Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging

Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes

BACKGROUND

Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450-850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen.

RESULTS

M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data.

CONCLUSIONS

HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually.

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

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

Fungal and Toxin Contaminants in Cereal Grains and Flours: Systematic Review and Meta-Analysis

Cereal grains serve as the cornerstone of global nutrition, providing a significant portion of humanity's caloric requirements. However, the presence of fungal genera, such Fusarium, Penicillium, Aspergillus, and Alternaria, known for their mycotoxin-producing abilities, presents a significant threat to human health due to the adverse effects of these toxins. The primary objective of this study was to identify the predominant fungal contaminants in cereal grains utilized in breadmaking, as well as in flour and bread. Moreover, a systematic review, including meta-analysis, was conducted on the occurrence and levels of mycotoxins in wheat flour from the years 2013 to 2023. The genera most frequently reported were Fusarium, followed by Penicillium, Aspergillus, and Alternaria. Among the published reports, the majority focused on the analysis of Deoxynivalenol (DON), which garnered twice as many reports compared to those focusing on Aflatoxins, Zearalenone, and Ochratoxin A. The concentration of these toxins, in most cases determined by HPLC-MS/MS or HPLC coupled with a fluorescence detector (FLD), was occasionally observed to exceed the maximum limits established by national and/or international authorities. The prevalence of mycotoxins in flour samples from the European Union (EU) and China, as well as in foods intended for infants, exhibited a significant reduction compared to other commercial flours assessed by a meta-analysis investigation.

Quantitative PCR (qPCR) for estimating the presence of Fusarium and its mycotoxins in barley grains

Members within the Fusarium sambucinum species complex (FSAMSC) are able to produce mycotoxins, such as deoxynivalenol (DON), nivalenol (NIV), zearalenone (ZEN) and enniatins (ENNs) in food products. Consequently, alternative methods for assessing the levels of these mycotoxins are relevant for quick decision-making. In this context, qPCR based on key mycotoxin biosynthetic genes could aid in determining the toxigenic fungal biomass, and could therefore infer mycotoxin content. The aim of this study was to verify the use of qPCR as a technique for estimating DON, NIV, ENNs and ZEN, as well as Fusarium graminearum sensu lato (s.l.) and F. poae in barley grains. For this purpose, 53 barley samples were selected for mycobiota, mycotoxin and qPCR analyses. ENNs were the most frequent mycotoxins, followed by DON, ZEN and NIV. 83% of the samples were contaminated by F. graminearum s.l. and 51% by F. poae. Pearson correlation analysis showed significant correlations for TRI12/15-ADON with DON, ESYN1 with ENNs, TRI12/15-ADON and ZEB1 with F. graminearum s.l., as well as ESYN1 and TRI12/NIV with F. poae. Based on the results, qPCR could be useful for the assessment of Fusarium presence, and therefore, provide an estimation of its mycotoxins' levels from the same sample.

Characterization of Rice-Magnaporthe oryzae Interactions by Hyperspectral Imaging

Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.

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.

Isolation of Main Pathogens Causing Postharvest Disease in Fresh Codonopsis pilosula during Different Storage Stages and Ozone Control against Disease and Mycotoxin Accumulation

Codonopsis pilosula is an important Chinese herbal medicine. However, fresh C. pilosula is prone to decay during storage due to microorganism infections, seriously affecting the medicinal value and even causing mycotoxin accumulation. Therefore, it is necessary to study the pathogens present and develop efficient control strategies to mitigate their detrimental effects on the herbs during storage. In this study, fresh C. pilosula was collected from Min County in Gansu Province, China. The natural disease symptoms were observed during different storage stages, and the pathogens causing C. pilosula postharvest decay were isolated from the infected fresh C. pilosula. Morphological and molecular identification were performed, and pathogenicity was tested using Koch's postulates. In addition, the control of ozone was examined against the isolates and mycotoxin accumulation. The results indicated that the naturally occurring symptom increased progressively with the extension of storage time. The mucor rot caused by Mucor was first observed on day 7, followed by root rot caused by Fusarium on day 14. Blue mold disease caused by Penicillum expansum was detected as the most serious postharvest disease on day 28. Pink rot disease caused by Trichothecium roseum was observed on day 56. Moreover, ozone treatment significantly decreased the development of postharvest disease and inhibited the accumulations of patulin, deoxynivalenol, 15-Acetyl-deoxynivalenol, and HT-2 toxin.

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.

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000-2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.

Assessment of Fusarium-Damaged Kernels in Common Wheat in Romania in the Years 2015 and 2016 with Extreme Weather Events

This article assesses the occurrence of Fusarium-damaged kernels (FDKs) in common wheat (Triticum aestivum) under the influence of environmental factors and extreme weather events in Romania (exceptionally high air temperatures and extreme pedological drought produced by a dipole block in summer 2015, and extreme precipitation and floods produced by an omega block in spring 2016). Wheat samples (N = 272) were analyzed for FDKs via visual estimation and manual weighing according to ISO 7970 and are statistically evaluated using SPSS. The dipole block in 2015 reduced the effects of environmental factors to non-significant correlations with FDKs, while the omega block in 2016 was non-significantly to very significantly correlated with FDKs in the northwestern and western regions. The occurrence of FDKs was favored for wheat cultivation in acidic soils and inhibited in alkaline soils. Wheat samples with FDKs ≥ 1% were sampled from crops grown in river meadows with high and very high risks of flooding. Knowing the contaminants’ geographical and spatial distributions under the influence of regular and extreme weather events is important for establishing measures to mitigate the effects of climate change and to ensure human and animal health.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

The severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement-often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000-1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390-1420 nm contributes most to the model's final decision.

Fusarium Head Blight: Effect of Infection Timing on Spread of Fusarium graminearum and Spatial Distribution of Deoxynivalenol within Wheat Spikes

Most studies of Fusarium head blight (FHB) focused on wheat infection at anthesis. Less is known about infections at later stages. In this study, the effect of infection timing on the development of FHB and the distribution of fungal biomass and deoxynivalenol (DON) along wheat spikes was investigated. Under greenhouse conditions, two wheat varieties were point-inoculated with Fusarium graminearum starting from anthesis until 25 days after anthesis. The fungus and fungal DNA were isolated from the centers and the bases of all the spikes but not from the tips for all inoculation times and both varieties. In each variety, the amount of fungal DNA and the content of DON and deoxynivalenol-3-glucoside (DON-3-G) were higher in the center than in the base for all inoculation times. A positive correlation was found between the content of fungal DNA and DON in the centers as well as the bases of both varieties. This study showed that F. graminearum grows downward within infected wheat spikes and that the accumulation of DON is largely confined to the colonized tissue. Moreover, F. graminearum was able to infect wheat kernels and cause contamination with mycotoxins even when inoculated 25 days after anthesis.

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.

Predicting micronutrients of wheat using hyperspectral imaging

Micronutrients are the key factors to evaluate the nutritional quality of wheat. However, measuring micronutrients is time-consuming and expensive. In this study, the potential of hyperspectral imaging for predicting wheat micronutrient content was investigated. The spectral reflectance of wheat kernels and flour was acquired in the visible and near-infrared range (VIS-NIR, 375-1050 nm). Afterwards, wheat micronutrient contents were measured and their associations with the spectra were modeled. Results showed that the models based on the spectral reflectance of wheat kernel achieved good predictions for Ca, Mg, Mo and Zn (r²>0.70). The models based on the spectra reflectance of wheat flour showed good predictive capabilities for Mg, Mo and Zn (r²>0.60). The prediction accuracy was higher for wheat kernels than for the flour. This study showed the feasibility of hyperspectral imaging as a non-invasive, non-destructive tool to predict micronutrients of wheat.

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

The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, one that was analysed for ergosterol and another that was analysed for DON by HPLC. This method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The prediction models presented a RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the EU legal limit for DON.