Regina Aroca-Santos

Identifying and Quantifying Adulterants in Extra Virgin Olive Oil of the Picual Varietal by Absorption Spectroscopy and Nonlinear Modeling

In this research, the detection and quantification of adulterants in one of the most common varieties of extra virgin olive oil (EVOO) have been successfully carried out. Visible absorption information was collected from binary mixtures of Picual EVOO with one of four adulterants: refined olive oil, orujo olive oil, sunflower oil, and corn oil. The data gathered from the absorption spectra were used as input to create an artificial neural network (ANN) model. The designed mathematical tool was able to detect the type of adulterant with an identification rate of 96% and to quantify the volume percentage of EVOO in the samples with a low mean prediction error of 1.2%. These significant results make ANNs coupled with visible spectroscopy a reliable, inexpensive, user-friendly, and real-time method for difficult tasks, given that the matrices of the different adulterated oils are practically alike.

Spectroscopic determination of the photodegradation of monovarietal extra virgin olive oils and their binary mixtures through intelligent systems.

A common phenomenon that takes place in bottled extra virgin olive oil (EVOO) is the photooxidation of its pigments, especially chlorophyll, which acts as a singlet-oxygen sensitizer. This translates into a severe decrease of quality, potentially leading to oxidized and rancid olive oils by the time they reach to the consumers. In this current research, the photochemical degradation has been monitored for 45 days in binary mixtures of four monovarietal EVOOs (Arbequina, Hojiblanca, Cornicabra, and Picual) through UV-Visible spectroscopy. A multilayer perceptron-based model was optimized to estimate the photodegradation suffered by the samples, in terms of photodegradation time, relying on the spectroscopic information gathered and attaining an error rate of 2.43 days (5.3%) in the determination of this parameter.

Neural networks applied to characterize blends containing refined and extra virgin olive oils

The identification and quantification of binary blends of refined olive oil with four different extra virgin olive oil (EVOO) varietals (Picual, Cornicabra, Hojiblanca and Arbequina) was carried out with a simple method based on combining visible spectroscopy and non-linear artificial neural networks (ANNs). The data obtained from the spectroscopic analysis was treated and prepared to be used as independent variables for a multilayer perceptron (MLP) model. The model was able to perfectly classify the EVOO varietal (100% identification rate), whereas the error for the quantification of EVOO in the mixtures containing between 0% and 20% of refined olive oil, in terms of the mean prediction error (MPE), was 2.14%. These results turn visible spectroscopy and MLP models into a trustworthy, user-friendly, low-cost technique which can be implemented on-line to characterize olive oil mixtures containing refined olive oil and EVOOs.

Algorithmic modeling of spectroscopic data to quantify binary mixtures of vinegars of different botanical origins

Multiple binary mixtures of different kinds of vinegars have been analyzed through UV-Vis absorption. Two types of mathematical models (multiple linear regression (MLR) and artificial neural networks (ANNs)) have been employed to identify and quantify the components of such blends. Six different vinegars were used to prepare these mixtures, each one with a particular botanical origin: white wine, red wine, apple cider, apple, molasses, and rice. The best results have been obtained with ANN based models, offering mean estimation error value averages of 1% (v/v) and mean correlation coefficients (R2) over 0.99. This model is adequate to perform the estimation and achieve an accurate and reliable tool. Nevertheless, although the MLR models provide worse results (0.88 in terms of R2 and 5% v/v error), they can be used depending on the application and required accuracy.

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. Using these waters, different SCW solutions (lower than 20% in weight) have been prepared and measured. The equipment used to sense the samples is based on combining fluorescence measurements, obtained with light emitting diodes (LEDs), and algorithms which rely on chaotic parameters. Every sample was measured by six different types of LEDs possessing distinct emission wavelengths (blue, orange, green, pink, white, and UV), leading to 1786 fluorescence spectra that were employed during the modeling phase. After the mathematical analysis, the dataset that generates the best statistical results was from the blue LED. This approach was dually validated via leave-one-out cross-validation as well as externally validation, and the results were very promising (error around 6.5% and 8% quantification error, respectively). Additionally, it is important to note that the sensor used has been designed and developed by a 3D printer and has the potential of being applied in situ for real-time and cost-effective analysis of natural bodies of water.

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%. Finally, the same approach was followed to analyze rice syrup, which is a common honey adulterant that is challenging to identify when mixed with honey. A LED-dependent and unique fluorescence spectrum was found for the syrup, which presumably qualifies this approach for the design of uncomplicated, fast, and cost-effective quality control and adulteration assessing tools for different types of honey.