PLSR and TBPANN were applied for determination of VTC and TCD.The optimal combination of feature wavelength was hunted by FMCIA.FMCIA-TBPANNyieldedhigher overallprediction performance.Spatial gradations of VTC and TCD were visualized on distribution map. This study was conducted to assess the potential feasibility of using hyperspectral imaging (900-1700nm) for rapid determination of the volatility of tuber compositions (VTC) and prediction of the tuber cooking degree (TCD) in low temperature baking (LTB). Tuber samples of six categories from different origins were imagedand calibrated. The partial least squares regression (PLSR) and three-layer back propagation artificial neural network (TBPANN) models were established to predict VTC and TCD using the entire spectral range and the feature wavelengths. The optimal combination of characteristic wavelengths (991, 1031, 1071, 1138, 1252, 1403, 1460 and 1641nm) were selected by first derivative and mean centering iteration algorithm (FMCIA) rather than other conventional methods. Based on the qualified eight wavelengths, the FMCIA-TBPANN approachyieldedgreater overallperformance for predicting both VTC and TCD.Furthermore, the distribution maps of VTC and TCD were generated using a resulting function to visualize each pixel of spectral image. This demonstrated the capability of spectral imaging technique for rapid and accurate evaluation of VTC and TCD during LTB.
['Wen-Hao Su', 'Da-Wen Sun']
Multivariate analysis of hyper/multi-spectra for determining volatile compounds and visualizing cooking degree during low-temperature baking of tubers