The chemical composition of rape stalk is the physiological basis for its lodging resistance. By taking the advantage of NIRS, we developed a rapid method to determine the content of six key composition without crushing the stalk. Rapeseed stalks in the mature stage of growth were collected from three cultivation modes over the course of 2 years. First, we used the near-infrared spectroscope to scan seven positions on the stalk samples and took their average to form the spectral data. The stalks were then crushed and sieved; then the ratio of carbon and nitrogen, ratio of acid-insoluble lignin and lignin, and the content of soluble sugar and cellulose were determined using the combustion method, weighing method, and colorimetric method, respectively. The partial least squares regression (PLSR) method was used to establish a prediction model between the spectral data and the chemical measurements, and all models were evaluated by an internal interaction verification and an external independent test set sample. To improve the accuracy of the model and reduce the computing time, some optimization methods have been applied. Some outliers were removed, and then the data were preprocessed to determine the best spectral information band and the optimal principal component number. The results showed that elimination of outliers effectively improved the precision of the prediction model and that no spectral pretreatment method exhibited the highest prediction accuracy. In summary, the NIRS-based prediction model could facilitate the rapid nondestructive detection in the key components of rapeseed stalk.
Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires
