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Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics
Low Field Time-Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry was used to determine moisture, fat, and defatted dry matter contents in “requeijão cremoso” (RC) processed cheese directly in commercial packaged (plastic cups or tubes with approximately 200 g). Forty-five samples of commercial...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318975/ https://www.ncbi.nlm.nih.gov/pubmed/35889306 http://dx.doi.org/10.3390/molecules27144434 |
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author | de Oliveira Machado, G. Teixeira, Gustavo Galastri Garcia, Rodrigo Henrique dos Santos Moraes, Tiago Bueno Bona, Evandro Santos, Poliana M. Colnago, Luiz Alberto |
author_facet | de Oliveira Machado, G. Teixeira, Gustavo Galastri Garcia, Rodrigo Henrique dos Santos Moraes, Tiago Bueno Bona, Evandro Santos, Poliana M. Colnago, Luiz Alberto |
author_sort | de Oliveira Machado, G. |
collection | PubMed |
description | Low Field Time-Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry was used to determine moisture, fat, and defatted dry matter contents in “requeijão cremoso” (RC) processed cheese directly in commercial packaged (plastic cups or tubes with approximately 200 g). Forty-five samples of commercial RC types (traditional, light, lactose-free, vegan, and fiber) were analyzed using longitudinal (T(1)) and transverse (T(2)) relaxation measurements in a wide bore Halbach magnet (0.23 T) with a 100 mm probe. The T(1) and T(2) analyses were performed using CWFP-T(1) (Continuous Wave Free Precession) and CPMG (Carr-Purcell-Meiboom-Gill) single shot pulses. The scores of the principal component analysis (PCA) of CWFP-T(1) and CPMG signals did not show clustering related to the RC types. Optimization by variable selection was carried out with ordered predictors selection (OPS), providing simpler and predictive partial least squares (PLS) calibration models. The best results were obtained with CWFP-T(1) data, with root-mean-square errors of prediction (RMSEP) of 1.38, 4.71, 3.28, and 3.00% for defatted dry mass, fat in the dry and wet matter, and moisture, respectively. Therefore, CWFP-T(1) data modeled with chemometrics can be a fast method to monitor the quality of RC directly in commercial packages. |
format | Online Article Text |
id | pubmed-9318975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93189752022-07-27 Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics de Oliveira Machado, G. Teixeira, Gustavo Galastri Garcia, Rodrigo Henrique dos Santos Moraes, Tiago Bueno Bona, Evandro Santos, Poliana M. Colnago, Luiz Alberto Molecules Article Low Field Time-Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry was used to determine moisture, fat, and defatted dry matter contents in “requeijão cremoso” (RC) processed cheese directly in commercial packaged (plastic cups or tubes with approximately 200 g). Forty-five samples of commercial RC types (traditional, light, lactose-free, vegan, and fiber) were analyzed using longitudinal (T(1)) and transverse (T(2)) relaxation measurements in a wide bore Halbach magnet (0.23 T) with a 100 mm probe. The T(1) and T(2) analyses were performed using CWFP-T(1) (Continuous Wave Free Precession) and CPMG (Carr-Purcell-Meiboom-Gill) single shot pulses. The scores of the principal component analysis (PCA) of CWFP-T(1) and CPMG signals did not show clustering related to the RC types. Optimization by variable selection was carried out with ordered predictors selection (OPS), providing simpler and predictive partial least squares (PLS) calibration models. The best results were obtained with CWFP-T(1) data, with root-mean-square errors of prediction (RMSEP) of 1.38, 4.71, 3.28, and 3.00% for defatted dry mass, fat in the dry and wet matter, and moisture, respectively. Therefore, CWFP-T(1) data modeled with chemometrics can be a fast method to monitor the quality of RC directly in commercial packages. MDPI 2022-07-11 /pmc/articles/PMC9318975/ /pubmed/35889306 http://dx.doi.org/10.3390/molecules27144434 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article de Oliveira Machado, G. Teixeira, Gustavo Galastri Garcia, Rodrigo Henrique dos Santos Moraes, Tiago Bueno Bona, Evandro Santos, Poliana M. Colnago, Luiz Alberto Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics |
title | Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics |
title_full | Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics |
title_fullStr | Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics |
title_full_unstemmed | Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics |
title_short | Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics |
title_sort | non-invasive method to predict the composition of requeijão cremoso directly in commercial packages using time domain nmr relaxometry and chemometrics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318975/ https://www.ncbi.nlm.nih.gov/pubmed/35889306 http://dx.doi.org/10.3390/molecules27144434 |
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