Cargando…

Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis

Consumption of brown rice (BR) contributes to the implementation of the grain-saving policy and improvement of residents’ nutrient status. However, the undesirable cooking properties, poor palatability, and presence of anti-nutritional factors limit the demand of BR products. To enhance its quality,...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Duqin, Ye, Yanjun, Wang, Luyao, Tan, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180828/
https://www.ncbi.nlm.nih.gov/pubmed/35681309
http://dx.doi.org/10.3390/foods11111560
_version_ 1784723616087146496
author Zhang, Duqin
Ye, Yanjun
Wang, Luyao
Tan, Bin
author_facet Zhang, Duqin
Ye, Yanjun
Wang, Luyao
Tan, Bin
author_sort Zhang, Duqin
collection PubMed
description Consumption of brown rice (BR) contributes to the implementation of the grain-saving policy and improvement of residents’ nutrient status. However, the undesirable cooking properties, poor palatability, and presence of anti-nutritional factors limit the demand of BR products. To enhance its quality, BR was solid-state fermented with single and mixed strains of Lb. plantarum, S. cerevisiae, R. oryzae, A. oryzae, and N. sitophila. Effects of solid-state fermentation (SSF) with different strains on the nutrition and sensory characteristics of BR were analyzed by spectroscopic method, chromatography, and sensory assessment. Contents of arabinoxylans, β-glucan, γ-oryzanol, phenolic, and flavonoid were significantly increased by 41.61%, 136.02%, 30.51%, 106.90%, and 65.08% after SSF, respectively (p < 0.05), while the insoluble dietary fiber and phytic acid contents reduced by 42.69% and 55.92%. The brightness and sensory score of BR significantly improved after SSF. Furthermore, cluster analysis (CA) and principal component analysis (PCA) were employed to evaluate BR quality. Three clusters were obtained according to CA, including BR fermented for 30 h and 48 h, BR fermented for 12 h, and the control group. Based on PCA, the best SSF processing technology was BR fermented with Lb. plantarum (0.5%, v/w) and S. cerevisiae (0.5%, v/w) at 28 °C for 48 h (liquid-to-solid ratio 3:10).
format Online
Article
Text
id pubmed-9180828
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91808282022-06-10 Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis Zhang, Duqin Ye, Yanjun Wang, Luyao Tan, Bin Foods Article Consumption of brown rice (BR) contributes to the implementation of the grain-saving policy and improvement of residents’ nutrient status. However, the undesirable cooking properties, poor palatability, and presence of anti-nutritional factors limit the demand of BR products. To enhance its quality, BR was solid-state fermented with single and mixed strains of Lb. plantarum, S. cerevisiae, R. oryzae, A. oryzae, and N. sitophila. Effects of solid-state fermentation (SSF) with different strains on the nutrition and sensory characteristics of BR were analyzed by spectroscopic method, chromatography, and sensory assessment. Contents of arabinoxylans, β-glucan, γ-oryzanol, phenolic, and flavonoid were significantly increased by 41.61%, 136.02%, 30.51%, 106.90%, and 65.08% after SSF, respectively (p < 0.05), while the insoluble dietary fiber and phytic acid contents reduced by 42.69% and 55.92%. The brightness and sensory score of BR significantly improved after SSF. Furthermore, cluster analysis (CA) and principal component analysis (PCA) were employed to evaluate BR quality. Three clusters were obtained according to CA, including BR fermented for 30 h and 48 h, BR fermented for 12 h, and the control group. Based on PCA, the best SSF processing technology was BR fermented with Lb. plantarum (0.5%, v/w) and S. cerevisiae (0.5%, v/w) at 28 °C for 48 h (liquid-to-solid ratio 3:10). MDPI 2022-05-25 /pmc/articles/PMC9180828/ /pubmed/35681309 http://dx.doi.org/10.3390/foods11111560 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
Zhang, Duqin
Ye, Yanjun
Wang, Luyao
Tan, Bin
Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis
title Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis
title_full Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis
title_fullStr Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis
title_full_unstemmed Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis
title_short Nutrition and Sensory Evaluation of Solid-State Fermented Brown Rice Based on Cluster and Principal Component Analysis
title_sort nutrition and sensory evaluation of solid-state fermented brown rice based on cluster and principal component analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180828/
https://www.ncbi.nlm.nih.gov/pubmed/35681309
http://dx.doi.org/10.3390/foods11111560
work_keys_str_mv AT zhangduqin nutritionandsensoryevaluationofsolidstatefermentedbrownricebasedonclusterandprincipalcomponentanalysis
AT yeyanjun nutritionandsensoryevaluationofsolidstatefermentedbrownricebasedonclusterandprincipalcomponentanalysis
AT wangluyao nutritionandsensoryevaluationofsolidstatefermentedbrownricebasedonclusterandprincipalcomponentanalysis
AT tanbin nutritionandsensoryevaluationofsolidstatefermentedbrownricebasedonclusterandprincipalcomponentanalysis