In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy
FANG Yuan, HE Zhang-ping, ZHU Shi-chao, LIANG Xian-rong, JIN Gang*
National Engineering Research Center of Novel Equipment for Polymer Processing, Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, Key Laboratory of Polymer Processing Engineering of Ministry of Education, South China University of Technology, Guangzhou 510640, China
Abstract：Misusing the wrong grades polymer during the polymer processing in the same production line may lead to poorer product performance and a lower qualification ratio. The traditional methods identifying the grades from same kind of polymer are usually time consuming and hysteretic. There has not yet been discovered a fast and real time method for grade identification. In this work, 5 different grades of GPPS were the research object. An in-line near-infrared spectral measurement system installed on the extruder was developed. Near-infrared spectroscopy was combined with chemometrics and machine learning algorithms. The different grades of GPPS could be fast and in-line identified during the extrusion process. First, the in- line near-infrared spectra of GPPS melts of 5 different grades were collected in real time by the developed system with a spectral range of 900~1 700 nm. After spectrum analysis, a K-means clustering algorithm in combination with PCA was performed to verify the distinguishability of in-line near-infrared spectra for different grades. Last, PLS-DA and RF algorithm were used to establish the grade identification models respectively, and the identification ability of these two models was compared. The results show that: ①After baseline correction, maximum and minimum normalization, and 7-point moving average smoothing, the characteristic peak values at 1 207，1 388，1 407, 1 429 nm of the in-line near-infrared spectra change in a step-like manner with the change of grades. With the first three principal components scores as input variables, the clustering accuracy by K-means can reach 88%. It shows the distinguishability of the in-line near-infrared spectral data of different grades of GPPS; ②The two prediction models established by PLS-DA and RF can both effectively identify the grades of GPPS. The classification accuracy on the validation set of the PLS-DA model with the optimal principal components of 3 can reach 90.4%. The classification accuracy on the validation set of the RF model with the first five principal components as input variables can reach 95.6%. The RF model shows better grade identification performance than that of the PLS-DA model. Therefore, combined with chemometrics and machine learning algorithms, the in-line near-infrared spectral measurement system can realize the rapid and in-line identification of GPPS grades. It provides a reference for the in-line identification of different grades of the same kind of polymer by near-infrared spectroscopy in a production line.
Key words：Near-infrared spectroscopy; Grade identification; General purpose polystyrene; Partial least square-discriminant analysis; Random forest
方 圆，何张平，朱世超，梁显荣，晋 刚. 近红外光谱的通用聚苯乙烯牌号在线识别方法[J]. 光谱学与光谱分析, 2021, 41(09): 2759-2763.
FANG Yuan, HE Zhang-ping, ZHU Shi-chao, LIANG Xian-rong, JIN Gang. In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2759-2763.
