Cereals are one of the primary food sources for humans and also constitute a significant part of animal feed. However, these products are particularly susceptible to attack by colonizing fungi such as Aspergillus, Fusarium, and Penicillium, which secrete toxic compounds known as mycotoxins. Mycotoxins are low-molecular-weight secondary metabolites that can cause severe harm to consumer health. In fact, some of them have been classified as carcinogenic by the International Agency for Research on Cancer (IARC). It is, therefore, crucial to monitor the environmental conditions that favor the development of fungi producing these 6 molecules and to analyze the products themselves, from raw materials to processed foods, before they reach consumers' tables. Currently, various types of analyses are applicable both to monitor potential fungal growth or detect its presence in agrifood samples, and to reduce the risk of mycotoxin contamination. This is achieved through the use of predictive models and early warning methods, often supported by specific machinery, screening techniques, and sensors. This paper presents the most commonly techniques used, with a focus on predictive models for contamination risk. These models are fundamental for minimizing the presence of mycotoxins in cereals and derived products.
I cereali sono una delle principali fonti alimentari per gli esseri umani e costituiscono anche una parte importante dei mangimi animali. Tuttavia, sono prodotti particolarmente suscettibili all'attacco di funghi colonizzatori come Aspergillus, Fusarium e Penicillium, i quali secernono composti tossici noti come micotossine. Le micotossine sono metaboliti secondari a basso peso molecolare che possono causare gravi danni alla salute dei consumatori. Infatti, alcune di esse sono state classificate come cancerogene dall'Agenzia Internazionale per la Ricerca sul Cancro (IARC). È, quindi, fondamentale monitorare le condizioni ambientali che favoriscono lo sviluppo dei funghi produttori di queste molecole e analizzare i prodotti stessi, dalle materie prime fino agli alimenti trasformati, prima che essi arrivino sulle tavole dei consumatori. Ad oggi, esistono diverse tipologie di analisi applicabili sia per monitorare possibili sviluppi fungini o rilevarne la presenza in campioni agro-alimentari, sia per ridurre il rischio di contaminazione da micotossine: questo si ottiene avvalendosi dell'utilizzo di modelli predittivi e metodi di allerta precoce, spesso supportati da macchinari, screening e sensori specifici. Pertanto, in questo elaborato, vengono riportate le tecniche più utilizzate, con un focus sui modelli predittivi del rischio di contaminazione. Queste tecniche sono fondamentali per ridurre al minimo la presenza di micotossine nei cereali e nei prodotti derivati.
MICOTOSSINE E SICUREZZA ALIMENTARE: TECNOLOGIE DI ALLERTA PRECOCE PER I CEREALI
CENTON, LARA
2024/2025
Abstract
Cereals are one of the primary food sources for humans and also constitute a significant part of animal feed. However, these products are particularly susceptible to attack by colonizing fungi such as Aspergillus, Fusarium, and Penicillium, which secrete toxic compounds known as mycotoxins. Mycotoxins are low-molecular-weight secondary metabolites that can cause severe harm to consumer health. In fact, some of them have been classified as carcinogenic by the International Agency for Research on Cancer (IARC). It is, therefore, crucial to monitor the environmental conditions that favor the development of fungi producing these 6 molecules and to analyze the products themselves, from raw materials to processed foods, before they reach consumers' tables. Currently, various types of analyses are applicable both to monitor potential fungal growth or detect its presence in agrifood samples, and to reduce the risk of mycotoxin contamination. This is achieved through the use of predictive models and early warning methods, often supported by specific machinery, screening techniques, and sensors. This paper presents the most commonly techniques used, with a focus on predictive models for contamination risk. These models are fundamental for minimizing the presence of mycotoxins in cereals and derived products.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/99422