Non-destructive evaluation of the physiochemical properties of milk drink flavored with date syrup utilizing NIR Spectroscopy and ANN analysis

Abstract

A milk drink flavored with dates syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow’s and camel’s milk at four percentages to form a nutritious, natural, sweet, and energy milk drink. Sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure physiochemical properties of dates fruits and milk drinks flavored with dates syrup, then to evaluate the physical properties of milk drinks utilizing a non-destructive NIR spectra. The study assessed the characteristics of the milk drink enhanced with date syrup by employing near-infrared spectra (NIR) spectra and analyzed utilizing partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The NIR spectrum proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model's future predictive performance compared to RMSEC, R2 ranged between 0.946 and 0.989. Consequently, non-destructive NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product's properties examined.