Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

Multispectral Sorting Based on Visibly High-Risk Kernels Sourced from Another Country Reduces Fumonisin and Toxigenic Fusarium on Maize Kernels

Fusarium species infect maize crops leading to the production of fumonisin by their toxigenic members. Elimination of microbes is critical in mitigating further postharvest spoilage and toxin accumulation. The current study investigates the efficacy of a previously described multispectral sorting technique to analyze the reduction of fumonisin and toxigenic Fusarium species found contaminating maize kernels in Kenya. Maize samples (n = 99) were collected from six mycotoxin hotspot counties in Kenya (Embu, Meru, Tharaka Nithi, Machakos, Makueni, and Kitui County) and analyzed for aflatoxin and fumonisin using commercial ELISA kits. Aflatoxin levels in majority (91%) of the samples were below the 10 ng/g threshold set by the Kenya Bureau of Standards and therefore not studied further. The 23/99 samples that had >2,000 ng/g of fumonisin were selected for sorting. The sorter was calibrated using kernels sourced from Ghana to reject visibly high-risk kernels for fumonisin contamination using reflectance at nine distinct wavelengths (470-1,550 nm). Accepted and rejected streams were tested for fumonisin using ELISA, and the presence of toxigenic Fusarium using qPCR. After sorting, there was a significant (p < 0.001) reduction of fumonisin, by an average of 1.8 log ng/g (98%) and ranging between 0.14 and 2.7 log ng/g reduction (28-99.8%) with a median mass rejection rate of 1.9% (ranged 0% to 48%). The fumonisin rejection rate ranged between 0 and 99.8% with a median of 77%. There was also a significant reduction (p = 0.005) in the proportion of DNA represented by toxigenic Fusarium, from a mean of 30-1.4%. This study demonstrates the use of multispectral sorting as a potential postharvest intervention tool for the reduction of Fusarium species and preformed fumonisin. The spectral sorting approach of this study suggests that classification algorithms based on high-risk visual features associated with mycotoxin can be applied across different sources of maize to reduce fumonisin.

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.

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.

Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management

Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.

Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R _Test ² = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R _Test ² = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R _Test ² = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R _Test ² = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R _Test ² = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.

Non-targeted metabolomics of moldy wheat by ultra-performance liquid chromatography - quadrupole time-of-flight mass spectrometry

Introduction

As one of the staple foods for the world's major populations, the safety of wheat is critical in ensuring people's wellbeing. However, mildew is one of the prevalent safety issues that threatens the quality of wheat during growth, production, and storage. Due to the complex nature of the microbial metabolites, the rapid identification of moldy wheat is challenging.

Methods

In this research, identification of moldy wheat samples was studied using ultra-performance liquid chromatography - quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with chemometrics. The non-targeted PCA model for identifying moldy wheat from normal wheat was established by using previously established compounds database of authentic wheat samples. The partial least squares-discriminant analysis (PLS-DA) was performed.

Results and discussion

By optimizing the model parameters, correct discrimination of the moldy wheat as low as 5% (w/w) adulteration level could be achieved. Differential biomarkers unique to moldy wheat were also extracted to identify between the moldy and authentic wheat samples. The results demonstrated that the chemical information of wheat combined with the existing PCA model could efficiently discriminate between the constructed moldy wheat samples. The study offered an effective method toward screening wheat safety.

An Efficient HPLC-PDA Coupled With Supel™ Tox DON SPE Approach for the Analysis of Deoxynivalenol Contamination in Cereal Grains and Feedstuffs in Jiangxi Province

Deoxynivalenol (DON) was commonly found in grains and feedstuffs, which can cause human chronic diseases. In this study, a quick and reliable method was developed for the determination of DON in grains and feedstuffs in Jiangxi Province market. The sample was extracted with acetonitrile-water (84:16, v/v), then purified by Supel™ Tox DON SPE column, and detected by high-performance liquid chromatography (HPLC). The results showed that the calibration curve of DON showed good linearity in the range of 0.01-10.0 μg/mL, and the correlation coefficient R² = 0.9999. The recovery of DON in the spiked maize sample was 94.8-98.5% by spiking with DON at 0.2 µg/g, 1.0 µg/g, and 2.0 µg/g. The RSD was between 2.5 and 3.3%. This method was used to analyze 120 samples, including 90 grains and 30 feedstuffs, collected from the Jiangxi Province market. The results showed that 81 samples of grains were positive with 90.0% positive rates, and 30 samples of feedstuff were positive with 100% positive rates. Maximum concentration of DON was 0.7 μg/g in oat and 6.9 μg/g in wheat feedstuffs, respectively. Fortunately, the positive samples of grains were safe levels in comparison with National standards for food safety limits of mycotoxins in food (1.0 μg/g), while, the feedstuff of oats was over the Maximum Guideline Level of 16.7% (the Maximum Residue Limit, MRL is 5.0 μg/g). The results of this study about current DON pollution in the grains and feedstuffs on the Jiangxi Province market have not been previously reported.

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.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

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.

Integrated Mycotoxin Management System in the Feed Supply Chain: Innovative Approaches

Exposure to mycotoxins is a worldwide concern as their occurrence is unavoidable and varies among geographical regions. Mycotoxins can affect the performance and quality of livestock production and act as carriers putting human health at risk. Feed can be contaminated by various fungal species, and mycotoxins co-occurrence, and modified and emerging mycotoxins are at the centre of modern mycotoxin research. Preventing mould and mycotoxin contamination is almost impossible; it is necessary for producers to implement a comprehensive mycotoxin management program to moderate these risks along the animal feed supply chain in an HACCP perspective. The objective of this paper is to suggest an innovative integrated system for handling mycotoxins in the feed chain, with an emphasis on novel strategies for mycotoxin control. Specific and selected technologies, such as nanotechnologies, and management protocols are reported as promising and sustainable options for implementing mycotoxins control, prevention, and management. Further research should be concentrated on methods to determine multi-contaminated samples, and emerging and modified mycotoxins.

Identification of type B trichothecenes and zearalenone in Chilean cereals by planar chromatography coupled to mass spectroscopy

High-performance thin-layer chromatography (HPTLC) and HPTLC coupled with mass spectrometry (MS) methods were described for the simultaneous determination of zearalenone (ZEA); type B trichothecenes (TCT-B); nivalenol (NIV) and deoxynivalenol (DON) along with its acetylated derivatives: 3-acetyldeoxynivalenol (3-ADON) and 15-acetyldeoxynivalenol (15-ADON). The extract samples were cleaned-up with Bond Elut Mycotoxin^® solid-phase extraction cartridges. Then, separation was performed on HPTLC silica gel 60 F₂₅₄ plates using toluene, ethyl acetate and formic acid (1:8:1 v/v/v) as mobile phase. Derivatisation was then performed with 10% aluminium trichloride in 50% methanol. Mycotoxin standards and spiked cereals grains were identified by UV spots at 366 nm, with retention factors (R_F) of 0.20 (NIV), 0.39 (DON), 0.45 (15-ADON), 0.50 (3-ADON) and 0.60 (ZEA). Some parameters of validation were determined. Calibration data (n = 5) fitted a linear regression model with determination coefficients, R² > 0.99. The recovery was determined in triplicate at two levels, ranging from 84.3 ± 2.2% to 114.2 ± 11.7%. Detection limits ranged from 80 to 120 μg kg^-1 and quantification limits ranged from 120.0 to 200 μg kg^-1. The analysis by HPTLC/electrospray (ESI)-MS in negative mode confirmed the presence of TCT-B and ZEA standards in Chilean cereals with mass signals at m/z 355, 371, 337, and 317 for DON, NIV, 3-ADON and 15-ADON, and ZEA, respectively.

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67-100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60-100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

Evaluation of the lignite biotransformation capacity of Fusarium sp. NF01 cultured on different growth substrates

The screening and studying the lignite solubilization/degradation capacities of indigenous microorganisms are key to exploring the in-situ biotransformation of lignite. Herein, a fungus was isolated from in-situ lignite samples and identified as Fusarium sp. NF01. This isolate was then cultured on four different carbon sources to evaluate its lignite-transformation capacity. When cultured on a solid agar medium containing sodium gluconate or sodium glutamate, Fusarium sp. NF01 completely liquefied 0.5 g of lignite within 6 days, and when cultured in a liquid medium containing sodium gluconate, the weight of lignite decreased by 28.4% within 7 days. Elemental analysis showed that the rate of lignite biodegradation was inversely proportional to the C:O ratio of the residual lignite samples. Additionally, a 5.9% biodesulfurization rate was achieved when Fusarium sp. NF01 was cultured in the presence of sodium gluconate. Finally, Fourier-transform infrared analysis of the residual lignite samples revealed relatively weak signal intensities of the signature peaks representing the following: aromatic ring side chains; ether, ester, and alcohol bonds; aromatic ring carbon-carbon double bonds; and aliphatic methyl and methylene. The results show that Fusarium sp. NF01 degrades lignite in a carbon-dependent manner and could be thus used for the bioconversion of subsurface coalbeds.

Standardization of near infrared hyperspectral imaging for wheat single kernel sorting according to deoxynivalenol level

The spatial recognition feature of near infrared hyperspectral imaging (HSI-NIR) makes it potentially suitable for Fusarium and deoxynivalenol (DON) management in single kernels to break with heterogeneity of contamination in wheat batches to move towards individual kernel sorting and provide more quick, environmental-friendly and non-destructive analysis than wet-chemistry techniques. The aim of this study was to standardize HSI-NIR for individual kernel analysis of Fusarium damage and DON presence, to predict the level of contamination and classify grains according to the EU maximum limit (1250 µg/kg). Visual inspection on Fusarium infection symptoms and HPLC analysis for DON determination were used as reference methods. The kernels were scanned in both crease-up and crease-down position and for different image captures. The spectra were pretreated by Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), 1st and 2nd derivatives and normalisation, and they were evaluated also by removing spectral tails. The best fitted predictive model was on SNV pretreated data (R² 0.88 and RMSECV 4.8 mg/kg) in which 7 characteristic wavelengths were used. Linear Discriminant Analysis (LDA), Naïve Bayes and K-nearest Neighbours models classified with 100% of accuracy 1st derivative and SNV pretreated spectra according to symptomatology and with 98.9 and 98.4% of correctness 1st derivative and SNV spectra, respectively. The starting point results are encouraging for future investigations on HSI-NIR technique application to Fusarium and DON management in single wheat kernels to overcome their contamination heterogeneity.

NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley

Deoxynivalenol (DON) is one of the mycotoxins produced mainly by the Fusarium graminearum species complex in small grain cereals, including barley. This toxin can cause alimentary disorders, immune function depression and gastroenteritis. The negative health effects associated with DON coupled to the increasing concern about green and rapid methods of analysis motivated this study. In this context, near infrared (NIR) spectroscopy data were applied for exploratory analysis to distinguish barley with high and low levels of DON contamination (> or <1250 µg/kg according to the European Union threshold), by Partial Least Squares-Discriminant Analysis (PLS-DA), and to verify the performance of Partial Least Squares-Regression (PLS-R) to predict DON concentration in barley samples. Maximum values of specificity and sensitivity were achieved in the calibration set; 90.9% and 81.9% were observed in the cross-validation set for the PLS-DA classification model. PLS-R quantification of DON in barley presented low values of error (RMSEC = 101.94 µg/kg and RMSEP = 160.76 µg/kg). Thus, we found that NIR in combination with adequate chemometric tools could be applied as a green technique to monitor DON contamination in barley.

Tri-octahedral bentonites as potential technological feed additive for Fusarium mycotoxin reduction

In 2009 the EU Regulation 386/2009 established a new functional group of feed additives called "substances for reduction of the contamination of feed by mycotoxin". Later, di-octahedral bentonite (1 m558) was authorised, as an anti-aflatoxin additive, being the only additive of this group authorised to date. This work aims to demonstrate the effectiveness of other bentonites, such as tri-octahedral bentonites, versus Fusarium-mycotoxins, since very few adsorbents have proved their effectiveness in relation to this group of mycotoxins. For this purpose, 7 bentonites (six of them tri-octahedral) and 7 commercial adsorbents, added at 0.02% (w/v), were assayed in an in vitro adsorption experiment using two simulated gastro-intestinal (GI) juices (pH 1.3 and 6.8) versus zearalenone (ZEN: 0.1-5 mg/L), fumonisin B₁ (FB₁: 1-10 mg/L) and deoxynivalenol (DON: 2-10 mg/L). Mycotoxin adsorption data were fitted to Langmuir and Freundlich isotherms. In vitro adsorption experiments showed that ZEN and FB₁ (in the latter case only in acid medium) were partially adsorbed, while the adsorption of DON was negligible. Moreover, the increase of adsorbent dose (up to 0.20%, w/v) significantly improved the in vitro adsorption of ZEN and FB₁, reaching >90% of adsorption. The present work proposes the use of some tri-octahedral bentonites as feed additives for Fusarium-mycotoxin reduction.

Advances in Analysis and Detection of Major Mycotoxins in Foods

Mycotoxins are the most widely studied biological toxins, which contaminate foods at very low concentrations. This review describes the emerging extraction techniques and the current and alternatives analytical techniques and methods that have been used to successfully detect and identify important mycotoxins. Some of them have proven to be particularly effective in not only the detection of mycotoxins, but also in detecting mycotoxin-producing fungi. Chromatographic techniques such as high-performance liquid chromatography coupled with various detectors like fluorescence, diode array, UV, liquid chromatography coupled with mass spectrometry, and liquid chromatography-tandem mass spectrometry, have been powerful tools for analyzing and detecting major mycotoxins. Recent progress of the development of rapid immunoaffinity-based detection techniques such as immunoassays and biosensors, as well as emerging technologies like proteomic and genomic methods, molecular techniques, electronic nose, aggregation-induced emission dye, quantitative NMR and hyperspectral imaging for the detection of mycotoxins in foods, have also been presented.

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.