Establishment of near-infrared classification model based on support vector machine for redried strips
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Abstract
In order to establish a support vector machine (SVM) classification model for redried strips, redried strips from Yunnan and Fujian Provinces were studied. The near-infrared spectra of strip samples were collected in the wavelength range of 4 000-10 000 cm-1, and the basic spectral data of redried strip samples were obtained. Under the supervised learning mode, the PCA-SVM classification model of redried strips was established through SVM algorithm after the first derivative +SG smoothing pretreatment. The training speed of the model was fast, the recognition accuracy rate of redried strips from Yunnan and Fujian reached 98.97% and 99.59% respectively, and the classification results were satisfying. The SVM classification model for redried strips can be used as an evaluation tool for quality stability of redried strips which can quickly and accurately identify the specifications of redried strips.
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