Predicting macronutrients and energy content of snack products using FT-NIR analysis and chemometric techniques

Abstract

Nowadays, the consumption of snack products is permanently increasing. Because of the growing trend of snack consumption, it is more and more difficult to guarantee the quality and safety of the products. Near infrared spectroscopy (NIRS) method, combined with chemometric techniques provide outstanding solutions, due to its rapidity and simple sample preparation. The objective of this study was to investigate the possibilities of using NIRS to predict fat, protein, carbohydrate, sugar and salt content of all in all 155 commercially available snack products from 25 countries. The prediction models were performed using partial least squares regression (PLSR) with different spectral pre-processing methods. Different pre-processing methods proved to be the best to predict the five macronutrients, however, the final models showed good accuracy |R2/Q2 > 0.94/0.82|. The energy content of the samples was calculated from the measured parameters and interval PLS regression was accomplished to improve prediction parameters. The methods developed are suitable for analyzing snacks made from single or mixed raw materials.