MODEL MAINTENANCE OF RC-PLSR FOR MOISTURE CONTENT MEASUREMENT OF DRIED SCALLOP

A prediction model for evaluating the moisture content in dried Haiwan scallops was established using hyper-spectral imaging (HSI) technology in a previously published study. The accuracy of such models is usually affected by differences in sample species, different environmental conditions such as temperature or humidity, and aging of instruments. In this study, the prediction ability of the RC-PLSR model is improved by correcting the spectra of the tested species of dried scallop (i.e., Xiayi) to solve the problem of model failure caused by sample differences. The results of model maintenance by direct standardization (DS) are compared with those of variety sensitive wavelength selection - piecewise direct standardization (VSWS-PDS). The results showed that after using VSWS-PDS to modify the spectral data of the dried scallop samples, the correlation coefficient of prediction (Rp) of the updated model increased from 0.0890 to 0.9190. However, the root mean square error of prediction (RMSEP) also increased, indicating a need for improved precision. The RC-PLSR model based on DS correction showed Rp of 0.790 and RMSEP of 9.7481%. Model maintenance using the DS method is suggested because DS generally outperformed VSWS-PDS, even with a lower correlation coefficient. Future work on error reduction and sample input is suggested for VSWS-PDS optimization.