NDLI: Simultaneous Optimization of SG Smoothing Parameters and PLS Factor Was Applied to NIRS Analysis of Soil Organic Matter

Content Provider	IEEE Xplore Digital Library
Author	Huazhou Chen Tao Pan Jiemei Chen Jun Xie Shuyi Li Fangbai Li
Copyright Year	2010
Abstract	The rapid and simple analytical method for soil organic matter by near infrared spectroscopy (NIRS) is very significant in precision agriculture. In this paper, the rapid determination method and the analysis model of soil organic matter were established by using the NIRS technology, partial least squares (PLS) regression and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavelength model, calibration set and prediction set were divided. By extending the number of smoothing points and the degree of polynomial, 483 smooth modes were calculated. The PLS models corresponding to all combinations of 483 SG smoothing modes and 1-30 PLS factor were established respectively. The optimal smoothing parameters were the second order derivative smoothing, 2 or 3 degree polynomial, 61 smoothing points, the optimal PLS factor, root mean squared error of predication (RMSEP) and correlation coefficient of predication (RP) were 19, 0.197 (%) and 0.925 respectively, which was obviously superior to the direct PLS model without SG smoothing and the optimal SG smoothing model within 25 smoothing points (the original smoothing method). This demonstrates that the extending of SG smoothing modes and large-scale simultaneous optimization selection of SG smoothing parameters and PLS factor was all very necessary, and can be effectively applied to the model optimization of NIRS analysis.
Starting Page	1
Ending Page	4
File Size	454198
Page Count	4
File Format	PDF
ISBN	9781424449637
DOI	10.1109/SOPO.2010.5504415
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Smoothing methods Soil measurements Biochemical analysis Food technology Predictive models Polynomials Agriculture Infrared spectra Least squares methods Chemical technology
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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