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Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations
The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) la...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471538/ https://www.ncbi.nlm.nih.gov/pubmed/34574094 http://dx.doi.org/10.3390/foods10091984 |
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author | Dong, Xiaoguang Gao, Libing Zhang, Haijun Wang, Jing Qiu, Kai Qi, Guanghai Wu, Shugeng |
author_facet | Dong, Xiaoguang Gao, Libing Zhang, Haijun Wang, Jing Qiu, Kai Qi, Guanghai Wu, Shugeng |
author_sort | Dong, Xiaoguang |
collection | PubMed |
description | The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose. |
format | Online Article Text |
id | pubmed-8471538 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84715382021-09-28 Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations Dong, Xiaoguang Gao, Libing Zhang, Haijun Wang, Jing Qiu, Kai Qi, Guanghai Wu, Shugeng Foods Article The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose. MDPI 2021-08-25 /pmc/articles/PMC8471538/ /pubmed/34574094 http://dx.doi.org/10.3390/foods10091984 Text en © 2021 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 Dong, Xiaoguang Gao, Libing Zhang, Haijun Wang, Jing Qiu, Kai Qi, Guanghai Wu, Shugeng Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations |
title | Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations |
title_full | Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations |
title_fullStr | Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations |
title_full_unstemmed | Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations |
title_short | Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations |
title_sort | comparison of sensory qualities in eggs from three breeds based on electronic sensory evaluations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471538/ https://www.ncbi.nlm.nih.gov/pubmed/34574094 http://dx.doi.org/10.3390/foods10091984 |
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