NONDESTRUCTIVE MOISTURE CONTENT DETERMINATION OF IN-SHELL PEANUTS BY RF IMPEDANCE METHOD C. V. K. Kandala, C. L. Butts ABSTRACT. Measuring the moisture content of peanuts without shelling the peanuts, particularly during buying/selling and drying, would be beneficial to the peanut industry. Previous research has shown that radio frequency (RF) impedance measurements of capacitance (C) and phase angle (q) using a parallel-plate system could be used to estimate the moisture content (MC) of a single in-shell peanut. This method was extended to determine the average MC of a small sample of peanut pods accommodated between the plates. For both single pods and small samples, the predicted MC values were within 1% of the standard air-oven values for over 85% of the samples tested in the moisture range between 5% and 22%. The current research discusses extending the measurement system to predict the average MC of in-shell peanuts in bulk quantities (about 200 g) from similar capacitance and phase angle measurements at 1 and 9 MHz. The parallel-plate system used in previous research was modified and fitted into a non-conducting cylindrical sample holder. Keywords. Capacitance, Impedance, Moisture content, Peanut pods, Phase angle.
P
eanuts (Arachis hypogaea L.) are an important crop in the southeastern U.S., and the peanut shelling and processing industry enjoys international recognition. Moisture content (MC) of peanuts is an important factor that can influence their marketing, processing, and storage. Freshly dug peanuts may have moisture contents as high as 40%. Usually, peanuts are inverted and left on the vines to partially cure in the windrow for a short period until their MC is between 15% and 20%. However, before they are subjected to the grading process for sale, their MC should be reduced to less than 10% (USDA, 2000). To achieve this, peanuts are dried artificially by placing them in trailers equipped with perforated floors over a plenum and forcing heated air up through the peanut mass. A commercial drying facility may, simultaneously, have as many as 200 loads in various stages of the curing process. It is imperative to monitor the MC of the peanuts in each trailer at regular intervals during the drying process to determine when the desired MC level has been achieved. Overdrying not only increases the drying costs but can also lower the quality of the peanuts (Butts, 1995). Periodic moisture measurement during drying is done by obtaining and shelling a 500 to 1000 g sample from each load. Approximately 250 g of peanut kernels are placed in electronic moisture testers to determine their moisture content. Most of the commercially available moisture testers measure the capacitance of the sample, which is a function
Submitted for review in January 2006 as manuscript number FPE 6259; approved for publication by the Food & Process Engineering Institute Division of ASABE in May 2006. Mention of company or trade name is for purpose of description only and does not imply endorsement by USDA The authors are Chari V. K. Kandala, ASABE Member Engineer, Agricultural Engineer, and Christopher L. Butts, ASABE Member Engineer, Agricultural Engineer, USDA-ARS National Peanut Research Laboratory, Dawson, Georgia. Corresponding author: Chari V. Kandala, NPRL, P.O. Box 509, Dawson, GA 39842; phone: 229-995-7400; fax: 229-995-7416; e-mail:
[email protected].
of the dielectric properties of the peanuts in the bulk sample and is highly correlated with the moisture content of the sample. Thus, the meter gives an average value of the MC of all the peanuts in the bulk sample. It would be very useful if the peanut kernel moisture could be estimated from physical measurements on the peanut pods, without cleaning and shelling. Experiments conducted earlier showed a good correlation between the peanut pod moisture and the peanut kernel moisture (Butts et al., 2004). Thus, from knowledge of the peanut pod moisture, the MC of the kernels inside the pod may be estimated to an acceptable accuracy. The method presented in this article could be useful in developing a practical instrument that would rapidly and nondestructively measure MC of in-shell peanuts. A nondestructive method would result in large savings in time and labor during the drying process and eliminate the loss of edible peanuts used for sampling.
MATERIALS AND METHODS
BASIC PRINCIPLES The variation of dielectric constant with MC of shelled, yellow-dent field corn at different frequencies from 1 MHz to 11 GHz was earlier investigated (Nelson, 1978). The increase in dielectric constant with MC was more pronounced at the lower frequencies of 1 and 5 MHz than at higher frequencies (fig. 1). Although these plots show the relationship between the dielectric constant of bulk corn samples and moisture content at different frequencies, similar relationships were expected for smaller samples and individual kernels (Nelson et al., 1992). Kandala et al. (1989) found that the variation in the dielectric constant at these frequencies was a useful parameter for estimating the MC of single kernels of corn. Similar variation in dielectric constant with frequency was assumed for peanut kernels (shelled peanuts) and was used to estimate their moisture content (Kandala and Nelson, 1990).
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2006 American Society of Agricultural and Biological Engineers ISSN 0001−2351
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Dielectric Constant ( e r)
Moisture Content (%)
Figure 1. Moisture dependence of the dielectric constant of shelled yellowdent field corn at indicated frequencies (Nelson, 1978).
The capacitance (C) of a parallel-plate capacitor with plate area A and plate separation d, filled with a dielectric material, and at a frequency f1 is given by: C1 = r1 0 A/d
+ A3 (θ1 − θ 2 )+ A4 (θ1 − θ 2 )2
(2)
(θ1 − θ2 ) + A5 (C1 − C2 )+ (D1 − D2 )
where r1 and r2 are the dielectric constants of the material between the plates at the two frequencies, and 0 is the permittivity of free space (8.854 × 10−12 farad m−1). Using these two equations, the difference in the dielectric constants is: r1 − r2 = (C1 − C2) d/(0 A)
(3)
Kandala and Nelson (1990) found that (C1 − C2) was a good estimate of the moisture content, but was highly influenced by the size and shape of the peanut kernels. Two other electrical parameters, dissipation factor (D) and phase angle (), were also measured at the two frequencies. The measurements were made on single peanut kernels with the kernel held between and in contact with the two parallelplates. The differences in the values of these two parameters at the two frequencies, (D1 − D2) and (1 − 2), were incorporated into the following empirical equation along with (C1 − C2) to calculate the moisture content (MC): MC = A0 + A1 (C1 − C2 )+ A2 (C1 − C2 )2 (θ1 − θ2 ) + A3 (C1 − C2 )+ K (D1 − D2 ) − (C1 − C2 )(D1 − D2 )
(4)
where A0 to A3 are calibration constants, and K = 1 for single peanut kernels. Measurements made on peanut pods (in-shell peanuts) are influenced by the presence of the shell around the kernels, and the MC determinations calculated with equation 4 did not compare well with the air-oven determinations. However, using a value of 2 for K in equation 4, the MC in single peanut pods could be obtained, to an accuracy within 1% of the air-oven values, for over 85% of the single peanut pods tested in the moisture range from 5% to 20% (Kandala, 2004). When two sets of parallel plates are placed inside a cylindrical tube made of a non-conducting material and
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MC = A0 + A1 (C1 − C2 )+ A2 (C1 − C2 )2
(1)
At a frequency f2, it is given by: C2 = r2 0 A/d
connected in parallel, they form a two-capacitor system and the capacitances are additive. However, some of the pods filling the cylinder would not be in contact with any of the four plates. They would lie in the middle of the pod mass, in contact with the pods around them, and there would be air gaps among the peanut pods. These air gaps act as additional capacitors, some in series and some in parallel, to the parallelplate capacitors. Using the differences in the values of C, , and D at the two frequencies (1 and 5 MHz) in equation 4 for this configuration minimizes the errors introduced by these air gaps. While the capacitance change represents the dielectric variation, the phase angle change accounts for the loss factor, and the dissipation factor represents the quality factor of the sample material. However, using C, , and D values measured at 1 and 9 MHz and adding the phase difference and quality factor parameters, as shown in equation 5, further improved the predictability:
+ A6 [(C1 − C2 )(D1 − D2 )]
(5)
Statistical significance of the terms in equation 5 was checked using the t statistic, with the condition that the probability of a greater absolute value of t under the null hypothesis for each variable had to be less than or equal to 0.0001. The calibration constants (A0 to A6) were evaluated by making measurements on peanut pod samples of known MC values, as determined by the air-oven method, at five different moisture levels in the range from 5% to 20%, and by applying a least squares computation. EQUIPMENT An Agilent 4285A precision LCR meter (Agilent Technologies, Palo Alto, Cal.) with 16048A test leads was used to make the measurements on in-shell peanuts harvested in 2004 and 2005. A cylindrical acrylic tube (fig. 2), fitted with two sets of parallel-plate electrodes, served as the sample holder. The tube is 305 mm long, 62 mm in diameter, and has a wall thickness of 5 mm. The electrode assembly consists of two pairs of rectangular aluminum plates, 88 mm long and 38 mm wide. The electrode pairs are connected together to form two parallel capacitors. The electrodes are glued into the inner walls of the cylinder, 38 mm from the ends, as shown in figure 2. This cylinder rests on top of a rectangular acrylic box and in a circular hole, centered about 35 mm from the front side of the box. The acrylic box is provided with an acrylic drawer that opens manually. The top of the drawer is covered with acrylic plate, 75 mm from the front end. When the drawer is pushed all the way in, this plate covers the hole at the bottom of the cylinder and prevents the peanuts placed in the cylinder from dropping into the drawer. Except for the electrodes, no metal parts were used in the assembly of the electrode system or the sample collecting system to prevent any interaction with the RF signal used in the measurements.
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Figure 2. RF impedance measuring system: (1) LCR meter, (2) cylinder with capacitors, and (3) computer.
With the drawer pushed all the way in, the cylinder was filled with the peanut samples and the impedance measurements were taken. After the completion of the measurements, the drawer was pulled out, allowing the peanuts to fall into the drawer. The drawer was emptied before another sample was placed in the cylinder for measurement. With the peanut pods occupying the space between the electrodes, the analyzer measured the capacitance, dissipation factor, and phase angle of this electrode system at 1 and 9 MHz, and a computer controlled and collected the data from the analyzer. A Mettler AE 163 electronic balance was also interfaced to the computer to record the wet and dryweights of the peanut pod samples to obtain their air-oven moisture values. Using these measurements and equation 5, the computer was programmed to calculate the moisture content in each peanut pod sample. Moisture contents are expressed in percentage wet basis throughout this article. PEANUT SAMPLES Peanuts of the Georgia Green cultivar that were harvested in 2004 and dried, cleaned, and stored at 4°C at the National Peanut Research Laboratory (group 1), and freshly harvested peanuts of the same cultivar in 2005 (group 2) were used for these studies. The initial moisture content of the peanuts in group 1 was about 7%, as indicated by their kernel MC measured with an electronic moisture tester (Grain Analysis Computer 2100, Dickey-John, Inc., Auburn, Ill.). These peanuts were divided into five sub-lots and placed in glass jars. Appropriate quantities of water were added to each jar, raising the moisture levels in steps of about 3%, to obtain a total of five moisture levels ranging between 7% and 20%. The jars were sealed and allowed to equilibrate at 4°C for one week. The jars were periodically rotated during this period to aid uniform distribution of moisture. The initial MC of the peanuts harvested in 2005 (group 2) was about 20%, as indicated by their kernel MC measured with the electronic moisture tester. Peanuts at the original harvested moisture level of 20% were placed in airtight jars. The remaining peanuts were dried using air blowers with air heated no more than 10°C above the ambient temperature and not exceeding 35°C. The drying containers were rotated
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periodically to maintain uniform heat distribution within the samples. Samples were removed from the dryers periodically to obtain peanuts of ten different moisture levels. The samples were then sealed in jars and kept in cold storage at 4°C. Thus, five moisture levels with nominal MC values of 8%, 10%, 13%, 15%, and 18% from the 2004 crop and ten moisture levels with nominal MC values of 5%, 6%, 8%, 10%, 10.5%, 14%, 15%, 16%, 17%, and 18% from the 2005 crop 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. PROCEDURE The peanut pods from the 2005 harvest were separated into two groups. The calibration group consisted of samples with nominal MC levels of 5%, 8%, 10.5%, 15%, and 17%, and the validation group had nominal MC levels of 6%, 10%, 14%, 16%, and 18%. The five MC levels from the 2004 harvest were used for validation. Measurements were made on 20 samples from each moisture level. Peanut samples were transferred from the jars into the cylindrical electrode system, filling the cylinder up to the top with about 180 to 200 g of peanut pods. Room temperature varied from 21°C to 23°C during the measurements. Peanut pods from the jar with a nominal MC level of 5% were transferred into the cylinder first, with the drawer fully inserted into the box, until the cylinder was filled. In this position, measurements were taken with the LCR meter at 1 and 9 MHz. The sample was then collected in the drawer, and the drawer was emptied and reset in the box. This procedure was repeated on the remaining 19 samples at this MC level and for all other MC levels in the calibration and validation groups from the 2005 harvest and for the validation group from the 2004 harvest. After completing the impedance measurements on the 20 samples from each of the MC levels, the oven moisture value for each level was determined by the standard air-oven method (ASAE Standards, 2002).
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Table 1. Comparison of mean MC values of the calibration group determined by the air-oven and parallel-plate cylinder methods using peanut pods from the 2005 harvest. Moisture Content (%) PredictPredicted by Equation 5 Lot Air-Oven ability[b] Mean[a] Std. Dev. Range No. Method (%) 1 6.39 6.35 0.32 1.11 100 2 10.5 10.36 0.30 0.94 100 3 14.62 14.52 0.20 0.10 80 4 15.54 15.33 0.51 2.02 90 5 16.37 16.02 0.40 1.53 90 [a] [b]
Mean of 20 sample measurements. Percentage of samples for which the MC predicted by equation 5 was within 1% of the air-oven value.
Table 2. Comparison of mean MC values calculated with equation 5 with the air-oven values for five validation MC levels using peanut pods from the 2005 harvest. Moisture Content (%) PredictPredicted by Equation 5 Air-Oven Lot ability[b] Mean[a] Std. Dev. Range Method No. (%) 1 6.39 6.35 0.32 1.11 100 2 10.5 10.36 0.30 0.94 100 3 14.62 14.52 0.20 0.10 80 4 15.54 15.33 0.51 2.02 90 5 16.37 16.02 0.40 1.53 90 [a] [b]
Mean of 20 sample measurements. Percentage of samples for which the MC predicted by equation 5 was within 1% of the air-oven value.
RESULTS AND DISCUSSION
Using the measured values of capacitance, phase angle, and dissipation factor and the air-oven MC values of the five calibration lots from the 2005 harvest, the values of the constants in equation 5, as determined by SAS procedures (SAS, 2001) for regression analysis, were: A0 = 13.698, A1 = 74.084, A2 = 9.271, A3 = −14.631, A4 =−0.232, A5 = 10.679, and A6 = 0.119. The coefficient of determination was 0.98. Using these values in equation 5, the MC of each pod sample in the five calibration lots was calculated, averaged over the 20 samples in each MC level, and the results are shown in table 1 along with the MC values determined by the air-oven method. The standard deviation and range (difference between the maximum and minimum) of the calculated values are also shown. The mean calculated values agree very closely with the air-oven MC values. The Bonferroni test (Neter et al., 1996)
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Oven % MC Predicted % MC
10 5 0
6
10
14
16
18
Nominal Oven % MC
Figure 3. Comparison of predicted mean moisture values with air-oven values for five validation samples from the 2005 crop with moisture content between 6% and 20%.
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[a] [b]
Mean of 20 sample measurements. Percentage of samples for which the MC predicted by equation 5 was within 1% of the air-oven value.
performed on the data, where the calculated and air-oven MC values differed by more than 1% (Kandala and Nelson, 2005), indicated that none of the calculated values could be identified as an outlier. An R2 value of 0.98, relatively small differences in the calculated and the oven MC values, and no visible outliers suggested the suitability of the model for MC predictions. The predictability shown in the last column is defined as the percentage of samples for which the moisture content was calculated within 1% of the air-oven MC value. The MC values of the pod samples in the five validation lots of the 2005 harvest, calculated using equation 5, are shown in table 2. The calculated mean moisture values were compared with the air−oven values, and the results are summarized in table 2, along with the standard deviation, range, and predictability. The predictability, in this case, is defined as the percentage of samples for which the moisture content was predicted within 1% of the air−oven MC value. The predictability was at least 80% for each lot and averaged 92% for all the five validation lots. A bar graph comparing the MC values determined by the air−oven and impedance methods at the five moisture levels in the validation group of the 2005 crop is shown in figure 3. Similarly, the MC values of the pod samples in the five validation lots of the 2004 harvest, calculated using equation 5, are shown in table 3. The calculated mean moisture values were compared with the air-oven values, and the results are summarized in table 3 along with the standard deviation, range, and predictability (percentage of samples for which the moisture content was predicted within 1% of the air-oven MC value). The predictability was 80% or higher for each lot and averaged 89% for the five validation lots. A bar graph comparing the MC values determined by the air-−
Predicted % MC
Predicted % MC
20
Table 3. Comparison of mean moisture contents calculated with equation 5 with the air-oven MC values for five validation lots. Peanut pods from 2004 harvest. Moisture Content (%) PredictPredicted by Equation 5 Air-Oven Lot ability[b] Mean[a] Std. Dev. Range Method No. (%) 1 8.30 8.27 0.13 0.46 100 2 9.84 9.43 0.43 1.22 100 3 13.05 13.29 0.81 2.07 80 4 14.93 15.17 0.86 2.67 80 5 17.99 18.28 0.87 2.30 85
20 18 16 14 12 10 8 6 4 2 0
Oven % MC Predicted % MC
8
10
13
15
18
Nominal Oven % MC
Figure 4. Comparison of predicted mean moisture values with air-oven values for five validation samples from 2004 crop with MC between 7% and 20%
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Table 4. Paired t-test results for prediction samples from each of the ten moisture levels. Moisture Content (%) Difference[b] Crop No. of
Nominal
Oven
Pred.[a] 8.27 9.43 13.29 15.17 18.28
Samples 20 20 20 20 20
t-value
8.3 9.84 13.05 14.93 17.99
Year 2004 2004 2004 2004 2004
Mean
8 10 13 15 18
−0.04 −0.41 0.24 0.24 0.29
−1.28 −3.90 1.33 1.25 1.49
6 10 14 16 18
6.39 10.50 14.62 15.54 16.37
6.35 10.36 14.52 15.33 16.02
2005 2005 2005 2005 2005
20 20 20 20 20
−0.32 −0.14 −0.10 −0.21 −0.35
−0.55 −2.09 −2.24 −1.84 −3.47
[a] [b]
Predicted by equation 5; mean of 20 sample measurements. Difference (% MC) between air-oven MC and calculated MC.
oven and impedance methods at the five moisture levels in the validation group of the 2004 crop is shown in figure 4. From figures 3 and 4, it can be inferred that the calculated MC values using the impedance parameters agree well with the air−oven moisture values in the MC range between 5% and 20% for crops from two different years. The MC levels for the 2004 crop were developed by starting from a dry stored sample and adding required quantities of water to the original sample. The samples from the 2005 crop were freshly harvested and contained about 20% MC. Samples having lower moisture levels were generated by controlled drying of the original samples. Paired t-tests were performed for testing the statistical significance of the differences between the predicted values and air-oven values at the 95% and 99% confidence levels. These tests were conducted for the ten validation lots (five from the 2004 crop and five from the 2005 crop) with nominal MC values in the range between 6% and 18% (table 4). The observed differences are not statistically significant at any moisture level except for the nominal 10% and the 18% levels. However, the predictability at the 10% and 18% levels were 100% and 90%, respectively, and standard deviations within the groups were about 0.4. The marginally larger t-values at these two MC levels were not indicative of poor predictability for equation 5.
CONCLUSIONS
By measuring capacitance, phase angle, and dissipation factor of a parallel−plate system fitted inside a non−conducting cylindrical tube, the average moisture content of approximately 200 g of peanuts could be predicted rapidly
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and nondestructively. The moisture range of the peanuts tested was between 6% and 18%, and the predicted MC values were within 1% of the air−oven MC values for almost 90% of the samples tested from the 2004 and 2005 harvests. The ability to estimate the moisture content without cleaning and shelling the peanuts would save a considerable amount of time and labor during the drying process and would save the destruction of considerable quantities of edible peanuts. The RF impedance measurement method provides a basis for the development of a practical instrument that can measure MC of in−shell peanuts. ACKNOWLEDGEMENTS The authors are grateful to Lawrence Dettore and Franteshia Thornton for their help during this work.
REFERENCES
ASAE Standards. 2002. S410.1: Moisture measurement − Peanuts. St. Joseph, Mich. ASAE. Butts, C. L. 1995. Incremental cost of overdrying farmers’ stock peanuts. Applied Eng. in Agric. 11(5): 671-675. Butts, C. L., J. I. Davidson, Jr., M. C. Lamb, C. V. Kandala, and J. M. Troeger. 2004. Estimating drying time for a stock peanut curing decision support system. Trans. ASAE 47(3): 925-932. Kandala, C. V. K. 2004. Moisture determination in single peanut pods by complex RF impedance measurement. IEEE Trans. Instrum. Meas. 53(6): 1493-1496. Kandala, C. V. K., and S. O. Nelson. 1990. Measurement of moisture content in single kernels of peanuts: A nondestructive electrical method. Trans. ASAE 33(2): 567-572. Kandala, C. V. K., and S. O. Nelson. 2005. Nondestructive moisture determination in small samples of peanuts by RF impedance measurement. Trans. ASAE 48(2): 715-718. Kandala, C. V. K., S. O. Nelson, and K. C. Lawrence. 1989. Nondestructive electrical measurement of moisture content in single kernels of corn. J. Agric. Eng. Res. 44: 125-132. Nelson, S. O. 1978. Frequency and moisture dependence of the dielectric properties of high-moisture corn. J. Microwave Power 13(2): 213-218. Nelson, S. O., C. V. K. Kandala, and K. C. Lawrence. 1992. Moisture determination in single grain kernels and nuts by RF impedance measurements. IEEE Trans. Instrum. Meas. 41(6): 1027-1031. Neter, J., M. H. Kutner, C. J. Nachtsheim, and W. Wasserman. 1996. Identifying outlying Y observations − Studentized deleted residuals. In Applied Linear Regression Models, 374-375. Chicago, Ill.: Irwin Book Team. SAS. 2001. Version 8. Cary, N.C.: SAS Institute, Inc. USDA. 2000. AMS farmers stock peanuts inspection instructions. Updated 2000. Washington, D.C.: USDA.
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