Search

bis-Phenol A (BPA), bis-Phenol S (BPS), and bis-Phenol F (BPF) are important polymer industry plasticizers. Regulatory measures have restricted the use of BPA in plastic formulations, especially for those which come in contact with food products. Rapid, accurate spectroscopic measurements are required for distinguishing which of the three are present. The bis-phenol groups are structurally identical. The second set of bis-groups (CH3-C-CH3, O=S=O, and H-C-H, respectively) are discretely different chemically, but vibrational modes corresponding to these groups are not unique identifiers, routinely overlapping with wavenumbers present in other members of the set. The dual modality method identifies the specific wavenumbers in which the Infrared (IR) signal is near zero and the Raman relative intensity is maximum, and those in which the Raman signal is minimum and the IR signal is maximum. The normalized intensity ratio between IR and Raman enhances the signal [BPA 10.6 (1508 cm−1); BPS 7.4 (751 cm−1); BPF 5.1 (1100 cm−1)]. The ratio between Raman and IR in BPF is also enhanced: 6.3 (845 cm−1). Discerning which specific wavenumbers are most enhanced is experimentally feasible, though not necessarily at present theoretically predictable. This study demonstrates that IR and Raman spectra are not just complimentary, but together they are confirmatory even when the normalized intensity ratios of corresponding wavenumbers are most different. [ABSTRACT FROM AUTHOR]

Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface–enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat. [ABSTRACT FROM AUTHOR]

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes. [ABSTRACT FROM AUTHOR]

Simple Summary: Bile acids have various physiological functions in the body. On the one hand, they can promote the digestion and absorption of fats as emulsifiers, and on the other hand, they participate in many metabolic pathways as signaling molecules. In addition, bile acids can shape the gut flora and improve gut health. This experiment aimed to investigate the effects of non-12α-hydroxylated bile acids on the growth and development, and intestinal flora of weaned piglets. This study showed that the addition of non-12α-hydroxylated bile acids could improve growth performance and nutrient digestibility in weaned piglets without negatively affecting the liver. In addition, the addition of non-12α-hydroxylated bile acids also improved the intestinal flora, increased the abundance of Lactobacillus_johnsonii, and increased the content of short-chain fatty acids. As an emulsifier and bioactive substance, bile acids (BAs) participate in the absorption of nutrients and in various physiological processes. The objective of this experiment was to investigate the effects of non-12α-hydroxylated BAs (including hyocholic acid, hyodeoxycholic acid and chenodeoxycholic acid, from now on referred to as NBAs) on growth performance, BAs metabolism and the intestinal flora of piglets. The experiment included four groups, with eight piglets per group. The four groups of pigs were fed 0, 60, 120 and 180 mg/kg of NBAs, respectively. The results show that adding NBAs significantly increased the final weight (FW), average daily feed intake (ADFI), average daily gain (ADG), and digestibility of crude fat (EE) and organic matter (OM) in piglets (p < 0.05). Adding NBAs significantly increased the villus height (VH) of the jejunum and ileum (p < 0.05). In addition, NBAs supplementation increased the content of urea nitrogen (BUN) and creatinine (CREA) as well as the ratio of urea nitrogen to creatinine (BUN/CREA) in serum (p < 0.05). Adding NBAs can affect the genes related to BAs enterohepatic circulation. Specifically, adding NBAs significantly decreased the relative mRNA abundance of FXR in the liver (p < 0.05), significantly increased the relative mRNA abundance of CYP27A1 (p < 0.05), and significantly increased the relative mRNA abundance of NTCP (p < 0.05). Adding NBAs also significantly decreased the relative mRNA abundance of FXR in the ileum (p < 0.05). In the full-length 16S rDNA sequencing analysis, ten biomarkers were found from the gate to the species level. NBAs mainly enriched Lactobacillus_Johnsonii and decreased the abundance of Streptococcus_alactolyticus. Short-chain fatty acids (SCFAs) content in the colon was significantly increased (p < 0.05). These results indicate that NBAs supplementation can improve the growth performance of piglets, promote the development of the bile acid replacement pathway and improve intestinal flora. [ABSTRACT FROM AUTHOR]

The food service industry must ensure that service facilities are free of foodborne pathogens hosted by organic residues and biofilms. Foodborne diseases put customers at risk and compromise the reputations of service providers. Fluorescence imaging, empowered by state-of-the-art artificial intelligence (AI) algorithms, can detect invisible residues. However, using AI requires large datasets that are most effective when collected from actual users, raising concerns about data privacy and possible leakage of sensitive information. In this study, we employed a decentralized privacy-preserving technology to address client data privacy issues. When federated learning (FL) is used, there is no need for data sharing across clients or data centralization on a server. We used FL and a new fluorescence imaging technology and applied two deep learning models, MobileNetv3 and DeepLabv3+, to identify and segment invisible residues on food preparation equipment and surfaces. We used FedML as our FL framework and Fedavg as the aggregation algorithm. The model achieved training and testing accuracies of 95.83% and 94.94% for classification between clean and contamination frames, respectively, and resulted in intersection over union (IoU) scores of 91.23% and 89.45% for training and testing, respectively, of segmentation of the contaminated areas. The results demonstrated that using federated learning combined with fluorescence imaging and deep learning algorithms can improve the performance of cleanliness auditing systems while assuring client data privacy. [ABSTRACT FROM AUTHOR]

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future. [ABSTRACT FROM AUTHOR]