Enhanced Raman And Mid-Infrared Spectroscopic Discrimination Of Geographical Origin Of Rice By Data Mining And Data Fusion

Data mining and fusion of Raman and mid-infrared (mid-IR) spectra was studied to improve the identification ability for geographical origins of rice. Relative standard deviation (RSD) analysis can predict whether there are outlier Raman spectra. Hierarchical clustering analysis (HCA) can find out the potential outlier data, and then RSD analysis can finally determine the outlier data. The recognition accuracy of the model built by eliminating the outlier data was higher than that of the model using all the data. The identification accuracy of the data fusion model was 97.8%, 4.5% higher than that of the Raman and mid-IR models. The model was further applied to identify the geographical origins of 10 japonica rice varieties, with an accuracy of 96.7%. A combination of data mining and data fusion can enhance the discrimination ability for the geographical origin of rice using a combination of Raman and mid-IR spectroscopy.