Mycobacterium avium subspecies paratuberculosis (MAP) is the causative agent of paratuberculosis, also known as Johne’s disease, in ruminants and is associated with Crohn’s disease in humans. Due to its resistance to pasteurization, MAP can be transmitted through contaminated milk and milk products, posing a food safety risk.
This study aimed to detect and assess the viability of MAP in retail pasteurized and raw tank cow milk in Şanlıurfa, Turkey, using the propidium monoazide (PMA)-quantitative polymerase chain reaction (qPCR) method. A total of 130 milk samples (50 pasteurized and 80 raw tank cow milk) were collected from local shops and dairies. Samples were tested for the presence of MAP, and viable bacteria were further quantified using PMA-qPCR.
MAP was not detected in any of the pasteurized milk samples. One (1.42%) raw milk sample tested positive for MAP, but further PMA-qPCR analysis indicated that the bacteria were not viable.
The PMA-qPCR method can effectively determine the viability of MAP in milk. Raw bulk milk was found to be at risk of MAP contamination; thus, it is recommended that raw milk be consumed with caution, ensuring proper hygiene and storage, and ideally, should not be consumed raw due to potential public health risks.
As the volume of imported food flowing into South Korea rapidly increases due to the expansion of free trade agreements, improving inspection efficiency through artificial intelligence technology emerges as a critical task, particularly as time and cost expenditures for safety inspections conducted by the Korean Ministry of Food and Drug Safety concurrently increase rapidly. The lack of a generalizable machine learning model for predicting the safety of food for human consumption constitutes a significant challenge for policymakers and responsible authorities.
This study developed an effective classification model for predicting non-conformance in customs inspection of imported seafood products. To address the severe class imbalance inherent in the inspection data, we applied class weight-based cost-sensitive learning and adopted an ensemble approach combining Decision Trees (DT), Random Forests (RF), Logistic Regression (LR), and Naive Bayes (NB) models.
Performance evaluation demonstrated that the soft voting ensemble technique achieved superior predictive performance in identifying non-conformance cases, with a recall of 75.57% and an Area Under the Curve (AUC) of 87.49%, significantly outperforming the hard voting method’s recall of 44.32% and AUC of 72.07%. Through SHapley Additive exPlanations (SHAP) analysis, we confirmed that various characteristics, including exporting country ratio, major product category, overseas manufacturer ratio, importer ratio, and seasonal variation, exerted substantial influence on the models’ decisions.
Notably, the Naive Bayes model component provided a more comprehensive analysis for identifying non-conformance by considering multiple dimensions and potential seasonality. This research guide for predicting seafood product import inspection results contributes to enhancing inspection efficiency for securing the safety of imported aquatic products.The proposed methodology demonstrates potential applicability to other regulatory inspection domains confronting similar data imbalance challenges.
Foods are frequently contaminated by natural toxins or toxic substances that have been illegally added, some of which are carcinogenic and pose potential cancer risks to consumers, particularly in low- and middle-income countries. This scoping review maps commonly reported food carcinogens and summarizes existing evidence on the role of these additives as risk factors for various cancers, such as breast, liver, lung, stomach, and colorectal (BLLSCr) cancers. Key findings highlight naturally occurring carcinogenic food contaminants (e.g., aflatoxins, heavy metals, bisphenol A, pesticide residues, polycyclic aromatic hydrocarbons (PAHs) from food processing) and economically motivated adulterants (e.g., Sudan dyes in palm oil, formaldehyde in meat) that frequently contaminate staple foods. There is evidence that these contaminants serve as a risk factor for certain cancers, especially BLLSCr cancers. Moreover, gender and socioeconomic disparities influence cancer prevalence, with men at higher risk for liver, lung, and gastric cancers, while breast cancer incidence rises among women in high-income settings. This review highlights two major contamination pathways: natural toxins and economically motivated adulterants. Meanwhile, strategies to prevent or control/reduce food contamination and associated dietary exposures to these carcinogens have been proposed. Exposure to nutritional carcinogens, as drivers of BLLSCr cancers, represents a food safety and public health challenge globally.