中国科学院机构知识库网格系统: 检索

Temporal and spatial variations in the terrestrial water storage across Central Asia based on multiple satellite datasets and global hydrological models 期刊论文 OAI收割
JOURNAL OF HYDROLOGY, 2021, 卷号: 596, 页码: 16
作者: Hu, Zengyun; Zhang, Zizhan; Sang, Yan-Fang; Qian, Jing; Feng, Wei

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收藏 | 浏览/下载：48/0 | 提交时间：2021/08/19

Terrestrial water storage Global hydrological model GRACE satellite dataset Partial least square regression Central Asia

The powdery mildew disease of rubber (Oidium heveae) is jointly controlled by the winter temperature and host phenology 期刊论文 OAI收割
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2021, 卷号: 65, 期号: 10, 页码: 1707-1718
作者: Zhai,De-Li; Thaler,Philippe; Luo,Yiqi; Xu,Jianchu

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收藏 | 浏览/下载：42/0 | 提交时间：2022/04/02

Rubber plantation Oidium heveae Partial least square (PLS) regression Winter warming Phenology

Measurement of moisture content in lubricating oils of high-speed rail gearbox by Vis-NIR spectroscopy 期刊论文 OAI收割
Optik, 2020, 卷号: 224
作者: Liu, Chenyang; Tang, Xingjia; Yu, Tao; Wang, Taisheng; Lu, Zhenwu

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收藏 | 浏览/下载：83/0 | 提交时间：2020/11/03

Vis-NIR spectroscopy Lubricating oils Moisture content The reflection probe Successive projections algorithm Partial least square regression Back propagation neural network

Phytoplankton community, structure and succession delineated by partial least square regression in Daya Bay, South China Sea 期刊论文 OAI收割
ECOTOXICOLOGY, 2020, 卷号: 29, 期号: 6, 页码: 751, 761
作者: Wu, Mei-Lin; Wang, Yu-Tu; Cheng, Hao; Sun, Fu-lin; Fei, Jiao

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收藏 | 浏览/下载：31/0 | 提交时间：2020/09/22

Nutrients Phytoplankton Diatoms Dinoflagellates Southeast Asian Monsoon Partial least square regression

Learning Instance Correlation Functions for Multilabel Classification 期刊论文 OAI收割
ieee transactions on cybernetics, 2017, 卷号: 47, 期号: 2, 页码: 499-510
作者: Liu, Huawen; Li, Xuelong; Zhang, Shichao

收藏 | 浏览/下载：25/0 | 提交时间：2017/04/06

l(1)-norm instance-based learning k-nearest neighbors (kNNs) multilabel classification partial least square (PLS) regression

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：41/0 | 提交时间：2016/04/24

Multiple linear stepwise regression Partial least square regression Estimation Soil element contents Visible/near-infrared spectroscopy

An experimental study on paddy soil moisture inversion based on emissive hyperspectra 会议论文 OAI收割
2012 First International Conference on Agro-Geoinformatics, New York
Huang Qi-ting; Luo Jian-cheng; Shi Zhou; Li Jun-li

收藏 | 浏览/下载：30/0 | 提交时间：2014/12/07

component Soil moisture Emissive hyper-spectra Partial least square regression ASTER TEMPERATURE

Band Selection Method for Retrieving Soil Lead Content with Hyperspectral Remote Sensing Data 会议论文 OAI收割
Earth Resources and Environmental Remote Sensing-Gis Applications, Bellingham
Zhang, Xia; Wen, Jianting; Zhao, Dong

收藏 | 浏览/下载：24/0 | 提交时间：2014/12/07

heavey metal inversion partial least square regression least square regression genetic algorithm band selection REFLECTANCE SPECTROSCOPY RIVER FLOODPLAINS REGRESSION

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：32/0 | 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：23/0 | 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.