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中文摘要：
采用近红外光谱在主成分空间的距离作为样本相似性的判据,建立了一种用于近红外光谱定量分析的局部建模方法。该方法首先对校正集的光谱进行主成分分析（PCA）,然后基于主成分空间中预测样本与校正集样本的距离选择校正子集并建立局部偏最小二乘（PLS）回归模型。对欧氏距离和马氏距离的比较表明,欧氏距离可以更好地表达样本之间的相似性。将所建立的方法用于烟草样品中氯和尼古丁含量的测定,结果表明局部建模方法比常用的全局建模方法具有更好的预测准确性,特别是在低含量成分的预测中具有明显优势。
英文摘要：
A local regression method based on distance criterion in principal component（PC） space for near-infrared（NIR） spectral quantitative analysis was proposed.In this method,principal component analysis（PCA） is firstly utilized to extract the information of the NIR spectra,and then,the calibration subsets are individually selected for each prediction sample according to the distance between the sample and calibration samples in the PCs space.Finally,the PLS local model for every prediction sample is established individually and the prediction of the sample is done with the local model.It was found that the Euclidean distance can more effectively measure the similarity of the samples than Mahalanobis distance.With an application of the local regression method to the quantitative determination of chlorine and nicotine in tobacco samples,it is proved that the prediction precision of local regression method is better than that of global regression methods,especially in the situation of predicting the low concentration components.
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