Predictive model of aflatoxin contamination risk associated with granary-stored corn with versicolorin A monitoring and logistic regression

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Ionic liquid-functionalized mesoporous multipod silica for simultaneously effective extraction of aflatoxin B1 and its two precursors from grain

BACKGROUND

Aflatoxin B1 (AFB1) and its precursors contaminate food and agricultural products, posing a significant risk to food safety and human health, but simultaneous and effective extraction and determination of AFB1 and its precursors with varied structures is still a challenging task.

RESULTS

In this study, a bisimidazolium-type ionic liquid functionalized mesoporous multipod silica (SiO2@mPMO-IL(im)2) was fabricated to extract AFB1 and its two precursors, i.e., averantin and sterigmatocystin. The SiO2@mPMO-IL(im)2 could simultaneously extract three targets with varied structures based on the multipods, mesopores, and multifunctional groups. The density functional theory calculations further verified the multiple interactions between SiO2@mPMO-IL(im)2 and targets. The fabricated SiO2@mPMO-IL(im)2 could effectively extract and determine three targets in grains by combing with dispersive solid-phase extraction and high-performance liquid chromatography. Good linearity (r2 > 0.9978), low LODs (0.9-1.5 μg kg-1) and LOQs (3.0-4.5 μg kg-1), satisfactory spiked recoveries (92.5%-106.8%) and high precisions (RSD<6.4%) were observed.

SIGNIFICANCE AND NOVELTY

This work demonstrates the feasibility of SiO2@mPMO-IL(im)2 for simultaneous and effective extraction of toxins with varied structures and provides a promising sample preparation for the analysis of AFB1 and its precursors in grain samples.

Fe3O4/ZIFs-based magnetic solid-phase extraction for the effective extraction of two precursors with diverse structures in aflatoxin B1 biosynthetic pathway

The aflatoxin B₁ (AFB₁) early warning technique based on precursors is an effective strategy for the prevention of AFB₁ contamination risk. The determination of precursors is imperative to ensure the efficiency of the early warning technique. Herein, a controllable magnetic adsorbent Fe₃O₄/ZIFs was first introduced for the effective extraction and determination of averantin (AVN) and sterigmatocystin (ST) precursors in cereal by combining magnetic solid-phase extraction (MSPE) and high-performance liquid chromatography (HPLC). Benefiting from the abundant adsorption sites and multifunctional groups matching the analytes, Fe₃O₄/ZIFs effectively and simultaneously extracted AVN and ST with great differences in polarity and structure via multiple interactions. AVN was extracted by Fe₃O₄/ZIFs mainly through π-π and hydrophobic interactions, while ST was extracted predominantly by electrostatic interactions and surface complexation. The limits of detection were 0.08 μg kg^-1 (AVN) and 0.36 μg kg^-1 (ST). The developed method exhibited satisfactory spiked recoveries (79.1%-105.4%) in the determination of AVN and ST in rice. This work provides a novel analytical strategy for further studying AFB₁ early warning technique and the formation and transformation of aflatoxins.

Aflatoxins in Maize: Can Their Occurrence Be Effectively Managed in Africa in the Face of Climate Change and Food Insecurity?

The dangers of population-level mycotoxin exposure have been well documented. Climate-sensitive aflatoxins (AFs) are important food hazards. The continual effects of climate change are projected to impact primary agricultural systems, and consequently food security. This will be due to a reduction in yield with a negative influence on food safety. The African climate and subsistence farming techniques favour the growth of AF-producing fungal genera particularly in maize, which is a food staple commonly associated with mycotoxin contamination. Predictive models are useful tools in the management of mycotoxin risk. Mycotoxin climate risk predictive models have been successfully developed in Australia, the USA, and Europe, but are still in their infancy in Africa. This review aims to investigate whether AFs’ occurrence in African maize can be effectively mitigated in the face of increasing climate change and food insecurity using climate risk predictive studies. A systematic search is conducted using Google Scholar. The complexities associated with the development of these prediction models vary from statistical tools such as simple regression equations to complex systems such as artificial intelligence models. Africa’s inability to simulate a climate mycotoxin risk model in the past has been attributed to insufficient climate or AF contamination data. Recently, however, advancement in technologies including artificial intelligence modelling has bridged this gap, as climate risk scenarios can now be correctly predicted from missing and unbalanced data.

Fungi population metabolomics and molecular network study reveal novel biomarkers for early detection of aflatoxigenic Aspergillus species

Mycotoxins threaten global food safety, public health and cause huge socioeconomic losses. Early detection is an effective preventive strategy, yet efficient biomarkers for early detection of aflatoxigenic Aspergillus species are lacking. Here, we proposed to use untargeted metabolomics and machine learning to mine biomarkers of aflatoxigenic Aspergillus species. We systematically delineated metabolic differences across 568 extensive field sampling A. flavus and performed biomarker analysis. Versicolorin B, 11-hydroxy-O-methylsterigmatocystin et.al metabolites shown a high correlation (from 0.71 to 0.95) with strains aflatoxin-producing capacity. Molecular networking analysis deciphered the connection of aflatoxins and biomarkers as well as potential emerging mycotoxins. We then developed a model using the biomarkers as variables to discern aflatoxigenic Aspergillus species with 97.8% accuracy. A validation dataset and metabolome from other 16 fungal isolates confirmed the robustness and specificity of these biomarkers. We further demonstrated the solution feasibility in agricultural products by early detection of biomarkers, which predicted aflatoxin contamination risk 35-47 days in advance. A developed operable decision rule by the XGBoost algorithm help regulators to intuitively assess the risk prioritization with 87.2% accuracy. Our research provides novel insights into global food safety risk assessment which will be crucial for early prevention and control of mycotoxins.

The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review

This review presents the current state of aflatoxin risk prediction models and their potential for value actors throughout the food chain in sub-Saharan Africa, with a specific focus on improving smallholder farmer management practices. Several empirical and mechanistic models have been developed either in academic research or by private sector aggregators and processors in high-income countries including Australia, the USA, and Southern Europe, but these models have been only minimally applied in sub-Saharan Africa, where there is significant potential and increasing need due to climate variability. Predictions can be made based on historic occurrence data using either a mechanistic microbiological framework for aflatoxin accumulation or an empirical model based on statistical correlations with climate conditions and local agronomic factors. Model results can then be distributed to smallholders through private, public, or mobile extension services, used by policymakers for strategy or policy, or utilised by private sector institutions for management decisions. Specific agricultural advice can be given during the three most critical points in the phenological cycle: preseason insight including sowing timing and crop varieties, preharvest advice about management and harvest timing, and postharvest optimal practices including storage, drying, and market information. Model development for sub-Saharan Africa is limited by a dearth of georeferenced aflatoxin occurrence data and real-time high resolution climate data; the wide diversity of farm typologies each with significant information and technology gaps; a prevalence of informal market structures and lack of economic incentives systems; and general lack of awareness around aflatoxins and best management practices to mitigate risk. Given advancements towards solving these challenges, predictive aflatoxin models can be integrated into decision support platforms to focus on optimisation of value for smallholders by minimising yield and nutritional losses, which can propagate value throughout the production and postharvest phases.

Near-infrared reflectance spectroscopy-based fast versicolorin A detection in maize for early aflatoxin warning and safety sorting

Aflatoxins (AFs) are potent carcinogens present in numerous crops. Access to accurate methods for evaluating contamination is a critical factor in aflatoxin risk assessment. Versicolorin A (Ver A), a precursor of aflatoxin B₁ (AFB₁), can be used as an indicator for the presence of AFB₁, even when the AF is not yet detectable. Currently employed Ver A detection methods are expensive, time consuming, and difficult to apply to numerous samples. Herein, Ver A was detected via near-infrared spectroscopy. Both quantitative and two-grade sorting methods were set-up using the extreme gradient boosting algorithm coupled with a support vector machine. This two-tiered method obtained a root-mean-square error of prediction value of 3.57 μg/kg for the quantitative model, and an accuracy rate of 90.32% for the sorting approach. This novel method is rapid, accurate, solvent free, requires no sample pretreatment, and detects Ver A in maize, making it convenient for practical use.