The drug supervision methods based on near-infrared spectroscopy analysis are heavily dependent on the chemometrics model which characterizes the relationship between spectral data and drug categories. The preliminary application of convolution neural network in spectral analysis demonstrates excellent end-to-end prediction ability, but it is sensitive to the hyper-parameters of the network. The transformer is a deep-learning model based on self-attention mechanism that compares convolutional neural networks (CNNs) in predictive performance and has an easy-todesign model structure. Hence, a novel calibration model named SpectraTr, based on the transformer structure, is proposed and used for the qualitative analysis of drug spectrum. The experimental results of seven classes of drug and 18 classes of drug show that the proposed SpectraTr model can automatically extract features from a huge number of spectra, is not dependent on pre-processing algorithms, and is insensitive to model hyperparameters. When the ratio of the training set to test set is 8:2, the prediction accuracy of the SpectraTr model reaches 100% and 99.52%, respectively, which outperforms PLS DA, SVM, SAE, and CNN. The model is also tested on a public drug data set, and achieved classification accuracy of 96.97% without preprocessing algorithm, which is 34.85%, 28.28%, 5.05%, and 2.73% higher than PLS DA, SVM, SAE, and CNN, respectively. The research shows that the SpectraTr model performs exceptionally well in spectral analysis and is expected to be a novel deep calibration model after Autoencoder networks (AEs) and CNN.

研究近红外光谱分析模型中的样本有效划分问题, 针对经典KS算法依据距离度量描述高维度光谱数据间差异时效果不尽人意甚至失去意义的问题, 结合目前相似性度量方法的不足, 构造出一种新的相似性度量函数, 采用光谱特征和性质特征相结合的方式计算样本间差异, 提出一种改进的KS算法以寻求样本差异的最佳表达方式。通过与其他改进方法的对比, 从有效性和对近红外光谱分析模型的影响两方面对所提改进算法进行分析, 验证了所提算法的合理性和优越性。

土壤是农业生产的基础, 采用近红外光谱技术实现对土壤养分的快速分析, 研制分立波长型近红外土壤养分测量系统, 指导农业生产过程, 有助于改变现有农业生产的粗放经营状态。 首先, 使用FOSS XDS近红外光谱分析仪对85份东北土壤样品采集光谱, 采用相关系数谱及连续投影法等化学计量学算法分析土壤的近红外光谱, 并优选出总氮和有机质的特征波长； 其次, 基于确定的特征波长研制了一套高信噪比近红外光谱测量系统, 对土壤样品的近红外光谱及测量结果进行了分析。 研制的测量系统测得土壤养分中总氮和有机质的预测均方差(RMSEP)分别为0.019%和0.36%, 预测相关系数(Rp)分别为0.851和0.923。 通过近红外光谱分析, 确定出能够代表土壤主要养分分析的特征光谱； 研制的测量系统可以用于土壤养分分析, 为进一步产业化应用打下基础。