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《Chinese Journal of Tropical Crops》 2018-01
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Prediction Models of Oil Content of Agarwood Based on Near Infrared Spectroscopy

LIN Yan;HE Zidi;MAO Jipeng;JIANG Kaibin;LIU Tianyi;HUANG Shaowei;College of Forestry and Landscape Architecture, South China Agricultural University/Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm;  
The spectral data of 64 agarwood samples between the spectrum of 950 nm to 1 650 nm were collected using DA7200 NIRS analyzer to estabilish a prediction model of near infrared spectroscopy of agarwood oil content. A regression model was established using the partial least squares( PLS) method, and selecting the best pretreatment method and the optimal number of principal components to set up a model of the near infrared spectra of the oil content. Results showed that the smoothing( S-G) method was best for spectral preprocessing,and when the best optimum principal component number was 7 can achieve the optimal mode. The related coefficient of calibration(RC) and root mean square error of calibration(RMSEC) was 0.980 9, 0.958 9; the related coefficient of validation( RV) and root mean square error of validation( RMSEV) was 0.697 4, 1.029 0. The prediction value has a significant correlation with the measured value, and the prediction accuracy of the model is high, which can meet the requirement of rapid prediction of agarwood quality.
【Fund】: 国家林业公益性行业科研专项“黎蒴等华南重要乡土树种良种选育”(No.201204303)
【CateGory Index】: R284
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