Predicting the dietary fiber content of fresh-cut bamboo shoots using a visible and near-infrared hyperspectral technique

The dietary fiber content in fresh-cut bamboo shoots is considered crucial for the quality of processed bamboo shoots products. This study aimed to explore the potential of applying two different hyperspectral techniques, namely visible near infrared (Vis-NIR) spectroscopy and near infrared (NIR) in the quick and non-destructive prediction of the dietary fiber content of fresh-cut bamboo shoots. The Vis-NIR and NIR hyperspectral data were collected to establish partial least square regression (PLSR) and principal component regression (PCR) calibration model for the average spectrum of fresh-cut bamboo shoots and their corresponding dietary fiber content. Subsequently, data fusion analysis, various pre-processing methods, and principal component analysis (PCA) were used to optimize the model. The results indicated that superior models were obtained based on low-level fusion data when compared with the corresponding methods based on single spectral data. The optimal SNV-PCA-PLSR model achieved a good performance with coefficient of determination of prediction ( R 2 p ) of 0.902, and root mean square errors of prediction (RMSEP) of 0.135. Therefore, hyperspectral technique combined with data fusion analysis can be a promising approach for non-invasive quality supervision of bamboo shoots products in varied processing states.