title: [Study on quantitative analysis with near-infrared spectra using latent root regression model].
abstract: The latent root regression model with near-infrared spectra of 40 soybean samples was founded for analyzing the content of soybean protein in this study. The contents of protein in another 32 soybean samples were predicted by this model. The predicting results were compared with PLS, which shows that the latent root regression model can practically be used for the quantitative analysis of the biological samples with near-infrared spectra. This method is a new kind of chemometrics calibration method, which is modified from PCR. Because the method takes the role of sample composition into account when extracting the principal component from the NIR spectra of samples, the model has a good result in analyzing samples. Further more, the results showed that it is necessary to take account of the role of sample composition when building quantitative analysis model using NIR spectra.
authors.affiliation: Information College, China Agricultural University, Beijing 100094, China.
authors.forename: Lu-da
authors.initials: LD
authors.lastname: Zhang
authors.forename: Xiao-ming
authors.initials: XM
authors.lastname: Qi
authors.forename: Jin-hua
authors.initials: JH
authors.lastname: Yang
authors.forename: Long-lian
authors.initials: LL
authors.lastname: Zhao
authors.forename: Jun-hui
authors.initials: JH
authors.lastname: Li
authors.forename: Yan-lu
authors.initials: YL
authors.lastname: Yan
mesh_terms: D002138:Calibration; D008956:Models, Chemical; D012044:Regression Analysis; D030262:Soybean Proteins / Q000032:analysis*; D013025:Glycine max / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D030262:Soybean Proteins