Analysis of complex, processed substances with the use of NIR spectroscopy and chemometrics: Classification and prediction of properties — The potato crisps example

Abstract

An NIR spectroscopic method was researched and developed for the analysis of potato crisps (chips) chosen as an example of a common, cheap but complex product. Four similar types of the ‘original flavour’ potato chips from different manufacturers were analysed by NIR spectroscopy; as well, the quality parameters — fat, moisture, acid and peroxide values of the extracted oil were predicted. Principal component analysis (PCA) of the NIR data displayed the clustering of objects with respect to the type of chips. NIR spectra were rank-ordered with the use of the sparingly applied multiple criteria decision making (MCDM) ranking methods, PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid), and a comprehensive quantitative description of the data was obtained. The four traditional parameters were predicted on the basis of the NIR spectra; the performance of the Partial Least Squares (PLS), and Kernel Partial Least Squares (KPLS) calibrations was compared with those from Least Squares-Support Vector Machines (LS-SVM) method. The LS-SVM calibrations, which model better data linearity and non-linearity, successfully predicted all four parameters. This work has demonstrated that NIR methodology with the use of chemometrics can describe comprehensively qualitative and quantitative properties of complex, processed substances as illustrated by the potato chips example, and indicated that this approach may be applied to other similar complex samples.