基于便携式近红外光谱仪的食品接触性塑料鉴别

采用便携式近红外光谱仪对聚对苯二甲酸乙二酯(PET)、高密度聚乙烯(HDPE)、低密度聚乙烯(LDPE)、聚氯乙烯(PVC)、聚苯乙烯(PS)和聚碳酸酯(PC)6类食品接触性塑料材质进行鉴别研究。用5点平滑、多元散射校正(MSC)、一阶导数和标准正态变量变换(SNV)4种方法对塑料样品光谱进行预处理;主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)分别用于塑料样品光谱空间分布分析和定性判别模型的建立。结果表明:光谱经SNV和MSC预处理后,6类塑料样品在前3个主成分空间得到了较好的分离;PLS-DA结合SNV预处理方法可得到精简的塑料材质定性判别模型,模型校正集和预测集的正确识别率(CRR)均为100%。该方法可为食品接触性塑料材质的快速鉴别提供参考。

Research on plastic material identification based on portable near infrared spectrometer

Portable NIR spectrometers was used for the identification of six kinds of food-contacted plastic material including polyethylene terephthalate (PET), high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyvinyl chloride (PVC), polystyrene (PS) and polycarbonate (PC). Four kinds of spectral preprocessing methods including 5-point smoothing, multivariate scattering correction (MSC), first-order derivative and standard normal variable transformation (SNV) were used to preprocess the spectra of plastic samples. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to analyze the spectral space distribution of plastic samples and establish qualitative discriminant models. The results showed that six kinds of food-contacted plastic material could be clearly separated in the first three principal component spaces after the pretreatment of SNV and MSC methods. The PLS-DA combined with SNV could be used to get a concise plastic material qualitative discriminant model, and the correct recognition rates (CRR) were 100% both for calibration and prediction datasets. The method was expected to be a reference method for rapid identification of food-contacted plastic materials.