Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review

Foods, Год журнала: 2024, Номер 13(20), С. 3339 - 3339

Опубликована: Окт. 21, 2024

Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites fungi) risk to the human population. Common methods mycotoxin detection include chromatographic separation, often combined mass spectrometry (accurate but time-consuming prepare sample requiring skilled technicians). Artificial intelligence (AI) has been introduced as new technique in food, providing high credibility accuracy. This review article provides an overview recent studies on use AI discovery food. approach demonstrated that variety technologies could be correlated. Deep learning models, machine algorithms, neural networks were implemented analyze elaborate datasets from different analytical platforms. In addition, this focuses advancement work concomitantly smart sensing or other non-conventional techniques such spectroscopy, biosensors, imaging rapid less damaging detection. We question requirement large diverse train discuss standardization methodologies, avenues regulatory approval AI-based approaches, among top-of-mind issues domain. research some interesting cases real commercial applications where able outperform traditional terms sensitivity, specificity, time required. aims provide insights future directions AI-enabled incorporating latest results stressing necessity multidisciplinary collaboration scientists, engineers, computer scientists. Ultimately, revolutionize systems monitoring mycotoxins, improving safeguarding global health.

Язык: Английский

Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm

Infrared Physics & Technology, Год журнала: 2024, Номер 142, С. 105532 - 105532

Опубликована: Авг. 24, 2024

Язык: Английский