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	Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards 期刊论文 OAI收割 MOLECULES, 2019, 卷号: 24, 期号: 9, 页码: 19 作者: Zhao, Yuhui; Zhao, Ziheng; Shan, Peng; Peng, Silong; Yu, Jinlong \| 收藏 \| 浏览/下载：58/0 \| 提交时间：2019/09/30 near-infrared (NIR) spectroscopy calibration transfer affine invariance multivariate calibration partial least squares (PLS)
	Qualitative determination of the components of textile products using near infrared spectroscopy (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photonics and Optoelectronics, SOPO 2010, June 19, 2010 - June 21, 2010, Chengdu, China 作者: Chen X.; Chen X.; Chen X.; Wang D. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 In the paper A total of 40 pure or two-mixed textile weaves were prepared and classification of these samples was introduced using near infrared spectroscopy (NIRS) all textile samples are selected among four classes which are Cotton-Terylene Cotton-Polyurethane Cotton and Terylene according to theirs components. Near infrared diffuse reflectance spectra of the samples were collected Mahalanobis distance was used to discriminate the samples coupled with principle components analysis (PCA) the results showed that near infrared spectroscopy could determine which class one textile weave belongs to easily comparing with traditional methods. In addition the expectation of the further research on qualitative determination of textile products by NIR technology was discussed in the end of this paper. 2010 IEEE.
	The study on the near infrared spectrum technology of sauce component analysis (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li S.; Wang C.; Chen X.; Chen X.; Chen X. 收藏 \| 浏览/下载：34/0 \| 提交时间：2013/03/25 The author Shangyu Li engages in supervising and inspecting the quality of products. In soy sauce manufacturing quality control of intermediate and final products by many components such as total nitrogen saltless soluble solids nitrogen of amino acids and total acid is demanded. Wet chemistry analytical methods need much labor and time for these analyses. In order to compensate for this problem we used near infrared spectroscopy technology to measure the chemical-composition of soy sauce. In the course of the work a certain amount of soy sauce was collected and was analyzed by wet chemistry analytical methods. The soy sauce was scanned by two kinds of the spectrometer the Fourier Transform near infrared spectrometer (FT-NIR spectrometer) and the filter near infrared spectroscopy analyzer. The near infrared spectroscopy of soy sauce was calibrated with the components of wet chemistry methods by partial least squares regression and stepwise multiple linear regression. The contents of saltless soluble solids total nitrogen total acid and nitrogen of amino acids were predicted by cross validation. The results are compared with the wet chemistry analytical methods. The correlation coefficient and root-mean-square error of prediction (RMSEP) in the better prediction run were found to be 0.961 and 0.206 for total nitrogen 0.913 and 1.215 for saltless soluble solids 0.855 and 0.199 nitrogen of amino acids 0.966 and 0.231 for total acid respectively. The results presented here demonstrate that the NIR spectroscopy technology is promising for fast and reliable determination of major components of soy sauce.
	Fast determination of total ginsenosides content in Ginseng powder by near infrared reflectance spectroscopy (EI CONFERENCE) 会议论文 OAI收割 ICO20: Biomedical Optics, August 21, 2005 - August 26, 2005, Changchun, China 作者: Chen X.-D.; Chen X.-D. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm1880 nm and 2230nm-2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR) principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm-2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.