近红外光谱的不同牌号聚乳酸识别方法

doi:10.3964/j.issn.1000-0593(2018)10-3053-05

Abstract
References
Related Citation (15)

Download: PDF (2108 KB)
Export: BibTeX | EndNote (RIS)

Abstract The type of plastics is the serial number that manufacturing companies formulated based on thenature and application of raw materials. Detecting the physical and chemical properties of plasticscan indirectly identify their types, but these test methods are time-consuming and destructive. In this work, near-infrared spectroscopy technology was used to identify different types of Poly(lactic acid)(PLA). In addition, three models，PCA-MD，PCA-ANN and PCA-SVM, were applied for the analysis and prediction of the sample. In the wavelength range of 900～1 700 nm, a total of 90 samples of three different types of PLA were used to establish the model and another 90 samples of these three types of PLA were taken for prediction and identification. Comparing the identification ability of three prediction models to PLA types, we can find that the scatter plot of the first two principal components scores of the validation set had an obvious clustering phenomenon after the PCA of the spectral data. The first nine principal component scores were taken as the input variables of Mahalanobis distance, ANN and SVM discriminants, and these discriminants effectively identified the type of PLA, among which the accuracy of the best discriminant——Mahalanobis distance can reach 98.9%. Therefore, near infrared spectroscopy can be used for nondestructive, fast and accurate identification of different types of PLA.

Key words：Near-infrared spectroscopy; PLA type identification; Mahalanobis distance; Artificial neural networks; Support vector machine

Received: 2017-10-19 Accepted: 2018-03-21

ZTFLH:

O657.3

Corresponding Authors: JIN Gang E-mail: pmrdd@scut.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)10-3053-05 OR https://www.gpxygpfx.com/EN/Y2018/V38/I10/3053