Authentication of recycled plastic content in water bottles using volatile fingerprint and chemometrics

Analytical strategy to assess the microbial degradation of poly(butylene-adipate-co-terephthalate)/poly(lactic acid) films

Plastic is widely used in agricultural applications, but its waste has an adverse environmental impact and a long-term detrimental effect. The development of biodegradable plastics for agricultural use is increasing to mitigate plastic waste. The most commonly used biodegradable plastic is poly(butylene adipate co-terephthalate)/poly(lactic acid) (PBAT/PLA) polymer. In this study, an analytical procedure based on dispersive liquid-liquid microextraction (DLLME) followed by gas chromatography-mass spectrometry (GC-MS) in combination with chemometrics has been optimized to assess the degradation level of PBAT/PLA films by monitoring their characteristic degradation products. Carboxylic acids (benzoic, phthalic, adipic, heptanoic, and octadecanoic acids) and 1,4-butanediol have been found to be potential markers of PBAT/PLA degradation. The DLLME-GC-MS analytical approach has been applied for the first time to assess the degradation efficiency of several microorganisms used as degradation accelerators of PBAT/PLA based on the assigned potential markers. This analytical strategy has shown higher sensitivity and precision than standard techniques, such as elemental analysis, allowing us to detect low degradation levels.

Application of chemometric tools in the QSAR development of VOC removal in plastic waste recycling

Deodorization and, in a broader sense, the removal of volatile organic compounds (VOCs) from plastic waste have become increasingly important in the field of plastic recycling, and various new decontamination techniques have been developed. Both in research and industrial practice, the selection of VOCs has been random or unsubstantiated, making it difficult to compare studies and assess decontamination processes objectively. Thus, this study proposes the use of Statistical Molecular Design (SMD) and Quantitative Structure - Activity Relationship (QSAR) as chemometric tools for the selection of representative VOCs, based on physicochemical properties. Various algorithms are used for SMD; hence, several frequently used D-Optimal Onion Design (DOOD) and Space-Filling (SF) algorithms were assessed. Hereby, it was validated that DOOD, by dividing the layers based on the equal-distance approach without so-called 'Adjacent Layer Bias', results in the most representative selection of VOCs. QSAR models that describe VOC removal by water-based washing of plastic waste as a function of molecular weight, polarizability, dipole moment and Hansen Solubility Parameters Distance were successfully established. An adjusted-R2 value of 0.77 ± 0.09 and a mean absolute error of 24.5 ± 4 % was obtained. Consequently, by measuring a representative selection of VOCs compiled using SMD, the removal of other unanalyzed VOCs was predicted on the basis of the QSAR. Another advantage of the proposed chemometric selection procedure is its flexibility. SMD allows to extend or modify the considered dataset according to the available analytical techniques, and to adjust the considered physicochemical properties according to the intended process.

Chemometrics-based Discrimination of Virgin and Recycled Acrylonitrile-Butadiene-Styrene Plastics Toys via Non-targeted Screening of Volatile Substances

Owing to the growing emphasis on child safety, it is greatly urgent to identify and assess the unknown compounds and discriminate the recycled materials for plastic toys. In this study, gas chromatography mass spectrometry coupled with static headspace has been optimized by response surface methodology for non-targeted screening of unknown volatiles in acrylonitrile-butadiene-styrene (ABS) plastic toys. Optimum conditions for static headspace were 120 °C for extraction temperature and 48 min for extraction time. A total of 83 volatiles in 11 categories were qualitatively identified by matching the NIST database library, retention index and standard materials. Considering high positive rate and potential toxicity, high-risk volatiles in ABS plastic toys were listed and traced for safety pre-warning. Moreover, the differential volatiles between virgin and recycled ABS plastics were screened out by orthogonal partial least-squares discrimination analysis. Principal component analysis, hierarchical cluster analysis and linear discrimination analysis were employed to successfully discriminate recycled ABS plastic toys based on the differential volatiles. The proposed strategy represents an effective and promising analytical method for non-targeted screening and risk assessment of unknown volatiles and discrimination of recycled materials combining with various chemometric techniques for children's plastic products to safeguard children's health.

Determination of extractable pollutants from microplastics to vegetables: Accumulation and incorporation into the food chain

The presence and impacts of microplastics (MPs) are being extensively researched and reviewed, especially in the marine environment. However, mobility, transportation routes, and accumulation of leaching compounds such as additives in plastic waste including MPs are scarcely studied. Information regarding ecotoxicity and leachability of compounds related to MPs contamination in the environment is limited. Current work presents the levels of leachates from plastic materials in edible-root and non-edible root vegetables. Samples were analyzed by static headspace and gas chromatography-mass spectrometry (SHS-GC-MS) and the presence of 93 putative compounds was accurately monitored in the samples by the usage of Mass Spectrometry-Data Independent Analysis software. The application of chemometrics to the SHS-GC-MS dataset allowed differentiation between the levels of plastic related compounds in edible root and non-edible root vegetables, the former showing a higher content of plastic leachates. For SHS sampling, 3 g of the sample were incubated at 130 °C for 35 min in the HS vial and toluene and naphthalene were added as internal standards for quantification purposes. The developed SHS-GC-MS methodology is straightforward, reliable, and robust and allowed the quantification of sixteen plastic associated compounds in the samples studied in a range from 0.14 to 28800 ng g-1 corresponding to 2,4-di-tert-butylphenol and p,α-dimethylstyrene, respectively. Several of the quantified compounds pointed out to potential contamination of polystyrene and/or polyvinyl chloride MPs.

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.

Tracing the origin of VOCs in post-consumer plastic film bales

Volatile organic compounds (VOCs), including odors, are still a key issue in plastic recycling, especially in case of flexible packaging. Therefore, this study presents a detailed qualitative and quantitative VOC analysis by applying gas chromatography on 17 categories of flexible plastic packaging that are manually sorted from bales of post-consumer flexible packaging (e.g., beverage shrink wrap, packaging for frozen food, packaging for dairy products, etc.). A total of 203 VOCs are identified on packaging used for food products, while only 142 VOCs are identified on packaging used for non-food products. Especially, more oxygenated compounds (e.g., fatty acids, esters, aldehydes) are identified on food packaging. With more than 65 VOCs, the highest number of VOCs is identified on packaging used for chilled convenience food and ready meals. The total concentration of 21 selected VOCs was also higher on packaging used for food products (totally 9187 μg/kg plastic) compared to packaging used for non-food packaging (totally 3741 μg/kg plastic). Hence, advanced sorting of household plastic packaging waste, e.g., via tracer-based sorting or watermarking, could open the door towards sorting on other properties than polymer type, such as mono- versus multi-material packaging, food versus non-food packaging or even their VOC profile, which might allow for tailoring washing procedures. Potential scenarios showed that sorting the categories with the lowest VOC load, which corresponds to half of the total mass of flexible packaging, could result in a VOC reduction of 56%. By producing less contaminated plastic film fractions and by tailoring washing processes recycled plastics can ultimately be used in a broader market segment.

Determination of potential volatile compounds in polyethylene terephthalate (PET) bottles and their short- and long-term migration into food simulants and soft drink

Head space (HS)-GC-MS was used to analyze possible migration of volatile compounds from polyethylene terephthalate (PET) bottles for soft drinks, and a total of six compounds were identified. Next, a rapid, simple, and accurate simultaneous method was established using purge-and-trap (PT)-GC-MS, to quantify their amounts in the liquid contents after short- and long-term storage in PET bottles. Starting with brand-new PET bottles, the maximum migration of 2-methyl-1,3-dioxolane into distilled water and 50 % aqueous ethanol after 2 years at 25 °C were 2.3 and 19 ng/mL, respectively. In commercially available bottled mineral water sold inside and outside Japan, we were able to detect 2-methyl-1,3-dioxolane in the same way. While nonanal was also detected in some products, 2-methyl-1,3-dioxolane was confirmed as the main volatile compound. Finally, the human exposure to 2-methyl-1,3-dioxolane was estimated based on the per capita intake of soft drinks in Japan and the migration amount in this study.

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.