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。

The effective sample partition in the near-infrared spectroscopy model is studied.When classical Kennard Stone (KS) algorithm uses the distance metric to describe the difference between high-dimensional spectral data, the effect is unsatisfactory or even meaningless.To solve the problem, and considering the shortcomings of current similarity measurement methods, we constructed a new similarity measure function.The spectral features and property features were combined to calculate the difference between the samples.An improved KS algorithm was thus proposed to find the best expression of sample difference. The improved algorithm was analyzed from the aspects of effectiveness and the impact on the near-infrared spectroscopy model by comparing with other improved methods, and the rationality and superiority of the proposed algorithm were verified.