Authentication of recycled and virgin polyethylene terephthalate based on UPLC-Q-TOF-MS using non-volatile organic compounds and chemometrics.

Plastic packaging waste, such as polyethylene terephthalate (PET) has increased significantly in recent decades, arousing a considerable and serious public concern regarding the environment, economy, and policy. Plastic recycling is a useful tool to mitigate this issue. Here, a feasible study was performed to investigate the potential of a novel method for identifying virgin and recycled PET. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was combined with various chemometrics, as a simple and reliable method that achieved a high discrimination rate for 105 batches of virgin PET (v-PET) and recycled PET (r-PET) based on 202 non-volatile organic compounds (NVOCs). Making use of orthogonal partial least-squares discrimination analysis (OPLS-DA) together with non-parametric tests, 26 marker compounds (i.e. 12 intentionally added substances (IAS) and 14 non-intentionally added substances (NIAS) as well as 31 marker compounds (i.e. 11 IAS and 20 NIAS) obtained from positive and combination of positive and negative ionization modes of UPLC-Q-TOF-MS, respectively, were successfully identified. Moreover, 100% accuracy was obtained using a decision tree (DT). Cross-discrimination based on misclassified samples using various chemometrics allowed the prediction accuracy to be improved and to identify a large sample set, thus greatly enhancing the application scope of this method. The possible origins of these detected compounds can be the plastic itself, as well as contamination from food, medicine, pesticides, industry-related substances, and degradation and polymerization products. As many of these compounds are toxic, especially those pesticide related, this indicates an urgent requirement for closed loop recycling. Overall, this analytical method provides a quick, accurate, and robust way to distinguish virgin from recycled PET and thus addresses the issue of potential virgin PET adulteration thereby detecting fraud in the area of PET recycling.

Machine learning directed discrimination of virgin and recycled poly(ethylene terephthalate) based on non-targeted analysis of volatile organic compounds.

The use of non-decontaminated recycled poly(ethylene terephthalate) (PET) in food packages arouses consumer safety concerns, and thus is a major obstacle hindering PET bottle-to-bottle recycling in many developing regions. Herein, machine learning (ML) algorithms were employed for the discrimination of 127 batches of virgin PET and recycled PET (rPET) samples based on 1247 volatile organic compounds (VOCs) tentatively identified by headspace solid-phase microextraction comprehensive two-dimensional gas chromatography quadrupole-time-of-flight mass spectrometry. 100% prediction accuracy was achieved for PET discrimination using random forest (RF) and support vector machine (SVM) algorithms. The features of VOCs bearing high variable contributions to the RF prediction performance characterized by mean decrease Gini and variable importance were summarized as high occurrence rate, dominant appearance and distinct instrument response. Further, RF and SVM were employed for PET discrimination using the simplified input datasets composed of 62 VOCs with the highest contributions to the RF prediction performance derived by the AUCRF algorithm, by which over 99% prediction accuracy was achieved. Our results demonstrated ML algorithms were reliable and powerful to address PET adulteration and were beneficial to boost food-contact applications of rPET bottles.