Determination of Chemical Properties of Desi Chickpea Flour (Besan) Using Near Infrared Spectroscopy and Chemometrics

Abstract

A method was developed to determine the protein, carbohydrate, fat and moisture content of desi chickpea flour (besan) using Near Infrared Spectrometer [NIRS] and multivariate regression namely, Principal Component Regression and Partial Least Square Regression Analysis . Spectra of the samples was collected in reflectance mode using lab built pre dispersive filter based NIRS in the wavelength range of 700-2500 nm. Reference analysis was collected using the Association of official Analytical Chemists (AOAC) methods. NIR spectral data and reference data was used to develop regression models using Partial Least Square Regression and Principal Component Regression. Prediction performance of the models was compared on the basis of the coefficient of correlation [R2] and Root Mean Square Error [RMSE] for calibration and validation sets. The R2 c values for prediction of moisture, fat, protein and carbohydrate content from PLSR model were 0.9858, 0.9863, 0.9888, 0.9915 respectively. PCR model resulted in R2 c 0.9739 for protein, 0.9833 for carbohydrate,0.9795 for fat and 0.9655 for moisture content. PLSR and PCR models results were accurate enough for prediction of the parameters. This study showed that NIR can be used to determine the chemical parameters of food material