TY - JOUR
T1 - Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy
AU - Georgouli, Konstantia
AU - Carrasco, Beatriz
AU - Vincke, Damien
AU - Martinez del Rincon, Jesus
AU - Koidis, Anastasios
AU - Baeten, Vincent
AU - Fernández Pierna, Juan Antonio
PY - 2020/1/15
Y1 - 2020/1/15
N2 - 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.
AB - 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.
U2 - 10.1016/j.chemolab.2019.103910
DO - 10.1016/j.chemolab.2019.103910
M3 - Article
VL - 196
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
M1 - 103910
ER -