Chari V. Kandala

Estimation of mass ratio of the total kernels within a sample of in-shell peanuts using RF impedance method

It would be useful to know the total kernel mass within a given mass of peanuts (mass ratio) while the peanuts are bought or being processed. In this work, the possibility of finding the mass ratio while the peanuts were in their shells was investigated. Capacitance, phase angle, and dissipation factor measurements on a parallel-plate capacitor holding in-shell peanut samples were made at frequencies from 1 to 10 MHz insteps of 1 MHz. A calibration equation was developed by multilinear regression analysis correlating the percentage ratio of the kernel weight with the measured capacitance, dissipation factor, and phase angle values of in-shell peanut samples with known kernel weights. The equation was used to predict the percentage mass ratio in the validation groups. Fitness of calibration model was verified using standard error of calibration, root mean square error of calibration, and leverage and influence plots. The predictability percentage, within 1% and 2% of the visual determination, was calculated by comparing the kernel mass ratio, obtained by the model equation and the reference value obtained by visual determination. Cross-validation gave 96% and 100% predictability, and external validation gave 87% and 98% predictability within 1% and 2% difference, respectively.

Analysis of Moisture Content, Total Oil and Fatty Acid Composition by NIR Reflectance Spectroscopy: A Review

Near Infrared (NIR) Reflectance spectroscopy has established itself as an important analytical technique in the field of food and agriculture. It is quicker and easier to use, and does not require processing the samples with corrosive chemicals such as acids or hydroxides. However, in earlier times, the samples had to be ground into powder form before making any measurements. Thanks to the development of new soft ware packages for use with NIR instruments, NIR techniques could be used in the analysis of intact grains and seeds. While most of the commercial instruments presently available work well with small grain size materials such as wheat and corn, they were found to be unsuitable for large kernel size products such as shelled or in-shell peanuts. In this chapter, principles of NIR Reflectance spectroscopy were reviewed, in particular reference to the water and oil bands. Also presented are some recent applications of NIR for the rapid and nondestructive measurement of moisture and total oil contents in shelled and in-shell peanuts. Applicability, and limitations of NIR reflectance method in the analysis of fatty acid composition of different varieties of peanuts while they are in their shells was also discussed. Ability to rapidly and nondestructively measure the water and total oil content, and analyze the fatty acid composition, will be immensely useful in the grading process of grains and nuts.

NIR reflectance method to determine moisture content in food products

Moisture content (MC) is an important quality factor that is measured and monitored, at various stages of processing and storage, in the food industry. There are some commercial instruments available that use near infrared (NIR) radiation measurements to determine the moisture content of a variety of grain products, such as wheat and corn, with out the need of any sample grinding or preparation. However, to measure the MC of peanuts with these instruments the peanut kernels have to be chopped into smaller pieces and filled into the measuring cell. This is cumbersome, time consuming and destructive. An NIR reflectance method is presented here by which the average MC of about 100 g of whole kernels could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. Initially, NIR reflectance measurements were made at 1 nm intervals in the wave length range of 1000 nm to 1800 nm and the data was modeled using partial least squares regression (PLSR). The predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R2 value of 0.96 and a standard error of prediction (SEP) of 0.83. Using the PLSR beta coefficients, five key wavelengths were identified and using multiple linear regression (MLR) method MC predictions were made. The R2 and SEP values of the MLR model were 0.84 and 1.62, respectively. Both methods performed satisfactorily and being rapid, nondestructive, and non-contact, may be suitable for continuous monitoring of MC of grain and peanuts as they move on conveyor belts during their processing.

Analysis of total oil and fatty acids composition by near infrared reflectance spectroscopy in edible nuts

Near Infrared (NIR) Reflectance spectroscopy has established itself as an important tool in quantifying water and oil present in various food materials. It is rapid and nondestructive, easier to use, and does not require processing the samples with corrosive chemicals that would render them non-edible. Earlier, the samples had to be ground into powder form before making any measurements. With the development of new soft ware packages, NIR techniques could now be used in the analysis of intact grain and nuts. While most of the commercial instruments presently available work well with small grain size materials such as wheat and corn, the method present here is suitable for large kernel size products such as shelled or in-shell peanuts. Absorbance spectra were collected from 400 nm to 2500 nm using a NIR instrument. Average values of total oil contents (TOC) of peanut samples were determined by standard extraction methods, and fatty acids were determined using gas chromatography. Partial least square (PLS) analysis was performed on the calibration set of absorption spectra, and models were developed for prediction of total oil and fatty acids. The best model was selected based on the coefficient of determination (R2), Standard error of prediction (SEP) and residual percent deviation (RPD) values. Peanut samples analyzed showed RPD values greater than 5.0 for both absorbance and reflectance models and thus could be used for quality control and analysis. Ability to rapidly and nondestructively measure the TOC, and analyze the fatty acid composition, will be immensely useful in peanut varietal improvement as well as in the grading process of grain and nuts.

Estimation of moisture and oil content of in-shell nuts with a capacitance sensor using discrete wavelet analysis

Moisture and oil contents are important quality factors often measured and monitored in the processing and storage of food products such as corn and peanuts. For estimating these parameters for peanuts nondestructively a parallel-plate capacitance sensor was used in conjunction with an impedance analyzer. Impedance, phase angle and dissipation factor were measured for the parallel-plate system, holding the in-shell peanut samples between its plates, at frequencies ranging between 1MHz and 30 MHz in intervals of 0.5 MHz. The acquired signals were analyzed with discrete wavelet analysis. The signals were decomposed to 6 levels using Daubechies mother wavelet. The decomposition coefficients of the sixth level were passed onto a stepwise variable selection routine to select significant variables. A linear regression was developed using only the significant variables to predict the moisture and oil content of peanut pods (inshell peanuts) from the impedance measurements. The wavelet analysis yielded similar R2 values with fewer variables as compared to multiple linear and partial least squares regressions. The estimated values were found to be in good agreement with the standard values for the samples tested. Ability to estimate the moisture and oil contents in peanuts without shelling them will be of considerable help to the peanut industry.

NIR Reflectance Spectroscopic Method for Nondestructive Moisture Content Determination of In-Shell Peanuts

Most of the commercial instruments presently available to determine the moisture content (MC) of peanuts need shelling and cleaning of the peanut samples, and in some cases some sort of sample preparation such as grinding. This is cumbersome, time consuming and destructive. It would be useful if the MC of the peanuts could be measured on the in-shell peanuts itself rapidly and nondestructively, particularly at the peanut buying points, where MC of the peanuts is an important factor in fixing the sale price. An NIR reflectance method is presented here by which the average MC of about 100 g of in-shell peanuts could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. NIR reflectance measurements were made at 1 nm intervals in the wavelength range of 1000 nm to 1800 nm and the spectral data was modeled using partial least squares regression (PLSR). Eight different models were developed by utilizing different data preprocessing methods such as Norris-Gap first derivative with a gap size of 3, peak normalization with 1680 nm (which is the no absorbance wavelength for water), and absorbance transformation. From these, a suitable model was selected based on model fitness measures. Predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R2 value of 0.91 and a standard error of prediction (SEP) of 1.37 for averaged spectra and an R2 value of 0.87 and SEP value of 1.61 for individual spectra. Both methods of analysis performed satisfactorily and being rapid, nondestructive and non-contact, may also be suitable for continuous monitoring of MC of in-shell peanuts as they move on conveyor belts during their processing.

Capacitance sensors for the nondestructive measurement of moisture content in in-shell peanuts

Moisture content (MC) in peanuts is measured at various stages of their processing and storage in the peanut industry. A method was developed earlier that would estimate the MC of a small sample of in-shell peanuts (peanut pods) held between two circular parallel-plates, from the measured values of capacitance and phase angle at two frequencies 1 and 5 MHz. These values were used in an empirical equation, developed using the capacitance and phase angle values of samples of known MC levels, to obtain the average MC values of peanut samples with moisture contents in the range of 7 to 18%. In the present work, two rectangular parallel-plates were mounted inside a vertical cylinder made of acrylic material and filled with about 100 g of in-shell peanuts and their average mc was determined from a similar empirical equation. The calculated MC values were compared with those obtained by the standard air-oven method. For over 85% of the samples tested in the moisture range between 6% and 22% the MC values were found to be within 1% of the air-oven values. Ability to determine the average MC of slightly larger quantities of in-shell peanuts without shelling and cleaning them, as being done presently, will save time, labor and sampling material for the peanut industry.