (Wang et al., 2003). 
 
 
 
 
 
Figure 11(a). Decrease in sample weight through the six RF heating cycles. 
Figure 11(b). Sample weight decrease after RF treatment. 
 
After RF heating/drying treatment, we measured the moisture content and found 
that the peanuts moisture decreased over time. However, this total weight loss during 
RF heating is not yet adequate to reach the required storage percentage moisture content 
(M.C.). The number of RF dehydration/stirring-cooling cycles should be increased to 
reach the desirable M.C. percentage.  In fact, RF treated peanuts reach this required 
M.C. within couple of days if stored in suitable environmental conditions. In our case 
we left RF treated peanuts samples in the lab for more than three weeks. These peanuts 
RF treated samples reached the required storage M.C. (11 %) after 2-3 days.  This can 
be seen from the graph (Figure 11 (a)).  
Decreasing in Moisture Content During RF Heating 
Process
3300
3350
3400
3450
3500
0 1 2 3 4 5 6Cycle No.
Sa
m
pl
e W
eig
ht
, g
40 ?C 50 ?C 60 ?C
Weight of Samples after RF Treatment
3000
3100
3200
3300
3400
0 5 10 15 20 25
Time, Days
W
eig
ht
 o
f S
am
ple
s, 
g
40 ?C
50 ?C
60 ?C
    
   Figure 11 (a) 
    
  Figure 11 (b) 
33  
Equilibrium moisture content analysis for peanuts in this research was measured 
according to ASABE standards (ASAE S410.1 DEC97). Procedures of this standard 
based on shelling 200 g. of peanuts and weighing each portion (shells and kernels) 
separately then oven drying them in separate metal containers for 6 hours at 130 ? 3 ?C. 
 
Table 4. Average RF dehydration results 
 
RF Heating Average Temperature   40 ?C 50 ?C 60 ?C 
Total Average RF Heating Time, Sec. 638 979 1112 
  Min. 10.6 16.3 18.5 
Total Average Weight Loss After RF Process, g. 110 163 196 
Average Time per one Cycle of RF Process, Sec. 107 163 214 
  Min. 1.78 2.72 3.56 
Total Average M.C. decrease,   3.68% 4.64% 5.98% 
 
Table 4 represents the average results of eight RF heating experiments for each 
treatment temperature [40, 50, 60 ?C]. The total average time required to reach the 
average dehydration temperatures using the standard 6 RF cycles was measured and 
shown in the table. Each RF cycle is followed by a stirring and cooling cycle for at least 
15 minutes.  
We can summarize the experiments results as follow: 
In order to dry peanuts using the standard 6 RF heating cycles at average drying 
temperature of 40 ?C we need to use the RF energy machine for about 10.6 minutes. In 
case we need to run the RF energy machine at average temperatures of 50 and 60 ?C we 
34  
will run it for a total of 16.3 and 18.5 minutes respectively. However this total time is 
the average of adding each time of the 6 RF heating cycles. The average time for each 
cycle is as shown in Table 4. 
Using the RF energy machine for these total average times will decrease the 
peanuts shell weight about an average of 3.68%, 4.64% and 5.98% for the three 
dehydration temperatures 40, 50, and 60 ?C respectively. 
These  above-motioned average results are the summary of a total of 24 
different experiments each consisting of standard 6 RF heating/cooling cycles. Table 5 
shows the entirety of the 24 experiments and their data.  
Appendix A shows all the experiments? date: peanuts type, heating temperature, 
maximum temperature difference (MTD) reading between 2 of the 4 total fiber optics 
channels during the 6 runs for each experiment, maximum average temperature (MAT) 
during the whole 6 runs for each experiments, total treatment/ RF heating period for the 
6 RF heating runs (TTT/6 runs), average treatment time per single RF heating run 
(AHT/run), percentage initial moisture content (IMC), percentage final moisture content 
(FMC), percentage water evaporated or weight loss at the end of the experiments 
(%EW), dry air temperature (DAT), and wet air temperature (WAT). 
From these results we can construct a graph representing the percentage weight 
loss in peanuts after the end of each complete RF treatment experiment. That would 
represent the amount of water evaporated by applying RF energy. This percentage value 
is variable and depends on many factors; these factors include but are not limited to the 
following: 
? Initial peanut moisture content 
35  
? Type and harvesting time of peanuts 
? RF heating/treatment average temperature (40, 50, 60 ?C) 
? Dry and wet air temperature 
 
The lowest initial moisture content in all 24 experiments was 15.24% and the 
highest was 24.92%.   However, the difference in percentage total water evaporated 
right after the end of RF treatment for these lowest and highest initial moisture contents 
ranged from 0.8 % in the case of 40 C average treatment temperature to 1.0 % in the 
case of 60 C average treatment temperature. In general and when other factors remained 
unchanged, the higher the initial moisture content the higher the RF loss factor thus the 
higher the percentage water evaporated from peanuts. 
Figure 12 shows the percentage water evaporated from the peanuts at the end of 
each RF treatment experiment. The big triangular points on the chart represent the 
average percentage weight loss or evaporated water at each average RF treatment 
temperature. These values are 3.68%, 4.64% and 5.61% for RF treatment temperature 
40, 50, and 60 ?C respectively. These results clearly show that increasing the RF 
treatment temperature by 10 ?C increases the percentage water evaporated by 
approximately one or 1.0%. 
The total RF treatment time for the 6 runs for each RF treatment temperature is 
represented in Table 5. The difference in time is mainly due to the difference in peanut 
types and initial moisture contents. 
36  
Table 5. Total RF heating/drying time for eight different experiments  
at the three RF heating temperatures.  
RF Treatment 
Temp., C RF Total Heating Time for 6 runs, (Sec) 
40 525 526 736 689 859 665 568 539 
50 860 951 1002 1171 984 941 964 956 
60 1067 1270 1427 1511 1293 1261 1227 1265 
 
 
Figure 12. Percentage water evaporated after RF 6 treatment cycles. 
 
Table 6 shows the percentage total weight loss or evaporated water at the end of 
the six RF treatment cycle for eight different experiment days. According to these 
% Evaporated Water after 6 RF Runs
3.68%
4.64%
5.61%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
30 40 50 60 70
RF Heating Temperature, C
%
 W
eig
ht
 L
os
s/ 
H2
O 
Ev
ap
.
37  
percentages, the higher the treating temperature is the higher the percentage of water 
evaporated. 
 
Table 6. Percentage total evaporated water after the six RF cycles 
RF Treatment 
Temp.  % Water Evaporated at end of the 6
th run of RF treatment 
40 3.46% 3.14% 3.05% 3.83% 3.83% 3.20% 4.55% 4.40% 
50 4.54% 4.29% 3.54% 4.80% 4.29% 3.77% 6.12% 5.80% 
60 5.26% 5.26% 4.83% 5.83% 5.23% 4.51% 7.06% 6.89% 
 
 Average values for the total amount of water evaporated, total treatment time 
after the end of the sixth RF treatment cycle and the average treatment time per run at 
different RF treatment temperatures are shown in Table 7. 
 
Table 7. Average percentage water evaporated and times for RF treatment.    
Averages at the end of RF Treatment 6 runs RF Treatment 
Temp. H2O Evaporated TTT (Sec.) TTT (Min.) TT / run (Sec.) 
40 3.68% 638 10.6 106 
50 4.64% 979 16.3 163 
60 5.61% 1290 21.5 215 
 
The rate of cooling for peanuts is slow and smooth compared to the rate of 
heating obtained form application of RF energy. Figure 13 shows the four fiber optic 
temperature readings during the RF cycle at average temperature of 50 ?C, followed by 
natural cooling after stopping the RF machine. From the results in this figure (Figure 
13) we noticed that it took more than 40 minutes for peanut temperature to decrease an 
average of 10 ?C without applying forced cooling. This supports the need for forced 
38  
cooling using a fan in order to reduce the duration of the cooling and stirring period.  
15-20 minutes is sufficient to ensure equal exposure of peanuts to the cooling fan, such 
that the temperature decreases to a value between 22 and 30 C depending on the RF 
treatment temperature and the number of cycles in the overall RF treatment process. 
30
35
40
45
50
55
60
65
0 500 1000 1500 2000 2500
Time,  (Sec.)
Te
m
pe
ra
tu
re
,  
  (
oC
)
 
Figure 13. Natural decrease in peanut kernels temperature immediately after the end of 
a RF heating cycle at 50 ?C. 
 
After RF treatment the M.C. is still higher than that required for storage (11%) 
Increasing the number of RE heating/stirring-cooling cycles will decrease the M.C. in 
peanuts and will increase the probability of killing the insects and over come the non-
uniformity problem. 
However, peanuts continue losing moisture after the RF treatment for about a 
month as we mentioned above. The rate of weight loss is relatively high in the 
39  
subsequent several days (2-3 days) after the RF treatment drying process then tends to 
be very slight after one week. It is important to mention here that the moisture content 
of peanuts is affected by many other outside factors such as the temperature in the 
environment and the humidity in the air. 
40  
THERMAL DEATH KINETICS OF RED FLOUR BEETLE (COLEOPTERA 
TENEBRIONIDAE) 
 
INTRODUCTION 
 
A major problem in the storage and marketing of  peanuts is infestation by a 
variety of postharvest pests. This includes field pests of possible phytosanitary 
importance such as navel orangeworm, Amyelois transitella (Walker), and codling 
moth, Cydia pomonella (L.), as well as common stored-product pests such as Indian 
meal moth, Plodia interpunctella (H?bner), and red flour beetle, Tribolium castaneum 
(Herbst). Currently, the dried fruit and tree nut industry relies on fumigation with 
methyl bromide and phosphine (hydrogen phosphide) for postharvest insect control. 
Regulatory actions against methyl bromide (UNEP, 1992) and hydrogen phosphide 
(EPA, 1998), as well as insect resistance to hydrogen phosphide, may make these 
fumigants costly or unavailable to the nut industry. In addition, as the organic industry 
expands, the need for nonchemical postharvest insect control methods increases.  
  Although non-chemical treatments for postharvest dried fruits and nuts have 
been investigated in the past, few have been implemented. Recent concerns over 
resistance, regulatory action and the needs of the organic industry have generated a 
renewed interest in developing alternative treatments (Johnson et. al., 2004). There are
41  
other non-chemical alternatives methods to control postharvest insects such as: ionizing 
radiation, cold storage, controlled atmospheres, and combination treatments. All have 
disadvantages including substantial capital investment, extensive alteration of existing 
facilities, lengthy treatment times, or concerns over consumer acceptance. Heat 
treatments using forced hot air also have been proposed, but the lengthy exposure times, 
exceeding one hour, required to heat nuts throughout may substantially reduce product 
throughput and/or quality or cause product damage (Johnson et al. 2003).  
Preferential heating of insects in nuts and fruits using RF is a promising thermal 
treatment procedure to control insects without affecting product quality. As mentioned 
before, post-harvested nuts and fruits are treated by chemical fumigation to control field 
and storage pests before being shipped to domestic and international markets. And 
because of the increasing public concern about adverse impacts of chemical fumigation 
on humans and the environment, there is a heightened interest in developing non?
chemical pest control methods, especially thermal methods. An important key to 
developing successful thermal treatments is to balance the need for a complete kill of 
insects with a minimal thermal impact on product quality. (Tang et al. 2005) 
The hypothesis of using RF to kill insects in nuts depends on the loss factor of 
the insects being 1.4 to 1.7 times greater than that for dry nut and fruits with 27- MHz 
RF energy (Wang et al. 2003).  
Researchers at Washington State University have proven this result for walnuts 
and codling moth larvae (Wang et al. 2002a). The most common insect in peanuts in 
southern states is Red Flour Beetle (RFB), (Tribolium Castaneum), (Herbst). This insect 
is one of the most common existing in grain products while are stored. It is one of the 
42  
most annoying pests in retail grocery stores and warehouses. RFB is very similar to 
confused flour beetle (Tribolium confusum) that first noted in the United States in 1893. 
Both insects occur throughout the world but the confused flour beetle is most abundant 
in the northern part of the United States, while the RFB is not commonly found north of 
the forty-first parallel. In general the RFB seems to be les common (Metcalf and Flint, 
1962). On the other hand it is important to confirm that the insect in this research is the 
RFB and that could be strong reasons why our results are different from those have 
been published before. The two insects may be distinguished in the adult stage by minor 
differences, such as distance between the two eyes and the shape of the antennae 
(Metcalf and Flint, 1962).  
The Industrial radio frequency heating system, extensively used in the food, 
textile, and wood processing industries, has been suggested for the control of 
postharvest insects and may avoid the problems associated with overheating and 
incomplete kill by providing more rapid product heating (10-20 ?C /min). Recently, 
radio frequency treatments have been shown to kill codling moth and navel 
orangeworm found inside in-shell walnuts (Wang et al. 2001, 2002a). 
 In order to develop thermal treatment using MW or RF heating we proposed to 
study the thermal death kinetics for targeted insects first. Methods used for studying 
thermal death kinetics of insects include directly exposing insects in a water bath for 
specific times, heating insects in submerged tubes in a water bath, or heating insects in 
fruits (Johnson et al. 2003). Washington State University (WSU) developed a heating 
block system (Ikediala et. al. 2000 and Wang et al. 2002b). The system directly heats 
exposed insects and provides precise heating rates in the range from 1 to 20 ?C /min. 
43  
This heating block system was used to determine thermal death kinetics of fifth instars 
of codling moth, naval orange-worm, and Indian meal moth (Johnson et al. 2003). 
 In this study we used the WSU heating system to identify the two most heat 
tolerant life stages: adults and old larvae, of red flour beetle according to Johnson et al. 
2003, and to determine its thermal death kinetics at a heating rate of 15 ?C /min.
44  
MATERIALS AND METHODS 
Heating Block System:  
The WSU heating block system (Figure 14) consisted of top and bottom 
aluminum blocks (each 254 by 254 by 18 mm), heating pads, an insect test chamber, 
and a data acquisition/control unit (Ikediala et al. 2000 and Wang et al. 2002b). 
Calibrated type-T thermocouples inserted through sensor paths were used to monitor the 
temperatures of the top and bottom blocks. Heating rate, set-point temperature, and 
exposure time were computer controlled by a customized Visual Basic program and 
PID controllers via a solid-state relay (i/32 temperature & process controller, Omega 
Engineering Inc., Stamford, CT).  
 
      
Figure 14. Heating block system.
45  
Experiment Insects: 
The Department of Entomology at Auburn University, AL. Prof. Henry 
Fadamiro was the sources of the red flour beetle. The red flour beetle used in these 
experiments was from a long-term laboratory culture maintained on a mixture of wheat 
bran and wheat flour. Cultures were kept in 0.5-1 liter glass canning jars at 25 ?C and 
50% RH. Two life stages were used in these experiments: adults and older larvae. 
Larvae age was in the range of 2-4 weeks. A control sample of 30 adults or older larvae 
was always maintained under the same conditions for more than two weeks after the 
experiments took place. The number of these adults and older larvae in the control 
plastic Petri dishes was monitored simultaneously with those used for the experiments.  
The experimental and control insects in the Petri dishes were kept with enough food and 
under suitable conditions to remain alive for at least two weeks after the experiments 
took place. 
 
Experimental Protocol: 
The heating rate chosen in this study was 15 ?C/min to simulate the rapid 
heating of nuts subjected to radio frequency and microwave energies and to be 
comparable to other research results on the same insects (RFB).  
Heating experiments took place for three different exposure temperatures (48, 
50 and 52 ?C) with three different time durations: 20, 30, and 40 minutes for 48 ?C, 4, 
8; 12 minutes for 50 ?C and 0.5, 1; and 2 minutes for 52 ?C. Approximately 50 test 
insects of each stage were treated at each exposure temperature-time combination, 
including controls. 
46  
Control insects were placed in Petri dishes with enough nutrition for more than 
two weeks. After two weeks all the control insects were still alive. Same environment 
including nutrition, Petri dishes, and temperature was applied to insects exposed in the 
heating block. 
The test was replicated three times, with a minimum of 200 insects treated at 
each exposure temperature-time combination. 
 Before starting each heating experiment, test insects were poured from the Petri 
dishes onto the bottom heating block, and then the top block was placed on the bottom 
block and the treatment program began. At the end of each exposure, test insects were 
quickly (within 10 seconds) transferred to plastic Petri dishes. Treated insects were held 
at 25 ?C, and 50% relative humidity (RH) until evaluation. Evaluation consisted of 
observing the insects for signs of movement. Insects? larvae or adults were considered 
dead if no movement was observed.  
The evaluation was conducted at four times: right after the end of each 
exposure; 12-24 hours after the exposure; four days after the exposure; and  after  two  
weeks of exposure.
47  
RESULTS AND DISCUSSION 
 
In this part RFB older larvae were exposed to a couple of different temperature-
time combination runs in the WSU heating system. The number of older larvae used in 
each run, and those older remained alive after the run are counted and shown in Table 8. 
Table 8 shows the number of older larvae used in each run, those remained alive after 
12-24 hours, those remained alive after four days, and those remained alive for more 
than two weeks, are all shown in Table 8. 
 
Table 8. Number of live RFB older larvae after heating at different times     
No. of live RFB Older Larvae  Exposure (Temp. & 
Time) 
Starting 
Quantity After Exp. Next Day After4 Days After  > 2 weeks 
48? C     20 min      
Sample 1 53 38 52 52 50 
Sample 2 55 40 53 52 50 
Sample 3 55 41 53 51 49 
      
48? C     30 min      
Sample 1 53 32 45 33 26 
Sample 2 54 34 47 40 38 
Sample 3 51 31 44 12 28 
      
48? C     40 min      
Sample 1 51 22 33 25 17 
Sample 2 51 19 25 22 19 
Sample 3 53 21 32 25 20 
      
50? C     4 min      
Sample 1 50 12 50 48 42 
Sample 2 51 7 51 47 41 
Sample 3 50 12 50 49 42 
48  
No. of live RFB Older Larvae  Exposure (Temp. & 
Time) 
Starting 
Quantity After 
Exp. Next Day 
After4 
Days 
After  > 2 
weeks 
50? C     8 min      
Sample 1 52 1 45 30 25 
Sample 2 55 2 46 28 26 
Sample 3 50 0 41 27 25 
      
50? C     12 min      
Sample 1 55 0 0 20 13 
Sample 2 53 0 0 21 13 
Sample 3 51 1 1 10 8 
      
52? C     0.5 min      
Sample 1 50 6 46 47 47 
Sample 2 50 5 49 49 48 
Sample 3 50 3 50 49 48 
      
52 ? C     1 min      
Sample 1 50 0 49 46 45 
Sample 2 50 0 50 45 44 
Sample 3 50 0 41 35 35 
      
52? C    2 min      
Sample 1 50 0 45 21 19 
Sample 2 50 0 49 28 24 
Sample 3 50 0 46 32 27 
      
Control Sample 1 50 50 50 50 50 
Control Sample 2 30 30 30 30 30 
 
It was found that the number of live older larvae is hard to discover immediately 
after the heating run. They might be thermally shocked so they do not move for awhile, 
then they will retain their normal activities within time (12-24 hours). That?s why the 
number of live larvae increased in the second column in Table 8 despite the fact that 
after 4 days some of those showed activities after one day were not moving.  
However, all the results after two weeks are completely different from those 
have been published before with similar experimental conditions (Johnson et al. 2003). 
The reasons for our different results could be due to the fact that these larvae pretend 
49  
not to move to protect themselves after this thermal shock, or they need time to 
rehabilitate and restore their normal activities. In the case of 50 ?C-12 minutes 
temperature-time combination and after 24 hours all larvae did not move but after four 
days 20-40% of them started to move. Also these RFB insects here in the south east of 
the US could be different in the way they resist the heat than those in north or west 
coast of the US. Table 9 shows the percentage of mortality of RFB older larvae at 
different exposure of temperature-time combination. 
 
Table 9. Percentage of mortality of RFB older larvae at different temperature-time 
combinations.  
Mortality of RFB Older Larvae 
Exposure (Temp. & Time) After Exp. Next Day After 4 
Days 
After > 2 
weeks 
48? C     20 min     
Sample 1 28.30% 1.89% 1.89% 5.66% 
Sample 2 27.27% 3.64% 5.45% 9.09% 
Sample 3 25.45% 3.64% 7.27% 10.91% 
     
48? C     30 min     
Sample 1 39.62% 15.09% 37.74% 50.94% 
Sample 2 37.04% 12.96% 25.93% 29.63% 
Sample 3 39.22% 13.73% 76.47% 45.10% 
     
48? C     40 min     
Sample 1 56.86% 35.29% 50.98% 66.67% 
Sample 2 62.75% 50.98% 56.86% 62.75% 
Sample 3 60.38% 39.62% 52.83% 62.26% 
     
50? C     4 min     
Sample 1 76.00% 0.00% 4.00% 16.00% 
Sample 2 86.27% 0.00% 7.84% 19.61% 
Sample 3 76.00% 0.00% 2.00% 16.00% 
     
50? C     8 min     
Sample 1 98.08% 13.46% 42.31% 51.92% 
Sample 2 96.36% 16.36% 49.09% 52.73% 
Sample 3 100.00% 18.00% 46.00% 50.00% 
     
50  
Mortality of RFB Older Larvae 
Exposure (Temp. & Time) After Exp. Next Day After 4 
Days 
After > 2 
weeks 
50? C     12 min     
Sample 1 100.00% 100.00% 63.64% 76.36% 
Sample 2 100.00% 100.00% 60.38% 75.47% 
Sample 3 98.04% 98.04% 80.39% 84.31% 
     
52? C     0.5 min     
Sample 1 88.00% 8.00% 6.00% 6.00% 
Sample 2 90.00% 2.00% 2.00% 4.00% 
Sample 3 94.00% 0.00% 2.00% 4.00% 
     
52? C     1 min     
Sample 1 100.00% 2.00% 8.00% 10.00% 
Sample 2 100.00% 0.00% 10.00% 12.00% 
Sample 3 100.00% 18.00% 30.00% 30.00% 
     
52? C    2 min     
Sample 1 100.00% 10.00% 58.00% 62.00% 
Sample 2 100.00% 2.00% 44.00% 52.00% 
Sample 3 100.00% 8.00% 36.00% 46.00% 
 
The runs results for the other life stage of RFB adults are shown in Table 10 
with the exposure temperature-time combination for each run with the number of RFB 
adults used in the heating block run (experiment). Those adults remain alive directly 
after the run, and those adults remain alive 12-24 hours after the run, and those adults 
remain alive after four days of the run, then those adults remain alive for more than two 
weeks after the run. 
51  
Table 10 Number of live RFB adults after heating at different temperature-time 
combinations. 
No. of alive RFB Older Larvae Exposure (Temp. & 
Time) 
Starting 
Quantity After Exp. Next Day After 4 Days After > 2 weeks 
48? C     20 min      
Sample 1 54 32 51 52 41 
Sample 2 52 28 51 51 47 
Sample 3 53 34 52 53 52 
      
48? C     30 min      
Sample 1 56 18 54 56 32 
Sample 2 52 16 48 50 39 
Sample 3 50 13 44 46 37 
      
48? C     40 min      
Sample 1 51 8 28 30 7 
Sample 2 52 11 41 44 24 
Sample 3 53 12 48 51 31 
      
50? C     4 min      
Sample 1 50 17 49 46 45 
Sample 2 51 19 51 50 49 
Sample 3 52 18 52 48 48 
      
50? C     8 min      
Sample 1 48 1 47 38 32 
Sample 2 50 0 49 34 30 
Sample 3 52 3 51 43 38 
      
50? C     12 min      
Sample 1 52 0 0 0 0 
Sample 2 53 0 0 0 2 
Sample 3 53 0 0 1 0 
      
52? C     0.5 min      
Sample 1 52 5 50 50 48 
Sample 2 53 9 53 51 50 
Sample 3 54 11 46 46 46 
      
52? C     1 min      
Sample 1 51 0 12 46 43 
Sample 2 54 0 9 48 44 
Sample 3 51 0 11 48 46 
      
52  
 
No. of alive RFB Older Larvae Exposure (Temp. & 
Time) 
Starting 
Quantity After Exp. Next Day After 4 Days After > 2 weeks 
52? C    2 min      
Sample 1 52 0 3 20 20 
Sample 2 55 0 0 10 10 
Sample 3 53 0 1 30 27 
      
Control Sample 1 50 50 50 50 50 
Control Sample 2 50 50 50 50 50 
 
We still can observe the same results we found for older RFB larvae in this case 
of RFB adults where the number of live insects/adults is different in each single run 
depending on the time after the run occurred. Those still alive after more than two 
weeks are more than the normal average numbers shown before in similar studies. 
The percentage mortality values of live RFB adults right after the run, after 12-
24 hours of the run, after four days of the run, and for more than two weeks are all 
shown in Table. 11. 
Table 11. Percentage of mortality of RFB adults at different temperature-time 
combinations.        
Mortality of RFB Adults 
Exposure (Temp. & Time) After Exp. 
Next Day 
After 4 
Days 
After > 2 
weeks 
48? C     20 min     
Sample 1 40.74% 5.56% 3.70% 24.07% 
Sample 2 46.15% 1.92% 1.92% 9.62% 
Sample 3 35.85% 1.89% 0.00% 1.89% 
     
48? C     30 min     
Sample 1 67.86% 3.57% 0.00% 42.86% 
Sample 2 69.23% 7.69% 3.85% 25.00% 
Sample 3 74.00% 12.00% 8.00% 26.00% 
     
48? C     40 min     
Sample 1 84.31% 45.10% 41.18% 86.27% 
Sample 2 78.85% 21.15% 15.38% 53.85% 
Sample 3 77.36% 9.43% 3.77% 41.51% 
53  
Mortality of RFB Adults 
Exposure (Temp. & Time) After Exp. Next Day After 4 
Days 
After > 2 
weeks 
50? C     4 min     
Sample 1 66.00% 2.00% 8.00% 10.00% 
Sample 2 62.75% 0.00% 1.96% 3.92% 
Sample 3 65.38% 0.00% 7.69% 7.69% 
     
50? C     8 min     
Sample 1 97.92% 2.08% 20.83% 33.33% 
Sample 2 100.00% 2.00% 32.00% 40.00% 
Sample 3 94.23% 1.92% 17.31% 26.92% 
     
50? C     12 min     
Sample 1 100.00% 100.00% 100.00% 100.00% 
Sample 2 100.00% 100.00% 100.00% 96.23% 
Sample 3 100.00% 100.00% 98.11% 100.00% 
     
52? C     0.5 min     
Sample 1 90.38% 3.85% 3.85% 7.69% 
Sample 2 83.02% 0.00% 3.77% 5.66% 
Sample 3 79.63% 14.81% 14.81% 14.81% 
     
52? C     1 min     
Sample 1 100.00% 76.47% 9.80% 15.69% 
Sample 2 100.00% 83.33% 11.11% 18.52% 
Sample 3 100.00% 78.43% 5.88% 9.80% 
     
52? C    2 min     
Sample 1 100.00% 94.23% 61.54% 61.54% 
Sample 2 100.00% 100.00% 81.82% 81.82% 
Sample 3 100.00% 98.11% 43.40% 49.06% 
 
The results shown before were for the three replicated runs of each exposure 
temperature-time combination. However, the average percentage mortality of these 
three replicated results is shown in Table 12. 
54  
Table 12. Average percentage of mortality of RFB older larvae and adults at different 
temperature-time combinations       
RFB Older Larvae RFB Adults Exposure, 
Temp. ?C 
& (min) 
After 
Exp. 
Next 
Day 
After 4 
Days 
After 2 
weeks 
After 
Exp. 
Next 
Day 
After 4 
Days 
After 2 
weeks 
48 ?C            
20 27.01% 3.05% 4.87% 8.55% 40.91% 3.12% 1.88% 11.86% 
30 38.63% 13.93% 46.71% 41.89% 70.36% 7.75% 3.95% 31.29% 
40 60.00% 41.97% 53.56% 63.89% 80.17% 25.23% 20.11% 60.54% 
         
50 ?C            
4 79.42% 0.00% 4.61% 17.20% 64.71% 0.67% 5.88% 7.20% 
8 98.15% 15.94% 45.80% 51.55% 97.38% 2.00% 23.38% 33.42% 
12 99.35% 99.35% 68.14% 78.72% 100 % 100% 99.37% 98.74% 
         
52 ?C            
0.5 90.67% 3.33% 3.33% 4.67% 84.34% 6.22% 7.48% 9.39% 
1 100% 6.67% 16.00% 17.33% 100% 79.41% 8.93% 14.67% 
2 100% 6.67% 46.00% 53.33% 100 % 97.45% 62.25% 64.14% 
 
According to these results and after two weeks of the run or exposure 
temperature-time combination we found best treatment results at 48 ?C -12 minutes 
combination with average percentage mortality of 78.72% for RFB older larvae and 
98.74% for RFB adults.  
One of RF applications is dehydration of peanuts but it could be also considered 
as a treatment procedure or pest control for FRB insects.  After comparing these results 
in Table 12, we will realize that 40 ?C RF treating temperature is completely not 
adequate pest control temperature or treatment protocol for RFB insects.   
The average percentage mortality at 50 ?C for both RFB older larvae and adults 
are the highest values according to our results. However, the total heating block time in 
this temperature-time combination was 12 minutes, while the total intermittent RF 
heating time was 16.3 minutes at 50 ?C with an average of (16.3/6 =) 2.7 minutes per 
55  
one RF heating cycle. This is completely different exposure time compared to the 12 
minutes direct heating exposure in WSU heating block. 
In addition to these results the quality of peanuts at 60 ?C is not acceptable at 
least for most of fancy food application of peanuts according to the percentage of 
broken and shelled peanuts due to the excessive thermal treatment. 
 That means the RF treatment temperature should not be higher than 52 ?C.  
However there is still a huge research area regarding both RF heating protocol and RFB 
pest control in peanuts. For example we need to investigate the quality of peanuts using 
RF heating/drying at 52 ?C assuming that the results of dehydration will be very close 
to those at 50 ?C.  
During the RF treatment experiment, the temperature rise inside kernels is 
certainly different than the insect temperature due to the different loss factor. However 
the loss factor of insects is higher than of kernels and consequently the temperature of 
insects (with all life stages) is higher than the temperature inside kernels. This fact is 
hypothesis because there are other factors manipulate the heat transfer to the insects and 
those other factors need to be defined in the future research work 
The question is whether or not the total RF heating time in 6 cycles will be a 
good pest control scenario. 
 It?s also important to mention that during weighing the different treated samples 
of peanuts for the couple of weeks after the RF treatment to check weight loss in the 
sample, we have seen very active alive RFB adults and some of them even flying out of 
the peanuts. This fact we have noticed was for all the samples, regardless of the 
treatment temperature. It is also important to report that those RFB insects (adults and 
56  
larvae) used in WSU heating block experiments, and stayed alive after the experiments, 
did show activity for more than two weeks and some of them were re-productive.
57  
CONCLUSIONS 
 
We found that RF energy is a very economic alternative to conventional process 
for dehydration of peanuts. Energy and time required to dry peanuts using RF is 
negligible to the conventional process; we are comparing maximum use of RF machine 
of 30 minutes to couple of days of using conventional methods. This is due to the fact 
that heat is evolved from inside the peanuts in the dielectric properties in RF heating 
application.  
However some difficulties face the use of this new method. Most of the 
difficulties are related to the non-uniformity of peanuts. This obstacle could be 
overcome using multiple RF heating/drying runs, each followed with intermittent 
stirring and cooling process for sufficient time.  
Our recommendation is using a certain screening and/or sorting process in the 
industrial scale RF treatment of peanuts in which peanuts can be classified into groups 
of different sizes and types. This will help reduce non-uniformity problem. RF heating 
process is a straight line relation between time and target or treatment required 
temperature. Using RF energy for dehydration of peanuts will save time, and energy. 
The thermal death kinetic for RFB insects needs a lot of attention in future 
research. Our results are different than those already published before. We found out 
RFB adults and larvae are resistant to RF energy; even at higher temperatures (60 ?C). 
58  
The reason of resistance of RFB could be due to the fact that the mechanism of 
heat transfer in WSU heat block system to the RFB is different from that in RF energy 
heating.  In other words, the heat gained by insects in WSU system is by heat 
conduction from heating elements through the whole experiment period. While in the 
real RF system the heat gained by insects is generated by the interaction between 
electromagnetic field and insects.  Future research should be conducted to take this 
point into consideration before the RF energy is applied as a pest control of RFB in 
peanuts. 
59  
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Technology. American Peanut Research and Education Society, Yoakum, TX, 
pp. 458?485. 
64 
APPENDIXES 
65 
APPENDIX A 
 
Standard RF Heating/Cooling Cycles Results Data for 24 Experiments       
 
Total of 24 RF treatment experiments have been performed according to our 
protocol to over come the non-uniformity problem, i.e. each experiment consist of 6 RF 
heating runs each followed by intermittent cooling and stirring run. Every 8 RF 
experiments have been performed at a different treatment temperature; 40, 50, or 60 ?C. 
A summary for all these experiments is illustrated, with all the results and 
experiment circumstances in the next table. 
The abbreviations used in this table are as follows: 
Peanut:  Peanuts type used in the experiments, there are 3 main types. 
MTD: Maximum temperature difference between any 2 fiber temperature 
optics readings during the RF heating run. 
ATD: Average maximum temperature difference between any 2 fiber 
temperature optics readings during the RF heating run. 
TTT/6 runs: Total treatment/heating time for the 6 runs, Seconds. 
AHT/run: Average heating time per one single run, Seconds. 
IMC: Initial percentage moisture content in peanuts. 
FMC: Final percentage moisture content in peanuts after RF treatment 
cycles. 
%EW: Percentage evaporated water, or % moisture content loss after RF 
treatment. 
66 
DAT: Dry air temperature, ?C. 
WAT: Wet air temperature, ?C. 
67 
 
Exp. Date Peanut 
RF H. 
Temp MTD AMTD 
TTT/ 6 
runs 
AHT 
/run IMC FMC % EW DAT WAT 
1 10/6 Ga. green 40 36.3 18.9 525 88 15.87% 3.46% 
2 10/6 Ga. green 50 33.2 14.9 860 143 14.78% 4.54% 
3 10/6 Ga. green 60 37.5 20.2 1067 178 
19.32% 
14.07% 5.26% 
25.5 19.8 
4 10/7 Ga. green 40 44.7 23.4 526 88 14.86% 3.14% 
5 10/7 Ga. green 50 42.2 26.8 951 159 13.71% 4.29% 
6 10/7 Ga. green 60 49.4 39.3 1270 212 
18.00% 
12.74% 5.26% 
25.6 21.4 
7 10/10 Ga. green 40 30.9 14.1 736 123 12.19% 3.05% 
8 10/10 Ga. green 50 47.4 34.1 1002 167 11.70% 3.54% 
9 10/10 Ga. green 60 31.5 16.1 1427 238 
15.24% 
10.41% 4.83% 
22 17.3 
10 10/12 GA01R 40 49.7 35.2 689 115 21.09% 3.83% 
11 10/12 GA01R 50 46.3 33.8 1171 195 20.12% 4.80% 
12 10/12 GA01R 60 56.1 41.8 1511 252 
24.92% 
19.09% 5.83% 
23.8 19 
13 10/12 GA01R 40 38.1 23 859 143 18.25% 3.83% 
14 10/12 GA01R 50 55.3 36.1 984 164 17.79% 4.29% 
15 10/12 GA01R 60 50.7 35.8 1293 216 
22.08% 
16.85% 5.23% 
24.7 18.7 
16 10/13 GA01R 40 50.8 32.2 665 111 19.51% 3.20% 
17 10/13 GA01R 50 77.6 55.3 941 157 18.94% 3.77% 
18 10/13 GA01R 60 59.4 44.8 1261 210 
22.71% 
18.20% 4.51% 
23.2 18 
19 10/14 C99R 40 26.7 17.2 568 95 16.17% 4.55% 
20 10/14 C99R 50 31.1 16.7 964 161 14.60% 6.12% 
21 10/14 C99R 60 44.8 34.1 1227 205 
20.72% 
13.66% 7.06% 
24.5 18.7 
22 10/14 C99R 40 19.2 9.2 539 90 16.02% 4.40% 
23 10/14 C99R 50 46.3 33.1 956 159 14.62% 5.80% 
24 10/14 C99R 60 57.1 40.4 1265 211 
20.42% 
13.53% 6.89% 
24.7 17.8 
 
68 
APPENDIX B 
 
RF Heating Curves 
The following charts present the complete set of RF heating cycles and/or 
experiments. Every chart represents one single RF heating cycle with 4 curves. Each 
consists of fiber optic temperature-time readings. The line in the bold black color 
represents trend line or he average values of temperature reading of these four channels. 
 We fitted the average temperature readings in straight line where intersection is 
the initial starting temperature inside the kernel at every RF heating run. Each RF 
treatment experiment consisted of 6 RF runs at an average temperature of 40, 50 or 60 ?C 
as we mentioned before in this thesis, so every one page stands for the results of one 
complete RF experiment. We have eight complete experiments at each temperature with 
total of 24 complete RF experiments and 144 charts.   
All the slopes of these trend lines (144 trend lines) have been gathered in to find 
the average slope. It has been found that 0.217 is the average value of the slopes of all the 
144 trend lines, so that the following straight-line equation stands for the RF heating 
pattern for peanuts regardless the final required average heating temperature: 
inTtT +?= 217.0  
where: 
T:  final temperature inside peanuts kernel after one RF heating run, ?C 
t:  the run time, Sec. 
Tin:  Initial temperature inside the peanuts kernel. 
69 
RF Heating 
y = 0.2494x + 22.726
R2 = 0.9965
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
iu
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2143x + 25.759
R2 = 0.9969
0
10
20
30
40
50
60
0 50 100 150
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.216x + 26.432
R2 = 0.9889
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2122x + 25.853
R2 = 0.9984
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2313x + 26.301
R2 = 0.9984
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1766x + 27.569
R2 = 0.9974
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
70 
RF Heating 
y = 0.2741x + 21.489
R2 = 0.9866
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.189x + 26.995
R2 = 0.9964
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1431x + 26.926
R2 = 0.9963
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
mp
. (C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1697x + 25.429
R2 = 0.9917
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1227x + 26.497
R2 = 0.9898
0
20
40
60
80
0 40 80 120 160 200
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2221x + 28.915
R2 = 0.9986
0
20
40
60
80
0 50 100 150 200
Time (Sec)
Te
mp
. (
Ce
lcu
is)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
71 
RF Heating 
y = 0.2635x + 21.612
R2 = 0.9898
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2237x + 26.58
R2 = 0.9969
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
mp
. (C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1909x + 26.498
R2 = 0.9983
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1533x + 26.954
R2 = 0.9974
0
20
40
60
80
0 40 80 120 160 200 240
Time (Sec)
Te
mp
. (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2348x + 28.478
R2 = 0.9977
0
20
40
60
80
100
0 40 80 120 160 200
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2383x + 28.31
R2 = 0.9993
0
20
40
60
80
0 40 80 120 160 200
Time (Sec)
Te
mp
. (
Ce
lcu
is)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
72 
RF Heating 
y = 0.3468x + 22.374
R2 = 0.992
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2071x + 26.774
R2 = 0.9917
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1592x + 27.238
R2 = 0.9867
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1838x + 27.135
R2 = 0.9938
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2051x + 27.609
R2 = 0.9966
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
mp
. (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1373x + 27.335
R2 = 0.9928
0
20
40
60
80
0 40 80 120
Time (Sec)
Te
mp
. (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
73 
RF Heating 
y = 0.252x + 22.146
R2 = 0.9922
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2872x + 27.529
R2 = 0.9969
0
20
40
60
80
0 40 80 120 160
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.1872x + 26.143
R2 = 0.9945
0
20
40
60
80
100
0 40 80 120 160 200
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2236x + 29.469
R2 = 0.9955
0
20
40
60
80
100
0 40 80 120 160
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1365x + 27.777
R2 = 0.9905
0
20
40
60
80
0 50 100 150 200
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1342x + 29.143
R2 = 0.9953
0
20
40
60
80
0 40 80 120 160 200
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
74 
RF Heating 
y = 0.2583x + 22.642
R2 = 0.9973
0
20
40
60
80
100
0 40 80 120 160 200
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2583x + 22.642
R2 = 0.9973
0
20
40
60
80
100
0 40 80 120 160 200
Time (Sec)
Te
mp
. (
Ce
lcu
is)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1785x + 29.612
R2 = 0.9977
0
20
40
60
80
0 40 80 120 160 200 240
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1455x + 28.244
R2 = 0.994
0
20
40
60
80
0 50 100 150 200 250
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.203x + 35.374
R2 = 0.9795
0
20
40
60
80
100
120
0 40 80 120 160 200 240
Time (Sec)
Te
mp
. (
Ce
lcu
is)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.173x + 27.899
R2 = 0.9975
0
20
40
60
80
100
120
0 40 80 120 160 200 240
Time (Sec)
Te
m
p. 
(C
elc
uis
)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
75 
RF Heating 
y = 0.3091x + 16.346
R2 = 0.9919
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.183x + 21.27
R2 = 0.9926
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1277x + 23.518
R2 = 0.9931
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
er
atu
re 
(C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1472x + 25.16
R2 = 0.8399
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.1908x + 23.473
R2 = 0.997
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2112x + 23.587
R2 = 0.9963
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
76 
RF Heating 
y = 0.2569x + 22.405
R2 = 0.9798
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1668x + 22.144
R2 = 0.9941
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.1679x + 23.213
R2 = 0.9943
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2225x + 23.826
R2 = 0.995
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2107x + 24.911
R2 = 0.9948
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1746x + 24.065
R2 = 0.9959
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
77 
RF Heating 
y = 0.1911x + 22.443
R2 = 0.9957
0
20
40
60
80
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2549x + 16.236
R2 = 0.9921
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1498x + 23.628
R2 = 0.9935
0
20
40
60
80
0 40 80 120 160 200 240 280
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1361x + 24.288
R2 = 0.9912
0
20
40
60
80
0 40 80 120 160 200 240 280 320
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.1938x + 25.684
R2 = 0.9986
0
20
40
60
80
0 40 80 120 160 200 240 280
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1521x + 26.057
R2 = 0.996
0
20
40
60
80
100
0 40 80 120 160 200 240 280
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
78 
RF Heating 
y = 0.2355x + 19.661
R2 = 0.9969
0
20
40
60
80
0 40 80 120
Time (sec)
Te
mp
era
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2197x + 24.165
R2 = 0.9924
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2354x + 27.14
R2 = 0.9676
0
20
40
60
80
0 20 40 60 80 100 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2198x + 32.263
R2 = 0.9828
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.159x + 27.526
R2 = 0.9785
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2801x + 30.587
R2 = 0.9752
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
79 
RF Heating 
y = 0.1882x + 21.02
R2 = 0.9953
0
20
40
60
80
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1981x + 26.666
R2 = 0.9919
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2337x + 30.856
R2 = 0.9566
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2236x + 29.469
R2 = 0.9955
0
20
40
60
80
100
0 40 80 120 160
Time (Sec)
Te
m
p.
 (C
elc
ui
s)
Average Channel 1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1771x + 27.853
R2 = 0.9918
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1301x + 27.794
R2 = 0.992
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
mp
era
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
80 
RF Heating 
y = 0.3692x + 22.108
R2 = 0.9975
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.3033x + 29.254
R2 = 0.9518
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2762x + 27.17
R2 = 0.9709
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1789x + 26.209
R2 = 0.9957
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
er
atu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1215x + 26.745
R2 = 0.9991
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2012x + 27.331
R2 = 0.9959
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
81 
RF Heating 
y = 0.2755x + 23.828
R2 = 0.9895
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.3223x + 27.597
R2 = 0.9848
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1961x + 28.442
R2 = 0.9642
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1565x + 26.884
R2 = 0.9979
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2114x + 32.288
R2 = 0.9673
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2589x + 30.839
R2 = 0.9849
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
82 
RF Heating 
y = 0.1848x + 22.687
R2 = 0.9988
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1854x + 27.811
R2 = 0.9927
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1595x + 28.292
R2 = 0.9962
0
20
40
60
80
100
0 40 80 120 160 200 240 280
Time (sec)
Te
mp
era
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.3356x + 28.386
R2 = 0.9905
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.1889x + 32.589
R2 = 0.9776
0
20
40
60
80
100
120
0 40 80 120 160 200 240 280
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2199x + 32.614
R2 = 0.9726
0
20
40
60
80
100
120
0 40 80 120 160 200 240
Time (sec)
Te
mp
er
atu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
83 
RF Heating 
y = 0.1926x + 22.711
R2 = 0.9979
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
era
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2089x + 32.36
R2 = 0.9537
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2365x + 33.248
R2 = 0.9389
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
era
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1767x + 28.047
R2 = 0.9981
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
 
 
 
84 
RF Heating 
y = 0.2176x + 23.914
R2 = 0.9945
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
mp
era
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.3038x + 36.693
R2 = 0.9154
0
20
40
60
80
100
120
0 40 80 120 160 200
Time (sec)
Te
mp
er
atu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.1882x + 29.103
R2 = 0.9957
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
mp
er
atu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.3309x + 30.306
R2 = 0.988
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
mp
er
atu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
 
 
 
 
 
 
 
 
85 
RF Heating 
y = 0.1827x + 23.391
R2 = 0.999
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.183x + 28.695
R2 = 0.9971
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2462x + 33.714
R2 = 0.9626
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.1921x + 32.051
R2 = 0.9837
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
 
 
 
86 
RF Heating 
y = 0.2598x + 17.286
R2 = 0.9843
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2587x + 24.884
R2 = 0.9986
0
20
40
60
80
0 40 80 120
Time (sec)
Te
mp
er
atu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2087x + 24.861
R2 = 0.9968
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2956x + 26.116
R2 = 0.9929
0
20
40
60
80
0 20 40 60 80
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2551x + 25.155
R2 = 0.9922
0
20
40
60
80
0 40 80
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2241x + 25.37
R2 = 0.9956
0
20
40
60
80
0 40 80 120
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
87 
RF Heating 
y = 0.2793x + 17.567
R2 = 0.9951
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2168x + 25.408
R2 = 0.9966
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
er
at
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2485x + 26.827
R2 = 0.9983
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2083x + 27.561
R2 = 0.9987
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2086x + 27.589
R2 = 0.9942
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.167x + 28.194
R2 = 0.9985
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
era
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
88 
RF Heating 
y = 0.2179x + 18.857
R2 = 0.9985
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2254x + 26.512
R2 = 0.998
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
mp
era
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2251x + 28.48
R2 = 0.9993
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
mp
era
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.175x + 25.183
R2 = 0.9991
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2356x + 29.686
R2 = 0.9963
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
mp
er
at
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2159x + 30.404
R2 = 0.9931
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
89 
RF Heating 
y = 0.2179x + 18.857
R2 = 0.9985
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2254x + 26.512
R2 = 0.998
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2251x + 28.48
R2 = 0.9993
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.175x + 25.183
R2 = 0.9991
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
mp
era
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
 
RF Heating 
y = 0.2356x + 29.686
R2 = 0.9963
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2159x + 30.404
R2 = 0.9931
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
90 
RF Heating 
y = 0.3316x + 21.646
R2 = 0.9951
0
20
40
60
80
0 20 40 60 80 100
Time (sec)
Te
m
pe
rat
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2174x + 25.305
R2 = 0.993
0
20
40
60
80
0 20 40 60 80 100 120
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2623x + 25.343
R2 = 0.9922
0
20
40
60
80
0 20 40 60 80 100
Time (sec)
Te
mp
era
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2436x + 25.926
R2 = 0.997
0
20
40
60
80
0 20 40 60 80 100
Time (sec)
Te
mp
era
tu
re 
(C
elc
ius
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2129x + 26.543
R2 = 0.9965
0
20
40
60
80
0 20 40 60 80 100
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2828x + 26.189
R2 = 0.9958
0
20
40
60
80
0 20 40 60 80 100
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
91 
RF Heating 
y = 0.2238x + 21.344
R2 = 0.9952
0
20
40
60
80
0 40 80 120 160
Time (sec)
Te
mp
era
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2939x + 27.08
R2 = 0.9923
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.1589x + 25.938
R2 = 0.9966
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
mp
er
at
ur
e (
Ce
lci
us
)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
RF Heating 
y = 0.2038x + 29.719
R2 = 0.9884
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
RF Heating 
y = 0.2653x + 27.256
R2 = 0.9955
0
20
40
60
80
100
0 40 80 120 160
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)
A
RF Heating 
y = 0.189x + 27.659
R2 = 0.9987
0
20
40
60
80
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
Average Channel1
Channel 2 Channel 3
Channel 4 Linear (Average)  
92 
RF Heating 
y = 0.2911x + 22.147
R2 = 0.9949
0
20
40
60
80
100
0 40 80 120 160 200
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
"Average" Channel1
Channel 2 Channel 3
Channel 4 Linear ("Average")
RF Heating 
y = 0.2369x + 26.659
R2 = 0.9986
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
iu
s)
"Average" Channel1
Channel 2 Channel 3
Channel 4 Linear ("Average")  
RF Heating 
y = 0.25x + 28.928
R2 = 0.9953
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re 
(C
elc
ius
)
"Average" Channel1
Channel 2 Channel 3
Channel 4 Linear ("Average")
RF Heating 
y = 0.1796x + 27.79
R2 = 0.9998
0
20
40
60
80
100
0 40 80 120 160 200 240
Time (sec)
Te
m
pe
ra
tu
re 
(C
elc
ius
)
"Average" Channel1
Channel 2 Channel 3
Channel 4 Linear ("Average")  
RF Heating 
y = 0.2706x + 30.737
R2 = 0.9829
0
20
40
60
80
100
120
0 40 80 120 160 200
Time (sec)
Te
mp
era
tu
re
 (C
elc
ius
)
"Average" Channel1
Channel 2 Channel 3
Channel 4 Linear ("Average")
RF Heating 
y = 0.1536x + 27.449
R2 = 0.998
0
20
40
60
80
100
0 40 80 120 160 200 240 280
Time (sec)
Te
m
pe
ra
tu
re
 (C
elc
ius
)
"Average" Channel1
Channel 2 Channel 3
Channel 4 Linear ("Average")