食品科学2026，Vol.47Issue(4)：39-48,10.DOI:10.7506/spkx1002-6630-20250921-152
基于CARS-1D-CNN与Vis/NIRS的贡梨SSC检测温度校正方法
Temperature Correction Method for the Detection of Soluble Solids Content in Gongli Pears Based on Vis/NIRS and CARS-1D-CNN
摘要
Abstract
In response to the problem that the detection of soluble solids content(SSC)in Gongli pears using visible/near-infrared spectroscopy(Vis/NIRS)is vulnerable to interferences from sample temperature fluctuations,a temperature correction method was proposed by integrating competitive adaptive reweighted sampling(CARS)with a one-dimensional convolutional neural network(1D-CNN)regression model,and six temperature gradients(5,10,15,20,25,and 30℃)were established for validation.The introduction of temperature labels as auxiliary variables in the model input helped the neural network perceive and adapt to spectral changes under different temperature conditions,thereby enhancing the robustness of the model to temperature perturbation.This method was compared and validated against other temperature correction methods such as global calibration,generalized least squares weighting(GLSW),and external parameter orthogonalization(EPO).The results showed that CARS-1D-CNN outperformed traditional methods such as EPO in terms of prediction accuracy and robustness,with correlation coefficient of prediction(Rp)of 0.885 9 and root mean square error(RMSE)of 0.548 3.Compared with the traditional method EPO used in this study,CARS-1D-CNN improved the correlation coefficient by 2.96%and reduced the prediction root mean square error of prediction by 2.73%.This method effectively mitigates the interference of temperature on the spectral model,improving its stability and prediction performance.关键词
竞争性自适应重加权采样-一维卷积神经网络/可见-近红外光谱/可溶性固形物含量/温度校正Key words
competitive adaptive reweighted sampling combined with 1D convolutional neural network/visible/near infrared spectroscopy/soluble solids content/temperature correction分类
数理科学引用本文复制引用
吴至境,刘富强,欧阳爱国,刘燕德..基于CARS-1D-CNN与Vis/NIRS的贡梨SSC检测温度校正方法[J].食品科学,2026,47(4):39-48,10.基金项目
"十四五"国家重点研发计划重点专项(2022YFD2001804) （2022YFD2001804）
