Microplastic (MP) contamination is a public issue for the environment and for human health. Plastic-based food filter bags, including polyethylene terephthalate, polypropylene, nylon 6 (NY6), and polyethylene, are widely used for soft drink sub-packaging, increasing the risk of MPs in foods and the environment. Three types of commercially available filter bags, including non-woven and woven bags, were collected, and MPs released after soaking were mapped using Raman imaging combined with chemometrics. Compared with peak area imaging at a single characteristic peak, Raman imaging combined with direct classical least squares calculation was more efficient and reliable for identifying MP features. Up to 94% of the bags released MPs after soaking, and there was no significant correlation with soaking conditions. Most MPs were tiny fragments and particles, and a few were fibrous MPs 620–840 μm in size. Woven NY6 filter bags had the lowest risk of releasing MPs. Source exploration revealed that most MPs originated from fragments and particles adsorbed on the surface of bags and strings. The results of this study are applicable to filter bag risk assessment and provide scientific guidance for regulating MPs in food.

Chinese steamed bread (CSB) is a traditional food of the Chinese nation, and the preservation of its quality and freshness during storage is very important for its industrial production. Therefore, it is necessary to study the storage characteristics of CSB. Non-destructive CT technology was utilized to characterize and visualize the microstructure of CSB during storage, and also to further study of quality changes. Two-dimensional and three-dimensional images of CSBs were obtained through X-ray scanning and 3D reconstruction. Morphological parameters of the microstructure of CSBs were acquired based on CT image using image processing methods. Additionally, commonly used physicochemical indexes (hardness, flexibility, moisture content) for the quality evaluation of CSBs were analyzed. Moreover, a correlation analysis was conducted based on the three-dimensional morphological parameters and physicochemical indexes of CSBs. The results showed that three-dimensional morphological parameters of CSBs were negatively correlated with moisture content (Pearson correlation coefficient range−0.86~−0.97) and positively correlated with hardness (Pearson correlation coefficient range−0.87~0.99). The results indicate the inspiring capability of CT in the storage quality evaluation of CSB, providing a potential analytical method for the detection of quality and freshness in the industrial production of CSB.

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm⁻¹ was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

Xiangyang fresh Huangjiu is a Chinese regional specialty fermented beverage and an intangible cultural heritage. The microbial community, sensory characteristics and volatile flavor compounds during the storage of two typs of Xiangyang fresh Huangjiu were investigated, and a correlative analysis was conducted between the microbial community and sensory characteristics as well as volatile flavor compounds. 14 dominant bacterial genera and 7 dominant fungal genera were indentified in both types of Huangjiu. The pronounced sourness in both types of Huangjiu during storage was strongly positively associated with Saccharomyces. The predominant flavor profile of 1-octen-3-one was significantly negatively correlated with Gluconobacterium, and the increase of Wickerhamomyces and Millerozyma, along with the decline of Enterobacter, Komagataeibacter, Pseudomonas, and Saccharomycopsis, contributed to the reduction of the pungent volatile compounds of dimethyl trisulfide and 2-methyl butanal. The results offer insights for quality enhancement, storage and consumption in the standardized industrial production of Xiangyang fresh Huangjiu.