Recent development of optical methods in rapid and non-destructive detection of aflatoxin and fungal contamination in agricultural products

Raman hyperspectral imaging as a potential tool for rapid and nondestructive identification of aflatoxin contamination in corn kernels

The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (non-aflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.

Prevalence and concentration of aflatoxin M1 in breast milk in Africa: a meta-analysis and implication for the interface of agriculture and health

Breast milk is one of the many distinct forms of food that can be contaminated with aflatoxin M1 (AFM1). They may be consumed by eating contaminated foods, such as contaminated meat and crops, which would then be present in breast milk and cause health problems, including nervous system disorders and cancers of the lungs, liver, kidneys, and urinary tract. However, the prevalently inconsistent explanation of prevalence and concentration remains a big challenge. Thus, this meta-analysis was conducted to determine the prevalence and concentration of harmful chemicals in breast milk in an African context. The databases MEDLINE, PubMed, Embase, SCOPUS, Web of Science, and Google Scholar were searched for both published and unpublished research. To conduct the analysis, the collected data were exported to Stata version 18. The results were shown using a forest plot and a prevalence with a 95% confidence interval (CI) using the random-effects model. The Cochrane chi-square (I2) statistics were used to measure the studies' heterogeneity, and Egger's intercept was used to measure publication bias. This review included twenty-eight studies with 4016 breast milk samples and newborns. The analysis showed the overall prevalence and concentration of aflatoxin M1 in breast milk were 53% (95% CI 40, 65; i2 = 98.26%; P = 0.001). The pooled mean aflatoxin M1 concentration in breast milk was 93.02 ng/l. According to this study, the eastern region of Africa was 62% (95% CI 39-82) profoundly affected as compared to other regions of the continent. In subgroup analysis by publication year, the highest level of exposure to aflatoxins (68%; 95% CI 47-85) was observed among studies published from 2010 to 2019. This finding confirmed that more than half of lactating women's breast milk was contaminated with aflatoxin M1 in Africa. The pooled mean aflatoxin M1 concentration in breast milk was 93.02 ng/l. According to this study, the eastern region of Africa was profoundly affected compared with other regions. Thus, the government and all stakeholders must instigate policies that mitigate the toxicity of aflatoxins in lactating women, fetuses, and newborns.

Colorimetric detection of the potent carcinogen aflatoxin B1 based on the aggregation of L-lysine-functionalized gold nanoparticles in the presence of copper ions

L-lysine functionalized gold nanoparticles (AuNPs-Lys) have been widely used for the detection of worldwide interest analytes. In this work, a colorimetric assay for the detection of the carcinogen aflatoxin B1 (AFB1) based on the aggregation of AuNPs-Lys in the presence of copper ions was developed. For this purpose, AuNPs were synthesized in citrate aqueous solution, functionalized, and further characterized by UV-Vis, fluorescence, Fourier transform infrared spectroscopy (FTIR), nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), and transmission electron microscopy (TEM). In general, AuNPS-Lys (~2.73 × 1011 particles) offered a clear colorimetric response in the presence of AFB1 and Cu2+ ions showing linearity in the range of 6.25 to 200 ng AFB1/mL, with a detection limit of 4.18 ng AFB1/mL via photometric inspection. Moreover, the performance of the proposed methodology was tested using the 991.31 AOAC official procedure based on monoclonal antibodies in maize samples artificially contaminated with AFB1. There was a good agreement between the measured AFB1 concentrations in both assays, the average recoveries for the colorimetric and immunoaffinity assays were between 91.2-98.4% and 96.0-99.2%, respectively. These results indicated that the colorimetric assay could be used as a rapid, eco-friendly, and cost-effective platform for the quantification of AFB1 in maize-based products.

The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques

Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.

Integrating molecular imprinting into flexible covalent organic frameworks for selective recognition and efficient extraction of aflatoxins

Covalent organic frameworks (COFs) are promising adsorbents for extraction, but their selectivity for molecular recognition remains a challenging issue due to the very limited structural design with rigid structure. Herein, we report an elegant strategy for the design and synthesis of molecularly imprinted flexible COFs (MI-FCOFs) via one-pot reaction between the flexible building block of 2,4,6-tris(4-formylphenoxy)- 1,3,5-triazine and linear 4-phenylenediamine for selective extraction of aflatoxins. The flexible chain structure enabled the developed MI-FCOF to adjust the shape and conformation of frameworks to suit the template molecule, giving high selectivity for aflatoxins recognition. Moreover, MI-FCOF with abundant imprinted sites and function groups exhibited an exceptional adsorption capacity of 258.4 mg g-1 for dummy template which is 3 times that of no-imprinted FCOF (NI-FCOF). Coupling MI-FCOF based solid-phase extraction with high-performance liquid chromatography gave low detection limits of 0.003-0.09 ng mL-1 and good precision with relative standard deviations ≤ 6.7% for the determination of aflatoxins. Recoveries for the spiked rice, corn, wheat and peanut samples were in the range of 85.4%- 105.4%. The high selectivity of the developed MI-FCOF allows matrix-free determination of AFTs in food samples. This work offers a new way to the design of MI-FCOF for selective molecular recognition.

Level of exposure to aflatoxins during pregnancy and its association with adverse birth outcomes in Africa: a meta-analysis

BACKGROUND

Aflatoxins are various poisonous carcinogens and mutagens produced by Aspergillus species. Exposure to aflatoxins during pregnancy results in adverse birth outcomes. This meta-analysis was carried out to determine the estimates of how much aflatoxin is harmful to the pregnancy and its outcome, including birthweight, birth length, low birthweight (LBW), small for gestational age (SGA), stunting, poverty, food insecurity, income, pesticides and stillbirth, in an African context.

METHODS

Both published and unpublished studies in Africa were searched on MEDLINE, PubMed, Embase, SCOPUS, Web of Science and Google Scholar. Stata version 18.2 software was used for cleaning and analysis. The prevalence with a 95% confidence interval (CI) was estimated using the random effects model and a forest plot was used to present the findings. In addition, the heterogeneity of the study was assessed using Cochrane I2 statistics and publication bias was assessed using Egger's intercept and funnel plot.

RESULTS

This review included 28 studies with a total of 6283 pregnant women and newborns. The analysis showed the overall level of exposure to aflatoxins was 64% (95% CI 48 to 78, τ2=0.66, I2=99.34%, p=0.001). In the subgroup analysis by publication year, the highest level of exposure to aflatoxins (82% [95% CI 69 to 92]) was observed among studies published from 2020 to 2023. This study also found that exposure to aflatoxins during pregnancy had an association with prematurity, LBW, SGA and stillbirth.

CONCLUSIONS

The data analysed in this study indicated that three of every five pregnant women had exposure to aflatoxins in Africa. Moreover, pregnant women exposed to aflatoxins had a higher likelihood of having a LBW and SGA newborn. Thus governments and all stakeholders should initiate policies that mitigate the toxicity of aflatoxins in pregnant women, foetuses and newborns.

Quality evaluation of fresh pistachios (Pistacia vera L.) cultivars coated with chitosan/TiO2 nanocomposite

Fresh pistachios are rich in dietary fiber, minerals and unsaturated fatty acids, but they have a short shelf life. This investigation examined the effect of pre-harvest foliar application with chitosan (500 and 1000 mg. L-1), nano-chitosan (250 and 500 mg. L-1), and chitosan/TiO2 nanocomposite (250 and 500 mg. L-1) coating films on the postharvest physiology and storage of fresh pistachios (Pistacia vera cvs. Akbari and Ahmad Aghaei) cultivar during storage at 4 ± 0.5 °C. It was found that, fresh pistachios' shelf life could by increased by up to 30 days by the use of chitosan/TiO2 nanocomposite coating for foliar application. The decay index of the composite coated fruits was 4-6 % lower than that of the control group, and after 50-60 days the bacterial contamination appeared in cultivars; respectively. The nanocomposite treatments reduced the fruits weight between 30 and 40 %, which was 15 % higher that of than uncoated fruits. The pre-harvest application of chitosan/TiO2 coating reduced microbial contamination, weight loss, phenylalanine ammonialyase (PAL) activity and saturated fatty acids, and increased unsaturated fatty acids, antioxidant properties, sensory properties, essential minerals, superoxide dismutase (SOD), quality indicators and shelf life. These results demonstrated that the chitosan/TiO2 (250 and 500 mg. L-1) coating film effectively preserved the nutrient composition, sensory quality, nutritional value, antioxidant capacity and shelf life of fresh pistachio.

A state-of-art-review on emerging contaminants: Environmental chemistry, health effect, and modern treatment methods

Pollution problems are increasingly becoming e a priority issue from both scientific and technological points of view. The dispersion and frequency of pollutants in the environment are on the rise, leading to the emergence have been increasing, including of a new class of contaminants that not only impact the environment but also pose risks to people's health. Therefore, developing new methods for identifying and quantifying these pollutants classified as emerging contaminants is imperative. These methods enable regulatory actions that effectively minimize their adverse effects to take steps to regulate and reduce their impact. On the other hand, these new contaminants represent a challenge for current technologies to be adapted to control and remove emerging contaminants and involve innovative, eco-friendly, and sustainable remediation technologies. There is a vast amount of information collected in this review on emerging pollutants, comparing the identification and quantification methods, the technologies applied for their control and remediation, and the policies and regulations necessary for their operation and application. In addition, This review will deal with different aspects of emerging contaminants, their origin, nature, detection, and treatment concerning water and wastewater.

Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking

The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (R_p) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.

Magnetic three-phase single-drop microextraction for highly sensitive detection of aflatoxin B1 in agricultural product samples based on peroxidase-like spatial network structure

In this work, a highly sensitive method for aflatoxin B₁ (AFB₁) detection was developed based on a peroxidase-like spatial network structure. The specific antibody and antigen of AFB₁ were coated on a histidine-modified Fe₃O₄ nanozyme to form the capture/detection probes. Based on the competition/affinity effect, the spatial network structure was constructed by the probes, which could be rapidly (8 s) separated by a magnetic three-phase single-drop microextraction process. In this single-drop microreactor, the network structure was applied to catalyze a colorimetric 3,3',5,5'-tetramethylbenzidine oxidation reaction for AFB₁ detection. The signal was amplified significantly due to the strong peroxidase-like ability of the spatial network structure and the enrichment effect of the microextraction. Thus, a low detection limit (0.034 pg/mL) was achieved. The matrix effect of real sample can be eliminated by the extraction approach, and the practicability of this method was proved by agricultural product samples analysis.

Aflatoxin detection technologies: recent advances and future prospects

Aflatoxins have posed serious threat to food safety and human health. Therefore, it is important to detect aflatoxins in samples rapidly and accurately. In this review, various technologies to detect aflatoxins in food are discussed, including conventional ones such as thin-layer chromatography (TLC), high performance liquid chromatography (HPLC), enzyme linked immunosorbent assay (ELISA), colloidal gold immunochromatographic assay (GICA), radioimmunoassay (RIA), fluorescence spectroscopy (FS), as well as emerging ones (e.g., biosensors, molecular imprinting technology, surface plasmon resonance). Critical challenges of these technologies include high cost, complex processing procedures and long processing time, low stability, low repeatability, low accuracy, poor portability, and so on. Critical discussion is provided on the trade-off relationship between detection speed and detection accuracy, as well as the application scenario and sustainability of different technologies. Especially, the prospect of combining different technologies is discussed. Future research is necessary to develop more convenient, more accurate, faster, and cost-effective technologies to detect aflatoxins.

Research update on aflatoxins toxicity, metabolism, distribution, and detection: A concise overview

Serious health risks associated with the consumption of food products contaminated with aflatoxins (AFs) are worldwide recognized and depend predominantly on consumed AF concentration by diet. A low concentration of aflatoxins in cereals and related food commodities is unavoidable, especially in subtropic and tropic regions. Accordingly, risk assessment guidelines established by regulatory bodies in different countries help in the prevention of aflatoxin intoxication and the protection of public health. By assessing the maximal levels of aflatoxins in food products which are a potential risk to human health, it's possible to establish appropriate risk management strategies. Regarding, a few factors are crucial for making a rational risk management decision, such as toxicological profile, adequate information concerning the exposure duration, availability of routine and some novel analytical techniques, socioeconomic factors, food intake patterns, and maximal allowed levels of each aflatoxin in different food products which may be varied between countries.

Current Developments of Analytical Methodologies for Aflatoxins' Determination in Food during the Last Decade (2013-2022), with a Particular Focus on Nuts and Nut Products

This review aims to provide a clear overview of the most important analytical development in aflatoxins analysis during the last decade (2013-2022) with a particular focus on nuts and nuts-related products. Aflatoxins (AFs), a group of mycotoxins produced mainly by certain strains of the genus Aspergillus fungi, are known to impose a serious threat to human health. Indeed, AFs are considered carcinogenic to humans, group 1, by the International Agency for Research on Cancer (IARC). Since these toxins can be found in different food commodities, food control organizations worldwide impose maximum levels of AFs for commodities affected by this threat. Thus, they represent a cumbersome issue in terms of quality control, analytical result reliability, and economical losses. It is, therefore, mandatory for food industries to perform analysis on potentially contaminated commodities before the trade. A full perspective of the whole analytical workflow, considering each crucial step during AFs investigation, namely sampling, sample preparation, separation, and detection, will be presented to the reader, focusing on the main challenges related to the topic. A discussion will be primarily held regarding sample preparation methodologies such as partitioning, solid phase extraction (SPE), and immunoaffinity (IA) related methods. This will be followed by an overview of the leading analytical techniques for the detection of aflatoxins, in particular liquid chromatography (LC) coupled to a fluorescence detector (FLD) and/or mass spectrometry (MS). Moreover, the focus on the analytical procedure will not be specific only to traditional methodologies, such as LC, but also to new direct approaches based on imaging and the ability to detect AFs, reducing the need for sample preparation and separative techniques.

Recent advances in simultaneous detection strategies for multi-mycotoxins in foods

Mycotoxin contamination has become a challenge in the field of food safety testing, given the increasing emphasis on food safety in recent years. Mycotoxins are widely distributed, in heavily polluted areas. Food contamination with these toxins is difficult to prevent and control. Mycotoxins, as are small-molecule toxic metabolites produced by several species belonging to the genera Aspergillus, Fusarium, and Penicillium growing in food. They are considered teratogenic, carcinogenic, and mutagenic to humans and animals. Food systems are often simultaneously contaminated with multiple mycotoxins. Due to the additive or synergistic toxicological effects caused by the co-existence of multiple mycotoxins, their individual detection requires reliable, accurate, and high-throughput techniques. Currently available, methods for the detection of multiple mycotoxins are mainly based on chromatography, spectroscopy (colorimetry, fluorescence, and surface-enhanced Raman scattering), and electrochemistry. This review provides a comprehensive overview of advances in the multiple detection methods of mycotoxins during the recent 5 years. The principles and features of these techniques are described. The practical applications and challenges associated with assays for multiple detection methods of mycotoxins are summarized. The potential for future development and application is discussed in an effort, to provide standards of references for further research.

Recent Progress on Techniques in the Detection of Aflatoxin B1 in Edible Oil: A Mini Review

Contamination of agricultural products and foods by aflatoxin B1 (AFB1) is becoming a serious global problem, and the presence of AFB1 in edible oil is frequent and has become inevitable, especially in underdeveloped countries and regions. As AFB1 results from a possible degradation of aflatoxins and the interaction of the resulting toxic compound with food components, it could cause chronic disease or severe cancers, increasing morbidity and mortality. Therefore, rapid and reliable detection methods are essential for checking AFB1 occurrence in foodstuffs to ensure food safety. Recently, new biosensor technologies have become a research hotspot due to their characteristics of speed and accuracy. This review describes various technologies such as chromatographic and spectroscopic techniques, ELISA techniques, and biosensing techniques, along with their advantages and weaknesses, for AFB1 control in edible oil and provides new insight into AFB1 detection for future work. Although compared with other technologies, biosensor technology involves the cross integration of multiple technologies, such as spectral technology and new nano materials, and has great potential, some challenges regarding their stability, cost, etc., need further studies.

Occurrence of mycotoxins in pulses

Pulses, dry grains of the Fabaceae family used for food and feed, are particularly important agricultural products with increasing commercial and nutritional relevance. Similar to other plant commodities, pulses can be affected by fungi in the field and during postharvest. Some of these fungi produce mycotoxins, which can seriously threaten human and animal health by causing acute poisoning and chronic effects. In this review, information referring to the analysis and occurrence of these compounds in pulses is summarized. An overview of the aims pursued, and of the methodologies employed for mycotoxin analysis in the different reports is presented, followed by a comprehensive review of relevant articles on mycotoxins in pulses, categorized according to the geographical region, among other considerations. Moreover, special attention was given to the effect of climatic conditions on microorganism infestation and mycotoxin accumulation. Furthermore, the limited literature available was considered to look for possible correlations between the degree of fungal infection and the mycotoxin incidence in pulses. In addition, the potential effect of certain phenolic compounds on reducing fungi infestation and mycotoxin accumulation was reviewed with examples on beans. Emphasis was also given to a specific group of mycotoxins, the phomopsins, that mainly impact lupin. Finally, the negative consequences of mycotoxin accumulation on the physiology and development of contaminated seeds and seedlings are presented, focusing on the few reports available on pulses. Given the agricultural and nutritional potential that pulses offer for human well-being, their promotion should be accompanied by attention to food safety issues, and mycotoxins might be among the most serious threats. Practical Application: According to the manuscript template available in the website, this section is for "JFS original research manuscripts ONLY; optional". Since we are publishing in CRFSFS this requirement will not be done.

Novel Sensor Approaches of Aflatoxins Determination in Food and Beverage Samples

The rapid quantification of toxins in food and beverage products has become a significant issue in overcoming and preventing many life-threatening diseases. Aflatoxin-contaminated food is one of the reasons for primary liver cancer and induces some tumors and cancer types. Advancements in biosensors technology have brought out different analysis methods. Therefore, the sensing performance has been improved for agricultural and beverage industries or food control processes. Nanomaterials are widely used for the enhancement of sensing performance. The enzymes, molecularly imprinted polymers (MIP), antibodies, and aptamers can be used as biorecognition elements. The transducer part of the biosensor can be selected, such as optical, electrochemical, and mass-based. This review explains the classification of major types of aflatoxins, the importance of nanomaterials, electrochemical, optical biosensors, and QCM and their applications for the determination of aflatoxins.

Biocontrol of toxinogenic Aspergillus flavus and Fusarium oxysporum f. sp. albedinis by two rare Saharan actinomycetes strains and LC-ESI/MS-MS profiling of their antimicrobial products

Fungi colonizing fruits in the field and post-harvest constitute a major threat to the global food sector. This study focuses on the biocontrol of Aspergillus flavus (aflatoxin-producing mold considered carcinogenic by IARC) and Fusarium oxysporum f. sp. albedinis (FOA) (phytopathogenic agent, causal of El Bayoud in the Algerian and Moroccan Sahara). These molds have a significant economic impact and pose a serious human health problem. The aim of this work is to study the antifungal activity of two rare actinomycetes strains; Saccharothrix sp. COL22 and Actinomadura sp. COL08 strains against toxinogenic A. flavus and F. oxysporum f. sp. albedinis. The strains are isolated from Citrullus colocynthis rhizosphere on different media: ISP2, GLM, TSA, Starch-casein-agar and WYE and with different treatments of the samples (physical, chemical treatment and enrichment). The antifungal tests against the pathogenic microorganisms were performed on ISP2, GLM and TSA medium by means of the agar cylinders method. The kinetics of antibiotic production were performed on ISP medium over 16 days. The characterization of the antimicrobial compounds by LC-ESI/MS-MS showed that the bacterial extracts contain Antibiotic SF 2738C, Tetrodecamycin and Aplysillamide B. The phenotypic and molecular studies showed that Saccharothrix sp. COL22 is closely related to the Saccharothrix longispora strain type and that Actinomadura sp. COL08 is closely related to the Actinomadura hibisca strain type. The two strains are rare and showed an interesting activity against toxinogenic A. flavus and F. oxysporum f. sp. albedinis.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

A review combining emerging techniques with classical ones for the determination of biscuit quality: advantages and drawbacks

The production of biscuit and biscuit-like products has faced many challenges due to changes in consumer behavior and eating habits. Today's consumer is looking for safe products not only with fresh-like and pleasant taste, but also with long shelf life and health benefits. Therefore, the potentiality of the use of healthier fat and the incorporation of natural antioxidant in the formulation of biscuit has interested, recently, the attention of researchers. The determination of the biscuit quality could be performed by several techniques (e.g., physical, chemical, sensory, calorimetry and chromatography). These classical analyses are unfortunately destructive, expensive, polluting and above all very heavy, to implement when many samples must be prepared to be analyzed. Therefore, there is a need to find fast analytical techniques for the determination of the quality of cereal products like biscuits. Emerging techniques such as near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopy (FFFS), coupled with chemometric tools have many potential advantages and are introduced, recently, as promising techniques for the assessment of the biscuit quality.

Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h.

Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System

The ripening process of dry-cured meat products is characterised by the development of fungi on the product's surface. This population plays a beneficial role, but, uncontrolled moulds represent a health risk, since some of them may produce mycotoxins, such as ochratoxin A (OTA). The aim of the present work is to assess the potential of near-infrared spectroscopy (NIRS) for the detection of OTA-producing mould species on dry-cured ham-based agar. The collected spectra were used to develop Support Vector Machines-Discriminant Analysis (SVM-DA) models by a hierarchical approach. Firstly, an SVM-DA model was tested to discriminate OTA and non-OTA producers; then, two models were tested to discriminate species among the OTA producers and the non-OTA producers. OTA and non-OTA-producing moulds were discriminated with 85% sensitivity and 86% specificity in the prediction. Furthermore, the SVM-DA model could differentiate non-OTA-producing species with a 95% sensitivity and specificity. Promising results were obtained for the prediction of the four OTA-producing species tested, with a 69% and 90% sensitivity and specificity, respectively. The preliminary approach demonstrated the high potential of NIR spectroscopy, coupled with Chemometrics, to be used as a real-time automated routine monitorization of dry-cured ham surfaces.

Aflatoxin contamination in food crops: causes, detection, and management: a review

Mycotoxins are secondary metabolites produced by several fungal species and molds. Under favorable conditions like high temperature and moisture, they contaminate a large number of food commodities and regional crops during pre and post-harvesting. Aflatoxin is the main mycotoxin that harm animal and human health due to its carcinogenic nature. Aflatoxins are mainly released by Aspergillus flavus and Aspergillus parasiticus. AFB1 constitutes the most harmful type of aflatoxins and is a potent hepato-carcinogenic, mutagenic, teratogenic and it suppresses the immune system. To maintain food safety and to prevent aflatoxin contamination in food crops, combined approaches of using resistant varieties along with recommended farming practices should be followed. This review concentrates on various aspects of mycotoxin contamination in crops and recent methods to prevent or minimize the contamination.

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level

Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. Firstly we found the best combination of 1D-CNN parameters were epoch = 30, learning rate = 0.00005 and 'relu' for active function, the highest test accuracy reached 96.35% for peanut, 92.11% for maize and 94.64% for mix data. Then we compared 1D-CNN with feature selection and methods in other papers, result shows that neural network has greatly improved the detection efficiency than feature selection. Finally we visualized the classification result of different training 1D-CNN networks. This research provides the core algorithm for the intelligent sorter with aflatoxin detection function, which is of positive significance for grain processing and the prenatal detoxification of foreign trade enterprises.

Investigation of feedstuff contaminated with aflatoxigenic fungi species in the semi-arid region in northeast of Iran

There is a close relationship between human health and the quality of the ration used by domestic animals. Also, molds, like genus of Aspergillus, infect animal and human food products with dangerous toxic substances, i.e., aflatoxins that are causing primary and secondary mycotoxicosis in animals and human. The aim of this study was to compare fungal species that contaminated and produced aflatoxin in livestock and poultry feed in Gonabad that is a semi-arid city in northeast of Iran. Sampling was randomly performed three times from two livestock feed mills and two poultry feed mills during summer and autumn. Samples were cultured in two forms of solid and suspension in Sabouraud dextrose agar with chloramphenicol medium (SC) for 5 days in 28 °C. Microscopic diagnostic test and also molecular diagnostic test were used to determine fungal species in culture based on β-tubulin gene sequencing. A total of 27.25% and 31.7% of two livestock feed and two poultry feed samples were contaminated with Aspergillus, respectively. Aspergillus flavus (n = 4), Aspergillus Fumigatus (n = 2), Aspergillus versicolor (n = 2), Aspergillus niger (n = 2), Aspergillus parasiticus (n = 1), Aspergillus ochraceus (n = 1), and Aspergillus terreus (n = 1) were detected by molecular PCR test. The rate of contamination to genus of Aspergillus in autumn was higher than summer (P value = 0.008). Poultry feed sample showed more contamination to Aspergillus species compared with livestock feed.

Physical and Chemical Methods for Reduction in Aflatoxin Content of Feed and Food

Aflatoxins (AFs) are among the most harmful fungal secondary metabolites imposing serious health risks on both household animals and humans. The more frequent occurrence of aflatoxins in the feed and food chain is clearly foreseeable as a consequence of the extreme weather conditions recorded most recently worldwide. Furthermore, production parameters, such as unadjusted variety use and improper cultural practices, can also increase the incidence of contamination. In current aflatoxin control measures, emphasis is put on prevention including a plethora of pre-harvest methods, introduced to control Aspergillus infestations and to avoid the deleterious effects of aflatoxins on public health. Nevertheless, the continuous evaluation and improvement of post-harvest methods to combat these hazardous secondary metabolites are also required. Already in-use and emerging physical methods, such as pulsed electric fields and other nonthermal treatments as well as interventions with chemical agents such as acids, enzymes, gases, and absorbents in animal husbandry have been demonstrated as effective in reducing mycotoxins in feed and food. Although most of them have no disadvantageous effect either on nutritional properties or food safety, further research is needed to ensure the expected efficacy. Nevertheless, we can envisage the rapid spread of these easy-to-use, cost-effective, and safe post-harvest tools during storage and food processing.

The Scourge of Aflatoxins in Kenya: A 60-Year Review (1960 to 2020)

Aflatoxins are endemic in Kenya. The 2004 outbreak of acute aflatoxicosis in the country was one of the unprecedented epidemics of human aflatoxin poisoning recorded in mycotoxin history. In this study, an elaborate review was performed to synthesize Kenya’s major findings in relation to aflatoxins, their prevalence, detection, quantification, exposure assessment, prevention, and management in various matrices. Data retrieved indicate that the toxins are primarily biosynthesized by Aspergillus flavus and A. parasiticus, with the eastern part of the country reportedly more aflatoxin-prone. Aflatoxins have been reported in maize and maize products (Busaa, chan’gaa, githeri, irio, muthokoi, uji, and ugali), peanuts and its products, rice, cassava, sorghum, millet, yams, beers, dried fish, animal feeds, dairy and herbal products, and sometimes in tandem with other mycotoxins. The highest total aflatoxin concentration of 58,000 μg/kg has been reported in maize. At least 500 acute human illnesses and 200 deaths due to aflatoxins have been reported. The causes and prevalence of aflatoxins have been grossly ascribed to poor agronomic practices, low education levels, and inadequate statutory regulation and sensitization. Low diet diversity has aggravated exposure to aflatoxins in Kenya because maize as a dietetic staple is aflatoxin-prone. Detection and surveillance are only barely adequate, though some exposure assessments have been conducted. There is a need to widen diet diversity as a measure of reducing exposure due to consumption of aflatoxin-contaminated foods.

A Flexible PI/Si/SiO2 Piezoresistive Microcantilever for Trace-Level Detection of Aflatoxin B1

In this paper, a polyimide (PI)/Si/SiO2-based piezoresistive microcantilever biosensor was developed to achieve a trace level detection for aflatoxin B1. To take advantage of both the high piezoresistance coefficient of single-crystal silicon and the small spring constant of PI, the flexible piezoresistive microcantilever was designed using the buried oxide (BOX) layer of a silicon-on-insulator (SOI) wafer as a bottom passivation layer, the topmost single-crystal silicon layer as a piezoresistor layer, and a thin PI film as a top passivation layer. To obtain higher sensitivity and output voltage stability, four identical piezoresistors, two of which were located in the substrate and two integrated in the microcantilevers, were composed of a quarter-bridge configuration wheatstone bridge. The fabricated PI/Si/SiO2 microcantilever showed good mechanical properties with a spring constant of 21.31 nN/μm and a deflection sensitivity of 3.54 × 10-7 nm-1. The microcantilever biosensor also showed a stable voltage output in the Phosphate Buffered Saline (PBS) buffer with a fluctuation less than 1 μV @ 3 V. By functionalizing anti-aflatoxin B1 on the sensing piezoresistive microcantilever with a biotin avidin system (BAS), a linear aflatoxin B1 detection concentration resulting from 1 ng/mL to 100 ng/mL was obtained, and the toxic molecule detection also showed good specificity. The experimental results indicate that the PI/Si/SiO2 flexible piezoresistive microcantilever biosensor has excellent abilities in trace-level and specific detections of aflatoxin B1 and other biomolecules.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals

Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.

Characteristics, Occurrence, Detection and Detoxification of Aflatoxins in Foods and Feeds

Mycotoxin contamination continues to be a food safety concern globally, with the most toxic being aflatoxins. On-farm aflatoxins, during food transit or storage, directly or indirectly result in the contamination of foods, which affects the liver, immune system and reproduction after infiltration into human beings and animals. There are numerous reports on aflatoxins focusing on achieving appropriate methods for quantification, precise detection and control in order to ensure consumer safety. In 2012, the International Agency for Research on Cancer (IARC) classified aflatoxins B1, B2, G1, G2, M1 and M2 as group 1 carcinogenic substances, which are a global human health concern. Consequently, this review article addresses aflatoxin chemical properties and biosynthetic processes; aflatoxin contamination in foods and feeds; health effects in human beings and animals due to aflatoxin exposure, as well as aflatoxin detection and detoxification methods.

Adverse Effects, Transformation and Channeling of Aflatoxins Into Food Raw Materials in Livestock

Aflatoxins are wide-spread harmful carcinogenic secondary metabolites produced by Aspergillus species, which cause serious feed and food contaminations and affect farm animals deleteriously with acute or chronic manifestations of mycotoxicoses. On farm, both pre-harvest and post-harvest strategies are applied to minimize the risk of aflatoxin contaminations in feeds. The great economic losses attributable to mycotoxin contaminations have initiated a plethora of research projects to develop new, effective technologies to prevent the highly toxic effects of these secondary metabolites on domestic animals and also to block the carry-over of these mycotoxins to humans through the food chain. Among other areas, this review summarizes the latest findings on the effects of silage production technologies and silage microbiota on aflatoxins, and it also discusses the current applications of probiotic organisms and microbial products in feeding technologies. After ingesting contaminated foodstuffs, aflatoxins are metabolized and biotransformed differently in various animals depending on their inherent and acquired physiological properties. These mycotoxins may cause primary aflatoxicoses with versatile, species-specific adverse effects, which are also dependent on the susceptibility of individual animals within a species, and will be a function of the dose and duration of aflatoxin exposures. The transfer of these undesired compounds from contaminated feed into food of animal origin and the aflatoxin residues present in foods become an additional risk to human health, leading to secondary aflatoxicoses. Considering the biological transformation of aflatoxins in livestock, this review summarizes (i) the metabolism of aflatoxins in different animal species, (ii) the deleterious effects of the mycotoxins and their derivatives on the animals, and (iii) the major risks to animal health in terms of the symptoms and consequences of acute or chronic aflatoxicoses, animal welfare and productivity. Furthermore, we traced the transformation and channeling of Aspergillus-derived mycotoxins into food raw materials, particularly in the case of aflatoxin contaminated milk, which represents the major route of human exposure among animal-derived foods. The early and reliable detection of aflatoxins in feed, forage and primary commodities is an increasingly important issue and, therefore, the newly developed, easy-to-use qualitative and quantitative aflatoxin analytical methods are also summarized in the review.

Aflatoxins in Food and Feed: An Overview on Prevalence, Detection and Control Strategies

Aflatoxins produced by the Aspergillus species are highly toxic, carcinogenic, and cause severe contamination to food sources, leading to serious health consequences. Contaminations by aflatoxins have been reported in food and feed, such as groundnuts, millet, sesame seeds, maize, wheat, rice, fig, spices and cocoa due to fungal infection during pre- and post-harvest conditions. Besides these food products, commercial products like peanut butter, cooking oil and cosmetics have also been reported to be contaminated by aflatoxins. Even a low concentration of aflatoxins is hazardous for human and livestock. The identification and quantification of aflatoxins in food and feed is a major challenge to guarantee food safety. Therefore, developing feasible, sensitive and robust analytical methods is paramount for the identification and quantification of aflatoxins present in low concentrations in food and feed. There are various chromatographic and sensor-based methods used for the detection of aflatoxins. The current review provides insight into the sources of contamination, occurrence, detection techniques, and masked mycotoxin, in addition to management strategies of aflatoxins to ensure food safety and security.

A Rapid and Nondestructive Method for Simultaneous Determination of Aflatoxigenic Fungus and Aflatoxin Contamination on Corn Kernels

Conventional methods for detecting aflatoxigenic fungus and aflatoxin contamination are generally time-consuming, sample-destructive, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening detection, real-time, and on-site analysis. Therefore, the potential of visible-near-infrared (Vis-NIR) spectroscopy over the 400-2500 nm spectral range was examined for determination of aflatoxigenic fungus infection and the corresponding aflatoxin contamination on corn kernels in a rapid and nondestructive manner. The two A. flavus strains, AF13 and AF38, were used to represent the aflatoxigenic fungus and nonaflatoxigenic fungus, respectively, for artificial inoculation on corn kernels. The partial least-squares discriminant analysis (PLS-DA) models based on different combinations of spectral range (I: 410-1070 nm; II: 1120-2470 nm), corn side (endosperm or germ side), spectral variable number (full spectra or selected variables), modeling approach (two-step or one-step), and classification threshold (20 or 100 ppb) were developed and their performances were compared. The first study focusing on detection of aflatoxigenic fungus-infected corn kernels showed that, in classifying the "control+AF38-inoculated" and AF13-inoculated corn kernels, the full spectral PLS-DA models using the preprocessed spectra over range II and one-step approach yielded more accurate prediction results than using the spectra over range I and the two-step approach. The advantage of the full spectral PLS-DA models established using one corn side than the other side were not consistent in the explored combination cases. The best full spectral PLS-DA model obtained was obtained using the germ-side spectra over range II with the one-step approach, which achieved an overall accuracy of 91.11%. The established CARS-PLSDA models performed better than the corresponding full-spectral PLS-DA models, with the better model achieved an overall accuracy of 97.78% in separating the AF13-inoculated corn kernels and the uninfected control and AF38-inoculated corn kernels. The second study focusing on the detection of aflatoxin-contaminated corn kernels showed that, based on the aflatoxin threshold of 20 and 100 ppb, the best overall accuracy in classifying the aflatoxin-contaminated and healthy corn kernels attained 86.67% and 84.44%, respectively, using the CARS-PLSDA models. The quantitative modeling results using partial least-squares regression (PLSR) obtained the correlation coefficient of prediction set ( R_P) of 0.91, which indicated the possibility of using Vis-NIR spectroscopy to quantify aflatoxin concentration in aflatoxigenic fungus-infected corn kernels.

Use of Visible-Near-Infrared (Vis-NIR) Spectroscopy to Detect Aflatoxin B1 on Peanut Kernels

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible-near-infrared (Vis-NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B₁ (AFB₁). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB₁-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB₁ contamination of peanut kernels.

Development of a chemiluminescent aptasensor for ultrasensitive and selective detection of aflatoxin B1 in peanut and milk

More and more attention about food safety leads to a research hotspot to develop new detection methods for food contaminant. To address the problems of serious interference and low sensitivity, a chemiluminescent aptasensor for the detection of aflatoxin B1(AFB1) in food was developed in this paper. It is based on horseradish peroxidase (HRP) catalyze the luminol chemiluminescence reaction. The hybridization chain reaction (HCR) signal amplification strategy has been used to improve the detection sensitivity. Magnetic separation could further reduce background signal obviously at the same time. AFB1 as a model of analyte to test the capability of our developed assay system. Under the optimal experimental conditions, CL intensity showed a good linear correlation with the concentrations of AFB1 ranging from 0.5 to 40 ng mL^-1. The limit of detection was estimated 0.2 ng mL^-1 based on 3 times of the signal-to-noise ratio which is lower than those of the previously reported sensors. It could be used to detect AFB1 content in real samples, such as peanuts and milk which were purchased in local supermarket. The results proved that the sensing system has good anti-interference and selectivity. In all, it has potential for practical application in food safety field.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (R_p) of 0.9260 and RMSEP of 87.9132 μg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.