Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

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• 作者：Mengli Fan ; Xiuwei Liu ; Xiaoming Yu ; Xiaoyu Cui ; Wensheng Cai&#8230
• 关键词：near ; infrared spectroscopy ; discrimination ; serum ; kidney disease ; chemometrics
• 刊名：Science China Chemistry
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：60
• 期：2
• 页码：299-304
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry/Food Science, general;
• 出版者：Science China Press
• ISSN：1869-1870
• 卷排序：60

文摘

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.