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Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating

International Journal of Agricultural and Biological Engineering 2024年第1期17卷 251-260页

作者： Hao Tian Shuai Wang Huirong Xu Yibin Ying College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China Key Laboratory of On-Site Processing Equipment for Agricultural Products Ministry of Agriculture and Rural AffairsHangzhou 310058China College of Food Science Shihezi UniversityShihezi 832003XinjiangChina

The aim of this study was in-line,rapid,and non-destructive detection for soluble solid content(SSC)in pomelos using visible and near-infrared spectroscopy(Vis-NIRS).However,the large size and thick rind of pomelo affect the stability of spectral acquisition and the biological variabilities affect the robustness of *** these issues,in this study,an efficient prototype in-line detection system in transmittance mode was designed and evaluated in comparison with an off-line detection *** from the years 2019 and 2020 were used for modeling and the external validation data were obtained by the inline detection system in *** wavelength selection methods of changeable size moving window(CSMW),random frog(RF),and competitive adaptive reweighted sampling(CARS)were used to improve the prediction accuracy of partial least squares regression(PLSR)*** best performance of internal prediction was obtained by CARS-PLSR and the determination coefficient of prediction(),root mean square error of prediction(RMSEP),and residual predictive deviation(RPD)were 0.958,0.204%,and 4.821,***,all models obtained large prediction biases in external *** latent variable updating(LVU)method was proposed to update models and improve the performance in external *** samples from the external validation set were randomly selected to update the *** with the recalibration method,LVU could effectively modify the original models which matched the SSC range of the external validation *** CSMW-PLSR models were more robust in external *** off-line model with LVU performed best with a root mean square error of validation(RMSEV)of 0.599%and the in-line model with recalibration obtained RMSEV of 0.864%.These results demonstrated the application potential of the transmittance Vis-NIRS for in-line rapid prediction of SSC in pomelos and the modeling and updating methods could be applied to samples with biological variabi

关键词： Vis-NIRS in-line detection external validation wavelength selection model updating pomelo SSC

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