Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review

Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.

Advancing Mycotoxin Detection in Food and Feed: Novel Insights from Surface-Enhanced Raman Spectroscopy (SERS)

The implementation of low-cost and rapid technologies for the on-site detection of mycotoxin-contaminated crops is a promising solution to address the growing concerns of the agri-food industry. Recently, there have been significant developments in surface-enhanced Raman spectroscopy (SERS) for the direct detection of mycotoxins in food and feed. This review provides an overview of the most recent advancements in the utilization of SERS through the successful fabrication of novel nanostructured materials. Various bottom-up and top-down approaches have demonstrated their potential in improving sensitivity, while many applications exploit the immobilization of recognition elements and molecular imprinted polymers (MIPs) to enhance specificity and reproducibility in complex matrices. Therefore, the design and fabrication of nanomaterials is of utmost importance and are presented herein. This paper uncovers that limited studies establish detection limits or conduct validation using naturally contaminated samples. One decade on, SERS is still lacking significant progress and there is a disconnect between the technology, the European regulatory limits, and the intended end-user. Ongoing challenges and potential solutions are discussed including nanofabrication, molecular binders, and data analytics. Recommendations to assay design, portability, and substrate stability are made to help improve the potential and feasibility of SERS for future on-site agri-food applications.

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.

Near infrared hyperspectral imaging as a sorting tool for deoxynivalenol reduction in wheat batches

The present study aimed to evaluate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics for classification of individual wheat kernels according to their deoxynivalenol (DON) level. In total, 600 wheat kernels from samples naturally contaminated over the maximum EU level were collected, and the DON content in each individual wheat kernel was analyzed by UHPLC. Linear discriminant analysis (LDA) was employed for building classification models of DON using the EU maximum level as cut off level, and they were tested on balanced and imbalanced test sets. The results showed that the models presented a balanced accuracy of 0.71, that would allow to obtain safe batches from contaminated batches once the unsafe kernels had been rejected, but often more than 30% of the batch would be rejected. The work confirmed that NIR-HSI could be a feasible method for monitoring DON in individual kernels and removing highly contaminated kernels prior to food chain entry.

Deoxynivalenol: Emerging Toxic Mechanisms and Control Strategies, Current and Future Perspectives

Deoxynivalenol (DON) is the most frequently present mycotoxin contaminant in food and feed, causing a variety of toxic effects in humans and animals. Currently, a series of mechanisms involved in DON toxicity have been identified. In addition to the activation of oxidative stress and the MAPK signaling pathway, DON can activate hypoxia-inducible factor-1α, which further regulates reactive oxygen species production and cancer cell apoptosis. Noncoding RNA and signaling pathways including Wnt/β-catenin, FOXO, and TLR4/NF-κB also participate in DON toxicity. The intestinal microbiota and the brain-gut axis play a crucial role in DON-induced growth inhibition. In view of the synergistic toxic effect of DON and other mycotoxins, strategies to detect DON and control it biologically and the development of enzymes for the biodegradation of various mycotoxins and their introduction in the market are the current and future research hotspots.

Enhancing traceability of wheat quality through the supply chain

With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

Rapid screening of benzyl dodecyl dimethyl ammonium bromide co-metabolic degrading bacteria based on NIR hyperspectral imaging technology

As a typical disinfectant, the use of benzyl dodecyl dimethyl ammonium bromide (BDAB) has dramatically increased since the emergence of SARS-CoV-2, posing a threat to environmental balance and human health. Screening BDAB co-metabolic degrading bacteria is required for efficient microbial degradation. Conventional methods for screening co-metabolic degrading bacteria are laborious and time-consuming, especially when the number of strains is large. This study aimed to develop a novel method for the rapid screening of BDAB co-metabolic degrading bacteria from the cultured solid medium using near-infrared hyperspectral imaging (NIR-HSI) technology. Based on NIR spectra, the concentration of BDAB in the solid medium can be well predicted by partial least squares regression (PLSR) models, non-destructively and rapidly, with R_c² > 0.872 and R_cv² > 0.870. The results show that the predicted BDAB concentrations decrease after degrading bacteria utilization, comparing with the regions where no degrading bacteria grew. The proposed method was applied to directly identify the BDAB co-metabolic degrading bacteria cultured on the solid medium, and two kinds of co-metabolic degrading bacteria RQR-1 and BDAB-1 were correctly identified. This method provides a high-efficiency method for screening BDAB co-metabolic degrading bacteria from a large number of bacteria.

Quantification and classification of deoxynivalenol-contaminated oat samples by near-infrared hyperspectral imaging

Deoxynivalenol (DON) is the most occurring mycotoxin in oat and oat-based products. Near-infrared hyperspectral imaging (NIR-HSI) has been proposed as a promising methodology for analysing DON contamination in the food industry. The present study aims to apply NIR-HSI for DON detection in oat kernels and to quantify and classify naturally DON-contaminated oat samples. Unground and ground oat samples were scanned by NIR-HSI before their DON content was determined by HPLC. The data were pre-treated and analysed by PLS regression and four classification methods. The most efficient DON prediction model was for unground samples (R² = 0.75 and RMSEP = 403.18 μg/kg), using twelve characteristic wavelengths with a special interest in 1203 and 1388 nm. The random forest algorithm of unground samples according to the EU maximum limit for unprocessed oats (1750 μg/kg) achieved a classification accuracy of 77.8 %. These findings indicate that NIR-HSI is a promising tool for detecting DON in oats.

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.

Deoxynivalenol: An Overview on Occurrence, Chemistry, Biosynthesis, Health Effects and Its Detection, Management, and Control Strategies in Food and Feed

Mycotoxins are fungi-produced secondary metabolites that can contaminate many foods eaten by humans and animals. Deoxynivalenol (DON), which is formed by Fusarium, is one of the most common occurring predominantly in cereal grains and thus poses a significant health risk. When DON is ingested, it can cause both acute and chronic toxicity. Acute signs include abdominal pain, anorexia, diarrhea, increased salivation, vomiting, and malaise. The most common effects of chronic DON exposure include changes in dietary efficacy, weight loss, and anorexia. This review provides a succinct overview of various sources, biosynthetic mechanisms, and genes governing DON production, along with its consequences on human and animal health. It also covers the effect of environmental factors on its production with potential detection, management, and control strategies.

Feasibility study on early identification of freshness decay of fresh-cut kiwifruit during cold chain storage by Fourier transform-near infrared spectroscopy combined with chemometrics

This work mainly aimed to evaluate the feasibility of Fourier transform-near infrared spectroscopy (FT-NIRS) combined with chemometrics in early identification of freshness decay of fresh-cut kiwifruit during simulated cold chain storage, with organoleptic evaluation as a reference. By linear discriminant analysis, the freshness decay could be identified after only 2 days of cold storage, corresponding to freshness level of 3.41 ± 0.27 N (hardness), 0.70 ± 0.05 g/kg (total acid), 8.62 ± 0.06 g/100 g (reducing sugars), 62.04 ± 1.03 mg/100 g (vitamin C) and 2.05 ± 0.11 log₁₀ CFU/g (total plate count). Organoleptic evaluators could not perceive the freshness decay that occurred after 2 days of cold storage. Moreover, the freshness decay could be well quantitatively predicted by partial least squares regression, with low RMSE_p (0.18-05.42) and high R² (0.90-0.96). FT-NIRS appears to be a promising option for early warning of the freshness decay of fresh-cut kiwifruit during cold chain storage, thereby preventing serious spoilage and ensuring fresh fruits for consumers. PRACTICAL APPLICATION: This work is based on the fact that fresh-cut kiwifruit is prone to freshness decay under unstable cold chain conditions, using FT-NIRS combined with chemometrics to identify the freshness decay early and rapidly, to a certain extent, early warn freshness decay and effectively prevent serious spoilage. The technology can be used for food regulatory agencies to monitor the freshness of fresh-cut kiwifruit, and can also be applied for fruit processing enterprises and dealers to ensure the freshness and high quality of fresh-cut kiwifruit.