Abstract:To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples,then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.
张红光,卢建刚* . 净信号的局部建模算法及其在近红外光谱分析中的应用 [J]. 光谱学与光谱分析, 2016, 36(02): 384-387.
ZHANG Hong-guang, LU Jian-gang* . Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(02): 384-387.
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