中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共6条，第1-6条

帮助

条数/页：排序方式：
	glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models 期刊论文 OAI收割 JOURNAL OF PLANT ECOLOGY, 2022, 卷号: 15, 期号: 6, 页码: 1-6 作者: Lai, Jiangshan; Zou, Yi; Zhang, Shuang; Zhang, Xiaoguang; Mao, Lingfeng \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2023/02/02 coefficient of determination commonality analysis fixed effect GLMM hierarchical partitioning marginal R-2 relative importance variance partitioning
	Does agroecosystem model improvement increase simulation accuracy for agricultural N2O emissions? 期刊论文 OAI收割 AGRICULTURAL AND FOREST METEOROLOGY, 2021, 卷号: 297, 页码: 12 作者: Zhang, Yajie; Yu, Qiang \| 收藏 \| 浏览/下载：37/0 \| 提交时间：2021/03/15 Nitrous oxide emission Agroecosystem model evolution Coefficient of determination Root mean square error Meta-analysis
	Determination of chitosan content with ratio coefficient method and HPLC 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES, 2020, 卷号: 164, 页码: 384-388 作者: Miao, Qin; Cui, Yulin; Zhang, Jingjing; Mi, Yingqi; Tan, Wenqiang \| 收藏 \| 浏览/下载：31/0 \| 提交时间：2021/06/07 Ratio coefficient Determination of chitosan content Hydrolysis rate HPLC Average deacetylation degree (DD)
	Remote chlorophyll-a retrieval in eutrophic inland waters by concentration classification Taihu Lake case study 会议论文 OAI收割 International Conference on Earth Observation Data Processing and Analysis, ICEODPA,, Wuhan, China, December 28, 2008 - December 30,2008 Du, Cong; Wang, Shixin; Zhou, Yi; Yan, Fuli 收藏 \| 浏览/下载：32/0 \| 提交时间：2014/12/07 In order to improve the precision of phytoplankton chlorophyll-a (chla) concentration retrieval this study classified the data into two groups (the high and the low) by chla concentration with the threshold of 50gA&bullL-1. And then build the statistical models for each group. Particularly a modifying factor OSS/TSS was used to unmixing the spectra in the low model to improve the low relationship between spectral reflectance and chla concentrations. As a result the concentration classification model allowed estimation of chla with a root mean square error (RMSE) of 21.12gA&bullL-1 and the determination coefficient (R2) was 0.92 comparing with RMSE of chla estimation was 35.72gA&bullL-1 and R2=0.72 in the traditional model. It shows that concentration classification is a helpful method for accurate remote chla retrieval in eutrophic inland waters. 2008 SPIE.
	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：35/0 \| 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：26/0 \| 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.