摘要
近红外光谱技术因快速、无损等特点,已广泛应用于烟草行业质量快速分析。然而,由于采收时间、环境差异等因素的影响,建立的近红外定量模型在新批次样本中的预测性能通常变差,因此必须对原有模型进行维护和更新。该研究采用半监督无参数校正增强(SS-PFCE)方法,通过约束优化,对主模型的回归系数进行修正。首先建立了2016年烟叶样本总糖含量的原始定量模型,其预测相关系数(Rp)为0.9978、预测均方根误差(RMSEP)为0.3108。采用SS-PFCE方法对模型更新后,分别预测2017年、2018年和2020年样本的总糖含量,3个测试集的Rp值比未更新模型提高了0.13%、1.32%和4.29%,RMSEP分别下降了15.26%、58.69%和36.53%。与重新建立的定量分析模型相比,更新后的模型具有更优的预测性能,同时大大降低了建模成本。研究表明,SS-PFCE方法可高效地实现不同年份烟叶样本的模型维护,在实际生产中具有重要的应用价值。
The near infrared spectroscopy technology has been widely applied to the quantitative analysis in tobacco industry because of its advantages of rapidness and non-destructiveness.However,the accuracy and robustness of the original model may deteriorate when predicting samples with new variations.When samples are obtained from different harvest years and different environmental conditions,new variations will be introduced.Therefore,there is a need to maintain the predictive performance of the original model when it works on a new batch.In this study,a method called semi-supervised parameter-free calibration enhancement(SS-PFCE)was used to update the original model.The regression coefficient of original model was corrected by constrained optimization.The original model for total sugar determination was firstly developed with the tobacco samples of 2016,which showed a predicted correlation coefficient(Rp)of 0.9978 and a root mean square error of prediction(RMSEP)of 0.3108.After updating the model by the SS-PFCE method,the total sugar contents in the samples of 2017,2018 and 2020 years were predicted,respectively.The Rp values of the three test sets were improved by 0.13%,1.32%and 4.29%,and the RMSEP were decreased by 15.26%,58.69%and 36.53%,respectively,compared with the non-updated model.Furthermore,the updated model by the SS-PFCE approach offered a better predictive performance than re-modeling method,while significantly reduced modeling costs.The results of this study showed that the SS-PFCE method could maintain the prediction accuracy for tobacco samples of different years efficiently,and it is of great practical application value in industrial production.
作者
耿莹蕊
沈欢超
倪鸿飞
王辉
吴继忠
张立立
李永生
何文苗
陈勇
刘雪松
GENG Ying-rui;SHEN Huan-chao;NI Hong-fei;WANG Hui;WU Ji-zhong;ZHANG Li-li;LI Yong-sheng;HE Wen-miao;CHEN Yong;LIU Xue-song(College of Pharmaceutical Sciences,Zhejiang University,Hangzhou 310058,China;Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University,Hangzhou 310018,China;Technology Center,China Tobacco Zhejiang Industrial Co.,Ltd.,Hangzhou 310008,China)
出处
《分析测试学报》
CAS
CSCD
北大核心
2022年第7期1066-1071,共6页
Journal of Instrumental Analysis
基金
浙江大学-浙江中烟联合实验室项目资助。
