Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.

Consumption of aflatoxin-contaminated food can cause severe illness when consumed by humans or livestock. Because the mycotoxin frequently occurs in cereal grains and other agricultural crops, it is crucial to develop portable devices that can be used non-destructively and in real-time to identify aflatoxin-contaminated food materials during early stages of harvesting or processing. In this study, an aflatoxin detection method was developed using a compact Raman device that can be used in the field. Data were obtained using maize samples naturally contaminated with aflatoxin, and the data were analyzed using a machine learning method. Of the multiple classification models evaluated, such as linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines and spectral preprocessing methods, the best classification accuracy was achieved at 95.7% using LDA in combination with Savitzky-Golay 2nd derivative (SG2) preprocessing. Partial least squares regression (PLSR) models demonstrated a close-range accuracy within the scope of standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing methods, with determination of coefficient values of R²_C and R²_V of 0.9998 and 0.8322 respectively for SNV, and 0.9916 and 0.8387 respectively for MSC. This study demonstrates the potential use of compact and automated Raman spectroscopy, coupled with chemometrics and machine learning methods, as a tool for rapidly screening food and feed for hazardous substances at on-site field processing locations.

Arsenic, cadmium, lead, and mercury contents of Ganoderma lucidum, Phellinus linteus, Umbilicaria esculenta, Trametes versicolor, Agaricus blazei, Hericium erinaciium, Paecilomyces japonica, Fuscoporia obliqua, Sarcodon imbricatu and Hypsizygus marmoreus mushroom samples from the South Korean market were analyzed using validated methods. Method validation was performed, and the limits of detection and quantification were determined to be 0.38–0.66 and 1.21–2.09 μg/kg, respectively. The recoveries of the target metals ranged from 91.3% to 103.5%. The contents of As, Cd, Hg, and Pb were significantly different among the ten mushroom species, and the ranged from 0.006 to 3.9, 0.025 to 5.8, 0.01 to 0.21, and 0.004 to 12.3 mg/kg dry weight, respectively. Additionally, the estimated daily exposures to these metals from consumption of mushroom used in this work were found to be safe in accordance with the provisional tolerable weekly intake. Considering the results in this study, it could be mentioned that the consumption of the ten mushrooms species from Korea presented no noticeable health risk to the general public.

This study presents a method for discriminating the geographical origin of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS) and multivariate analysis, such as orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap analysis, and canonical discriminant analysis (CDA). Herein, 102 samples were analyzed for the content of 33 elements using optimized conditions of 200 Hz (repetition rate), 50 μm (spot size), and 90% (energy). Significant differences in count per second (cps) values of the elements were observed between domestic and imported peppers, with variations of up to 5.66 times (¹³³Cs). The OPLS-DA model accuracy achieved an R² of 0.811 and a Q² of 0.733 for distinguishing dried chili peppers of different geographical origins. The variable importance in projection (VIP) and s-plot identified elements 10 and 3 as key to the OPLS-DA model, and in the heatmap, six elements were estimated to be significant in discriminating between domestic and imported samples. Furthermore, CDA showed a high accuracy of 99.02%. This method can ensure food safety for consumers, and accurately determine the geographic origin of agricultural products.