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

Qualitative and quantitative analysis of mineral oil pollution in peanut oil by Fourier transform near-infrared spectroscopy

Emerging contaminants pose a potential threat to the safety of edible oils. This study combined Fourier Transform Near-Infrared (FT-NIR) spectroscopy with chemometrics for the qualitative and quantitative analysis of five contaminants in peanut oil. The results show that the Partial Least Squares Discriminant Analysis (PLS-DA) classifier effectively differentiates between normal and contaminated samples with a classification accuracy of 100 %. In specific contaminant identification, PLS-DA achieved 100 % accuracy for diesel, white mineral oil, and lubricating oil, and 97.04 % for kerosene and engine oil. Quantitative results revealed that Support Vector Regression (SVR) exhibited high precision in predicting diesel (RP = 0.9852), white mineral oil (RP = 0.9908), and lubricating oil (RP = 0.9929), while Partial Least Squares Regression (PLSR) demonstrated good predictive ability for kerosene (RP = 0.9335) and engine oil (RP = 0.9270). Therefore, NIR spectroscopy can be an effective tool for monitoring the safety of edible oils.

Determination of aflatoxin B1 in wheat using Raman spectroscopy combined with chemometrics

Aflatoxin B1 (AFB1) is carcinogenic and highly susceptible to production in wheat. In this study, the quantitative detection of contaminant AFB1 in wheat was investigated by Raman spectroscopy combined with chemometric method realization. Firstly, Savitzky-Golay smoothing (SG) and baseline calibration methods were used to perform the necessary preprocessing of the collected raw Raman spectra. Then, three variable optimization methods, i.e., competitive adaptive reweighted sampling (CARS), iteratively variable subset optimization (IVSO), and bootstrap soft shrinkage (BOSS), were applied to the preprocessed wheat Raman spectra. Finally, partial least squares regression (PLSR) models were developed to determine AFB1 in wheat samples. The results showed that all three variable optimization algorithms significantly improved the predictive performance of the models. The BOSS-PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination (RP2) was 0.9927, the root mean square error of prediction (RMSEP) was 2.4260 μg/kg, and the relative prediction deviation (RPD) was 11.5250, respectively. In conclusion, the combination of Raman spectroscopy and chemometrics can realize the rapid quantitative detection of AFB1 in wheat.

Feasibility study on non-destructive detection of microplastic content in flour based on portable Raman spectroscopy system combined with mixed variable selection method

Microplastics, as emerging environmental pollutants, have garnered considerable attention due to their contamination of both the environment and food. Microplastics can infiltrate the human food chain through multiple pathways, potentially posing health risks to humans. Currently, non-destructive testing of microplastics in food is considered challenging. This study aims to investigate the feasibility of employing a portable Raman spectroscopy system for non-destructive detection of microplastic content (polystyrene, PS; polyethylene, PE) in flour. In this study, a portable spectrometer was used to collect flour spectra of different abundances of microplastics. To enhance the predictive performance of the partial least squares (PLS) model, a mixed variable selection strategy that combined the wavelength interval selection method (Synergy interval partial least squares, siPLS) and the wavelength point selection method (Least absolute shrinkage and selection operator, LASSO; Multiple feature-spaces ensemble by least absolute shrinkage and selection operator, MFE-LASSO) was proposed. Four regression models (PLS, siPLS, siPLS-LASSO, siPLS-MFE-LASSO) were developed and compared for detecting PS and PE content in flour. The siPLS-MFE-LASSO model exhibited the best generalization performance in the prediction set, and was considered to have the best generalization performance (PS: RP2 = 0.9889, RMSEP=0.0344 %; PE: RP2 = 0.9878, RMSEP=0.0361 %). In conclusion, this study has demonstrated the potential of using a portable Raman spectrometer in conjunction with a mixed variable selection algorithm for non-destructive detection of PS and PE content in flour, providing more possibilities for non-destructive detection of microplastic content in food.

Determination of aflatoxin B1 in peanuts based on millimetre wave

This study introduces a novel method for swift detection of AFB1 in peanuts using millimetre wave technology. The research team devised a portable millimetre-wave detection device employing a double-external-difference mixing structure. The device measured millimetre-wave transmission coefficients in the 20 GHz - 40 GHz frequency range for peanut samples. Results showed that the PCA-KNN model excelled in qualitative AFB1 detection, achieving 100 % accuracy in the prediction set. In quantitative analysis, by condensing the feature variables into a 16-dimensional space, the BOSS-PSO-SVR model enhanced performance. Compared to the full transmission coefficient SVR model, the BOSS-PSO-SVR model exhibited improved coefficients of determination (RP2), reducing root mean square error of prediction (RMSEP) from 36.49 μg∙kg-1 to 19.08 μg∙kg-1, and enhancing relative prediction deviation (RPD) from 3.17 to 6.06. This study concludes that the integration of a custom miniaturized millimetre-wave device with appropriate chemometric methods facilitates rapid and accurate detection of peanut AFB1 levels.

Quantitative detection of heavy metal Cd in vegetable oils: A nondestructive method based on Raman spectroscopy combined with chemometrics

Heavy metal contaminants in vegetable oils can cause irreversible damage to human health. In this study, the quantitative detection of Cd in vegetable oils was investigated based on Raman spectroscopy combined with chemometric methods. The necessary preprocessing of the Raman signal was performed using baseline calibration and the Savitzky-Golay method. Three variable optimization methods were applied to the preprocessed Raman spectra. Namely, bootstrap soft shrinkage, multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator, and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares regression (PLSR) modeling for the determination of Cd in vegetable oils. The results show that three variable optimization algorithms improved the predictive performance of the model. Among them, the CARS-PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination (R P 2 $R_{\mathrm{P}}^2$ ) was 0.9995, the root mean square error of prediction was 0.3533 mg/kg, and the relative prediction deviation was 44.3748, respectively. In summary, rapid quantitative analysis of Cd contamination in vegetable oils can be realized based on Raman spectroscopy combined with chemometrics.

Raman Hyperspectral Imaging as a Potential Tool for Rapid and Nondestructive Identification of Aflatoxin Contamination in Corn Kernels

The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.

Quantitative detection of aflatoxin B1 in peanuts using Raman spectra and multivariate analysis methods

Aflatoxin B1 (AFB1), among the identified aflatoxins, exhibits the highest content, possesses the most potent toxicity, and poses the gravest threat. It is commonly found in peanuts and their derivatives. This study employs Raman spectroscopy to monitor the AFB1 levels in moldy peanuts, providing a reliable theoretical basis for peanut storage management. Firstly, different degrees of moldy peanuts are spectrally characterized using a portable Raman spectrometer. Subsequently, a two-step hybrid strategy for feature selection is proposed, combining backward interval partial least squares (BiPLS) and variable combination population analysis (VCPA), aiming to simplify model complexity and enhance predictive accuracy. Finally, partial least squares (PLS) regression models are constructed based on different feature intervals and wavelength points. The research results reveal that the PLS regression model using the optimized feature intervals and wavelength points exhibits improved predictive capability and generalization performance. Notably, the BiPLS-VCPA-PLS model, established through the two-step optimization, selects nine wavelength variables, achieving a root mean square error of prediction (RMSEP) of 33.3147 μg∙kg-1, a correlation coefficient of the prediction set (RP) of 0.9558, and a relative percent deviation (RPD) of 3.4896. These findings demonstrate that the two-step feature optimization method, combining feature interval selection and feature wavelength selection, can more accurately identify optimal variables, thus enhancing detection efficiency and predictive precision.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 μg∙kg-1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.

Target-Induced Electrochemical Sensor Based on Foldable Aptamer and MoS2@MWCNTs-PEI for Enhanced Detection of AFB1 in Peanuts

Herein, a sensitive and selective electrochemical sensor based on aptamer folding was constructed to detect aflatoxin B1 (AFB1) in peanuts. Specifically, polyethylenimine-functionalized multiwalled carbon nanotubes modified with molybdenum disulfide (MoS2@MWCNTs-PEI) were used as the electrode matrix to enable a large specific surface area, which were characterized by the Randles-Sevcik equation. Additionally, AuNPs were used to immobilize the aptamer via the Au-S covalent bond and provide a favorable microenvironment for signal enhancement. Methylene blue (MB) was modified at the proximal 3' termini of the aptamer as the capture probe, while the signal transduction of the sensor was obtained through changes in conformation and position of MB induced by the binding between AFB1 and the probe. Changes in spatial conformation could be recorded by electrochemical methods more readily. This electrochemical aptasensor demonstrated remarkable sensitivity to AFB1 with an extensive detection range (1 pg/mL to 100 ng/mL) and a lower limit detection (1.0 × 10-3 ng/mL). Moreover, using the constructed aptasensor, AFB1 was identified successfully in peanut samples, with recoveries ranging from 95.83 to 107.53%, illustrating its potential use in determining AFB1 in food.

Study on qualitative identification of aflatoxin solution based on terahertz metamaterial enhancement

Aflatoxin is the main carcinogen that contaminates agricultural products and foods such as peanuts and corn. There are many kinds of aflatoxins, mainly including aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2 (AFG2). Different types of aflatoxins have different toxicity and different levels of contamination to agricultural products as well as food. Therefore, the rapid, non-destructive and highly sensitive qualitative identification of aflatoxin species is of great significance to maintain people's life and health. The conventional terahertz detection method can only qualitatively identify the samples at the milligram level, but it is not suitable for the qualitative analysis of trace samples. In this paper, a terahertz metamaterial sensor with "X" composite double-peak structure was designed based on electromagnetic theory to investigate the feasibility of THz-TDS technology based on a metamaterial sensor for the qualitative identification of trace aflatoxin B2, G1 and G2 solutions. Firstly, the terahertz transmission spectra of eight different concentrations of aflatoxin B2, G1 and G2 were collected respectively, and then the differences of terahertz transmission spectra of different aflatoxin species were investigated. Finally, the terahertz transmission spectra of aflatoxin B2, G1 and G2 solutions were modeled and analyzed using chemometric methods. It was found that there were significant differences in the transmission peak curves of different kinds of aflatoxin. Through the comparative analysis of different models, it was concluded that the prediction accuracy of the CARS-RBF-SVM model was the highest, and the accuracy of the calibration set reached 100%. 119 out of 120 predicted samples were correctly predicted, and the prediction accuracy was 99.17%. This study verified the feasibility of qualitative identification of trace aflatoxin B2, G1 and G2 solutions by a metamaterial sensor based on the "X" composite double-peak structure combined with THz-TDS technology, and provided a theoretical basis and a new detection method for the qualitative identification of trace aflatoxins. This will facilitate the rapid, non-destructive and highly sensitive qualitative detection of different kinds of aflatoxins in food and agricultural products. At the same time, this study has important implications for promoting the qualitative detection of other trace substances.

Is chemical analysis suitable for detecting mycotoxins in agricultural commodities and foodstuffs?

The assessment of the risks of mycotoxins to humans through consuming contaminated foods resulted in specific legislation that evaluates the presence, quantities, and type of mycotoxins in agricultural commodities and foodstuffs. Thus, to ensure compliance with legislation, food safety and consumer health, the development of suitable analytical procedures for identifying and quantifying mycotoxins in the free or modified form, in low-concentration and in complex samples is necessary. This review reports the application of the modern chemical methods of analysis employed in mycotoxin detection in agricultural commodities and foodstuffs. It is reported extraction methods with reasonable accuracy and those present characteristics according to guidelines of Green Analytical Chemistry. Recent trends in mycotoxins detection using analytical techniques are presented and discussed, evaluating the robustness, precision, accuracy, sensitivity, and selectivity in the detection of different classes of mycotoxins. Sensitivity coming from modern chromatographic techniques allows the detection of very low concentrations of mycotoxins in complex samples. However, it is essential the development of more green, fast and more suitable accuracy extraction methods for mycotoxins, which agricultural commodities producers could use. Despite the high number of research reporting the use of chemically modified voltammetric sensors, mycotoxins detection still has limitations due to the low selectivity from similar chemical structures of mycotoxins. Furthermore, spectroscopic techniques are rarely employed due to the limited number of reference standards for calibration procedures.

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

Handheld Fluorescence Spectrometer Enabling Sensitive Aflatoxin Detection in Maize

Aflatoxins are among the main carcinogens threatening food and feed safety while imposing major detection challenges to the agrifood industry. Today, aflatoxins are typically detected using destructive and sample-based chemical analysis that are not optimally suited to sense their local presence in the food chain. Therefore, we pursued the development of a non-destructive optical sensing technique based on fluorescence spectroscopy. We present a novel compact fluorescence sensing unit, comprising both ultraviolet excitation and fluorescence detection in a single handheld device. First, the sensing unit was benchmarked against a validated research-grade fluorescence setup and demonstrated high sensitivity by spectrally separating contaminated maize powder samples with aflatoxin concentrations of 6.6 µg/kg and 11.6 µg/kg. Next, we successfully classified a batch of naturally contaminated maize kernels within three subsamples showing a total aflatoxin concentration of 0 µg/kg, 0.6 µg/kg and 1647.8 µg/kg. Consequently, our novel sensing methodology presents good sensitivity and high potential for integration along the food chain, paving the way toward improved food safety.

Carboxyl porphyrin as signal molecule for sensitive fluorescent detection of aflatoxin B1 via ARGET-ATRP

In this work, a novel fluorescent biosensor for sensitive detecting of aflatoxin B₁ (AFB₁) was constructed through activators regenerated by electron transfer for atom transfer radical polymerization (ARGET-ATRP) for the first time. The AFB₁ antigen was immobilized on the carboxy magnetic beads (MBs) by forming a sandwich-type "aptamer-antigen-antibody" immune system. Then, acrylamid (AM) was introduced through ARGET-ATRP to provide binding sites for the signaling molecules. Finally, carboxy porphyrins (TPP*) were connected with monomers through an amide bond and fixed on the MBs. Under the optimal experimental conditions, the fluorescence intensity and the logarithm of the concentration of AFB₁ showed a good relationship from 100 fg mL^-1 to 100 ng mL^-1, with the limit of detection (LOD) as low as 8.38 fg mL^-1. In addition, the method shows good selectivity and excellent reproducibility. More importantly, the biosensor has applied to the quantitative analysis of AFB₁ in four Chinese medicines, and this strategy could potentially serve as a novel means for sensitive detecting of AFB₁ in complex matrices.