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

Konstantia Georgouli, Beatriz Carrasco, Damien Vincke, Jesus Martinez del Rincon, Anastasios Koidis, Vincent Baeten, Juan Antonio Fernández Pierna

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.
Original languageEnglish
Article number103910
Number of pages11
JournalChemometrics and Intelligent Laboratory Systems
Volume196
Early online date20 Dec 2019
DOIs
Publication statusPublished - 15 Jan 2020

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