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Published in:
02-02-2018
Predicting the content of camelina protein using FT-IR spectroscopy coupled with SVM model
Published in: Cluster Computing | Special Issue 4/2019
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Abstract
133 camelina samples were used to build the Fourier transform infrared (FT-IR) prediction model. Several methods have been used for the establishment of the predicting model, but support vector machine was rarely used in FT-IR area to build the prediction model. The aim of this study was to develop a new model for predicting protein with higher accuracy. In the spectra region 690–1700 cm\(^{-1}\), the SVM method was better than that of PLS and PCR. In the development of SVM, the \(\hbox {R}_{\mathrm{RMSEC}}^{2}\) and \(\hbox {R}_{\mathrm{RMSEP}}^{2}\) of the model were 0.83963 and 0.96578 respectively, and the RPD was 5.5016. The RPD was greater than that of PLS and PCR. The FT-IR was effective in predicting the content of camelina protein and SVM was a better method to build prediction model.