中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共8条，第1-8条

帮助

条数/页：排序方式：
	Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion 期刊论文 OAI收割 BIORESOURCE TECHNOLOGY, 2021, 卷号: 326, 页码: 10 作者: Yang, Gaixiu; Li, Ying; Zhen, Feng; Xu, Yonghua; Liu, Jinming \| 收藏 \| 浏览/下载：73/0 \| 提交时间：2021/10/27 Anaerobic co-digestion Biochemical methane potential Multivariate linear regression Partial least squares Near-infrared spectroscopy
	Estimating purple-soil moisture content using Vis-NIR spectroscopy 期刊论文 OAI收割 JOURNAL OF MOUNTAIN SCIENCE, 2020, 卷号: 17, 期号: 9, 页码: 2214-2223 作者: Gou, Yu; Wie, Jie; Li, Jin-lin \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2020/11/24 Purple soil Soil moisture Vis-NIR spectroscopy Stepwise multiple linear regression Partial least squares regression
	Frequentist model averaging estimation for the censored partial linear quantile regression model 期刊论文 OAI收割 JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2017, 卷号: 189, 页码: 1-15 作者: Sun, Zhimeng; Sun, Liuquan; Lu, Xiaoling; Zhu, Ji; Li, Yongzhuang \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2018/07/30 Model averaging Model selection Partial linear model Quantile regression Random censoring
	Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula 期刊论文 OAI收割 CATENA, 2016, 卷号: 137, 页码: 340-349 作者: Yu, X; Liu, Q; Wang, YB; Liu, XY; Liu, X 收藏 \| 浏览/下载：43/0 \| 提交时间：2016/04/24 Multiple linear stepwise regression Partial least square regression Estimation Soil element contents Visible/near-infrared spectroscopy
	Variable selection in high-dimensional partially linear additive models for composite quantile regression 期刊论文 OAI收割 COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2013, 卷号: 65, 页码: 56-67 作者: Guo, Jie; Tang, Manlai; Tian, Maozai; Zhu, Kai 收藏 \| 浏览/下载：24/0 \| 提交时间：2016/11/17 Adaptive Lasso Composite quantile regression High-dimension Semiparametric additive partial linear model Spline approximation Variable selection
	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：41/0 \| 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：30/0 \| 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.
	Asymptotics of the goodness-of-fit test for a partial linear model with randomly censored data 期刊论文 OAI收割 SCIENCE IN CHINA SERIES A-MATHEMATICS, 2003, 卷号: 46, 期号: 2, 页码: 145-158 作者: Chen, M; Yuen, KC; Zhu, LX \| 收藏 \| 浏览/下载：13/0 \| 提交时间：2018/07/30 partial linear regression random censoring empirical process model checking