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Outline
The traditional winemaking process of grading and storage is a crucial link that affects the quality of Chinese baijiu blending and finished wine, but the grading and storage process of Chinese baijiu are mainly relied on workers experience in traditional winemaking factories, causing difficulties in many characteristic quantification and form scientific basis for grading storage. Based on deep learning technology, a gas chromatograph was used to quantitatively detect and analyse the content of flavour components in the base wine of small-curve clear-flavoured Chinese baijiu, and a regression model for the quality grading of the base wine was established using PLSR based on the content of the components detected in the base wine, screening the first eight components that have the most significant impact on the quality grading of the base wine: ethyl acetate, ethyl lactate, ethyl valerate, isobutyl alcohol, ethyl caprylate, ethylene acetal, ethyl acetate, sec-butanol, n-propanol, and then the PSO-RBF neural network-based base wine quality classification model was explored. Finally, according to the predicted outputs of the test samples, our algorithm achieved 98.6% accuracy in base wine classification, which was significantly better than the 94.3% of the traditional RBF neural network. As a result, the base wine classification grading model was successfully constructed.
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| 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 |
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