Search Results (195)

As an important component of the global fishery economy, the crab breeding and processing industry faces the dual challenges of sustainable development and technological upgrading. This paper first systematically analyzes the regional distribution and core biological characteristics of major global economic crab species, laying a foundation for the targeted design of processing technologies and equipment. Secondly, based on advances in crab processing technology, the industry is categorized into two systems: live crab processing and dead crab processing. Live crab processing has formed a full-chain technological system of “fishing–temporary rearing–depuration–grading–packaging”. Dead crab processing focuses on high-value utilization: high-pressure processing enhances the quality of crab meat; liquid nitrogen quick-freezing combined with modified atmosphere packaging extends shelf life; and biological fermentation and enzymatic hydrolysis facilitate the green extraction of chitin from crab shells. In terms of intelligent equipment application, sensor technology enables full coverage of aquaculture water quality monitoring, precise classification during processing, and vitality monitoring during transportation. Automation technology reduces labor costs, while fuzzy logic algorithms ensure the process stability of crab meat products. The integration of the Internet of Things (IoT) and big data analytics, combined with blockchain technology, enables full-link traceability of the “breeding–processing–transportation” chain. In the future, cross-domain technological integration and multi-equipment collaboration will be the key to promoting the sustainable development of the industry. Additionally, with the support of big data and artificial intelligence, precision management of breeding, processing, logistics, and other links will realize a more efficient and environmentally friendly crab industry model. Full article

Advertising images constitute an important factor influencing consumer purchase intentions in commercial settings. Drawing on the perspective of self-conscious emotions, this study examines the impact of advertising image types for fresh agricultural products on consumer purchase intentions and explores the underlying mechanisms. Advertising images are classified into three categories: meat-typical, animal-typical, and composite. Evidence from two randomized experiments reveals the following findings: (a) The effectiveness of the three advertising image types in promoting purchase intentions follows the order of meat-typical > animal-typical > composite; (b) guilt mediates the relationship between advertising image types and purchase intentions, such that composite images evoke greater guilt than meat-typical and animal-typical images, thereby reducing consumer willingness to purchase; and (c) self-construal partially moderates the mediating effect of guilt, in that interdependent self-construal consumers exposed to composite advertising images are more likely to experience heightened guilt and consequently exhibit lower purchase intentions. This study extends the application of animal-related classifications in advertising and marketing research and provides new empirical evidence and practical insights for the design of advertising strategies for fresh agricultural products. Full article

This study aimed to describe a new broiler meat quality class—red, soft, and exudative (RSE) meat—and to propose novel classification criteria. Two-step cluster analysis assigned 132 broilers into five meat quality classes using ultimate pH, drip loss, and L* values: pale, soft, and exudative (PSE); pale, firm, and nonexudative (PFN); RSE; red, firm, and nonexudative (RFN); and dark, firm, and dry (DFD) meat. PSE meat showed the lowest plasma superoxide dismutase activity, highest malondialdehyde activity, greater live and carcass weights, higher breast and leg yields, the lowest initial and ultimate pH, highest initial temperature, the lightest colour (the highest L* and b* values, and the lowest a* value), and the greatest drip, thawing, and cooking losses. RFN meat had the highest superoxide dismutase activity, lowest malondialdehyde activity, and remained within the optimal range for ultimate pH, drip loss, and L* value, generally occupying a midpoint between PSE and DFD meat. RSE meat shared the poor water-holding capacity of PSE but differed by showing a colour similar to RFN and an optimal ultimate pH. PFN meat had firmness comparable to RFN, with appropriate water-holding capacity and optimal ultimate pH, but an undesirably pale colour resembling PSE. DFD meat displayed the highest initial and ultimate pH, lowest drip, thawing, and cooking losses, darkest colour (the lowest L* value), and lowest protein content. This study provides the first evidence of RSE meat in broilers and proposes a classification system based on ultimate pH, drip loss, and L* values to distinguish five quality classes. Further studies are required to validate these findings and develop preventive strategies. Full article

Camphora tenuipilis, a unique aromatic plant in the traditional Xishuangbanna dish “Duo Sheng” (raw minced meat dish), lacks scientific evidence to support its traditional use and potential application as a natural preservative/antioxidant. This study aims to fill this gap by analyzing the chemical composition and bioactivities of its leaf essential oils (EOs), verifying its traditional use, and exploring the bioactivities specific to different chemotypes. Leaf samples were collected from the Xishuangbanna Tropical Botanical Garden (XTBG), Chinese Academy of Sciences, and local markets. Gas chromatography–mass spectrometry (GC-MS) analysis identified 53 compounds, leading to the classification of the EOs into five chemotypes: linalool, geraniol, citral, elemicin, and methyl cinnamate. Notably, the elemicin-type EO (YC02, with an elemicin content of 94.56 ± 0.98%) exhibited the strongest antioxidant properties. The EOs demonstrated antibacterial activity against four foodborne pathogens: Bacillus cereus, Bacillus subtilis, Escherichia coli, and Staphylococcus aureus; except for YC04, the other EOs effectively inhibited pathogen growth to varying extents. Cytotoxicity tests revealed half-maximal inhibitory concentrations (IC₅₀) for HaCaT cells ranging from 0.163 to 0.847 mg/mL. This study scientifically validates the traditional use of C. tenuipilis in “Duo Sheng” and supports its potential as a natural food preservative, antioxidants, and antimicrobial agents. Full article

Foodborne pathogens represent a significant public health risk in both developed and developing countries. Among these pathogens, enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in humans and one of the leading causes of mortality in newly weaned pigs. The main sources of ETEC contamination include environments with poor hygiene and contaminated water, meat, cereals, and vegetables. Therefore, this review manuscript focuses on the pathogenesis of ETEC in humans and pigs. The main virulence factors responsible for ETEC-associated infections, such as colonization factors and toxins, will be described for both species, with particular emphasis on the toxins as well as, their classification and structural characterization. More specifically, this study will outline the main physiological alterations and adaptive mechanisms induced by these enterotoxins, namely heat-stable toxin (ST) and heat-labile toxin (LT), in the three most affected systems: the gastrointestinal system, the enteric nervous system (ENS), and the immune system. This set of findings provides a deeper insight into the pathogenesis of this relevant foodborne pathogen, which is crucial for empowering food scientists and stakeholders to more effectively mitigate associated risks. As such, it provides valuable understanding of toxin activity, serving as a means to raise awareness of food safety practices and strengthening risk communication, surveillance and intervention strategies, thereby ensuring consumer protection. Additionally, this knowledge enables the development of preventive strategies to reduce ETEC infections, thereby decreasing the need for clinical management among consumers exposed to this bacterium. Ultimately, it contributes to the preservation of public health, the reduction of antimicrobial use, and the lowering of antimicrobial resistance gene prevalence. Full article

Objectives: The aim of this study was to assess nutrient intake among individuals adhering to the Christian Orthodox Church (COC) fasting and to investigate potential differences in dietary intake according to Body Mass Index (BMI) classification. Methods: This cross-sectional study enrolled participants through announcements at public universities, churches, and monasteries, targeting both urban and religious adult populations. A total of 228 adults with a BMI exceeding 25 kg/m² were enrolled. Of these, 121 had followed COC fasting practices for at least 10 years or since childhood, while 107 non-fasters were age-matched. Exclusion criteria included age under 18 years, refusal to provide consent, absence from measurements, non-communicable diseases, food allergies, pregnancy, or lactation. Results: Overweight and obesity rates were similar in both groups. Furthermore, there were no statistically significant differences in body composition measurements [body fat %, fat mass (kg), fat free mass (kg), waist circumference]. Diastolic and systolic blood pressure was significantly higher in non-fasters. Non-fasters reported higher intake of sugar, dietary protein, fats (saturated and polyunsaturated), and cholesterol. Fasters consumed lower amounts of vitamin A, vitamins B (B2, B3, B6, B12, folate, pantothenic acid), iron, phosphorus, sodium, zinc, and calcium. Serum folic acid levels were higher, and fasting glucose and phosphorus levels were lower in fasters. Distinct dietary patterns were observed between groups, with fasters consuming more fish and traditional plant-based foods, while non-fasters consumed higher amounts of meat, dairy products, and alcohol. Conclusions: COC fasting is associated with favorable dietary and metabolic profiles, including improved glucose regulation. However, its impact on weight status appears limited. Full article

(1) Background: This study compared subjective and objective texture classifications of hospital-provided pureed meat dishes and evaluated the impact of adding a food-shaping agent on the consistency of the food. (2) Methods: In total, 18 common pureed meat dishes (pork, chicken, and fish) from a medical center were tested. Subjective classification was conducted according to the International Dysphagia Diet Standardisation Initiative (IDDSI) level 4 criteria, and an objective texture analysis was performed using a Texture Profile Analysis (TPA), with hardness values interpreted via the Universal Design Foods (UDF) framework. (3) Results: Only six of the 18 dishes (33%) met all IDDSI level 4 tests in their original form, despite visually resembling purees. After the addition of 1% of a food-shaping agent, all samples passed IDDSI criteria, indicating enhanced textural consistency and a reduced risk of swallowing complications. TPA data confirmed that all samples, both with and without the food-shaping agent, met UDF stage 4 hardness standards (<5 × 10³ N/m²), ensuring appropriate structural integrity for safe swallowing. The addition of food-shaping agents significantly increased the hardness and adhesiveness (p < 0.001), while the cohesiveness remained unchanged. (4) Conclusions: These findings highlight discrepancies between visual/subjective assessments and objective measurements and support the use of combined IDDSI- and TPA-based verification to improve dietary safety and reproducibility in dysphagia care. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

This study investigates the effects of species, geographical origin, and processing on the proximate composition of frog leg meat, with a focus on developing predictive models for processing status. Data were systematically compiled from 18 published studies, yielding 32 entries across 10 edible frog species and multiple processing methods. Proximate composition parameters (moisture, protein, fat, ash) were compared between processed and unprocessed samples, and classification models were trained using moisture content as the primary predictor. Logistic regression and several machine learning algorithms, including Stochastic Gradient Descent, Support Vector Machine, Random Forest, and Decision Tree, were benchmarked under a Leave-One-Study-Out (LOSO) cross-validation framework. Results demonstrated that moisture content alone was sufficient to accurately distinguish processing status, with a critical threshold of ~73% separating processed from unprocessed frog legs. Logistic regression achieved perfect specificity and precision (100%) with an overall accuracy of 96.8%, while other classifiers also performed strongly (>90% accuracy). These findings confirm moisture as a species- and origin-independent marker of processing, offering a simple, rapid, and cost-effective tool for authenticity verification and quality control in frog meat and potentially other niche protein products. Future work should expand sample coverage, validate thresholds across processing types, and integrate biochemical and sensory quality assessments. Full article

Food fraud in meat products presents serious economic and public health challenges, underscoring the need for rapid and reliable detection methods. This study investigates the potential of Raman spectroscopy combined with machine learning to accurately discriminate between pure and mixed minced meat preparations. We evaluated three classification algorithms: Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forests (RFs). Raman spectra were collected from 19 distinct samples consisting of different ratios of pork, beef, and lamb minced meat. Our findings suggest that homogenization markedly enhances spectral consistency and classification accuracy. In the pure meat samples case, all three models (SVM, ANN, and RF) achieved notable increases in classification accuracies (from 0.50–0.70 to above 0.85), a dramatic improvement over unhomogenized samples. In more complex homogenized mixtures, SVM delivered the highest performance, achieving an accuracy of up to 0.88 for 50:50 mixtures and 0.86 for multi-ratio samples, often outperforming both ANN and RF. While the underlying interpretation of the classification models remains complex, the findings consistently underscore the critical role of homogenization on model performance. This work demonstrates the robust potential of the Raman spectroscopy-coupled machine learning approach for the rapid and accurate identification of minced meat species. Full article

The colorimetric food freshness indicator (CFFI) is a promising technology in intelligent food packaging, offering the capability for real-time monitoring of food freshness through colorimetric changes. This technology holds significant promise in mitigating food waste and enhancing transparency across the supply chain. This paper provides a comprehensive review of the classification system for the CFFI, encompassing colorimetric films and sensor arrays. It explores their applications across key perishable food categories, including meats, seafoods, fruits, and vegetables. Furthermore, this paper offers an in-depth analysis of three critical challenges currently hindering technological advancement: safety concerns, stability issues, and limitations in sensitivity and selectivity. In addressing these challenges, this paper proposes forward-looking solutions and outlines potential research directions aimed at overcoming these bottlenecks, thereby fostering substantial progress in the development of this field. Full article

Background/Objectives: Recent studies linked a diet rich in ultra-processed foods (UPFs) with depression and diabetes. Although common risk factors, such as aging, are defined for both diseases, how UPFs are associated with the bidirectional relationship between them is not known. This study aimed to investigate whether UPF intake modifies the association between depression and diabetes within the Brazilian adult population. Methods: This cross-sectional analysis utilized data from the 2019 Brazilian National Health Survey, involving over 87,000 adults (aged 18–92 years). Participants provided self-reported data on diabetes and depression diagnoses, dietary habits (assessed by qualitative FFQ), as well as demographic, and socioeconomic variables. Multivariate logistic regression models were used to evaluate the associations, employing two classification methods—UPF1 and UPF2—based on different thresholds of weekly consumption, for high/low UPF intake. Analyses were stratified by age groups to identify variations in associations. Results: There was a significant association between depression and diabetes, especially among participants with high UPF consumption. Models adjusted by demographic characteristics, as well as meat and vegetable consumptions, demonstrated elevated odds ratios (ORs) for diabetes among individuals with depression consuming high levels of UPF, compared to those with a low UPF intake (OR: 1.258; 95% CI: 1.064–1.489 for UPF1 and OR: 1.251; 95% CI: 1.059–1.478 for UPF2). Stratified analysis by age further amplified these findings, with younger individuals showing notably stronger associations (non-old adult group OR: 1.596; 95% CI: 1.127–2.260 for UPF1, and OR: 6.726; 95% CI: 2.625–17.233 for UPF2). Conclusions: These findings suggest that high UPF intake may influence the relationship between depression and diabetes, especially in younger adults. Future longitudinal studies are warranted to establish causality, investigate underlying biological mechanisms, and examine whether improving overall nutrient intake through dietary interventions can reduce the co-occurrence of depression and diabetes. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

In the process of chicken breeding, there has been a great deal of abuse of antibiotics. Antibiotics can enter the human body along with the chicken meat, comprising a possible risk to human health. In this paper, principal component analysis (PCA)–linear discriminant analysis (LDA) was chosen to classify neomycin (NEO) and chloramphenicol (CAP) residues in chicken meat. A total of 400 chicken meat samples were used for the classification, of which 268 samples and 132 samples were used as the training sets and the test sets, respectively. The experimental condition of SERS spectrum collection was optimized, including the use of a gold colloid and active agent, and an improvement in the adsorption time. The optimal measurement conditions for the SERS spectra were an adsorption time of 4 min and the use of a 14th-generation gold colloid as the enhanced substrate without a surfactant. For three groups of different spectral preprocessing methods, the classification accuracies of PCA-LDA models for test sets were 78.79% for baseline correction, 84.85% for the second derivative and 100% for the second derivative combined with baseline correction. LDA was used to establish a classification model to realize the quick determination of NEO and CAP residues in chicken meat by SERS. The results showed that the characteristic peaks at 546 and 666 cm⁻¹ could be used to distinguish NEO and CAP residues in chicken meat. The classification model based on PCA-LDA had higher classification accuracy, sensitivity and specificity using a second derivative combined with baseline correction as the spectral preprocessing method, which shows that the SERS method based on PCA-LDA could be used to perform the classification of NEO and CAP residues in chicken meat quickly and effectively. It also verified the feasibility of PCA-LDA to effectively classify chicken meat samples into four types. This research method could provide a reference for the measurement of such antibiotic residues in chicken meat in the future. Full article

Beef meat quality and value are influenced by the breed, sex, and age of slaughtered animals. This study aimed to evaluate lipofuscin pigment autofluorescence as a method for age classification in beef meat samples and to determine the sex of market-obtained meat using PCR-based amelogenin gene amplification. Deboned beef meat samples from M. longissimus dorsi and M. biceps femoris were collected from 67 slaughtered cows with known age and sex. Additionally, 48 market samples were tested for sex identification and age classification using the same methods. Lipofuscin deposition was first observed at 1.5 years, and autofluorescence analysis effectively distinguished between meat from younger animals (1.5–2.2 years) and older ones (3–13 years), with a statistically significant difference (p < 0.001). Lipofuscin levels and excitation intensity increased with age, and no differences were found between the two muscles analyzed. The sex determination results were fully consistent with the records, and 55.2% of animals aged 3 years and older were identified as female. These findings demonstrate the reliability of lipofuscin autofluorescence for binary age determination in beef and support the potential of combining age and sex classification to identify meat derived from older dairy cows in the marketplace. Full article