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Background Nitrate is converted to nitrite in the human body and subsequently can react with amines and amides in the gastrointestinal tract to form N -nitroso compounds (NOCs), which are known to be carcinogenic in animals. Humans can be exposed to nitrate via consumption of drinking water and diet, especially green leafy vegetables and cured meat. The contribution of nitrate from drinking water in combination with meat intake has not been investigated thoroughly. Therefore, in the present pilot study, we examined the effect of nitrate from drinking water, and its interaction with the consumption of white and processed red meat, on the endogenous formation of NOCs, taking into account the intake of vitamin C, a nitrosation inhibitor. Methods Twenty healthy subjects were randomly assigned to two groups consuming either 3.75 g/kg body weight (maximum 300 g per day) processed red meat or unprocessed white meat per day for two weeks. Drinking water nitrate levels were kept low during the first week (< 1.5 mg/L), whereas in week 2, nitrate levels in drinking water were adjusted to the acceptable daily intake level of 3.7 mg/kg bodyweight. At baseline, after 1 and 2 weeks, faeces and 24 h urine samples were collected for analyses of nitrate, apparent total N -nitroso compounds (ATNC), compliance markers, and genotoxic potential in human colonic Caco-2 cells. Results Urinary nitrate excretion was significantly increased during the high drinking water nitrate period for both meat types. Furthermore, levels of compliance markers for meat intake were significantly increased in urine from subjects consuming processed red meat (i.e. 1-Methylhistidine levels), or unprocessed white meat (i.e. 3-Methylhistidine). ATNC levels significantly increased during the high drinking water nitrate period, which was more pronounced in the processed red meat group. Genotoxicity in Caco-2 cells exposed to faecal water resulted in increased genotoxicity after the interventions, but results were only significant in the low drinking water nitrate period in subjects consuming processed red meat. Furthermore, a positive correlation was found between the ratio of nitrate/vitamin C intake (including drinking water) and the level of ATNC in faecal water of subjects in the processed red meat group, but this was not statistically significant. Conclusions Drinking water nitrate significantly contributed to the endogenous formation of NOC, independent of the meat type consumed. This implies that drinking water nitrate levels should be taken into account when evaluating the effect of meat consumption on endogenous formation of NOC. Trial registration Dutch Trialregister: 29707 . Registered 19th of October 2018. Retrospectively registered.

Relatively small lifestyle modifications related to weight reduction, physical activity and diet have been shown to decrease the risk of type 2 diabetes. Connected with diet, low consumption of meat has been suggested as a protective factor of diabetes. The aim of the present study was to examine the association between the consumption of total meat or the specific types of meats and the risk of type 2 diabetes. The Alpha-Tocopherol, Beta-Carotene Cancer Prevention cohort included middle-aged male smokers. Up to 12 years of follow-up, 1098 incident cases of diabetes were diagnosed from 24 845 participants through the nationwide register. Food consumption was assessed by a validated FFQ. In the age- and intervention group-adjusted model, high total meat consumption was a risk factor of type 2 diabetes (relative risk (RR) 1·50, 95 % CI 1·23, 1·82, highest v. lowest quintile). The result was similar after adjustment for environmental factors and foods related to diabetes and meat consumption. The RR of type 2 diabetes was 1·37 for processed meat (95 % CI 1·11, 1·71) in the multivariate model. The results were explained more by intakes of Na than by intakes of SFA, protein, cholesterol, haeme Fe, Mg and nitrate, and were not modified by obesity. No association was found between red meat, poultry and the risk of type 2 diabetes. In conclusion, reduction of the consumption of processed meat may help prevent the global epidemic of type 2 diabetes. It seems like Na of processed meat may explain the association.

This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs. The data were evaluated by principal component analysis (PCA) and the partial least squares regression (PLSR) method. For the prediction of sex, the best model was the Standard Normal Variate (SNV) because it presented a relatively high coefficient of determination for prediction, presenting a percentage of correctness of 75.51% and an error of 24.49%. Regarding age, none of the models were able to differentiate the samples through Vis-NIRS. The findings confirm that Vis-NIRS prediction models are a valuable tool for differentiating carcasses based on sex. To further enhance the precision of these predictions, we recommend using Vis-NIRS equipment with the full infrared wavelength range to collect and predict sex and age in intact beef samples.

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908–1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%–93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%–100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy’s potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.

This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).

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.

Red meat and processed meat have been suggested to increase risk of colorectal cancer (CRC), especially colon cancer. However, it remains unclear whether these associations differ according to meat subtypes or colon subsites. The present study addressed this issue by undertaking a pooled analysis of large population‐based cohort studies in Japan: 5 studies comprising 232 403 participants (5694 CRC cases) for analysis based on frequency of meat intake, and 2 studies comprising 123 635 participants (3550 CRC cases) for analysis based on intake quantity. Study‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using the Cox proportional hazards model and then pooled using the random effect model. Comparing the highest vs lowest quartile, beef intake was associated with an increased risk of colon cancer in women (pooled HR 1.20; 95% CI, 1.01‐1.44) and distal colon cancer (DCC) risk in men (pooled HR 1.30; 95% CI, 1.05‐1.61). Frequent intake of pork was associated with an increased risk of distal colon cancer in women (pooled HR 1.44; 95% CI, 1.10‐1.87) for “3 times/wk or more” vs “less than 1 time/wk”. Frequent intake of processed red meat was associated with an increased risk of colon cancer in women (pooled HR 1.39; 95% CI, 0.97‐2.00; P trend = .04) for “almost every day” vs “less than 1 time/wk”. No association was observed for chicken consumption. The present findings support that intake of beef, pork (women only), and processed red meat (women only) might be associated with a higher risk of colon (distal colon) cancer in Japanese. As shown in figure A and figure B, comparing the highest versus lowest quartile, beef intake was associated with an increased risk of colon cancer in women (pooled HR 1.20, 95% CI 1.01‐1.44) and distal colon cancer (DCC) risk in men (pooled HR 1.30, 95% CI 1.05‐1.61). Figure C shows that frequent intake of pork was associated with an increased risk of DCC in women (pooled HR 1.44, 95% CI 1.10‐1.87) for “3 times/week or more” versus “<1time/week”. Figure D shows that frequent intake of processed red meat was associated with an increased risk of colon cancer in women (pooled HR 1.39, 95% CI 0.97‐2.00; p‐trend=0.04) for “almost every day” versus “<1 time/week”.

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen’s kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model’s values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

The study aimed to evaluate the effectiveness of the Europ carcass classification system (ECCS) in discriminating between carcass characteristics and meat quality of fat‐tailed (FT) and thin‐tailed (TT) lambs. In this study, 45 single male lambs of the breeds Akkaraman (n = 14), Karayaka (n = 15), and Herik (n = 16) were used. The lambs were fed and slaughtered at 40 kg. After analysis, two groups were obtained in respect of meat quality and carcass characteristics. One was Akkaraman and Herik as FT, and the other was Karayaka. The effect of fatness class (FC) on carcass characteristics in FT and TT breeds was generally significant. The effect of FC on meat quality characteristics was significant only in a* and b* and expressed juice traits in TT lambs, while no meat quality parameters were affected in FT lambs. While the effect of conformation on carcass traits was significant in terms of trimmed meat, bone and fat percentages in FT lambs, the effect of conformation class (CC) on meat quality traits was insignificant in both tail structures. In conclusion, FC is more effective than CC in distinguishing carcass and meat quality traits in FT and TT lambs according to EECS. This system could be improved especially meat quality characteristics both FT and TT lamb carcasses. The European carcass classification system was originally developed for European thin‐tailed sheep breeds. Since the distribution of fat tissue in thin‐tailed lambs differs from fat‐tailed lamb, this may create a disadvantage regarding the effectiveness of the EUROP classification system for fat‐tailed breeds.

In Morocco the meat business risks being targeted by fraud and adulteration, leading customers to probe the authenticity of the meat. The traditional styles for verifying meat types are expensive and consuming time. In this work, we propose a method based on computer vision and deep learning, which allows the bracket and isolation between turkey and chicken and Fayoumi and chicken farmer meat. We created a model grounded on the pre-trained Mobile Net V2 model and trained it with a Dataset containing the collected images of the four poultries. The evaluation of this model has given satisfactory results and has demonstrated that the model is suitable to predict the meat class with a delicacy of over 98%. The algorithm can be generalized to separate between authentic and fake meat.