NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination

BACKGROUND

Maize is frequently contaminated with deoxynivalenol (DON) and fumonisins B1 (FB1) and B2 (FB2). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally friendly analysis method.

RESULTS

In the present study, near-infrared hyperspectral imaging (NIR-HSI) was used to develop regression and classification models for DON, FB1 and FB2 in maize kernels. The best regression models presented the following root mean square error of cross validation and ratio of performance to deviation values: 0.848 mg kg-1 and 2.344 (DON), 3.714 mg kg-1 and 2.018 (FB1) and 2.104 mg kg-1 and 2.301 (FB2). Regarding classification, European Union legal limits for DON and FB1 + FB2 were selected as thresholds to classify maize kernels as acceptable or not. The sensitivity and specificity were 0.778 and 1 for the best DON classification model and 0.607 and 0.938 for the best FB1 + FB2 classification model.

CONCLUSION

NIR-HSI can help reduce DON and fumonisins contamination in the maize food and feed chain. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran

Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling. Forty-two samples with DON contamination in the 40-1650 µg/kg range were considered. A chemometric processing of spectroscopic data allowed distinguishing of samples based on their DON content using a cut-off level set at 400 µg/kg DON. Although much lower than the EU limit of 750 µg/kg for wheat bran, this cut-off limit was considered useful whether accepting the sample as safe or implying further inspection by means of more accurate but also more expensive standard analytical techniques. Chemometric data processing using Principal Component Analysis and Quadratic Discriminant Analysis demonstrated a classification rate of 79% in cross-validation. To the best of our knowledge, this is the first time that a pocket-size fluorometer was used for DON screening of wheat bran.

Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains

Wheat has played an important role in human agriculture since ancient times. Increasing rates of processed wheat product fabrication require more and more laboratory studies of product quality. This, in turn, requires the use, in production and in field conditions, of sufficiently accurate, fast and relatively low-cost quality control methods, including the detection of fungal diseases. One of the most widespread fungal diseases of wheat in the world is ergot caused by the fungi genus Claviceps. Optical methods are promising for this disease identification due to the relative ease of implementation and the possibility of performing fast analyses in large volumes. However, for application in practice, it is necessary to identify and substantiate characteristic spectral markers that make it possible to judge the sample contamination. In this regard, within the framework of this study, the methods of IR absorption spectroscopy in the MIR region and reflection spectroscopy in the UV-vis-NIR ranges, as well as luminescence spectroscopy, were used to study ergot-infected grains of winter wheat of the "Moskovskaya 56" cultivar. To justify the choice of the most specific spectral ranges, the methods of chemometric analysis with supervised classification, namely PCA-LDA and PCA-SVM, were applied. The possibility of separating infected grains according to the IR absorption, reflection spectra in the UV-vis-NIR ranges and visible luminescence spectra was tested.

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.

Using Molecular Spectroscopic Techniques (NIR and ATR-FT/MIR) Coupling with Various Chemometrics to Test Possibility to Reveal Chemical and Molecular Response of Cool-Season Adapted Wheat Grain to Ergot Alkaloids

The objectives of this study were to explore the possibility of using near infrared (NIR) and Fourier transform mid-infrared spectroscopy-attenuated total reflectance (ATR-FT/MIR) molecular spectroscopic techniques as non-invasive and rapid methods for the quantification of six major ergot alkaloids (EAs) in cool-season wheat. In total, 107 wheat grain samples were collected, and the concentration of six major EAs was analyzed using the liquid chromatography-tandem mass spectrometry technique. The mean content of the total EAs-ergotamine, ergosine, ergometrine, ergocryptine, ergocristine, and ergocornine-was 1099.3, 337.5, 56.9, 150.6, 142.1, 743.3, and 97.45 μg/kg, respectively. The NIR spectra were taken from 680 to 2500 nm, and the MIR spectra were recorded from 4000-700 cm-1. The spectral data were transformed by various preprocessing techniques (which included: FD: first derivative; SNV: standard normal variate; FD-SNV: first derivative + SNV; MSC: multiplicative scattering correction; SNV-Detrending: SNV + detrending; SD-SNV: second derivative + SNV; SNV-SD: SNV + first derivative); and sensitive wavelengths were selected. The partial least squares (PLS) regression models were developed for EA validation statistics. Results showed that the constructed models obtained weak calibration and cross-validation parameters, and none of the models was able to accurately predict external samples. The relatively low levels of EAs in the contaminated wheat samples might be lower than the detection limits of the NIR and ATR-FT/MIR spectroscopies. More research is needed to determine the limitations of the ATR-FT/MIR and NIR techniques for quantifying EAs in various sample matrices and to develop acceptable models.

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.

Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut

BACKGROUND

Aspergillus flavus is a major pollutant in moldy peanuts, and it has a large influence on the taste of food. The secondary metabolites of Aspergillus flavus, including aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2), are highly toxic and can expose humans to high risk. The total mold count (TMC) is an important index to determine the contamination degree and hygiene quality of peanut.

RESULTS

Quantitative calibration models were established based on full-band wavelengths and characteristic wavelengths, combined with chemometric methods, to explore the feasibility of the use of near-infrared spectroscopy (NIRS) for rapid detection of the TMC in peanuts. The successive projection algorithm (SPA) and elimination of uninformative variables (UVE) algorithms were used to extract the characteristic wavelengths. In comparison, the model built by original spectrum, selected with the UVE algorithm, gave the best result, with a correlation coefficient in a prediction set (R_P ) of 0.9577, a root mean square error for the prediction set (RMSEP) of 0.2336 Log CFU/g, and a residual predictive deviation (RPD) of 3.5041.

CONCLUSIONS

The results showed that NIRS is a rapid, practicable method for the quantitative detection of peanut Aspergillus flavus contamination. It is a promising method for detecting moldy peanuts and increasing peanut safety. © 2019 Society of Chemical Industry.

Shortwave Near-Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 Contamination in Polished Rice

The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B₁ (AFB₁) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB₁ and 15 samples artificially contaminated with AFB₁. Quantitative calibration models to detect AFB₁ were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 μg/kg, and a bias of -0.778 μg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 μg/kg, bias = 0.015 μg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 μg/kg, bias = 0.008 μg/kg). A classification-based partial least squares discriminant analysis model of AFB₁ contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB₁ contamination.

Fourier transform near-infrared and mid-infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

BACKGROUND

Deoxynivalenol (DON) is the most common Fusarium mycotoxin occurring in wheat and wheat-derived products, with several adverse and toxic effects in animals and humans. Although bran fractions produced by milling wheat have numerous health benefits, cereal bran is the part of the grain with the highest concentration of DON, thus representing a risk for consumers. Increased efforts have been made to develop analytical methods suitable for rapid DON screening.

RESULTS

The applicability of Fourier transform near-infrared (FTNIR), or mid-infrared (FTMIR) spectroscopy, and their combination for rapid analysis of DON in wheat bran, was investigated for the classification of samples into compliant and non-compliant groups regarding the EU legal limit of 750 µg kg^-1 . Partial least squares-discriminant analysis (PLS-DA) and principal component-linear discriminant analysis (PC-LDA) were employed as classification techniques using a cutoff value of 400 µg kg^-1 DON to distinguish the two classes. Depending on the classification model, overall discrimination rates were from 87% to 91% for FTNIR and from 86% to 87% for the FTMIR spectral range. The FTNIR spectroscopy gave the highest overall classification rate of wheat bran samples, with no false compliant samples and 18% false noncompliant samples when the PC-LDA classification model was applied. The combination of the two spectral ranges did not provide a substantial improvement in classification results in comparison with FTNIR.

CONCLUSIONS

Fourier transform near-infrared spectroscopy in combination with classification models was an efficient tool to screen many DON-contaminated wheat bran samples and assess their compliance with EU regulations. © 2018 Society of Chemical Industry.

Near-infrared spectroscopy allows detection and species identification of Epichloë endophytes in Lolium perenne

BACKGROUND

Perennial ryegrass (Lolium perenne) is systemically infected by seed-transmitted fungal endophytes (Epichloë sp.). The presence of Epichloë endophytes alters the nutritive quality of its hosts by modifying several plant traits. The aim of this research was to develop a fast method based on near-infrared reflectance spectroscopy (NIRS) to discriminate between perennial ryegrass plants infected (E+) or not infected (E-) with two endophyte species, Epichloë festucae var. lolii, and Epichloë typhina, using a heterogonous set of perennial ryegrass samples collected from wild grasslands and cultivars. Epichloë festucae var. lolii cultures show two morphotypes, M1 and M3, and Epichloë typhina cultures have a different M2 morphotype.

RESULTS

Near-infrared reflectance spectra from E+ and E- ryegrass plants were recorded. Applying the best NIRS model for the detection of Epichloë, 93.3% of E+ plants were classified correctly. The NIRS morphotype classification was correct for 92.9% of M1 morphotype and 100% of M2 morphotypes. The NIRS classification of M3 morphotypes was not as accurate, but it was in accordance with the fungal species classification, identifying some M3 as M1 morphotypes.

CONCLUSION

Near-infrared reflectance spectroscopy can detect the presence of Epichloë fungal endophytes directly in samples of perennial ryegrass, and it is adequate for discriminating among fungal species. © 2018 Society of Chemical Industry.

Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques

The applicability of near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was explored in this study to develop rapid, low-cost and non-destructive spectroscopic methods for classification and quantification of aflatoxins in brown rice. A total of 132 brown rice samples within the aflatoxin concentration range of 0-2435.8 μg/kg were prepared by artificially inoculated with A. flavus and A. parasiticus strains of fungus. For the classification of samples at varying levels of aflatoxin B₁, the linear discriminant analysis model obtained correct classification rate of 96.9 and 90.6% for NIR and MIR spectroscopy, respectively. For the simultaneous determination of aflatoxins B₁, B₂, G₁, G₂ and the total aflatoxins, partial least squares regression also showed good predictive accuracy for both NIR (r_v  = 0.936-0.973, RPD = 2.5-4.0) and MIR spectroscopy (r_v  = 0.922-0.970, RPD = 2.5-4.0). The overall results indicated that the two spectroscopic techniques offered the feasibility to be used as alternative tools for rapid detection of various aflatoxin contaminations in grain.

Production of fumonisins by endophytic strains of Tolypocladium cylindrosporum and its relation to fungal virus infection

Fumonisins were first discovered in Fusarium verticillioides, a fungus associated to disease and asymptomatic infections in maize. Afterwards, other fungal taxa have been found to produce fumonisins. The entomopathogenic ascomycete Tolypocladium cylindrosporum has been isolated from soil and also as an endophyte from leaves of grasses. The objectives of this work were to determine the in vitro production of fumonisin B (FB) mycotoxins and the immunosuppressive compound cyclosporine A (CyA) in several strains of T. cylindrosporum, and to examine the effect of fungal virus infection and temperature in FB production. FB₁ was detected in 30% of the strains, ranging from 0.16 to 5.52 μg cm^-2 in solid media, and FB₂ was detected in 78% of the strains, ranging from 0.764 to 40.92 μg cm^-2. CyA was not detected in any strain. The mean FB₂ concentration of the endophytic strain Tc37W was three times greater (p < 0.05) than that of any other strain. Up to 34% more of FB₂ was detected in strains infected by the virus TcV3 than in the corresponding virus-free versions. The effect of temperature on FB₂ content was interactively significantly dependent on fungal strain and growth medium; in the YES medium, the FB₂ of virus-infected strains Tc37-1V and Tc37W increased by 67 and 16%, respectively, at 26 °C as compared to 20 °C. The FB concentration in some fungal strains was similar to that in fungi associated to food and feed intoxications.

Qualitative and quantitative analysis of endophyte alkaloids in perennial ryegrass using near-infrared spectroscopy

BACKGROUND

Near-infrared reflectance spectroscopy (NIRS) has been widely used in forage quality control because it is faster, cleaner and less expensive than conventional chemical procedures. In Lolium perenne (perennial ryegrass), one of the most important forage grasses, the infection by asymptomatic Epichloë fungal endophytes alters the plant nutritional quality due to the production of alkaloids. In this research, we developed a rapid method based on NIRS to detect and quantify endophyte alkaloids (peramine, lolitrem B and ergovaline) using a heterogeneous set of L. perenne plants obtained from wild grasslands and cultivars.

RESULTS

NIR spectra from dried grass samples were recorded and classified according to the absence or presence of alkaloids, based on reference methods. The best discriminant equations for detection of alkaloids classified correctly 94.4%, 87.5% and 92.9% of plants containing peramine, lolitrem B and ergovaline, respectively. The quantitative NIR equations obtained by modified partial least squares (MPLS) algorithm had coefficients of correlation of 0.93, 0.41, and 0.76 for peramine, lolitrem B and ergovaline respectively.

CONCLUSION

NIRS is a suitable tool for qualitative analysis of endophyte alkaloids in grasses and for the accurate quantification of peramine and ergovaline. © 2017 Society of Chemical Industry.

Analytical techniques for deoxynivalenol detection and quantification in wheat destined for the manufacture of commercial products

The concern regarding toxicity from the presence of deoxynivalenol (DON) in wheat that affects both economy and public health leads to the need to find appropriate detection methods for determining the degree of DON contamination in terms of the equipment available and the speed required for obtaining the incidence. The objective of this study was to compare the performance of two alternative analytical techniques for DON quantification for use in the food industry with a reference technique. Samples of wheat and the commercial by-products were analysed by high-performance liquid chromatography (HPLC) with an ultraviolet detector as the reference method and the results compared with those obtained from a rapid lateral-flow immunochromatographic device (Reveal Q+) and of a Fourier-transform-infrared (FTIR) spectroscopy technique. Pearson’s correlation coefficient between the HPLC and Reveal-Q+ data (0.45), although significant (P<0.0003), was lower than that obtained between HPLC and the FTIR method (0.94, P<0.0001). Both methods were considered efficient in quantifying DON levels in wheat-flour samples. This study was aimed at assisting the producers in choosing an appropriate tool for the purpose of analysis and upon consideration of the available equipment.

Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy

BACKGROUND

Ochratoxin A (OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner.

RESULTS

PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient (r) of 0.814, a standard error of prediction (SEP and bias of 1.965 µg kg^-1 and 0.358 µg kg^-1 , respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non-OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%.

CONCLUSION

The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry.

Quantitative Determination of Fusarium proliferatum Concentration in Intact Garlic Cloves Using Near-Infrared Spectroscopy

Fusarium proliferatum is considered to be a pathogen of many economically important plants, including garlic. The objective of this research was to apply near-infrared spectroscopy (NIRS) to rapidly determine fungal concentration in intact garlic cloves, avoiding the laborious and time-consuming procedures of traditional assays. Preventive detection of infection before seeding is of great interest for farmers, because it could avoid serious losses of yield during harvesting and storage. Spectra were collected on 95 garlic cloves, divided in five classes of infection (from 1-healthy to 5-very highly infected) in the range of fungal concentration 0.34-7231.15 ppb. Calibration and cross validation models were developed with partial least squares regression (PLSR) on pretreated spectra (standard normal variate, SNV, and derivatives), providing good accuracy in prediction, with a coefficient of determination (R²) of 0.829 and 0.774, respectively, a standard error of calibration (SEC) of 615.17 ppb, and a standard error of cross validation (SECV) of 717.41 ppb. The calibration model was then used to predict fungal concentration in unknown samples, peeled and unpeeled. The results showed that NIRS could be used as a reliable tool to directly detect and quantify F. proliferatum infection in peeled intact garlic cloves, but the presence of the external peel strongly affected the prediction reliability.

Mycotoxin Contamination in the EU Feed Supply Chain: A Focus on Cereal Byproducts

Mycotoxins represent a risk to the feed supply chain with an impact on economies and international trade. A high percentage of feed samples have been reported to be contaminated with more than one mycotoxin. In most cases, the concentrations were low enough to ensure compliance with the European Union (EU) guidance values or maximum admitted levels. However, mycotoxin co-contamination might still exert adverse effects on animals due to additive/synergistic interactions. Studies on the fate of mycotoxins during cereal processing, such as milling, production of ethanol fuels, and beer brewing, have shown that mycotoxins are concentrated into fractions that are commonly used as animal feed. Published data show a high variability in mycotoxin repartitioning, mainly due to the type of mycotoxins, the level and extent of fungal contamination, and a failure to understand the complexity of food processing technologies. Precise knowledge of mycotoxin repartitioning during technological processes is critical and may provide a sound technical basis for feed managers to conform to legislation requirements and reduce the risk of severe adverse market and trade repercussions. Regular, economical and straightforward feed testing is critical to reach a quick and accurate diagnosis of feed quality. The use of rapid methods represents a future challenge.

Mycotoxins in silage: checkpoints for effective management and control

Silage has a substantial role in ruminant nutrition. Silages as a source of mycotoxigenic fungi and mycotoxins merit attention. Fungal growth and mycotoxin production before and during storage are a well-known phenomenon, resulting in reduced nutritional value and a possible risk factor for animal health. Mycotoxin co-contamination seems to be unavoidable under current agricultural and silage-making practices. Multi-mycotoxin contamination in silages is of particular concern due to the potential additive or synergistic effects on animals. In regard to managing the challenge of mycotoxins in silages, there are many factors with pre- and post-harvest origins to take into account. Pre-harvest events are predominantly dictated by environmental factors, whereas post-harvest events can be largely controlled by the farmer. An effective mycotoxin management and control programme should be integrated and personalised to each farm at an integrative level throughout the silage production chain. Growing crops in the field, silage making practices, and the feed out phase must be considered. Economical and straightforward silage testing is critical to reach a quick and sufficiently accurate diagnosis of silage quality, which allows for ‘in field decision-making’ with regard to the rapid diagnosis of the quality of given forage for its safe use as animal feed. Regular sampling and testing of silage allow picking up any variations in mycotoxin contamination. The use of rapid methods in the field represents future challenges. Moreover, a proper nutritional intervention needs to be considered to manage mycotoxin-contaminated silages. At farm level, animals are more often exposed to moderate amounts of several mycotoxins rather than to high levels of a single mycotoxin, resulting more frequently in non-specific digestive and health status impairment. Effective dietary strategies to promote rumen health, coupled with the administration of effective and broad-spectrum mycotoxin detoxifiers, are essential to minimise the negative impact of mycotoxins.

Developments in detection and determination of aflatoxins

Since the discovery of aflatoxins in the 1960s, much research has focused on detecting the toxins in contaminated food and feedstuffs in the interest of public safety. Most traditional detection methods involved lengthy culturing and/or separation techniques or analytical instrumentation and complex, multistep procedures that required destruction of samples for accurate toxin determination. With more regulations for acceptable levels of aflatoxins in place, modern analytical methods have become quite sophisticated, capable of achieving results with very high precision and accuracy, suitable for regulatory laboratories and for post-harvest sample testing in developed countries. Unfortunately, many countries around the world that are affected by the aflatoxin problem do not have ready access to high performance liquid chromatography and mass spectrometry instrumentation and require alternate, readily available and simple detection methods that may be used by small holdings farmers in developing countries. This paper presents an overview of the existing detection and/or determination methods for aflatoxins. The traditional, quantitative, chemically-based analytical strategies for detecting aflatoxins in maize and their evolution to the modern instrumentation routinely used in developed countries are reviewed. Additionally, novel, more streamlined, user-friendly and in some instances, non-destructive, methods that may be useful for semi-quantitative or qualitative, quick-screening of contaminated maize samples appropriate also for use in developing countries, are discussed.

Recent advances in the rapid acoustic screening of deoxynivalenol in wheat grains

Deoxynivalenol (DON) is a natural and ubiquitous toxic metabolite produced by filamentous fungi of the genus Fusarium. Approximately one quarter of the world's food crops (mainly cereals) are affected by mycotoxins such as DON. A rapid and non-destructive method to evaluate the quality and safety of grains is therefore required to eliminate these toxins from the food chain. The first portable acoustic device that predicts the concentration of DON in cereal grains has been developed using a broadband capacitive ultrasonic transducer. An acoustic method was optimised for the rapid prediction of DON in wheat. To measure the performance of this method, a model system comprising 0-100% scabby wheat grains was prepared and a single laboratory validation was carried out. The best regression model between DON concentrations determined by the reference ELISA method and the acoustic technique was obtained at an acoustic frequency of 32.2 kHz, with a correlation coefficient of 0.9852 and a repeatability coefficient of variation of 2.1-9.3%, which is much better than the results achieved by prototype acoustic spectrometers. These data show that acoustic technology allows the online monitoring of DON in cereal grains, such as wheat, because it is possible to analyse multilayer grain beds. Sound absorption depends on the grain size and moisture content, so it is advisable to use the equipment at the point of harvest, where one strain of cereals usually dominates and the grains have a more homogeneous morphology and uniform moisture content.