Hyperspectral imaging for the classification of individual cereal kernels according to fungal and mycotoxins contamination: A review

Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception-ResNet Model

Aflatoxin B1, a toxic carcinogen frequently contaminating almonds, nuts, and food products, poses significant health risks. Therefore, a rapid and non-destructive detection method is crucial to detect aflatoxin B1-contaminated almonds to ensure food safety. This study introduces a novel deep learning approach utilizing 3D Inception-ResNet architecture with fine-tuning to classify aflatoxin B1-contaminated almonds using hyperspectral images. The proposed model achieved higher classification accuracy than traditional methods, such as support vector machine (SVM), random forest (RF), quadratic discriminant analysis (QDA), and decision tree (DT), for classifying aflatoxin B1 contaminated almonds. A feature selection algorithm was employed to enhance processing efficiency and reduce spectral dimensionality while maintaining high classification accuracy. Experimental results demonstrate that the proposed 3D Inception-ResNet (Lightweight) model achieves superior classification performance with a 90.81% validation accuracy, an F1-score of 0.899, and an area under the curve value of 0.964, outperforming traditional machine learning approaches. The Lightweight 3D Inception-ResNet model, with 381 layers, offers a computationally efficient alternative suitable for real-time industrial applications. These research findings highlight the potential of hyperspectral imaging combined with deep learning for aflatoxin B1 detection in almonds with higher accuracy. This approach supports the development of real-time automated screening systems for food safety, reducing contamination-related risks in almonds.

Rapid qualitative and quantitative detection for adulteration of Atractylodis Rhizoma using hyperspectral imaging combined with chemometric methods

In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R2) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models' RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR.

Quality control of cooked rice: Exploring physicochemical changes of the intrinsic component in production

Sensory deterioration exists in marketed cooked rice. The migration and interaction of intrinsic components occur under multiple conditions in each industrial production process and cause relevant physicochemical changes in cooked rice. This review aims to establish a scientific knowledge system of intrinsic component transition and migration in cooked rice kernel during processing to solve qualitative deficiencies in cooked rice products. The main influencing factors of intrinsic component structural change in cooked rice and the quality control points that should be considered are summarized. Further studies are needed to establish proper evaluation standards for cooked rice products to meet the growing consumer demands.

Detection of aflatoxin B1 level and revelation of its dynamic accumulation process using visible/near-infrared hyperspectral and microscopic imaging

Understanding and controlling the dynamic process of aflatoxin B1 (AFB1) accumulation by Aspergillus flavus (A. flavus) remains challenging. In this study, the A. flavus development and AFB1 accumulation were investigated using visible/near-infrared (Vis/NIR) hyperspectral imaging (HSI) on culture media, Potato Dextrose Agar (PDA), PDA + l-glutamine (Gln), and PDA + rapamycin (RAPA). In addition, the levels of AFB1 in various heterogeneous regions of colonies were measured and their microscopic morphology was characterized. In the temporal and spatial domains, fungal colonies exhibited a concentric circular response pattern. A continuous increase in AFB1 content was observed in the PDA and PDA + Gln groups as culture time increased. The growth of A. flavus and aflatoxin accumulation were promoted by adding Gln to PDA. However, adding RAPA inhibited the development of fungi and the production of AFB1. The distribution of AFB1 across the fungal colony was uneven, and this heterogeneity was associated with the aging and autolysis of the hyphae. Principal component analysis showed that spectral bands of 480, 623, 674, 726 nm were related to the color changes of hyphae and spores during colony growth; however, wavelengths of 840, 882, 867, 972 nm were key bands related to changes in nutritional composition. Multiple preprocessing techniques and modeling methods employed to construct regression models for predicting AFB1 contents showed that the first-derivative and partial least squares regression (PLSR) provided the best results. A visualization map of AFB1 levels established using the optimal model showed a spatial pattern similar to the measurement results. This study highlights the application potential of Vis/NIR HSI for monitoring A. flavus growth and AFB1 content.

Mapping Variability of Mycotoxins in Individual Oat Kernels from Batch Samples: Implications for Sampling and Food Safety

Oats are susceptible to contamination by Fusarium mycotoxins, including deoxynivalenol (DON), zearalenone (ZEN), and T-2/HT-2 toxins, posing food safety risks. This study analyses the variation in levels of 14 mycotoxins in 200 individual oat kernels from two DON-contaminated batch samples (mean = 3498 µg/kg) using LC-MS/MS. The samples also contained deoxynivalenol-3-glucoside (DON-3G), 3-acetyldeoxynivalenol (3-ADON), 15-acetyldeoxynivalenol (15-ADON), and ZEN. Contamination levels varied notably among individual kernels, with DON detected in 70% of them, followed by DON-3G (24.5%) and 3-ADON (20.5%). Importantly, 8% of kernels exceeded the EU legal limit for DON (1750 µg/kg), and some occasionally surpassed limits for ZEN and T-2/HT-2. Correlation analyses revealed strong associations between DON and its derivatives but weaker correlations with other toxins. Mycotoxin ratios varied widely, indicating that although they often co-occur, their concentrations differ between kernels. Contamination did not significantly impact kernel weight, though a slight trend toward lower weights in contaminated kernels was noted. Additionally, sampling statistics showed that as the percentage of selected kernels increased, the probability of batch sample rejection for DON contamination rose significantly. The study highlights the heterogeneity of mycotoxin contamination in oat batches, emphasising the importance of accurate detection and regulatory compliance to ensure safer oat-based products.

Identifying defects and varieties of Malting Barley Kernels

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

Direct and indirect technical guide for the early detection and management of fungal plant diseases

Fungal plant diseases are a major threat to plants and vegetation worldwide. Recent technological advancements in biotechnological tools and techniques have made it possible to identify and manage fungal plant diseases at an early stage. These techniques include direct methods, such as ELISA, immunofluorescence, PCR, flow cytometry, and in-situ hybridization, as well as indirect methods, such as fluorescence imaging, hyperspectral techniques, thermography, biosensors, nanotechnology, and nano-enthused biosensors. Early detection of fungal plant diseases can help to prevent major losses to plantations. This is because early detection allows for the implementation of control measures, such as the use of fungicides or resistant varieties. Early detection can also help to minimize the spread of the disease to other plants. The techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases. These techniques can help to ensure food security and protect our valuable plant resources.

Prediction of Deoxynivalenol Contamination in Wheat via Infrared Attenuated Total Reflection Spectroscopy and Multivariate Data Analysis

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 μg/kg but also provided valuable insights into the relevant parameters shaping these models.

Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees

Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014−2015 and 2017−2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.