生物量(biomass)是微生物高密度发酵过程中必须监控的关键指标之一,常用细胞光密度(optical density,OD)来测定。相较于传统的取样离线检测,实时在位监测技术具有无损、反馈快速和污染减少等优点,能够有效调控和优化发酵过程。为实现发...生物量(biomass)是微生物高密度发酵过程中必须监控的关键指标之一,常用细胞光密度(optical density,OD)来测定。相较于传统的取样离线检测,实时在位监测技术具有无损、反馈快速和污染减少等优点,能够有效调控和优化发酵过程。为实现发酵过程中关键指标的实时反馈,本研究构建了一个基于漫反射近红外光谱仪的在位监测平台,用于虾青素的高密度发酵过程中生物量的监测。通过实施光谱异常值剔除、比较不同的光谱预处理方法以及间隔偏最小二乘法(interval partial least squares,i-PLS)光谱波段分析,结果表明在1417-1650 nm波长范围内存在虾青素发酵生物量的特征波段。在此波段基础上建立的生物量动态预测模型交互验证决定系数(determination coefficient of cross validation,Rcv2)和交叉验证均方根误差(root mean square error of cross validation,RMSECV)分别为0.973和9.32。经过3批次发酵的外部验证表明采用i-PLS方法建立的生物量模型在细胞光密度(OD600)2.46-180.50范围内进行监测,OD平均绝对误差(mean absolute error,MAE)为6.28,展现出较高的预测准确性和稳定性。这表明该模型在虾青素高密度发酵过程生物量监测中具有应用前景。展开更多
[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensem...[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.展开更多
文摘生物量(biomass)是微生物高密度发酵过程中必须监控的关键指标之一,常用细胞光密度(optical density,OD)来测定。相较于传统的取样离线检测,实时在位监测技术具有无损、反馈快速和污染减少等优点,能够有效调控和优化发酵过程。为实现发酵过程中关键指标的实时反馈,本研究构建了一个基于漫反射近红外光谱仪的在位监测平台,用于虾青素的高密度发酵过程中生物量的监测。通过实施光谱异常值剔除、比较不同的光谱预处理方法以及间隔偏最小二乘法(interval partial least squares,i-PLS)光谱波段分析,结果表明在1417-1650 nm波长范围内存在虾青素发酵生物量的特征波段。在此波段基础上建立的生物量动态预测模型交互验证决定系数(determination coefficient of cross validation,Rcv2)和交叉验证均方根误差(root mean square error of cross validation,RMSECV)分别为0.973和9.32。经过3批次发酵的外部验证表明采用i-PLS方法建立的生物量模型在细胞光密度(OD600)2.46-180.50范围内进行监测,OD平均绝对误差(mean absolute error,MAE)为6.28,展现出较高的预测准确性和稳定性。这表明该模型在虾青素高密度发酵过程生物量监测中具有应用前景。
文摘[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.
