Multiblock Analysis Applied to TD-NMR of Butters and Related Products

Abstract

This work presents a novel and rapid approach to predict fat content in butter products based on nuclear magnetic resonance longitudinal (T1) relaxation measurements and multi-block chemometric methods. The potential of using simultaneously liquid (T1L) and solid phase (T1S) signals of fifty samples of margarine, butter and concentrated fat by Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Selective Covariance Selection (SO-CovSel) methods was investigated. The two signals (T1L and T1S) were also used separately with PLS and CovSel regressions. The models were compared in term of prediction errors (RMSEP) and repeatability error (σrep). The results obtained from liquid phase (RMSEP ≈ 1.33% and σrep≈ 0.73%) are better than those obtained with solid phase (RMSEP ≈ 5.27% and σrep≈ 0.69%). Multiblock methodologies present better performance (RMSEP ≈ 1.00% and σrep≈ 0.47%) and illustrate their power in the quantitative analysis of butter products. Moreover, SO-Covsel results allow for proposing a measurement protocol based on a limited number of NMR acquisitions, which opens a new way to quantify fat content in butter products with reduced analysis times.

Special Issue “Application of Spectroscopy in Food Analysis”

“…The second paper where chemometric regression methods are used for the quantification of a constituent in food is the one by Roger and collaborators [6]. In this work, a NMR-based method for predicting fat content in butter, margarine, and milk-derived samples is proposed.…”

Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations

“…Consequently, SO-PLS and SO-CovSel, which were conceived to overcome these drawbacks, were tested. Compared to other data fusion approaches, these have the advantage of removing redundant information among the predictor blocks; due to the nature of the multi-set data set, this represents a crucial characteristic, which makes this approach particularly advisable for the aim of the present work [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”