An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize

Quantitative SERS sensor for mycotoxins with extraction and identification function

The development of new sensors for on-site food toxin monitoring that combine extraction, analytes distinction and detection is important in resource-limited environments. Surface-enhanced Raman scattering (SERS)-based signal readout features fast response and high sensitivity, making it a powerful method for detecting mycotoxins. In this work, a SERS-based assay for the detection of multiple mycotoxins is presented that combines extraction and subsequent detection, achieving an analytically relevant detection limit (∼ 1 ng/mL), which is also tested in corn samples. This sensor consists of a magnetic-core and mycotoxin-absorbing polydopamine-shell, with SERS-active Au nanoparticles on the outer surface. The assay can concentrate multiple mycotoxins, which are identified through multiclass partite least squares analysis based on their SERS spectra. We developed a strategy for the analysis of multiple mycotoxins with minimal sample pretreatment, enabling in situ analytical extraction and subsequent detection, displaying the potential to rapidly identify lethal mycotoxin contamination on site.

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman)

A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.

Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety

Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.

Current Developments of Analytical Methodologies for Aflatoxins' Determination in Food during the Last Decade (2013-2022), with a Particular Focus on Nuts and Nut Products

This review aims to provide a clear overview of the most important analytical development in aflatoxins analysis during the last decade (2013-2022) with a particular focus on nuts and nuts-related products. Aflatoxins (AFs), a group of mycotoxins produced mainly by certain strains of the genus Aspergillus fungi, are known to impose a serious threat to human health. Indeed, AFs are considered carcinogenic to humans, group 1, by the International Agency for Research on Cancer (IARC). Since these toxins can be found in different food commodities, food control organizations worldwide impose maximum levels of AFs for commodities affected by this threat. Thus, they represent a cumbersome issue in terms of quality control, analytical result reliability, and economical losses. It is, therefore, mandatory for food industries to perform analysis on potentially contaminated commodities before the trade. A full perspective of the whole analytical workflow, considering each crucial step during AFs investigation, namely sampling, sample preparation, separation, and detection, will be presented to the reader, focusing on the main challenges related to the topic. A discussion will be primarily held regarding sample preparation methodologies such as partitioning, solid phase extraction (SPE), and immunoaffinity (IA) related methods. This will be followed by an overview of the leading analytical techniques for the detection of aflatoxins, in particular liquid chromatography (LC) coupled to a fluorescence detector (FLD) and/or mass spectrometry (MS). Moreover, the focus on the analytical procedure will not be specific only to traditional methodologies, such as LC, but also to new direct approaches based on imaging and the ability to detect AFs, reducing the need for sample preparation and separative techniques.

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.

Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy

Aflatioxin B₁ (AFB₁) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB₁-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB₁ or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB₁ in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (R_c² = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB₁ infection in the early stages.

Recent developments in multi-omics and breeding strategies for abiotic stress tolerance in maize (Zea mays L.)

High-throughput sequencing technologies (HSTs) have revolutionized crop breeding. The advent of these technologies has enabled the identification of beneficial quantitative trait loci (QTL), genes, and alleles for crop improvement. Climate change have made a significant effect on the global maize yield. To date, the well-known omic approaches such as genomics, transcriptomics, proteomics, and metabolomics are being incorporated in maize breeding studies. These approaches have identified novel biological markers that are being utilized for maize improvement against various abiotic stresses. This review discusses the current information on the morpho-physiological and molecular mechanism of abiotic stress tolerance in maize. The utilization of omics approaches to improve abiotic stress tolerance in maize is highlighted. As compared to single approach, the integration of multi-omics offers a great potential in addressing the challenges of abiotic stresses of maize productivity.

Determination of aflatoxin B1 (AFB1) in maize based on a portable Raman spectroscopy system and multivariate analysis

Aflatoxin B₁ (AFB₁) is the most widely distributed, most toxic, and most harmful, and it is widely present in moldy grains. This study proposes a new method for quantitative and rapid determination of the AFB₁ content in maize based on Raman spectroscopy. The Raman spectra of maize samples with different mildew degrees were collected by a portable laser Raman spectroscopy system. Three different spectral selection methods, which were bootstrapping soft shrinkage (BOSS), variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS), were applied to optimize the characteristic wavelength variables of the pretreated Raman spectra. The support vector machine (SVM) detection models based on different optimized characteristic wavelength variables were established, and the results of each detection model were compared. The results obtained showed that the performance of the SVM models established by optimized features was significantly better than the performance of the SVM model built by full-spectrum data. Among them, the SVM model based on the characteristic wavelength variables optimized by the CARS method had the best performance, and its root mean square error of prediction (RMSEP) was 3.5377 μg∙kg^-1, the determination coefficient of prediction (R_P²) was 0.9715, and the relative prediction deviation (RPD) was 5.8258. The overall results reveal that the rapid quantitative detection of the AFB₁ in maize by Raman spectroscopy has a promising application prospect. In addition, the implementation of the characteristic wavelength optimization of Raman spectra in the model calibration process can effectively improve the detection accuracy of chemometric models.

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models

ABSTRACT

This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models.

HIGHLIGHTS

Rapid and Quantitative Detection of Aflatoxin B1 in Grain by Portable Raman Spectrometer

Many foodstuffs are extremely susceptible to contamination with aflatoxins, in which aflatoxin B₁ is highly toxic and carcinogenic. Therefore, it is crucial to develop a rapid and effective analytical method for detecting and monitoring aflatoxin B₁ in food. Herein, a surface-enhanced Raman spectroscopic (SERS) method combined with QuEChERS (quick, easy, cheap-effective, rugged, safe) sample pretreatment technique was used to detect aflatoxin B₁. Sample preparation was optimized into a one-step extraction method using an Au nanoparticle-based solution (Au sol) as the SERS detection substrate. An affordable portable Raman spectrometer was then used for rapid, label-free, quantitative detection of aflatoxin B₁ levels in foodstuffs. This method showed a good linear log relationship between the Raman signal intensity of aflatoxin B₁ in the 1-1000 µg L^-1 concentration range with a limit of detection of 0.85 µg kg^-1 and a correlation coefficient of 0.9836. Rapid aflatoxin B₁ detection times of ∼10 min for wheat, corn, and protein feed powder samples were also achieved. This method has high sensitivity, strong specificity, excellent stability, is simple to use, economical, and is suitable for on-site detection, with good prospects for practical application in the field of food safety.

Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil

This study presents a method to determine adulteration of olive oil (obtained from Olea europea, i.e. olives) with rapeseed oil (obtained from Brassica napus) or with corn oil (also named maize oil, obtained from Zea mays, i.e. maize) using Raman spectroscopy and a mathematical method based on exponential equation fit. The samples were prepared by mixing olive oil with volume fractions (0-100%) of rapeseed or corn oil. The oils were differentiated spectroscopically using intensity ratio for specific Raman peaks; Raman spectroscopy is able to detect changes within a liquid molecular environment without the need for sample treatment. It was possible to determine rapeseed or corn oil volume fractions added into the olive oil using the method proposed. Thus, the potential of Raman spectroscopy as a technique for determining adulteration of olive oil was corroborated clearly, opening the potential to investigate adulteration of other liquid foods, without any need for sample preparation.

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

Aflatoxins (AFs) are potent carcinogens present in numerous crops. Access to accurate methods for evaluating contamination is a critical factor in aflatoxin risk assessment. Versicolorin A (Ver A), a precursor of aflatoxin B₁ (AFB₁), can be used as an indicator for the presence of AFB₁, even when the AF is not yet detectable. Currently employed Ver A detection methods are expensive, time consuming, and difficult to apply to numerous samples. Herein, Ver A was detected via near-infrared spectroscopy. Both quantitative and two-grade sorting methods were set-up using the extreme gradient boosting algorithm coupled with a support vector machine. This two-tiered method obtained a root-mean-square error of prediction value of 3.57 μg/kg for the quantitative model, and an accuracy rate of 90.32% for the sorting approach. This novel method is rapid, accurate, solvent free, requires no sample pretreatment, and detects Ver A in maize, making it convenient for practical use.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (R_p) of 0.9260 and RMSEP of 87.9132 μg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

Affinity capture of aflatoxin B1 and B2 by aptamer-functionalized magnetic agarose microspheres prior to their determination by HPLC

A novel adsorbent is described for magnetic solid-phase extraction (MSPE) of the aflatoxins AFB₁ and AFB₂ (AFBs). Magnetic agarose microspheres (MAMs) were functionalized with an aptamer to bind the AFBs which then were quantified by HPLC and on-line post-column photochemical derivatization with fluorescence detection. Streptavidin-conjugated MAMs were synthesized first by a highly reproducible strategy. They possess strong magnetism and high surface area. The MAMs were characterized by transmission electron microscopy, scanning electron microscopy, optical microscopy, laser diffraction particle size analyzer, Fourier transform infrared spectrometry, vibrating sample magnetometry and laser scanning confocal microscopy. Then, the AFB-aptamers were immobilized on MAMs through biotin-streptavidin interaction. Finally, the MSPE is performed by suspending the aptamer-modified MAMs in the sample. They are then collected by an external magnetic field and the AFBs are eluted with methanol/buffer (20:80). Several parameters affecting the coupling, capturing and eluting efficiency were optimized. Under the optimized conditions, the method is fast, has good linearity, high selectivity, and sensitivity. The LODs are 25 pg·mL^-1 for AFB₁ and 10 pg·mL^-1 for AFB₂. The binding capacity is 350 ± 8 ng·g^-1 for AFB₁ and 384 ± 8 ng·g^-1 for AFB₂, and the precision of the assay is <8%. The method was successfully applied to the analysis of AFBs in spiked maize samples. Graphical abstract Schematic of novel aptamer functionalized magnetic agarose microspheres (Apt-MAM) as magnetic adsorbents for simultaneous and specific affinity capture of aflatoxins B₁ and B₂ (AFBs).

Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques

The applicability of near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was explored in this study to develop rapid, low-cost and non-destructive spectroscopic methods for classification and quantification of aflatoxins in brown rice. A total of 132 brown rice samples within the aflatoxin concentration range of 0-2435.8 μg/kg were prepared by artificially inoculated with A. flavus and A. parasiticus strains of fungus. For the classification of samples at varying levels of aflatoxin B₁, the linear discriminant analysis model obtained correct classification rate of 96.9 and 90.6% for NIR and MIR spectroscopy, respectively. For the simultaneous determination of aflatoxins B₁, B₂, G₁, G₂ and the total aflatoxins, partial least squares regression also showed good predictive accuracy for both NIR (r_v  = 0.936-0.973, RPD = 2.5-4.0) and MIR spectroscopy (r_v  = 0.922-0.970, RPD = 2.5-4.0). The overall results indicated that the two spectroscopic techniques offered the feasibility to be used as alternative tools for rapid detection of various aflatoxin contaminations in grain.

Developments in mycotoxin analysis: an update for 2015-2016

This review summarises developments in the determination of mycotoxins over a period between mid-2015 and mid-2016. Analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone are covered in individual sections. Advances in proper sampling strategies are discussed in a dedicated section, as are methods used to analyse botanicals and spices and newly developed liquid chromatography mass spectrometry based multi-mycotoxin methods. This critical review aims to briefly discuss the most important recent developments and trends in mycotoxin determination as well as to address limitations of presented methodologies.

A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits

The rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B₁ in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1750 µg kg^-1 and, for the first time, 77% of 92 peanut samples at 8 µg kg^-1 of aflatoxin B₁. A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 µg kg^-1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.