Sequential data-fusion of near-infrared and mid-infrared spectroscopy data for improved prediction of quality traits in tuber flours

This study evaluates the near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (MIR) complementary spectral ranges to predict six different quality traits, which include chemical components such as amylose, starch, protein, glucose, cellulose, and moisture contents, in tubers and root flours. The sequential orthogonalized partial least square regression (SOPLS), a recently developed multi-sensor data-fusion approach, was adapted to improve the performance of the model in predicting the chemical properties of the flour samples. Furthermore, the performance of the SOPLS model was compared to that of traditional PLS modeling. Compared to the earlier results acquired using individual sensor modeling (with the traditional PLS model), the SOPLS fusion model showed significant improvement in the prediction performance for all cases except glucose. Particularly, the highest improvement in performance was observed for the prediction of cellulose, showing a 22.8% increase in coefficient of determination for prediction (R2 p) and 66.5% decrease in root mean square of prediction (RMSEP) values. Therefore, we concluded that the data-fusion approach used in this study exhibited better performance compared to the model using individual sensors. Furthermore, the multi-sensor fusion with the sequential approach is not limited to NIR and MIR data only and can be used for complementary information fusion to further improve the performance of the model.