Cargando…

(1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities

In this study, the (1)H HRMAS-NMR (High-resolution Magic Angle Spinning-Nuclear Magnetic Resonance) spectra of 52 cheese samples obtained from the South Korean dairy farms were evaluated for their metabolic profiling and intensities associating with the sensory qualities. The NMR profiles displayed...

Descripción completa

Detalles Bibliográficos
Autores principales: Kandasamy, Sujatha, Yoo, Jayeon, Yun, Jeonghee, Kang, Han Byul, Seol, Kuk-Hwan, Ham, Jun-Sang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254036/
https://www.ncbi.nlm.nih.gov/pubmed/32489280
http://dx.doi.org/10.1016/j.sjbs.2020.04.043
_version_ 1783539450868924416
author Kandasamy, Sujatha
Yoo, Jayeon
Yun, Jeonghee
Kang, Han Byul
Seol, Kuk-Hwan
Ham, Jun-Sang
author_facet Kandasamy, Sujatha
Yoo, Jayeon
Yun, Jeonghee
Kang, Han Byul
Seol, Kuk-Hwan
Ham, Jun-Sang
author_sort Kandasamy, Sujatha
collection PubMed
description In this study, the (1)H HRMAS-NMR (High-resolution Magic Angle Spinning-Nuclear Magnetic Resonance) spectra of 52 cheese samples obtained from the South Korean dairy farms were evaluated for their metabolic profiling and intensities associating with the sensory qualities. The NMR profiles displayed a broad range of compounds comprising amino acids, carbohydrates, organic acids, and phospholipids. Afterwards, the cheese samples were categorized into three groups (more likeness - G1, moderate likeness - G2, less likeness - G3), in relating to their sensory scores. The NMR data of the samples were later investigated through multivariate statistical tools to define the variations in metabolic fingerprints of every cheese sample and their intensities hailing in individual sensory groups. The unsupervised PCA employing all cheese samples unveiled the uniqueness in metabolite profiles of the brown and cheddar type cheeses (outliers). Moreover, Gouda and other types of cheeses displayed samples positioning in respective of their metabolite profiles. The pairwise comparison of sensory groups in the supervised models perceived better separation in OPLS-DA than PLS-DA. The corresponding VIP (PLS-DA) and loading (OPLS-DA) plots revealed amino acids and organic acids (lactate, citrate) as significant variables. The discrimination of G 1 Gouda type of cheeses against G 2 and G 3 was highly associated with their citrate levels. Further investigation using heatmaps displayed clear differentiation between each sensory group in terms of the levels of amino acids, lactate, citrate, phospholipids, and glycerol, conveying these variations are likely due to proteolytic and metabolic processes in cheese ripening. This study concluded that (1)H HRMAS-NMR metabolite profile of the Korean cheeses is consistence with their sensory qualities. Further, the candidate metabolites identified in this study confers their potential application as biomarkers in cheese industries for faster and effective validation of sensory characteristics.
format Online
Article
Text
id pubmed-7254036
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-72540362020-06-01 (1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities Kandasamy, Sujatha Yoo, Jayeon Yun, Jeonghee Kang, Han Byul Seol, Kuk-Hwan Ham, Jun-Sang Saudi J Biol Sci Article In this study, the (1)H HRMAS-NMR (High-resolution Magic Angle Spinning-Nuclear Magnetic Resonance) spectra of 52 cheese samples obtained from the South Korean dairy farms were evaluated for their metabolic profiling and intensities associating with the sensory qualities. The NMR profiles displayed a broad range of compounds comprising amino acids, carbohydrates, organic acids, and phospholipids. Afterwards, the cheese samples were categorized into three groups (more likeness - G1, moderate likeness - G2, less likeness - G3), in relating to their sensory scores. The NMR data of the samples were later investigated through multivariate statistical tools to define the variations in metabolic fingerprints of every cheese sample and their intensities hailing in individual sensory groups. The unsupervised PCA employing all cheese samples unveiled the uniqueness in metabolite profiles of the brown and cheddar type cheeses (outliers). Moreover, Gouda and other types of cheeses displayed samples positioning in respective of their metabolite profiles. The pairwise comparison of sensory groups in the supervised models perceived better separation in OPLS-DA than PLS-DA. The corresponding VIP (PLS-DA) and loading (OPLS-DA) plots revealed amino acids and organic acids (lactate, citrate) as significant variables. The discrimination of G 1 Gouda type of cheeses against G 2 and G 3 was highly associated with their citrate levels. Further investigation using heatmaps displayed clear differentiation between each sensory group in terms of the levels of amino acids, lactate, citrate, phospholipids, and glycerol, conveying these variations are likely due to proteolytic and metabolic processes in cheese ripening. This study concluded that (1)H HRMAS-NMR metabolite profile of the Korean cheeses is consistence with their sensory qualities. Further, the candidate metabolites identified in this study confers their potential application as biomarkers in cheese industries for faster and effective validation of sensory characteristics. Elsevier 2020-06 2020-05-11 /pmc/articles/PMC7254036/ /pubmed/32489280 http://dx.doi.org/10.1016/j.sjbs.2020.04.043 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Kandasamy, Sujatha
Yoo, Jayeon
Yun, Jeonghee
Kang, Han Byul
Seol, Kuk-Hwan
Ham, Jun-Sang
(1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities
title (1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities
title_full (1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities
title_fullStr (1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities
title_full_unstemmed (1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities
title_short (1)H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities
title_sort (1)h hrmas-nmr based metabolic fingerprints for discrimination of cheeses based on sensory qualities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254036/
https://www.ncbi.nlm.nih.gov/pubmed/32489280
http://dx.doi.org/10.1016/j.sjbs.2020.04.043
work_keys_str_mv AT kandasamysujatha 1hhrmasnmrbasedmetabolicfingerprintsfordiscriminationofcheesesbasedonsensoryqualities
AT yoojayeon 1hhrmasnmrbasedmetabolicfingerprintsfordiscriminationofcheesesbasedonsensoryqualities
AT yunjeonghee 1hhrmasnmrbasedmetabolicfingerprintsfordiscriminationofcheesesbasedonsensoryqualities
AT kanghanbyul 1hhrmasnmrbasedmetabolicfingerprintsfordiscriminationofcheesesbasedonsensoryqualities
AT seolkukhwan 1hhrmasnmrbasedmetabolicfingerprintsfordiscriminationofcheesesbasedonsensoryqualities
AT hamjunsang 1hhrmasnmrbasedmetabolicfingerprintsfordiscriminationofcheesesbasedonsensoryqualities