This review highlights methods for the detection of the main mycotoxins: aflatoxins, fumonisins, ochratoxin, patulin, trichothecene, zearalenone, citrinin. Detection methods such as competitive or noncompetitive immunoassays, different biosensors, and the use of optoelectronics devices to miniaturize the equipment in a portable way, are described. The importance of spectroscopic techniques are underlined and the maxima permissible by various committees are indicated.

This manuscript describes the use of a portable and low cost fluorescence setup to quantify the concentration of ochratoxin A (OTA) in beverage samples using an in-house developed system and different color models. It is reported that OTA is naturally fluorescent, for that reason an ultraviolet light at 365 nm was used to excite the samples and a Complementary Metal Oxide Semiconductor (CMOS) sensor was used to get a photograph of the OTA under excitation conditions, which is controlled by an executable interface designed in MATLAB. For each concentration of OTA, the coordinates with respect to each model color were obtained and plotted to quantify the mycotoxin present in the sample. It was possible to observe that despite the fact no extraction column was employed, the Red, Green, Blue (RGB) model shows a proportional relation to the evaluated concentrations. Despite the fact more analysis and other methods are required to quantify the OTA concentration, the brightness and a,b for the color-opponent dimensions (L*a*b) and Hue, Saturation, Value (HSV) tests provide results whereby it is possible to identify the concentration of OTA in beverage samples such as beer and wine.

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image’s pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers’ performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen’s kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.