Process optimization and quality analysis of strawberry lees steamed buns
This study employed an artificial neural network and a genetic algorithm to optimize the production of steamed buns made with strawberry lees. The optimal parameters identified were: 17% strawberry lees addition, dough fermentation at 36°C for 120 min, and a 50-min proofing period. The experimental...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | CyTA - Journal of Food |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19476337.2025.2541888 |
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| author | Qianqian Tong Linlin Yin Hong Rong Jingjing Yang Mengmeng Gu Chaojia Shi |
| author_facet | Qianqian Tong Linlin Yin Hong Rong Jingjing Yang Mengmeng Gu Chaojia Shi |
| author_sort | Qianqian Tong |
| collection | DOAJ |
| description | This study employed an artificial neural network and a genetic algorithm to optimize the production of steamed buns made with strawberry lees. The optimal parameters identified were: 17% strawberry lees addition, dough fermentation at 36°C for 120 min, and a 50-min proofing period. The experimental sensory score (84.03 ± 1.76) was close to the predicted score (83.32). Analyses indicated that strawberry lees enhanced the properties and palatability of the buns compared to traditional yeast buns, although they contained more fiber and were of lower quality than commercial varieties. The free amino acid profiles varied, with glutamic acid exhibiting the highest taste activity value. A total of 79 volatile compounds were identified, primarily esters and alcohols, with heptanoic acid and octanoic acid ethyl ester contributing to a unique aroma. These buns address the issue of lees disposal and establish a foundation for the large-scale production of nutritious steamed buns. |
| format | Article |
| id | doaj-art-cdcb35fc7f1b416a8a0eaf2eaac9d1cf |
| institution | Kabale University |
| issn | 1947-6337 1947-6345 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | CyTA - Journal of Food |
| spelling | doaj-art-cdcb35fc7f1b416a8a0eaf2eaac9d1cf2025-08-20T04:02:41ZengTaylor & Francis GroupCyTA - Journal of Food1947-63371947-63452025-12-0123110.1080/19476337.2025.2541888Process optimization and quality analysis of strawberry lees steamed bunsQianqian Tong0Linlin Yin1Hong Rong2Jingjing Yang3Mengmeng Gu4Chaojia Shi5Bioengineering School, Huainan Normal University, Huainan, Anhui, ChinaBioengineering School, Huainan Normal University, Huainan, Anhui, ChinaBioengineering School, Huainan Normal University, Huainan, Anhui, ChinaBioengineering School, Huainan Normal University, Huainan, Anhui, ChinaBioengineering School, Huainan Normal University, Huainan, Anhui, ChinaBioengineering School, Huainan Normal University, Huainan, Anhui, ChinaThis study employed an artificial neural network and a genetic algorithm to optimize the production of steamed buns made with strawberry lees. The optimal parameters identified were: 17% strawberry lees addition, dough fermentation at 36°C for 120 min, and a 50-min proofing period. The experimental sensory score (84.03 ± 1.76) was close to the predicted score (83.32). Analyses indicated that strawberry lees enhanced the properties and palatability of the buns compared to traditional yeast buns, although they contained more fiber and were of lower quality than commercial varieties. The free amino acid profiles varied, with glutamic acid exhibiting the highest taste activity value. A total of 79 volatile compounds were identified, primarily esters and alcohols, with heptanoic acid and octanoic acid ethyl ester contributing to a unique aroma. These buns address the issue of lees disposal and establish a foundation for the large-scale production of nutritious steamed buns.https://www.tandfonline.com/doi/10.1080/19476337.2025.2541888Steamed bunsartificial neural networkgenetic algorithmquality characteristicsflavor compounds |
| spellingShingle | Qianqian Tong Linlin Yin Hong Rong Jingjing Yang Mengmeng Gu Chaojia Shi Process optimization and quality analysis of strawberry lees steamed buns CyTA - Journal of Food Steamed buns artificial neural network genetic algorithm quality characteristics flavor compounds |
| title | Process optimization and quality analysis of strawberry lees steamed buns |
| title_full | Process optimization and quality analysis of strawberry lees steamed buns |
| title_fullStr | Process optimization and quality analysis of strawberry lees steamed buns |
| title_full_unstemmed | Process optimization and quality analysis of strawberry lees steamed buns |
| title_short | Process optimization and quality analysis of strawberry lees steamed buns |
| title_sort | process optimization and quality analysis of strawberry lees steamed buns |
| topic | Steamed buns artificial neural network genetic algorithm quality characteristics flavor compounds |
| url | https://www.tandfonline.com/doi/10.1080/19476337.2025.2541888 |
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