Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

ABSTRACT

Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500 nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345 nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345 nm for carbohydrates, 1180–1590 nm and 1860–2094 nm for fat, and 1700–2345 nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths.

KEYWORDS:

• Chickpea flour
• interval partial least square regression
• near infrared spectroscopy
• principal component regression
• synergy interval partial least square regression