Convolutional decoding of thermographic images to locate and quantify honey adulterations.

In this research, 56 samples of pure honey have been mixed with different concentrations of rice syrup simulating a set of adulterated samples. A thermographic camera was used to extract data regarding the thermal development of the honey. The resulting infrared images were processed via convolutional neural networks (CNNs), a subset of algorithms within deep learning.

Combination of LEDs and cognitive modeling to quantify sheep cheese whey in watercourses.

The concentration of sheep cheese whey (CW) in water obtained from two Spanish reservoirs, two Spanish rivers, and distilled water has been estimated by combining spectroscopic measurements, obtained with light-emitting diodes (LEDs), and linear or non-linear algorithms. The concentration range of CW that has been studied covers from 0 to 25% in weight. Every sample was measured by six different types of LEDs possessing different emission wavelengths (blue, orange, green, pink, white, and UV).

Chaotic parameters from fluorescence spectra to resolve fraudulent mixtures of fresh and expired protected designation of origin extra virgin olive oils.

One of the most profitable products from the Mediterranean basin is extra virgin olive oil (EVOO), and, therefore, some of them have protected designation of origin (PDO) labels. In order to prevent fraudulent practices, a method to quantify adulterants has been developed. 459 binary blends composed of PDO EVOO in date (Saqura, Oleoestepa, and Duque de Baena) mixed with expired PDO EVOO (Quinta do Vallouto, Señorío de Segura, and Planeta) to serve as adulterants (<17%) have been analyzed.

Chaotic parameters extracted from fluorescence spectra to quantify sheep cheese whey in natural bodies of water.

Sheep cheese whey (SCW) is a by-product from the dairy industry, and due to its composition, it is very hazardous for natural bodies of water. However, illegal discharges of this product have been commonly reported in watercourses and reservoirs. To prevent this type of actions, a simple and affordable sensor has been designed and validated using diverse water samples from different sources containing SCW, such as water from two Spanish reservoirs and two Spanish rivers located in the province of Madrid.

Characterization of an array of honeys of different types and botanical origins through fluorescence emission based on LEDs.

A set of 10 honeys comprising a diverse range of botanical origins have been successfully characterized through fluorescence spectroscopy using inexpensive light-emitting diodes (LEDs) as light sources. It has been proven that each LED-honey combination tested originates a unique emission spectrum, which enables the authentication of every honey, being able to correctly label it with its botanical origin. Furthermore, the analysis was backed up by a mathematical analysis based on partial least square models which led to a correct classification rate of each type of honey of over 95%.