Reducing microplastics in tea infusions released from filter bags by pre-washing method: Quantitative evidences based on Raman imaging and Py-GC/MS.

Microplastics released from plastic-based filter bags during tea brewing have attracted widespread attention. Laser confocal micro-Raman and direct classical least squares were used to identify and estimate micron-sized microplastics. Characteristic peaks from pyrolysis-gas chromatography/mass spectrometry of polyethylene terephthalate, polypropylene, and nylon 6 were selected to construct curves for quantification submicron-sized microplastics. The results showed that microplastics released from tea bags in the tea infusions ranged from 80 to 1288 pieces (micron-sized) and 0 to 63.755 µg (submicron-sized) per filter bag. Nylon 6 woven tea bags released far fewer microplastics than nonwoven filter bags. In particular, a simple strategy of three pre-washes with room temperature water significantly reduced microplastic residues with removal rates of 76 %-94 % (micron-sized) and 80 %-87 % (submicron-sized), respectively. The developed assay can be used for the quantitative evaluation of microplastics in tea infusions, and the pre-washing reduced the risk of human exposure to microplastics during tea consumption.

Spectroscopic studies on thermal degradation and quantitative prediction on acid value of edible oil during frying by Raman spectroscopy.

The health risks posed by harmful substances resulting from the thermal degradation of frying oils are of great concern. Characteristic peak intensity ratios (PIRs) screened from Raman spectra were used to characterize the thermal degradation. High correlation coefficients between PIRs and acid values (AVs) of 0.972 (linear fitting), 0.984 (logarithmic function fitting), and 0.954 (linear fitting) for fried soybean oil, canola oil, and palm oil, were obtained at the PIRs of I1267/I1749, I1267/I1659, and I1267/I1749, respectively. The highly correlated PIRs common to the three oils were determined by Pearson's correlation coefficient combined with heat maps. To accommodate both linear and nonlinear features, a global model for predicting AVs of multi-varieties frying oils was constructed using a least-squares support vector machine algorithm, and the results performed well with a root mean square error of prediction of 0.016 and a ratio of prediction to deviation of 11.351. The whole results demonstrate that Raman spectroscopy could characterize the thermal degradation and has excellent quantitative analysis ability for food control based on AV in frying oils, thus providing a new approach to quality control of frying oils.