Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy

Abstract

Hydrocolloids such as natural gums and carrageenans are used extensively in the food industry in various mixtures that are difficult to be characterised due to their similar chemical structure. The aim of this study was to develop an analytical framework for the identification and quantification of these compounds in complex mixtures using Near-infrared (NIR) spectroscopy and chemometrics. Partial Least Squares (PLS) regression accompanied by Continuous Locality Preserving Projections (CLPP) dimensionality reduction technique is proposed as chemometric framework. Four different analytical models based on this framework are developed and compared for the analysis of spectral fingerprints of food hydrocolloids mixtures. Classification results showed that this method allowed the discrimination of hydrocolloids in blends with a 100% of correct classification. The same scheme also allows the quantitative determination of the different types of food hydrocolloids (3 types) and/or their individual compounds (8 different compounds) with a relative low root mean square error of prediction (RMSEP) of 0.028 and 0.038 respectively.