Skip to Main content Skip to Navigation
Journal articles

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 (T-1) relaxation measurements and multi-block chemometric methods. The potential of using simultaneously liquid (T-1L) and solid phase (T-1S) 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 (T-1L and T-1S) were also used separately with PLS and CovSel regressions. The models were compared in term of prediction errors (RMSEP) and repeatability error (sigma(rep)). The results obtained from liquid phase (RMSEP approximate to 1.33% and sigma(rep)approximate to 0.73%) are better than those obtained with solid phase (RMSEP approximate to 5.27% and sigma(rep) approximate to 0.69%). Multiblock methodologies present better performance (RMSEP approximate to 1.00% and sigma(rep) approximate to 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.
Document type :
Journal articles
Complete list of metadata

https://hal.inrae.fr/hal-02945031
Contributor : Silvia Mas Garcia <>
Submitted on : Thursday, May 27, 2021 - 5:00:04 PM
Last modification on : Friday, May 28, 2021 - 3:38:37 AM

File

2020_Roger_AS.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

Citation

Jean-Michel Roger, Sílvia Mas Garcia, Mireille Cambert, Corinne Rondeau-Mouro. Multiblock analysis applied to TD-NMR of butters and related products. Applied Sciences, MDPI, 2020, 10 (15), pp.5317. ⟨10.3390/app10155317⟩. ⟨hal-02945031⟩

Share

Metrics

Record views

67

Files downloads

4