Image-Based Screening for the Identification of Bright Greenish Yellow Fluorescence on Pistachio Nuts and Cashews

Aflatoxin detection technologies: recent advances and future prospects

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

A Low-Cost, Portable Device for Detecting and Sorting Aflatoxin-Contaminated Maize Kernels

Aflatoxin contamination of maize is a major food safety issue worldwide. The problem is of special significance in African countries because maize is a staple food. This manuscript describes a low-cost, portable, non-invasive device for detecting and sorting aflatoxin-contaminated maize kernels. We developed a prototype employing a modified, normalized difference fluorescence index (NDFI) detection method to identify potentially aflatoxin-contaminated maize kernels. Once identified, these contaminated kernels can be manually removed by the user. The device consists of a fluorescence excitation light source, a tablet for image acquisition, and detection/visualization software. Two experiments using maize kernels artificially infected with toxigenic Aspergillus flavus were implemented to evaluate the performance and efficiency of the device. The first experiment utilized highly contaminated kernels (71.18 ppb), while mildly contaminated kernels (1.22 ppb) were used for the second experiment. Evidently, the combined approach of detection and sorting was effective in reducing aflatoxin levels in maize kernels. With a maize rejection rate of 1.02% and 1.34% in the two experiments, aflatoxin reduction was achieved at 99.3% and 40.7%, respectively. This study demonstrated the potential of using this low-cost and non-invasive fluorescence detection technology, followed by manual sorting, to significantly reduce aflatoxin levels in maize samples. This technology would be beneficial to village farmers and consumers in developing countries by enabling safer foods that are free of potentially lethal levels of aflatoxins.

Fast location and segmentation of high-throughput damaged soybean seeds with invertible neural networks

BACKGROUND

Fast identification of damaged soybean seeds has undeniable importance in seed sorting and food quality. Mechanical vibration is generally used in soybean seed sorting, but this can seriously damage soybean seeds. The convolutional neural network (CNN) is considered an effective method for location and segmentation tasks. However, a CNN requires a large amount of ground truth data and has high computational cost.

RESULTS

First, we propose a self-supervision manner to automatically generate ground truths, which can theoretically create an almost unlimited number of labeled images. Second, instead of using popular CNNs, a novel invertible convolution (involution)-enabled scheme is proposed by using the bottleneck block of the residual networks. Third, a feature selection feature pyramid network (FS-FPN) based on involution is designed, which selects features more flexibly and adaptively. We further merge involution-based backbones and FS-FPN into a unified network, achieving an end-to-end seed location and segmentation model; the best mean average precision of location and segmentation achieved was 85.1% and 81% respectively.

CONCLUSION

The experimental results demonstrate that the proposed method greatly improves the performance of the baseline network with faster speed and fewer parameters, enabling it to detect soybean seeds more effectively. © 2022 Society of Chemical Industry.

Analysis and classification of peanuts with fungal diseases based on real-time spectral processing

The study presents an approach to the analysis and classification of peanuts performed in order to detect kernels with fungi diseases, i.e. kernels prone to contamination with mycotoxigenic Aspergillus flavus (Aspergillus parasiticus). The aim of this study was to evaluate the effectiveness of luminescent spectroscopy with a violet laser (405 nm wavelength) as the excitation source of the fluorescence when applied for real-time detection of mould in peanuts performed by means of multispectral processing based on machine learning methods. We suggest a laboratory unit used to form, register, and process the luminescence spectra of peanuts in visible and near-infrared wavelength ranges in the real-time mode. The study demonstrated that contaminated peanuts have increased luminous intensity and show a redshift in the fluorescence peaks of the contaminated samples as compared to the pure ones. The difference in the fluorescence spectra of pure and contaminated kernels is compatible with the results obtained when traditional UV-light sources are used (365 nm). To classify peanuts by their spectral characteristics, neural network algorithms were used combined with dimensionality reduction methods. The paper presents the probabilities of incorrect recognition of the peanuts' type depending on the number of relevant secondary features determined when reducing the dimensionality of the initial data. When 10 spectral components were used, the error ratios were 0.7% or 0.3% depending on the method of reducing the dimensionality of the initial data.

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

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

Application of multiplexing fiber optic laser induced fluorescence spectroscopy for detection of aflatoxin B1 contaminated pistachio kernels

To explore the effect of signal acquisition way on screening ability, the multiplexing fiber optic laser induced fluorescence spectroscopy (LIFS) system with one-, two- and three-probe, were employed respectively to detect artificially aflatoxin B₁ (AFB₁, 5, 10, 20, 30, and 50 ppb) contaminated 300 pistachio kernels in this study. Compared to one- and two-probe modes, highest accuracy (≥97.0%) by support vector machine (SVM) employing 390-660 nm were obtained using three-probe, which also showed the most attractive precision (root mean square error of prediction (RMSEP) < 4.5 ppb) for AFB₁ by stepwise multiple linear regression (SMLR) using 174-1100 nm. These suggested that the effective collection of spatial information could improve the performance of model, and the three-probe LIFS had a preliminary feasibility for discriminating pistachios contaminated with low concentration of AFB₁. Further study on classifying naturally contaminated samples is needed to validate the applicability of this system.

Innovative technologies to manage aflatoxins in foods and feeds and the profitability of application - A review

Aflatoxins are mainly produced by certain strains of Aspergillus flavus, which are found in diverse agricultural crops. In many lower-income countries, aflatoxins pose serious public health issues since the occurrence of these toxins can be considerably common and even extreme. Aflatoxins can negatively affect health of livestock and poultry due to contaminated feeds. Additionally, they significantly limit the development of international trade as a result of strict regulation in high-value markets. Due to their high stability, aflatoxins are not only a problem during cropping, but also during storage, transport, processing, and handling steps. Consequently, innovative evidence-based technologies are urgently required to minimize aflatoxin exposure. Thus far, biological control has been developed as the most innovative potential technology of controlling aflatoxin contamination in crops, which uses competitive exclusion of toxigenic strains by non-toxigenic ones. This technology is commercially applied in groundnuts maize, cottonseed, and pistachios during pre-harvest stages. Some other effective technologies such as irradiation, ozone fumigation, chemical and biological control agents, and improved packaging materials can also minimize post-harvest aflatoxins contamination in agricultural products. However, integrated adoption of these pre- and post-harvest technologies is still required for sustainable solutions to reduce aflatoxins contamination, which enhances food security, alleviates malnutrition, and strengthens economic sustainability.

A recent review of non-biological remediation of aflatoxin-contaminated crops

Aflatoxins are highly toxic, mutagenic, teratogenic and carcinogenic compounds produced predominantly as secondary metabolites by certain species of fungi belonging to the Aspergillus genus. Owing to the significant health risks and economic impacts associated with the presence of aflatoxins in agricultural commodities, a considerable amount of research has been directed at finding methods to prevent toxicity. This review compiles the recent literature of methods for the detoxification and management of aflatoxin in post-harvest agricultural crops using non-biological remediation.