Aflatoxin M1 standard

Aflatoxins are secondary metabolites produced by Aspergillus fungi, and aflatoxin B-1 (AFB(1)) is the most potent carcinogen among these compounds. As one of the most widely distributed hazardous chemicals in nature, it can be introduced into the human body via the food chain, thereby posing a threat to human health. Therefore, improving the detection level of AFB(1) has become a critical control point for food safety, medical examination, import and export inspection, and quarantine diagnosis. Herein, we designed a magnetic beads (MBs)-based flow cytometry fluorescence immunoassay (FCMFI). In this assay, we developed a magnetic bead modified with BSA-AFB(1) to form a competitive immune pattern in the sample solution with free AFB(1). A secondary antibody labeled with fluorescein isothiocyanate (FITC) binds to the above targets to provide a signal for the fluorescent immunoassay. Changes in fluorescence intensity received by the flow cytometer can be used to quantify the AFB(1) content. In this work, the limit of detection (LOD) of FCMFI was 0.2034 mu g/L for AFB(1) in the buffer, and the limit of quantitation (LOQ) was 0.5659 mu g/L. We also optimized some reaction conditions. Under the best conditions, the developed FCMFI method could analyze AFB(1) efficiently with considerable sensitivity. The developed FCMFI was verified by comparing with the colorimetric indirect enzyme linked immunosorbent assay (id-ELISA), and commercial kit. The results show that the FCMFI method has excellent performance features, such as high sensitivity and low LOD. Hence, the convenience and reliability of the developed AFB(1) detection method were fully demonstrated.

Aflatoxin-B1 contamination in maize is a major food safety issue across the world. Conventional detection technique of toxins requires highly skilled technicians and is time-consuming. Application of appropriate chemometrics along with hyperspectral imaging (HSI) can identify aflatoxin-B1 infected maize kernels. Present study was undertaken to classify 240 maize kernels inoculated with six different concentrations (25, 40, 70, 200, 300 and 500 ppb) of aflatoxin-B1 by using Vis-NIR HSI. The reflectance spectral data were pre-processed (multiplicative scatter correction (MSC), standard normal variate (SNV), Savitsky-Golay smoothing and their combinations) and classified using partial least square discriminant analysis (PLS-DA) and k-nearest neighbour (k-NN). PLS model was also developed to predict the concentration of aflatoxin-B1in naturally contaminated maize kernels inoculated with Aspergillus flavus. The potential wavelength (508 nm) was selected based on principal component analysis (PCA) loadings to distinguish between sterile and infected maize kernels. PCA score plots revealed a distinct separation of low contaminated samples (25, 40 and 70 ppb) from highly contaminated samples (200, 300 and 500 ppb) without any overlapping of data. The maximum classification accuracy of 94.7% was obtained using PLS-DA with SNV pre-processed data. Across all the combinations of pre-processing and classification models, the best efficiency (98.2%) was exhibited by k-NN model with raw data. The developed PLS model depicted good prediction accuracy (R-CV(2) = 0.820, SECV = 79.425, RPDCV = 2.382) during Venetian-blinds cross-validation. The results of pixel-wise classification (k-NN) and concentration distribution maps (PLS with raw spectra) were quite close to the result obtained by reference method (HPLC analysis) of aflatoxin-B1 detection.