کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1234796 968836 2009 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine
چکیده انگلیسی

A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis–NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x–y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard–Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy - Volume 74, Issue 2, 1 October 2009, Pages 344–348
نویسندگان
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