基于PSO-RBF的小曲清香型白酒基酒品质分级模型研究

Quality Grading Model of Base Wines of Xiaoqu Clear-flavored Chinese Baijiu Based on PSO-RBF

摘要:

传统酿酒工艺的分级存储影响白酒成品质量，但白酒分级存储主要依赖人工经验，许多特征难以进行量化，难以形成科学的分级存储依据。基于深度学习技术，采用气相色谱仪对小曲清香型白酒基酒中风味物质成分含量进行定量检测分析，根据检测出的基酒中物质成分含量利用PLSR建立基酒的品质分级回归模型，筛选前8种对基酒品质等级划分影响最显著的成分：乙酸乙酯、乳酸乙酯、戊酸乙酯、异丁醇、己酸乙酯、乙缩醛、仲丁醇和正丙醇。然后对基于PSO-RBF神经网络的基酒品质分级模型进行探究，最终根据测试样本的预测输出结果，表明算法基酒分类的精确率达到了98.6%，显著优于传统RBF神经网络的94.3%，成功构建了基酒的分级模型。

Abstract:

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.