Determination of 18 photoinitiators in food paper packaging materials by FastPrep-based extraction combined with GC-MS

Perovskite Nanocrystals: Superior Luminogens for Food Quality Detection Analysis

With the global limited food resources receiving grievous damage from frequent climate changes and ascending global food demand resulting from increasing population growth, perovskite nanocrystals with distinctive photoelectric properties have emerged as attractive and prospective luminogens for the exploitation of rapid, easy operation, low cost, highly accurate, excellently sensitive, and good selective biosensors to detect foodborne hazards in food practices. Perovskite nanocrystals have demonstrated supreme advantages in luminescent biosensing for food products due to their high photoluminescence (PL) quantum yield, narrow full width at half-maximum PL, tunable PL in the entire visible spectrum, easy preparation, and various modification strategies compared with conventional semiconductors. Herein, we have carried out a comprehensive discussion concerning perovskite nanocrystals as luminogens in the application of high-performance biosensing of foodborne hazards for food products, including a brief introduction of perovskite nanocrystals, perovskite nanocrystal-based biosensors, and their application in different categories of food products. Finally, the challenges and opportunities faced by perovskite nanocrystals as superior luminogens were proposed to promote their practicality in the future food supply.

Occurrence, Fate, Human Exposure, and Toxicity of Commercial Photoinitiators

Photoinitiators (PIs) are a family of anthropogenic chemicals used in polymerization systems that generate active substances to initiate polymerization reactions under certain radiations. Although polymerization is considered a green method, its wide application in various commercial products, such as UV-curable inks, paints, and varnishes, has led to ubiquitous environmental issues caused by PIs. In this study, we present an overview of the current knowledge on the environmental occurrence, human exposure, and toxicity of PIs and provide suggestions for future research based on numerous available studies. The residual concentrations of PIs in commercial products, such as food packaging materials, are at microgram per gram levels. The migration of PIs from food packaging materials to foodstuffs has been confirmed by more than 100 reports of food contamination caused by PIs. Furthermore, more than 20 PIs have been detected in water, sediment, sewage sludge, and indoor dust collected from Asia, the United States, and Europe. Human internal exposure was also confirmed by the detection of PIs in serum. In addition, PIs were present in human breast milk, indicating that breastfeeding is an exposure pathway for infants. Among the most available studies, benzophenone is the dominant congener detected in the environment and humans. Toxicity studies of PIs reveal multiple toxic end points, such as carcinogenicity and endocrine-disrupting effects. Future investigations should focus on synergistic/antagonistic toxicity effects caused by PIs coexposure and metabolism/transformation pathways of newly identified PIs. Furthermore, future research should aim to develop "greener" PIs with high efficiency, low migration, and low toxicity.

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky-Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition.

Multi-Residue Analysis of Chemical Additives in Edible Vegetable Oils Using QuEChERS Extraction Method Followed by Supercritical Fluid Chromatography

Since the quality and safety of food highly depend on its preservation and protection, the use of food packaging materials increases the risk of chemical contamination of the packaged food by migration. Herein, we focused on antioxidants, photoinitiators, UV absorbers and plasticizers which are extensive additives used in food packaging materials. In the present study, a rapid, simple, green and reliable method was developed and validated for the determination of twelve chemical additives in edible vegetable oils using SFC together with a modified QuEChERS procedure. Under the optimum conditions, twelve additives were separated within 10 min, and the consumption of the organic solvent was significantly reduced, which improved the environmentally friendliness. The performance of the developed method was evaluated. Good linearity (r > 0.999) was obtained in the range of 0.20−20.0 µg/mL and 0.50−20.0 µg/mL, respectively. The limits of detection and limits of quantification of the twelve additives in vegetable oils were 0.05−0.15 µg/mL and 0.15−0.50 µg/mL, respectively. Recoveries of all the chemical additives for the spiked samples were between 60.9% and 106.4%, with relative standard deviations (RSD) lower than 9.9%. The results demonstrated that the proposed method was efficient, reliable and robust for the routine analysis of additives in edible vegetable oils and can be an alternative to the multi-residue analysis of chemical additives for other packaged foods.