Percorrer por autor "Garcia-Cabezon, Cristina"
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- Analysis of milk adulteration by means of a potentiometric electronic tonguePublication . Perez-Gonzalez, Clara; Garcia-Hernandez, Celia; Garcia-Cabezon, Cristina; Rodriguez-Mendez, M.L.; Dias, L.G.; Martin-Pedrosa, FernandoMilk adulteration presents substantial challenges in the food industry, prompting the need for efficient de- tection methods. This study introduces a potentiometric electronic tongue for rapid and accurate detection of milk adulteration. Using polymeric membranes with various integrated additives, the electronic tongue distinguished between different milk types and detected common adul- terants. Experimental results demonstrated its effective- ness in discriminating raw, pasteurized, and medicated cow milk, as well as goat milk. Moreover, it success- fully identified adulterants, such as water and cow milk, in goat milk samples. Chemometric analyses, including principal component analysis and partial least squares regression, correlated sensor responses with traditional milk parameters such as fat, protein, and lactose content with an R 2 of up to 0.97 on the validation step. Strong correlations validated the electronic tongue’s potential for rapid milk quality assessment. This innovative ap- proach offers a cost-effective, reliable solution for de- tecting milk adulteration in contrast to current techniques that require numerous time-consuming experiments.
- Analysis of milk using a portable potentiometric electronic tongue based on five polymeric membrane sensorsPublication . Pérez-González, C.; Salvo-Comino, Coral; Martin-Pedrosa, Fernando; Dias, L.G.; Rodriguez-Perez, M.A.; Garcia-Cabezon, Cristina; Rodriguez-Mendez, Maria LuzA portable potentiometric electronic tongue (PE-tongue) was developed and applied to evaluate the quality of milk with different fat content (skimmed, semi-skimmed, and whole) and with different nutritional content (classic, calcium-enriched, lactose-free, folic acid-enriched, and enriched in sterols of vegetal origin). The system consisted of a simplified array of five sensors based on PVC membranes, coupled to a data logger. The five sensors were selected from a larger set of 20 sensors by applying the genetic algorithm (GA) to the responses to compounds usually found in milk including salts (KCl, CaCl2, and NaCl), sugars (lactose, glucose, and galactose), and organic acids (citric acid and lactic acid). Principal component analysis (PCA) and support vector machine (SVM) results indicated that the PE-tongue consisting of a five-electrode array could successfully discriminate and classify milk samples according to their nutritional content. The PE-tongue provided similar discrimination capability to that of a more complex system formed by a 20-sensor array. SVM regression models were used to predict the physicochemical parameters classically used in milk quality control (acidity, density, %proteins, %lactose, and %fat). The prediction results were excellent and similar to those obtained with a much more complex array consisting of 20 sensors. Moreover, the SVM method confirmed that spoilage of unsealed milk could be correctly identified with the simplified system and the increase in acidity could be accurately predicted. The results obtained demonstrate the possibility of using the simplified PE-tongue to predict milk quality and provide information on the chemical composition of milk using a simple and portable system.
- Analysis of phenolic content in grape seeds and skins by means of a bio-electronic tonguePublication . Garcia-Cabezon, Cristina; Teixeira, Guilherme Gobbi; Dias, L.G.; Salvo-Comino, Coral; García-Hernandez, Celia; Rodriguez-Mendez, Maria Luz; Martin-Pedrosa, FernandoA bio-electronic tongue has been developed to evaluate the phenolic content of grape residues (seeds and skins) in a fast and easy way with industrial use in mind. A voltammetric electronic tongue has been designed based on carbon resin electrodes modified with tyrosinase combined with electron mediators. The presence of the phenoloxydase promotes the selectivity and specificity towards phenols. The results of multivariate analysis allowed discriminating seeds and skins according to their polyphenolic content. Partial least squares (PLS) has been used to establish regression models with parameters related to phenolic content measured by spectroscopic methods i.e., total poliphenol content (TPC) and Folin–Ciocalteu (FC) indexes. It has been shown that electronic tongue can be successfully used to predict parameters of interest with high correlation coefficients (higher than 0.99 in both calibration and prediction) and low residual errors. These values can even be improved using genetic algorithms for multivalent analysis. In this way, a fast and simple tool is available for the evaluation of these values. This advantage may be due to the fact that the electrochemical signals are directly related to the phenolic content.
- Enose lab made with vacuum sampling: quantitative applicationsPublication . Teixeira, Guilherme Gobbi; Peres, António M.; Estevinho, Leticia M.; Geraldes, Pedro; Garcia-Cabezon, Cristina; Martin-Pedrosa, Fernando; Rodriguez-Mendez, Maria Luz; Dias, L. G.A lab-made electronic nose (Enose) with vacuum sampling and a sensor array, comprising nine metal oxide semiconductor Figaro gas sensors, was tested for the quantitative analysis of vapor–liquid equilibrium, described by Henry’s law, of aqueous solutions of organic compounds: three alcohols (i.e., methanol, ethanol, and propanol) or three chemical compounds with different functional groups (i.e., acetaldehyde, ethanol, and ethyl acetate). These solutions followed a fractional factorial design to guarantee orthogonal concentrations. Acceptable predictive ridge regression models were obtained for training, with RSEs lower than 7.9, R2 values greater than 0.95, slopes varying between 0.84 and 1.00, and intercept values close to the theoretical value of zero. Similar results were obtained for the test data set: RSEs lower than 8.0, R2 values greater than 0.96, slopes varying between 0.72 and 1.10, and some intercepts equal to the theoretical value of zero. In addition, the total mass of the organic compounds of each aqueous solution could be predicted, pointing out that the sensors measured mainly the global contents of the vapor phases. The satisfactory quantitative results allowed to conclude that the Enose could be a useful tool for the analysis of volatiles from aqueous solutions containing organic compounds for which Henry’s law is applicable.
- Looking for the optimal harvest time of red grapes with an enzymatic electrochemical multisensory systemPublication . Garcia-Hernandez, Celia; Perez-Gonzalez, Clara; Martin-Pedrosa, Fernando; Dias, L.G.; Barajas-Tola, E.; Rodriguez-Mendez, M.L.; Garcia-Cabezon, CristinaThe timing of the grape harvest is a critical decision for winemakers, as it greatly impacts the quality and organoleptic characteristics of the resulting red wines. One key indicator for determining the optimal harvest time is the phenolic content of the grapes. As the berries ripen, phenolic compounds in the grape skin cells migrate from the seeds to the pulp and finally to the skin. However, monitoring these phenolic changes, particularly in the seeds, presents a challenge. This research addresses this problem by developing a novel technique to track the phenolic composition of seeds during ripening. The objective is to provide a reliable method for winemakers to monitor the phenolic evolution and improve harvest decision-making. In this study, a multisensory system consisting of four electrochemical enzymatic carbon paste sensors, modified with tyrosinase and electrocatalytic materials (AuNPs, lutetium phthalocyanine, and nickel oxide nanoparticles), was employed to analyze the phenolic content in grape seed extracts. The system monitored weekly changes in the phenolic composition of the seeds from three red grape varieties—Cabernet Sauvignon, Tempranillo, and Prieto Picudo—during their ripening from veraison to being overripe. Using voltammetric techniques, the electrochemical responses were characterized by shifts in peak positions and intensity changes, reflecting the oxidation/reduction of phenols. Principal Component Analysis (PCA) demonstrated the ability of the array of sensors to discriminate phenolic changes across ripening stages, while Partial Least Squares (PLS) regression provided robust correlation models between the electrochemical responses and seed phenolic content, with correlation coefficients ranging from 0.93 to 0.99. The developed methodology successfully tracked phenolic changes, offering a promising tool for monitoring grape seed maturation and assisting in determining the optimal harvest time.