Spectroscopy and Spectral Analysis, Volume. 41, Issue 9, 2759(2021)
In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy
Figures & Tables(9)
Fig. 1. Schematic diagram of in-line near-infrared spectral measurement system
Fig. 2. (a) In-line NIR spectra of different grades of GPPS, (b) Changes in characteristic peaks of in-line NIR spectra of different grades of GPPS
Fig. 3. Explained variance contribution of principal components of in-line NIR spectra
Fig. 4. K-means clustering result of in-line NIR spectra of different grades of GPPS
Fig. 5. Relationship between PRESS and the number of principal components
Fig. 6. Identification results for different grades of GPPS in validation set
(a): PLS-DA model; (b): RF model
Fig. 7. Relationship between maximum depth of CART and classification accuracy of different grades of GPPS
Table 1. Identification results of different grades of GPPS based on the PLS-DA algorithm
View tableView in ArticleTable 1. Identification results of different grades of GPPS based on the PLS-DA algorithm
分类
变量牌号 验证集
个数识别正确
个数识别正确
率/%1 158K 50 38 76 2 5250 50 38 76 3 525 50 50 100 4 PG-33 50 50 100 5 GP-150 50 50 100
Table 2. Identification results of different grades of GPPS based on the RF algorithm
View tableView in ArticleTable 2. Identification results of different grades of GPPS based on the RF algorithm
分类
变量牌号 验证集
个数识别正确
个数识别正确
率/%1 158K 50 50 100 2 5250 50 46 92 3 525 50 50 100 4 PG-33 50 50 100 5 GP-150 50 43 86
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Yuan FANG, Zhang-ping HE, Shi-chao ZHU, Xian-rong LIANG, Gang JIN. In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2021, 41(9): 2759
Paper Information
Category: Research Articles
Received: Aug. 24, 2020
Accepted: --
Published Online: Oct. 29, 2021
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