Full
Outline
To establish a sensory evaluation system suitable for commercially available steamed red bean buns and explore the correlation between sensory indicators and instrumental analysis parameters.
Sensory evaluation indicators were screened and their weights were determined based on the fuzzy binary contrast decision method, and an evaluation system was constructed in combination with expert consultation. The 12 kinds of commercially available steamed red bean bun samples were selected for sensory evaluation and instrumental analysis(texture, color difference, electronic nose, electronic tongue). Correlation analysis was used to clarify the relationship between sensory indicators and analysis parameters.
A sensory evaluation system comprising 8 indicators for steamed red bean buns was established, in which taste had the highest weight (0.20). Among the 12 kinds of commercial samples, sample 8 received the highest comprehensive sensory score. Texture analysis indicated that samples with lower hardness and chewiness generally achieved higher sensory scores. Colorimetric analysis showed that the L* of all 12 kinds of samples exceeded 80. Correlation analysis revealed that the sensory score for crust color was highly significantly positively correlated with instrumental whiteness (P<0.001), chewiness was highly significantly positively correlated with hardness (P<0.001), aroma was highly significantly negatively correlated with the response values of electronic nose sensors W1W, W1S and W5S (P<0.001), and taste was significantly positively correlated with the reference signal value cycles per second (CPS) from the electronic tongue (P<0.05).
This study establishes a sensory evaluation system for commercially available steamed red bean buns and preliminarily validates the correlation between sensory indicators and analysis parameters, which can serve as a methodological reference for product quality evaluation and process optimization. Given that this study is based on a cross-sectional comparison of 12 kinds of commercial samples, further validation of the system’s applicability will benefit from expanded sample ranges and the inclusion of consumer preference comparisons.
9
2
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
