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Non Destructive Determination of Peanut Moisture Content Using Near Infrared Reflectance Spectroscopy

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1008520.(doi:10.13031/2013.29882)
Authors:   Chari V.K Kandala, Jaya Sundaram, Govindarajan K Naganathan, Jeyam Subbiah
Keywords:   Keywords NIR reflectance spectroscopy, in-shell peanuts, kernels, non-destructive, moisture content, partial least square regression, Calibration model, Standard error of prediction. Coefficient of determination, spectral pretreatment

A custom made Near Infrared reflectance spectroscope, measuring reflectance value of the energy incident on a peanut sample over the wavelengths 1000 nm to 2500 nm, is used to estimate the moisture content (MC) of in-shell peanuts and peanut kernels of Valencia type, non-destructively. About 150g of peanuts were filled into a Petri dish, in at least two layers, placed on a platform under the light source of the instrument, and NIR light reflected from the sample was collected by the NIR instrument. Valencia peanut samples with MC range between 5% and 23% were used as the calibration set of samples. A calibration model was developed with the measured spectral values and their MC reference values, determined earlier by standard air-oven method. Partial least square (PLS) regression method was used to develop models for MC prediction. Peanuts were then shelled, and similar measurements were made on the kernels. MC values of peanut samples in the moisture range of 6% to 21%, not used in the calibration, were predicted by the developed model and compared with their standard air-oven values for validation. The best calibration model was selected based on the calculated standard Error of Prediction (SEC), coefficient of determination (R2) and bias values. For Valencia peanut kernels the best model was the model developed using the spectral data with reflection plus derivative pretreatment. For in-shell peanuts the model developed by reflection plus normalization pretreatment was found to be the best.

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