Near-infrared reflectance spectroscopy-based fast versicolorin A detection in maize for early aflatoxin warning and safety sorting

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Reducing soil and leaf shadow interference in UAV imagery for cotton nitrogen monitoring

Introduction

Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.

Methods

In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R 2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.

Results

(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R 2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

Aflatoxins in the rice production chain: A review on prevalence, detection, and decontamination strategies

Rice (Oryza sativa L.) is one of the most consumed cereals that along with several important nutritional constituents typically provide more than 21% of the caloric requirements of human beings. Aflatoxins (AFs) are toxic secondary metabolites of several Aspergillus species that are prevalent in cereals, including rice. This review provides a comprehensive overview on production factors, prevalence, regulations, detection methods, and decontamination strategies for AFs in the rice production chain. The prevalence of AFs in rice is more prominent in African and Asian than in European countries. Developed nations have more stringent regulations for AFs in rice than in the developing world. The contamination level of AFs in the rice varied at different stages of rice production chain and is affected by production practices, environmental conditions comprising temperature, humidity, moisture, and water activity as well as milling operations such as de-husking, parboiling, and polishing. A range of methods including chromatographic techniques, immunochemical methods, and spectrophotometric methods have been developed, and used for monitoring AFs in rice. Chromatographic methods are the most used methods of AFs detection followed by immunochemical techniques. AFs decontamination strategies adopted worldwide involve various physical, chemical, and biological strategies, and even using plant materials. In conclusion, adopting good agricultural practices, implementing efficient AFs detection methods, and developing innovative aflatoxin decontamination strategies are imperative to ensure the safety and quality of rice for consumers.

Ionic liquid-functionalized mesoporous multipod silica for simultaneously effective extraction of aflatoxin B1 and its two precursors from grain

BACKGROUND

Aflatoxin B1 (AFB1) and its precursors contaminate food and agricultural products, posing a significant risk to food safety and human health, but simultaneous and effective extraction and determination of AFB1 and its precursors with varied structures is still a challenging task.

RESULTS

In this study, a bisimidazolium-type ionic liquid functionalized mesoporous multipod silica (SiO2@mPMO-IL(im)2) was fabricated to extract AFB1 and its two precursors, i.e., averantin and sterigmatocystin. The SiO2@mPMO-IL(im)2 could simultaneously extract three targets with varied structures based on the multipods, mesopores, and multifunctional groups. The density functional theory calculations further verified the multiple interactions between SiO2@mPMO-IL(im)2 and targets. The fabricated SiO2@mPMO-IL(im)2 could effectively extract and determine three targets in grains by combing with dispersive solid-phase extraction and high-performance liquid chromatography. Good linearity (r2 > 0.9978), low LODs (0.9-1.5 μg kg-1) and LOQs (3.0-4.5 μg kg-1), satisfactory spiked recoveries (92.5%-106.8%) and high precisions (RSD<6.4%) were observed.

SIGNIFICANCE AND NOVELTY

This work demonstrates the feasibility of SiO2@mPMO-IL(im)2 for simultaneous and effective extraction of toxins with varied structures and provides a promising sample preparation for the analysis of AFB1 and its precursors in grain samples.

Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning

Bakanae disease is a common seed-borne disease of rice. Rapid and accurate detection of bakanae pathogens carried by rice seeds is essential for the health of rice germplasm resources and the safety of rice production. This study aims to propose a general framework for species identification of major bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning. Seven varieties of rice seeds and four kinds of bakanae pathogens were analyzed. One-dimensional deep convolution neural networks (DCNNs) were first constructed using complete datasets. They achieved accuracies larger than 96.5% on the testing sets of most datasets, exceeding the conventional SVM and PLS-DA models. Then the developed DCNNs were transferred to detect other complete training sets. Most of the deep transferred models achieved comparable or even better performance than the original DCNNs. Two smaller target training sets were further constructed by randomly selecting spectra from the complete training sets. As the size of the target training sets reduced, the accuracies of all models on the corresponding testing sets also decreased gradually. Visualization analysis were conducted using the t-distribution stochastic neighbor embedding (t-SNE) algorithm and a proposed gradient-weighted activation wavelength (Grad-AW) method. They all showed that deep transfer learning could utilize the representation patterns in the source datasets to improve the target tasks. The overall results indicated that the bakanae pathogens were all identified accurately under our proposed framework. Hyperspectral imaging combined with deep transfer learning provided a new idea for the quality detection of large-scale seeds in modern seed industry.

A Novel and Highly Efficient Microextraction Method for the Determination of Aflatoxin Precursor Averantin in Fatty Grain Samples

Averantin (AVN) is an important aflatoxin biosynthetic precursor and has been listed in the screening range of mycotoxins. Herein, a novel ionic liquid-based one-, two-, and three-phase transition microextraction (IL-OTTPTME) method was combined with high-performance liquid chromatography for the extraction and determination of AVN in fatty grain samples. The formation of a homogeneous solution and three-phase system during the IL-OTTPTME process allowed both efficient extraction and coextracted lipid cleanup. Density functional theory calculations and distribution coefficient determination results demonstrated that AVN extraction by IL mainly occurred through hydrogen-bond and π-π interactions. Under optimized conditions, the LOD and LOQ of the proposed method were 0.5 and 1.5 ng/g, respectively. Finally, the method was used to determine AVN in several grains with different fat contents, achieving satisfactory relative recoveries (86.0-107.8%) and RSDs (1.2-6.2%, n = 3).

Optical nanosensors based on noble metal nanoclusters for detecting food contaminants: A review

Food contaminants present a significant threat to public health. In response to escalating global concerns regarding food safety, there is a growing demand for straightforward, rapid, and sensitive detection technologies. Noble metal nanoclusters (NMNCs) have garnered considerable attention due to their superior attributes compared to other optical materials. These attributes include high catalytic activity, excellent biocompatibility, and outstanding photoluminescence properties. These features render NMNCs promising candidates for crafting nanosensors for food contaminant detection, offering the potential for the development of uncomplicated, swift, sensitive, user-friendly, and cost-effective detection approaches. This review investigates optical nanosensors based on NMNCs, including the synthesis methodologies of NMNCs, sensing strategies, and their applications in detecting food contaminants. Furthermore, it involves a comparative assessment of the applications of NMNCs in optical sensing and their performance. Ultimately, this paper imparts fresh perspectives on the forthcoming challenges. Hitherto, optical (particularly fluorescent) nanosensors founded on NMNCs have demonstrated exceptional sensing capabilities in the realm of food contaminant detection. To enhance sensing performance, future research should prioritize atomically precise NMNCs synthesis, augmentation of catalytic activity and optical properties, development of high-throughput and multimode sensing, integration of NMNCs with microfluidic devices, and the optimization of NMNCs storage, shelf life, and transportation conditions.

Fe3O4/ZIFs-based magnetic solid-phase extraction for the effective extraction of two precursors with diverse structures in aflatoxin B1 biosynthetic pathway

The aflatoxin B₁ (AFB₁) early warning technique based on precursors is an effective strategy for the prevention of AFB₁ contamination risk. The determination of precursors is imperative to ensure the efficiency of the early warning technique. Herein, a controllable magnetic adsorbent Fe₃O₄/ZIFs was first introduced for the effective extraction and determination of averantin (AVN) and sterigmatocystin (ST) precursors in cereal by combining magnetic solid-phase extraction (MSPE) and high-performance liquid chromatography (HPLC). Benefiting from the abundant adsorption sites and multifunctional groups matching the analytes, Fe₃O₄/ZIFs effectively and simultaneously extracted AVN and ST with great differences in polarity and structure via multiple interactions. AVN was extracted by Fe₃O₄/ZIFs mainly through π-π and hydrophobic interactions, while ST was extracted predominantly by electrostatic interactions and surface complexation. The limits of detection were 0.08 μg kg^-1 (AVN) and 0.36 μg kg^-1 (ST). The developed method exhibited satisfactory spiked recoveries (79.1%-105.4%) in the determination of AVN and ST in rice. This work provides a novel analytical strategy for further studying AFB₁ early warning technique and the formation and transformation of aflatoxins.

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.

Quantitative detection of zearalenone in wheat grains based on near-infrared spectroscopy

Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (R_P) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 μg·kg^-1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains.

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.

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other's advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

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.

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.