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Nondestructive Measurement of Moisture Content Using a Parallel-Plate Capacitance Sensor for Grain and Nuts Chari V. Kandala and Jaya Sundaram
Abstract—A simple, low-cost instrument that measures impedance and phase angle was used along with a parallel-plate capacitance system to estimate the moisture content (MC) of in-shell peanuts and yellow-dent field corn. Moisture content of the field crops is important and is measured at various stages of their processing and storage. A sample of about 150 g of in-shell peanuts or corn was placed separately between a set of parallel plate electrodes and the impedance and phase angle of the system were measured at frequencies 1 and 5 MHz. A semi-empirical equation was developed for peanuts and corn separately using the measured impedance and phase angle values, and the computed capacitance and the MC values obtained by standard air-oven method. The multilinear regression (MLR) method was used for the empirical equation development using an Unscrambler 9.7 data analyzer. In this paper, a low-cost impedance analyzer designed and assembled in our laboratory was used to measure the impedance and phase angles. MC values of corn samples in the moisture range of 7% to 18% and in-shell peanuts in the moisture range of 9% to 20%, not used in the calibration, were predicted by the equations and compared with their standard air-oven values. For over 96% of the samples tested from both crops, the predicted MC values were within 1% of the air-oven values. This method, being nondestructive and rapid, will have considerable application in the drying and storage processes for peanuts, corn, and similar field crops. Index Terms—Capacitance, corn, impedance analyzer, moisture content, parallel-plate electrodes, peanuts, phase angle.
I. INTRODUCTION
M
OISTURE content is an important factor to be measured and controlled for a variety of grain such as corn and wheat, and nuts such as peanuts and pecans. Corn and peanuts are popular crops produced in North America and cultivated in considerably large areas. Both these food materials and their products are exported for consumption throughout the country and abroad. MC measurement of these crops is important in harvesting, storing, and processing. For yellow-dent field corn a MC value not exceeding 14.5% during purchase or sale, and below 13% for storage is recommended [1]. Peanuts have to be dried to reduce their MC to less than 10.5% to meet the grading Manuscript received August 13, 2009; revised October 20, 2009; accepted January 05, 2010. Date of current version May 21, 2010. The associate editor coordinating the review of this paper and approving it for publication was Prof. Ralph Etienne-Cummings. The authors are with the National Peanut Research Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Dawson, GA 39842 USA (e-mail:
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2010.2041446
standards [2]. Thus, various methods and devices were developed since the beginning of the last century for the measurement of MC.1 In 1908, Briggs [3] discovered a linear relationship between the logarithm of the electrical resistance of wheat and its MC, and since then, many types of electrical moisture meters were developed to rapidly determine the MC of different types of grain. Several years later, moisture dependence of the dielectric constant of aqueous materials was established [4], and this led to the development of moisture meters that made use of certain dielectric related measurements for moisture determination. Presently available MC measuring instruments in the U.S. commonly use capacitance sensing for moisture measurement, but moisture sensing by dc conductance or resistance are common in Japan and some other countries. A commercial instrument developed in Japan is widely used for measurement of MC in single kernels of corn and other grain material (Shizuoka-Seiki Model CTR-800). This machine senses the conductance of the kernel as it is crushed between two roller electrodes and determines the MC of the kernel. This method is for single kernels only and is destructive. Using presently available capacitance type moisture meters for peanuts is laborious and time consuming, because the peanuts have to be shelled and cleaned before measurements. The test samples are usually discarded, resulting in cumulative loss of edible grains. A method was developed earlier [5] to determine the MC of in-shell peanuts (peanut pods) from the impedance measurements made on a parallel-plate system holding the peanuts between them, using either an impedance meter or an LCR bridge made by Hewlett-Packard2 and Agilent Technologies, respectively. The measurements made were on seven or eight pods held between the parallel-plates and thus it was possible to measure the average moisture of a small sample. This small parallel-plate system would be useful for detecting any high moisture peanuts blended in a larger sample of peanuts that might have escaped detection while MC measurements were made on bulk samples (300 to 500 g). However, it would be convenient to measure MC on moderately large quantities of grain samples, say 100 to 150 g, that would yield better estimation of the average MC. While the commercial impedance meters performed well in the establishment of the impedance method for moisture determination, they have several extra features that are not needed for this work but make them expensive. Thus, a low-cost meter that would measure the impedance parameters at the required frequencies on a moderately large sample of grains would 1Moisture
contents are expressed in percent wet basis throughout this paper. of company or trade names is for purpose of description only and does not imply endorsement by USDA. 2Mention
1530-437X/$26.00 © 2010 IEEE
KANDALA AND SUNDARAM: NONDESTRUCTIVE MEASUREMENT OF MOISTURE CONTENT
Fig. 1. Moisture dependence of the dielectric constant of yellow-dent field corn at indicated frequencies [7].
be useful in the estimation of their moisture content. A method was developed to measure the MC of peanuts using a low cost impedance meter [6] to measure the impedance and phase angle at 1, 5, and 9 MHz of a parallel-plate capacitor holding the peanuts between the plates. By this method, the predicted MC values for 92% of the samples tested were found to be within 1% of the standard MC values. To further improve the measurement techniques, in this work, the impedance and phase angles were measured at only two frequencies (1 and 5 MHz) and an attempt was made to predict the MC of in-shell peanuts and corn samples in the moisture ranges of 8% to 24% and 6% to 20%, respectively, which is commonly required in the industry. This method can be extended to the MC determination of grain such as wheat and barley and can help to develop a commercial instrument that can measure MC of a variety of grain and nuts. II. MATERIALS AND METHODS A. Basic Principles The dielectric constant of a hygroscopic material depends on its moisture content. Moisture dependence of the dielectric constant of shelled yellow-dent field-corn between 1 MHz and 11 GHz was well documented earlier [7]. The variation in the dielectric constant at 1, 5, and 9 MHz were earlier found to be useful parameters in the estimation of the MC of grain such as corn and wheat [8]. The increase in dielectric constant with MC was more pronounced at the lower frequencies of 1, 5, and 20 MHz than at higher frequencies (Fig. 1). For a parallel-plate capacitor with plate area A and plate separation d, filled with a dielectric material, the difference in dielectric constants at two frequencies can be written as (1) where and are the dielectric constants of the material beand are the capacitance of the partween the plates and is the permittivity allel-plate system at the two frequencies. farad/m). of free space ( If the space between the parallel-plates is filled with a dielecwould tric of the same A and D but with different MC, have given a good estimate of the moisture content present in the dielectric. In the case of materials such as grain and nuts, was highly influenced by the size and shape of the grains. Two other electrical parameters, dissipation factor, D and phase angle were also measured at the two frequencies, 1 and 5 MHz. The measurements were made on grains that held between and in contact with two parallel-plates. The dissipation
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factor D, based on a parallel equivalent RC circuit, is the tangent and related to the phase angle as of the loss angle . The differences in the values of these two parameters at the two frequencies, and , were , incorporated into an empirical equation along with and from this equation the moisture content MC of the grain sample was calculated [9]. The phase angle change, accounts for the loss factor while the dissipation factor differrepresents the quality factor of the grain mateence, rial. Empirical equations were developed using measured values of capacitance, phase angle and dissipation factors of calibration samples at known moisture levels. MC of unknown samples was then predicted by these equations. The method worked satisfactorily for both single peanut kernels and peanut pods (in-shell peanuts) [9], [10]. When two parallel-plates were placed on the inside walls of a cylindrical tube made of a nonconducting material, they form a capacitor system. As the space between these plates is filled with grain samples, some of them filling the cylinder would not be in contact with either of the two plates. They would be lying in the middle, and in contact with the grain around them. Air gaps would be generated randomly between the grains. These air gaps act as additional capacitors, some in series and some in parallel, to the parallel-plate capacitors. The low-cost impedance meter designed by us would measure impedance (Z) and phase angle of a parallel-plate system at frequencies of 1, 5, and 9 MHz from which the capacitance (C) of the system could be computed. The method was extended to larger samples of peanuts by using a cylindrical tube with the parallel-plate electrodes fixed to its inner walls [11], [12]. Using the differences in the values of C, , and Z at the frequencies 1 and 5 MHz in an empirical equation, the average MC of 100 to 150 g of corn and in-shell peanut samples could be determined to an acceptable accuracy. B. Equipment Three frequencies, 1, 5, and 9 MHz were generated by crystal oscillators as described previously (12). One and 5 MHz signals generated as shown in the block diagram Fig. 2(a), were applied to the parallel-plate electrode system alternately by switching through a multiplexer. at the two The values of impedance (Z) and phase angle frequencies were read from the instrument and from the values of Z and the real and imaginary parts of the impedance R and and X, at each frequency is calculated as . The value of capacitance C, of the parallel-plate system with the grain sample between them is given as (2) The power supply consisted of two 12-V rechargeable lead-acid batteries from which the voltages, required to operate the circuits were derived, making the instrument portable. A laptop computer was used to register data from the system, compute the calibration constants and calculate the moisture content. The experimental setup is shown in Fig. 2(b). A cylindrical acrylic tube, fitted with a set of parallel-plate electrodes, served as the moisture-sensor (12). The cylinder (T1) is 190-mm long and 50-mm in diameter. The parallel-plates are
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week. The jars were periodically rotated during this period to aid uniform distribution of moisture. Thus, 12 moisture levels with nominal MC values of 8%, 9%, 11%, 12%, 13%, 15%, 16%, 17%, 18%, 19%, 20%, and 23% were available for calibration and validation. The jars were removed from cold storage and the pods were allowed to reach room temperature in the jars before the measurements were made. D. Corn Samples Yellow-dent field corn samples harvested in 2007 from Nebraska were used in this study. The initial moisture content of the corn lot, as determined with an electronic moisture meter, was about 6%. This corn lot was divided into 12 sublots and was placed in glass jars. Similarly, like for peanuts, appropriate quantities of water were added to each jar, to raise the moisture levels in steps of about 1% moisture, to obtain a total of 12 moisture levels ranging between 6% and 20%. Thus, 12 moisture levels with nominal MC values of 6%, 7%, 8%, 9%, 10%, 11%, 13%, 15%, 16%, 17%, 18%, and 19% were available for calibration and validation. E. Measurements
Fig. 2. (a) Block diagram of the impedance and phase angle measuring circuit at 1, 5, and 9 MHz Z is the impedance of the sample to be measured and is calculated as Z = R (e =e ) where R is the value of the range resistor adjusted for each frequency. Phase angle, is proportion to e . (b) RF Impedance measuring system: 1) impedance meter, 2) cylinder with electrodes, and 3) computer.
two rectangular aluminum plates, 140-mm-long and 50-mmwide fixed to the inside walls of the cylinder. The distance of separation between the parallel-plates for T1 was 42-mm. The cylinder was filled with the sample and the impedance measurements were taken. With the sample occupying the space between the electrodes, the analyzer measured the impedance and phase angle of this electrode system at 1 and 5 MHz, and a computer controlled and collected the data from the analyzer. A Mettler AE 163 electronic balance was also interfaced to this computer to record the wet and dry weights of the grain samples to obtain their air-oven moisture values. Using these measurements in an empirical equation, the computer was programmed to calculate the moisture content in the corn and peanut samples. C. Peanut Samples Peanuts of the Georgia Green cultivar harvested in 2007, dried, cleaned, and stored at 4 C at the National Peanut Research Laboratory, were used for these studies. The initial moisture content of these peanuts was about 7%. These peanuts were divided into 12 sublots and were placed in glass jars. Appropriate quantities of water were added to each jar, to raise the moisture levels in steps of about 2% moisture, to obtain a total of 11 moisture levels ranging between 8% and 24%. The jars were sealed and allowed to equilibrate at 4 C for one
The peanut pod samples, after conditioning, were separated into two groups. The calibration group consisted of samples taken from the nominal MC levels of 8%, 11%, 13%, 16%, 18, 19%, and 23% and the validation group was from nominal MC levels of 9%, 12%, 15%, 17%, and 20%. The MC values of the samples in each of these 12 moisture levels (called the sublots), were obtained by the standard air-oven method [13]. Three samples, each weighing about 100 g were taken from each sublot and placed in small aluminum containers and their wet weights were taken. The containers were placed in a hot-air oven at 130 C for 6 h. At the end of the heating period the containers were removed and weighed again to determine the dry weight of the samples. Moisture content (wet weight basis) was determined for each sample as the percentage ratio of the weight loss to the original wet weight of the sample. The average of the three moisture values, for each sublot thus obtained, were labeled as the oven MC value of that sublot. Impedance measurements were made on 30 samples from each sublot. Peanut samples were transferred from the jars into the cylindrical electrode system, till the space between the two plates of the cylinder was completely filled. The cylinder accommodated about 100 to 150 g of peanut pods. The room temperature during the measurements varied from 21 C to 23 C. Peanut pods from one of the jars were transferred into the cylinder with the drawer sitting fully inside the box until the space between the two plates of the cylinder was filled. In this position, measurement of impedance were taken on the moisture meter at (Z) and phase angle 1 and 5 MHz. The sample was then collected in the drawer by gently pulling it out and tapping on the cylinder for the peanuts to drop down. The drawer was emptied and reset in the box. The procedure was repeated on all peanut samples (sublots) in rest of the jars. Similarly, the corn samples, after conditioning, were separated into two groups. The calibration group consisted of samples taken from the nominal MC levels of 6%, 9%, 11%, 13%, 16%, 18%, and 19% and the validation group was from
KANDALA AND SUNDARAM: NONDESTRUCTIVE MEASUREMENT OF MOISTURE CONTENT
TABLE I COMPARISON OF MEAN MC VALUES DETERMINED BY THE AIR-OVEN AND PARALLEL-PLATE CYLINDER METHODS FOR CALIBRATION GROUPS OF PEANUTS
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TABLE II FITNESS MEASURES FOR THE CALIBRATION SET OF PEANUTS
TABLE III COMPARISON OF MEAN MC VALUES DETERMINED BY THE AIR-OVEN AND PARALLEL-PLATE CYLINDER METHODS FOR VALIDATION GROUPS OF PEANUTS
nominal MC levels of 7%, 8%, 10%, 15%, and 17%. The MC values of the samples in each of these 12 moisture levels (called the sublots), were obtained by the standard air-oven method at 103 C for 72 h [14]. As in the case of peanut pods, dry weight of the corn and moisture content of the corn on wet weight basis was determined. The average of three moisture values, for each sublot thus obtained, were labeled as the oven MC value of that sublot. Impedance measurements were made on 30 samples from each sublot. The computer was programmed to make 30 measurements and record the average value for each sample. Measurements of impedance (Z) and phase angle were taken on the moisture meter at 1 and 5 MHz as per the procedure followed for peanut samples. Using the MC values and the measured impedance values of the calibration group, the calibration constants were determined for peanut pods and corn samples, using MLR techniques with Unscrambler [15] software. By using the respective constants and the measured impedance values of the samples in the validation group, the MC of each peanut and corn sample was calculated and compared with the MC values obtained by the air-oven method. III. RESULTS AND DISCUSSION A. Peanut Pods From the measured values of impedance and phase angle the capacitance value for each sample was obtained using (2). From the capacitance value, measured values of impedance and phase angle, and the oven determined MC value of the samples in the calibration lots the semi-empirical equation developed by the multilinear regression (MLR) model was
(3) where and are the capacitance, phase angle, and and are the capaciimpedance values at 1 MHz and tance, phase angle, and impedance values at 5 MHz. Using (3), the MC of each pod sample in the seven calibration lots was calculated, averaged over the 30 samples in each MC level, and the results are shown in Table I along with the MC values determined by the air-oven method. The standard deviations and the difference between the oven and calculated MC values (difference) are also shown. The predictability shown in the last column of Tables I and III is defined as the percentage of samples for which moisture content was calculated within 1% of the air-oven MC values. An value of 0.97, a low value
TABLE IV FITNESS MEASURES FOR THE VALIDATION SET OF PEANUTS
of SEC (shown in Table II), relatively small differences in the calculated and the oven MC values, and acceptable standard deviation values suggest the suitability of (3) for MC predictions. The standard error of calibration (SEC)3 was 0.82 as shown in Table II which contains fitness measures for the calibration set. The mean calculated values agree very closely with the oven MC values. The MC values of the pod samples in the five validation lots calculated by using (3) are shown in Table III. The calculated mean moisture values were compared with the air-oven values and the results are summarized in Table III, along with standard deviations, difference and predictability. The predictability was better than 95% for all the levels. The errors in individual samples in a moisture level could be due to non-homogeneity of moisture distribution in the sample. The standard error of prediction (SEP)4 was 0.70 as shown in Table IV. The bias value of , close to zero, indicates the closeness of the mean calculated values to the standard values confirming the fitness of the prediction equation (3) [16]. Comparison of the MC values determined by the air-oven and impedance methods using the parallel-plate cylindrical sample holder is shown in Fig. 3. It can be seen that the average values predicted by (3) agreed well with the air-oven values. The time taken for one sample measurement was approximately one minute. There is no need to shell, weigh, or measure the volume of the peanuts. B. Yellow-Dent Field Corn From the measured values of impedance and phase angles the capacitance value for each sample was obtained using (2). Using the capacitance value, measured values of impedance and phase angle, at the frequencies 1 and 5 MHz and the oven determined
0 0
3SEC = ((1)=(n p 1) e ) where n is the number of observations, p is the number of variables in the regression equation with which the calibration is performed, and e is the difference between the observed and reference value for the ith observation. 4SEP = ((1)=(n 1) (e e) ) where n is the number of observations, e is the difference in the moisture content predicted and that determined by the reference method for the ith sample, and e is the mean of e for all of the samples.
0
0
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Fig. 3. Comparison of oven and predicated MC values for the calibration and predication sublot of peanuts.
Fig. 4. Comparison of oven and predicated MC values for both the calibration and predication sublots of corn. TABLE VI COMPARISON OF AVERAGE MC OF CORN DETERMINED BY THE IMPEDANCE METER AND THE AIR-OVEN METHODS (CALIBRATION SET)
TABLE V FITNESS MEASURES FOR THE CALIBRATION SET OF CORN
MC value of the samples in the calibration lots a MLR model was developed as
(4) and are the capacitance, phase angle, and where and are the capaciimpedance values at 1 MHz and tance, phase angle, and impedance values at 5 MHz. Using the MLR (4), MC values of the samples in both calibration and validation sets were determined, and plotted against their standard air-oven MC values (Fig. 4). The fitness measures for the calibration set are shown in Table V. The calibration set value of 0.98 and the standard error of calibration had an (SEC) was 0.61. The average MC values (of 30 samples) at each moisture level for the calibration set, as calculated by the MLR (4) along with
the corresponding air-oven moisture values, their standard deviations and the difference between the two moisture values, are shown in Table VI. The predictability shown in Tables VI and VIII are the percentage of samples in each moisture group whose values are predicted within 1% of their air-oven values. For the validation set, the fitness measures are shown in Table VII. The validation value of 0.96 and the standard error of prediction set had an (SEP) was 0.70. Here too, a low bias value of 0.15 indicates the closeness of the mean calculated values to the standard values confirming the fitness of the prediction (4). The average MC values (30 samples) of each of the six moisture levels of the validation group (which are not used in the
KANDALA AND SUNDARAM: NONDESTRUCTIVE MEASUREMENT OF MOISTURE CONTENT
TABLE VII FITNESS MEASURES FOR THE VALIDATION SET OF CORN
TABLE VIII COMPARISON OF AVERAGE MC OF CORN DETERMINED BY THE IMPEDANCE METER AND THE AIR-OVEN METHODS (VALIDATION SET)
calibration) as predicted by (4) are shown in Table VIII along with their standard air-oven values. Also shown are the standard deviations, the difference between the two moisture values and the predictabilities. The SEP is very close to the SEC value and both the calibration and the validation lots have a good value. The predictability for the validation sets are above 93% for any moisture level and averaged 98.6% over all the levels. The overall predictability for peanuts (Table III) was 96.6% while for corn it was 98.6% (Table VIII). Variations in temperature and relative humidity, while measurements were made, may have caused some variations in the predicted values of the MC. The oil content and protein in the peanuts might have some influence on the predicted MC values. Including temperature as a variable in the prediction equations for both the crops could improve the predictability. In case of peanuts, measuring the oil content (OC) of peanuts by a similar method and including the OC also as a variable in their prediction equation could further improve the MC predictions, both in the laboratory and field environments. IV. CONCLUSION Using a simple low-cost impedance meter and a capacitance sensor, the moisture content of in-shell peanuts and corn samples can be determined rapidly and nondestructively. This method is suitable for similar types of other grains and nuts. Moisture content of peanuts can be determined without shelling and cleaning them, as is being presently done. There are no requirements on the weight or volume of the sample being tested. This system is nondestructive and thus could be used repetitively on about 100 g to 150 g of samples to obtain a better average MC value than from measurements on a bulk quantity. The MC of the corn samples tested was between 6% and 20% and the peanut samples was between 8% and 24% (most useful range for drying purposes in each case). The predicted MC values were within 1% of the air-oven values for over, 98% of the samples tested for corn, and 96% for in-shell peanuts. A practical instrument developed on these lines could be of considerable use in both grain and peanut industries. REFERENCES [1] “Identity preserved grain laboratory/corn tests,” Illinois Crop Improvement Assoc., Inc. [Online]. Available: http://www.ilcrop.com [2] “AMS farmers stock peanuts inspection instructions,” USDA, Washington, DC, 2000.
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[3] L. G. Briggs, “An electrical resistance method for the rapid determination of the moisture content of grain,” Bur. Plant Industry Cir. No. 20, 1908, USDA. [4] S. O. Nelson, “Use of electrical properties for grain-moisture measurement,” J. Microw. Power, vol. 12, no. 1, pp. 67–72, 1977. [5] C. V. K. Kandala and S. O. Nelson, “RF impedance method for nondestructive moisture content determination for in-shell peanuts,” Meas. Sci. Technol., vol. 18, pp. 991–996, 2007. [6] C. V. K. Kandala and C. L. Butts, “Nondestructive moisture content determination of in-shell peanuts by RF impedance method,” Trans. Am. Soc. Agricult. Eng., vol. 49, no. 3, pp. 729–733, 2006. [7] S. O. Nelson, “Frequency and moisture dependence of the dielectric properties of high-moisture corn,” J. Microw. Power, vol. 13, no. 2, pp. 213–218, 1978. [8] C. V. K. Kandala, S. O. Nelson, and K. C. Lawrence, “Moisture determination in single kernels of corn: A nondestructive method,” Trans. Amer. Soc. Agricult. Eng., vol. 31, no. 6, pp. 1890–1895, 1988. [9] C. V. K. Kandala and S. O. Nelson, “Measurement of moisture content in single kernels of peanuts: A nondestructive electrical method,” Trans. Amer. Soc. Agricult. Eng., vol. 33, no. 2, pp. 567–572, 1990. [10] C. V. K. Kandala, “Moisture determination in single peanut pods by complex RF impedance measurement,” IEEE Trans. Instrum. Meas., vol. 53, no. 6, pp. 1493–1496, 2004. [11] C. V. K. Kandala, C. L. Butts, and S. O. Nelson, “Capacitance sensor for nondestructive measurement of moisture content in nuts and grain,” IEEE Trans. Instrum. Meas., vol. 56, no. 5, pp. 1809–1813, 2007. [12] C. V. K. Kandala, C. L. Butts, and M. C. Lamb, “Moisture content determination for in-shell peanuts with a low-cost impedance analyzer and capacitor sensor,” Trans. Amer. Soc. Agricult. Eng., vol. 51, no. 4, pp. 1377–1381, 2008. [13] “Moisture measurements: Peanuts,” ASAE Std. St. Joseph, MI ASAE. Amer. Society of Agricultural Eng., pp. 604–605, Feb. 2003, Available: ASAE S410.1. [14] “Moisture measurement—Un-ground grain and seeds,” ASAE Standards 1990, St. Joseph, MI, ASABE. [15] “The Unscrambler,” ver. 9.7, Camo Software, Inc.. [16] D. S. Moore, The Basic Practice of Statistics, 2nd ed. New York: Freeman, 2000. Chari V. Kandala was born in Kakinada, India. He received the M.S. degree in physics from the Indian Institute of Technology, Madras, India, and another M.S. degree in physics and Ph.D. degree in biological and agricultural engineering from the University of Georgia, Athens. He was an Assistant Professor of Physics at the G.B.P. University, Pantnagar, India, during 1968 to 1975, and a Senior Engineer in Electronics and Instrumentation at ICRISAT, Hyderabad, India, from 1975 to 1983. Since 1985, he has been associated with the Agricultural Research Service of the U.S. Department of Agriculture. Presently, he is a Research Agricultural Engineer with the National Peanut Research Laboratory, Dawson, GA, working on developing sensors for nondestructive measurement of quality of nuts and grain using RF Impedance and NIR methods. Dr. Kandala is a Senior Member of IEEE, Member of American Society of Biological and Agricultural Engineers, and the International Society for Optical Engineering.
Jaya Sundaram received the B.S. and M.S. degrees in agricultural engineering from Tamil Nadu Agricultural University, India, in 1997 and 1999, respectively, and the Ph.D. degree in food engineering from the Indian Institute of Technology, Kharagpur, India, in 2003. From 2004 to 2008, she worked as a Research Associate in the Food Science Program at the University of British Columbia, Canada, in the area of biomaterials processing for food, pharmaceutical, and biomedical applications. Since March 2008, she has been with the Agricultural Research Service of USDA, National Peanut Research Laboratory, Dawson, GA. Her research interests are development of nondestructive methods for grading and quality analysis of peanuts and grains. Dr. Sundaram received a Postdoctoral Fellowship Award from the Natural Science and Engineering Research Council of Canada (NSERC) in 2005.
