Scholarlink

<p>Daqu serves as a key saccharifying and fermenting agent in Baijiu production, and its reducing sugar content has a significant impact on the aroma and quality of the liquor. However, traditional methods for measuring reducing sugar content in Daqu are complex and suffer from time lag, which makes monitoring and analyzing reducing sugar levels during fermentation difficult. Given the influence of environmental variables on the reducing sugar content in Daqu, this study aimed to propose a time-series prediction method by combining Daqu microenvironment parameters with a stacked ensemble learning approach. The microenvironment parameters were recorded and analyzed, and RF-Relief was used to identify the most important monitoring points from various environmental variables. Single models, including support vector regression (SVR), eXtreme Gradient Boosting (XGBoost), random forest (RF), and ridge regression (RR), as well as a stacking ensemble model (Stacking), were established, and their prediction accuracies for reducing sugar content were compared. The results showed that the stacking model significantly outperformed the single models, achieving <i>R</i><sup>2</sup> values of 0.9610, 0.9661, and 0.9501 for the upper, middle, and lower layers, respectively. This represented an improvement by 17.08%, 11.58%, and 16.47% over SVR; by 20.09%, 15.23%, and 14.86% over XGBoost; by 10.12%, 6.05%, and 9.09% over RF; and by 7.80%, 10.60%, and 15.81% over RR. The proposed model enabled accurate real-time prediction of reducing sugar content, thereby overcoming the limitations of traditional methods and providing reliable data support for the intelligent regulation of the Daqu fermentation process.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic Prediction of Reducing Sugar Content in Daqu Based on a Time Series–Microenvironment Coupled Stacking Model

  • 期刊论文

  • 发表日期：

    2026年1月2日

查看全文
  • Haili Yang,
  • Hao Xia,
  • Sai Liu,
  • Shan Chen,
  • Xinjun Hu,
  • Liangliang Xie,
  • Xilong Liao,
  • Lei Fei,
  • Fuhao Han,
  • Jianping Tian,
  • Manjiao Chen,
  • Yuqi Zhou

摘要

Daqu serves as a key saccharifying and fermenting agent in Baijiu production, and its reducing sugar content has a significant impact on the aroma and quality of the liquor. However, traditional methods for measuring reducing sugar content in Daqu are complex and suffer from time lag, which makes monitoring and analyzing reducing sugar levels during fermentation difficult. Given the influence of environmental variables on the reducing sugar content in Daqu, this study aimed to propose a time-series prediction method by combining Daqu microenvironment parameters with a stacked ensemble learning approach. The microenvironment parameters were recorded and analyzed, and RF-Relief was used to identify the most important monitoring points from various environmental variables. Single models, including support vector regression (SVR), eXtreme Gradient Boosting (XGBoost), random forest (RF), and ridge regression (RR), as well as a stacking ensemble model (Stacking), were established, and their prediction accuracies for reducing sugar content were compared. The results showed that the stacking model significantly outperformed the single models, achieving R2 values of 0.9610, 0.9661, and 0.9501 for the upper, middle, and lower layers, respectively. This represented an improvement by 17.08%, 11.58%, and 16.47% over SVR; by 20.09%, 15.23%, and 14.86% over XGBoost; by 10.12%, 6.05%, and 9.09% over RF; and by 7.80%, 10.60%, and 15.81% over RR. The proposed model enabled accurate real-time prediction of reducing sugar content, thereby overcoming the limitations of traditional methods and providing reliable data support for the intelligent regulation of the Daqu fermentation process.