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.

中文翻译：

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近红外反射光谱法预测油菜茎秆抗倒塌关键成分的模型

油菜茎的化学成分是其抗倒伏的生理基础。利用NIRS的优势，我们开发了一种快速的方法来确定六种关键成分的含量而不会压坏茎。在2年的过程中，从三种耕作模式中收集了处于成熟阶段的油菜茎。首先，我们使用近红外光谱仪扫描茎样品上的七个位置，并取它们的平均值以形成光谱数据。然后将茎杆粉碎并过筛。然后分别采用燃烧法，称量法和比色法测定碳氮比，酸不溶性木质素和木质素的比例以及可溶性糖和纤维素的含量。使用偏最小二乘回归（PLSR）方法在光谱数据和化学测量之间建立预测模型，所有模型均通过内部相互作用验证和外部独立测试集样本进行评估。为了提高模型的准确性并减少计算时间，已应用了一些优化方法。除去一些离群值，然后对数据进行预处理，以确定最佳光谱信息带和最佳主成分数。结果表明，离群值的消除有效地提高了预测模型的精度，并且没有任何光谱预处理方法能够显示出最高的预测精度。综上所述，