Application of NIR Reflectance Spectroscopy on Determination of Moisture Content of In-Shell Peanuts: A Non-Destructive Analysis

Abstract

NIR spectroscopy was used to measure the moisture content (MC) of Virginia and Valencia type in-shell peanuts. Peanuts were conditioned to various moisture levels between 7% and 26% (wet basis), and the MC was verified using the standard oven method. Sample from the various moisture levels were separated into two groups, as calibration and validation. NIR absorption spectral data from 400 nm to 2500 nm were collected using peanuts within the calibration and validation sample sets. Measurements were obtained on 30 replicates within each moisture level. Partial least squares (PLS) analysis was performed on the calibration set, and models were developed using the raw spectral data and its derivative function data. The standard error of calibration (SEC) and R2 of the calibration models were calculated to select the best calibration model for each peanut market type. Both Valencia and Virginia types gave an R2 value of 0.99 for the derivative spectral data treatment as well as for the raw data. The selected models were used to predict the moisture content of peanuts in the validation sample set. Predicted and reference moisture contents were compared. Relative percent deviation (RPD) and standard error of prediction (SEP) were calculated to validate the goodness of fit of the prediction model. The raw reflectance spectra model gave an RPD of 5.55 with a corresponding SEP of 0.97 for Valencia type peanuts, which is an indicator that the model is good for quality control and analysis. For Virginia type peanuts, the derivative reflectance spectra model gave the highest RPD value of 5.75 and the lowest SEP of 0.771. Thus, these two models were selected for the respective peanut types as the best models for prediction of moisture content.