Detection of aflatoxin M1 in milk using spectroscopy and multivariate analyses

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy

The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.

Interaction study of aflatoxin M1 with milk proteins using ATR-FTIR

Aflatoxin M1 (AFM1) is a metabolite of carcinogenic aflatoxin B1 (AFB1) and appears in milk of dairy animals on ingestion of feed contaminated with AFB1. It has been reported to have affinity towards milk proteins, the exact mechanism of which is still unknown. In the present study, ATR-FTIR coupled with chemometrics is utilized to understand AFM1 interaction with milk proteins. The second order derivative spectra of the spectral window 1700-1600 cm-1 confirms the affinity of AFM1 towards milk proteins. The results of principal component analysis suggested that spectral window of 1700-1600 cm-1 is informative and provides an indication of the conformational changes brought by AFM1 in the secondary structure of milk proteins.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-022-05587-x.

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.

Urinary Aflatoxin M1 Concentration and Its Determinants in School-Age Children in Southern Ethiopia

Aflatoxins are mycotoxins that can contaminate grains, legumes, and oil seeds. These toxic compounds are an especially serious problem in tropical and sub-tropical climates. The objective of this study was to raise awareness of aflatoxin exposure among primary school children in Shebedino woreda, southern Ethiopia, by measuring urinary aflatoxin M1 (AFM1). The study employed a cross-sectional design and systematic random sampling of children from eight schools in the district. The mean ± SD age of the children was 9.0 ± 1.8 years. Most (84.6%) households were food insecure with 17.9% severely food insecure. Urinary AFM1 was detected in more than 93% of the children. The median [IQR] concentration of AFM1/Creat was 480 [203, 1085] pg/mg. Based on a multiple regression analysis: DDS, consumption of haricot bean or milk, source of drinking water, maternal education, and household food insecurity access scale scores were significantly associated with urinary AFM1/Creat. In conclusion, a high prevalence of urinary AFM1 was observed in this study. However, the relation between AFM1 and dietary intake was analyzed based on self-reported dietary data; hence, all of the staple foods as well as animal feeds in the study area should be assessed for aflatoxin contamination.

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

Impact of Germination on the Microstructural and Physicochemical Properties of Different Legume Types

The microstructural and physicochemical compositions of bean (Phaseolus vulgaris), lentil (Lens culinaris Merr.), soybean (Glycine max L.), chickpea (Cicer aretinium L.) and lupine (Lupinus albus) were investigated over 2 and 4 days of germination. Different changes were noticed during microscopic observations (Stereo Microscope, SEM) of the legume seeds subjected to germination, mostly related to the breakages of the seed structure. The germination caused the increase in protein content for bean, lentil, and chickpea and of ash content for lentil, soybean and chickpea. Germination increased the availability of sodium, magnesium, iron, zinc and also the acidity for all legume types. The content of fat decreased for lentil, chickpea, and lupine, whereas the content of carbohydrates and pH decreased for all legume types during the four-day germination period. Fourier transform infrared spectroscopic (FT-IR) spectra show that the compositions of germinated seeds were different from the control and varied depending on the type of legume. The multivariate analysis of the data shows close associations between chickpea, lentil, and bean and between lupine and soybean samples during the germination process. Significant negative correlations were obtained between carbohydrate contents and protein, fat and ash at the 0.01 level.

A Flexible PI/Si/SiO2 Piezoresistive Microcantilever for Trace-Level Detection of Aflatoxin B1

In this paper, a polyimide (PI)/Si/SiO2-based piezoresistive microcantilever biosensor was developed to achieve a trace level detection for aflatoxin B1. To take advantage of both the high piezoresistance coefficient of single-crystal silicon and the small spring constant of PI, the flexible piezoresistive microcantilever was designed using the buried oxide (BOX) layer of a silicon-on-insulator (SOI) wafer as a bottom passivation layer, the topmost single-crystal silicon layer as a piezoresistor layer, and a thin PI film as a top passivation layer. To obtain higher sensitivity and output voltage stability, four identical piezoresistors, two of which were located in the substrate and two integrated in the microcantilevers, were composed of a quarter-bridge configuration wheatstone bridge. The fabricated PI/Si/SiO2 microcantilever showed good mechanical properties with a spring constant of 21.31 nN/μm and a deflection sensitivity of 3.54 × 10-7 nm-1. The microcantilever biosensor also showed a stable voltage output in the Phosphate Buffered Saline (PBS) buffer with a fluctuation less than 1 μV @ 3 V. By functionalizing anti-aflatoxin B1 on the sensing piezoresistive microcantilever with a biotin avidin system (BAS), a linear aflatoxin B1 detection concentration resulting from 1 ng/mL to 100 ng/mL was obtained, and the toxic molecule detection also showed good specificity. The experimental results indicate that the PI/Si/SiO2 flexible piezoresistive microcantilever biosensor has excellent abilities in trace-level and specific detections of aflatoxin B1 and other biomolecules.

Rapid screening on aflatoxins' presence in Pistachia vera nuts using diffuse reflectance infrared Fourier transform spectroscopy and chemometrics

Aflatoxin contamination in pistachios has been analyzed in this work, using Diffuse Reflectance Infrared Spectroscopy (DRIFTS) with chemometrics. Forty-nine Greek pistachio samples of different aflatoxin concentrations were classified into aflatoxin and non-aflatoxin groups using the 3035-2821, 1770-1721, 1570-1481 and 1260-1061 cm^-1 spectral regions in Kubelka-Munk conversion and first derivative form. A chemometric model was developed using twenty-eight samples as calibration, 11 as validation and 10 as test set. The discrimination analysis separated correctly the 100% of the calibration and the validation set and the 80% of the test set. The proposed chemometric model is simple, rapid, economical and environmentally friendly since it does not require chemical pre-treatment of the samples. It is expected that the present method may be proved useful in food industry and the inspection authorities as a rapid decision-making tool to detect batches that must be rejected and enhance consumers' protection from aflatoxin contaminated pistachios.

Fabrication of microfluidic device for Aflatoxin M1 detection in milk samples with specific aptamers

This study describes the colorimetric detection of aflatoxin M1 (Afl M1) in milk samples using a microfluidic paper-based analytical device (µPAD). Fabrication of µPADs was done using a simple and quick approach. Each μPAD contained a detection zone and a sample zone interconnected by microchannels. The colorimetric assay was developed using unmodified AuNPs as a probe and 21-mer aptamer as a recognition molecule. The free aptamers were adsorbed onto the surface of AuNPs in absence of Afl M1, even at high salt concentrations. The salt induced aggregation of specific aptamers occurred in presence of Afl M1. Under optimum conditions, the analytical linear range was found to be 1 µM to 1 pM with limit of detection 3 pM and 10 nM in standard buffer and spiked milk samples respectively. The proposed aptamer based colorimetric assay was repeatable, quick, selective, and can be used for on-site detection of other toxins in milk and meat samples.

Detection Methods for Aflatoxin M1 in Dairy Products

Mycotoxins are toxic compounds produced mainly by fungi of the genera Aspergillus, Fusarium and Penicillium. In the food chain, the original mycotoxin may be transformed in other toxic compounds, reaching the consumer. A good example is the occurrence of aflatoxin M1 (AFM1) in dairy products, which is due to the presence of aflatoxin B1 (AFB1) in the animal feed. Thus, milk-based foods, such as cheese and yogurts, may be contaminated with this toxin, which, although less toxic than AFB1, also exhibits hepatotoxic and carcinogenic effects and is relatively stable during pasteurization, storage and processing. For this reason, the establishment of allowed maximum limits in dairy products and the development of methodologies for its detection and quantification are of extreme importance. There are several methods for the detection of AFM1 in dairy products. Usually, the analytical procedures go through the following stages: sampling, extraction, clean-up, determination and quantification. For the extraction stage, the use of organic solvents (as acetonitrile and methanol) is still the most common, but recent advances include the use of the Quick, Easy, Cheap, Effective, Rugged, and Safe method (QuEChERS) and proteolytic enzymes, which have been demonstrated to be good alternatives. For the clean-up stage, the high selectivity of immunoaffinity columns is still a good option, but alternative and cheaper techniques are becoming more competitive. Regarding quantification of the toxin, screening strategies include the use of the enzyme-linked immunosorbent assay (ELISA) to select presumptive positive samples from a wider range of samples, and more reliable methods-high performance liquid chromatography with fluorescence detection or mass spectroscopy-for the separation, identification and quantification of the toxin.

Rapid quality evaluation of Shenzhiling oral liquid by attenuated total reflectance-infrared spectroscopy with chemometrics

In this study, a useful method of Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with chemometrics was proposed for rapid quantification of two-indicator components as well as discrimination of Shenzhiling oral liquid in shelf life and over shelf life. Fifteen batches of samples were employed to construct quantitative and discriminant models. Two ingredients (paeoniflorin and cinnamic acid) for quality control were modeled by partial least square regression (PLSR). The discrimination of samples between in shelf life and over shelf life was carried out by using discriminant analysis (DA). The samples were divided into calibration set and validation set according to batches. Different data pre-processing algorithms such as standard-normal-variate (SNV), multiplicative scatter correction (MSC), Savitzkye-Golay (SG) smoothing with derivative methods were applied to reduce the influence of systematic disturbances. Variable selection methods including correlation coefficient (CC), competitive adaptive reweighted sampling (CARS) and interval partial least squares regression (iPLS) were all performed for optimizing the PLSR models and DA model. The results demonstrated that ATR-FTIR combined with chemometrics could be a rapid, convenient and nondestructive approach to evaluate the quality of Shenziling oral liquid.

Comparison of transmission FTIR and ATR spectra for discrimination between beef and chicken meat and quantification of chicken in beef meat mixture using ATR-FTIR combined with chemometrics

Detecting meat adulteration for quality control and accurate labeling is important and needs convenient analytical methods. This study aimed to investigate and compare the application of the transmission and ATR approaches of FTIR followed by principal component analysis (PCA) to not only discriminate between chicken and beef meat but also quantizing chicken portion of mixtures. Two different approaches are presented; spectra preprocessing with focus on wavenumber region of 1700-1071 cm^-1, and no preprocessed where PCA was applied on the whole spectra range of mid-FTIR. The results suggest that applying PCA on specified preprocessed spectra could detect hidden relationships between variables in chicken and beef in both approaches. PCA successfully clustered these kinds of meats when applied on transmission mode spectra without any preprocessing treatment, while applying it on ATR mode's raw spectra failed to cluster them. Additionally, the preprocessed ATR-FTIR spectrum was used to prepare regression models by Partial Least Square Regression (PLS-R) and artificial neural networks (ANN) for predicting presence and percentage of chicken meat in the beef meat mixture. The results demonstrated the superiority of ANN over PLS-R in this assessment with an R2 of 0.999.

A simple aptamer-based fluorescent aflatoxin B1 sensor using humic acid as quencher

This work described a fluorometric and aptamer-based assay for aflatoxin B1 (AFB1). Aptamer-modified carbon dots (DNA-CDs) were first synthesized as fluorescence probes, then reacted with humic acid (HAs) which acted as quencher of the blue fluorescence of the CDs. It was found that HAs can readily adsorb ssDNA aptamers due to the presence of a rich surface chemistry (quinoidal units, aromatic rings and sugar moieties). This resulted in quenching of the fluorescence of the CDs (with excitation/emission peaks at 360/450 nm), probably due to π interactions. If the nanoprobe was reacted with AFB1, the DNA-CDs detached from the HAs and fluorescence was restored. Under optimized experimental conditions, the assay had a linear response in the 0.1-0.8 ng mL^-1 AFB1 concentration range, with a low limit of detection of 70 pg mL^-1.

Development of a chemiluminescent aptasensor for ultrasensitive and selective detection of aflatoxin B1 in peanut and milk

More and more attention about food safety leads to a research hotspot to develop new detection methods for food contaminant. To address the problems of serious interference and low sensitivity, a chemiluminescent aptasensor for the detection of aflatoxin B1(AFB1) in food was developed in this paper. It is based on horseradish peroxidase (HRP) catalyze the luminol chemiluminescence reaction. The hybridization chain reaction (HCR) signal amplification strategy has been used to improve the detection sensitivity. Magnetic separation could further reduce background signal obviously at the same time. AFB1 as a model of analyte to test the capability of our developed assay system. Under the optimal experimental conditions, CL intensity showed a good linear correlation with the concentrations of AFB1 ranging from 0.5 to 40 ng mL^-1. The limit of detection was estimated 0.2 ng mL^-1 based on 3 times of the signal-to-noise ratio which is lower than those of the previously reported sensors. It could be used to detect AFB1 content in real samples, such as peanuts and milk which were purchased in local supermarket. The results proved that the sensing system has good anti-interference and selectivity. In all, it has potential for practical application in food safety field.