An electronic tongue for juice level evaluation in non-alcoholic beverages

An electronic tongue with 36 cross-sensibility polymeric membranes was built and used for semi-quantitative analysis of beverages. The objective was to differentiate 4 non-alcoholic beverage groups with different added fruit juice contents: higher than 30%, between 14%-30%, 5%-10% and 0.1%-2%. A set of 16 Portuguese beverages (4 for each group), purchased in commercial supermarkets, was analyzed and the respective signal profiles recorded by the electronic tongue device. The data obtained were treated by stepwise linear discriminant analysis, allowing a 100% overall correct classification for the original grouped cases and a 93.8% for the “leaving one-out” cross-validation procedur

Evaluation of an electronic tongue for honey classification according to its pólen analysis

Electronic tongues (ET) have attracted great interest due to its potential to obtain global information from complex samples that could hardly be obtained by traditional instrumental methods of analysis. These multi-sensor arrays provide a huge amount of sample information which, by applying chemometric methods, allows sample identification/classification, taste evaluation as well as, multicomponent analysis. The method of operation consists in obtaining a signal pattern which corresponds to the ove rall information on the sample using chemical sensors with high stability and cross sensitivity to different species in solution. In this work, a potenciometric electronic tongue or taste sensor array was used. The device had 20 sensors, based on all~solid ~ state electrodes with lipid polymeric membranes formed on solid conducting silver supports. This analytical system was used to analyse unifloral honeys, which honey pollen profiles were obtained by pa11inic analysis, that are representative of eight main types of pollens: Castanea sp., Echium sp., Erica sp., Eucalyptus sp., Lavandula sp., Prunus sp.t Rubus sp. and Trifolium sp .. The signal profile information obtained from the ET analysis of the honey samples was related with the pollinic analysis, using linear discriminant analysis. The results showed that ET could be used for classifying the type of honey according to their pollen profile, when the main pollen is in great abundance, being a possible alternative to traditional honey classification techniques that are time consuming and require expert labour. The influence of the second main pollen showed to be relevant in honey classification

Sugars' quantifications using a potentiometric electronic tongue with cross-selective sensors: Influence of an ionic background

Glucose, fructose and sucrose are sugars with known physiological e ects, and their consumption has impact on the human health, also having an important e ect on food sensory attributes. The analytical methods routinely used for identification and quantification of sugars in foods, like liquid chromatography and visible spectrophotometry have several disadvantages, like longer analysis times, high consumption of chemicals and the need for pretreatments of samples. To overcome these drawbacks, in this work, a potentiometric electronic tongue built with two identical multi-sensor systems of 20 cross-selectivity polymeric sensors, coupled with multivariate calibration with feature selection (a simulated annealing algorithm) was applied to quantify glucose, fructose and sucrose, and the total content of sugars as well. Standard solutions of ternary mixtures of the three sugars were used for multivariate calibration purposes, according to an orthogonal experimental design (multilevel fractional factorial design) with or without ionic background (KCl solution). The quantitative models’ predictive performance was evaluated by cross-validation with K-folds (internal validation) using selected data for training (selected with the K-means algorithm) and by external validation using test data. Overall, satisfactory predictive quantifications were achieved for all sugars and total sugar content based on subsets comprising 16 or 17 sensors. The test data allowed us to compare models’ predictions values and the respective sugar experimental values, showing slopes varying between 0.95 and 1.03, intercept values statistically equal to zero (p-value 0.05) and determination coe cients equal to or greater than 0.986. No significant di erences were found between the predictive performances for the quantification of sugars using synthetic solutions with or without KCl (1 mol L1), although the adjustment of the ionic background allowed a better homogenization of the solution’s matrix and probably contributed to an enhanced confidence in the analytical work across all of the calibration working range.This research work was funded by strategic project CIMO–PEst-OE/AGR/UI0690/2014 and Associate Laboratory LSRE-LCM–UID/EQU/50020/2019, financially supported by the FEDER—Fundo Europeu de Desenvolvimento Regional through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI); and by national funds through FCT—Fundação para a Ciência e a Tecnologia, Portugalinfo:eu-repo/semantics/publishedVersio

An electronic tongue for beer differentiation

In this work an electronic tongue, based on a potentiometric solid-state mufti-sensor array, with 36 polymeric membranes, was built for developing an analytical tool to apply in process monitoring and quality control. As a first approach, this tool was applied together with a supervised pattern recognition tool to semi-quantitatively differentiate beers with different alcoholic levels

Aplicação de uma língua electrónica na classificação de méis monoflorais

Analisou-se o perfil polínico de 51 amostras de mel nacional. Os pólens mais frequentes no mel são originários das plantas dos géneros Lavandula, Cistus, Echium, Erica, Castanea, Thymus, Prunus, Cytisus, Carduus, Trifolium e Citrus. Considerando a classificação de mel monofloral de Lavandula (pólen predominante superior a 15%), de Erica e de Echium (pólen predominante superior a 45%) seleccionaram-se 38 méis monoflorais: 18, 9 e 11 destes méis foram classificados como méis monoflorais de Lavandula, Erica e Echium, respectivamente. Contudo há méis que podem ser classificados como mel monofloral de dois géneros de pólen (Lavandula-Echium, Erica-Lavandula, Echium-Lavandula). Os méis monoflorais foram analisados com um sistema de multi-sensores químicos não específicos de sensibilidade cruzada (Língua Electrónica) e os resultados foram tratados através da análise de componentes principais e análise discriminante. Verificou-se que é possível discriminar razoavelmente o mel monofloral de acordo com o género do pólen predominante

Sugar analysis by a multi-sensor system: applying to honey samples

One of the emerging approaches for analysis of liquid samples with complex matrices is the Electronic Tongue (ET) since it allows evaluating tastes by calibration, mimicking the human tongue. The ET records a pattern of signals that depends on the matrix solution composition, which information is extracted into qualitative and quantitative information by multivariate statistical methods. The chemical sensors used in these devices, usually, differ from those of the traditional chemical sensors because they have the ability to obtain global information about the solution (cross-sensibility sensors, the signal results from sensitization to various substances). Selectiveions sensors (high selectivity for detecting substances) may also be included in the analytical system, allowing cross-information as well as specific information about the sample matrix. Recent works with ET showed the wide range of applications such as, classification of honey (Dias, 2008), detection of milk adulterations (Dias, 2009), detection of protein levels (gliadins) in different foodstuffs (Peres, 2011) and classification of soft drinks accordingly to different added fruit juice contents (Dias, 2011). Moreover, the analytical performance suggests that ETs could have a wider set of applications as, quantification if the substances to be analysed are major compounds in the sample. With the aim of test this hypothesis, an all-solid-state potentiometric ET was developed and has being tested to quantify fructose and glucose contents, which are important constituents of the food products, as an alternative tool for the quantification of these sugars in real samples. Several multivariate data treatments for quantitative analysis of these two sugars (MLR, PLS and others) are considered as well as, their application to the results obtained in honey samples analysis.Collaboration of the Portuguese National Beekeepers Federation in providing honey samples is gratefully acknowledge

An electronic tongue for honey classification

An electronic tongue system was developed based on 20 all-solid-state potentiometric sensors and chemometric data processing, with polymeric membranes applied on solid conducting silver-epoxy supports and a Ag=AgCl reference electrode. The sensor array was applied to 52 commercial honey samples obtained randomly from different regions of Portugal. These samples were analysed independently for their pollen profiles by biological techniques and the data collected with the tongue were evaluated for discrimination of the samples with multivariate statistical methods (principal component analysis and linear discriminant analysis), to investigate whether the device may provide an analytical alternative for classification of honey samples with respect to pollen type, a task which is time consuming and requires skilled labour when performed by biological techniques. It was found that the tongue has a reasonable efficiency for classification of honey samples of the most common three types (with Erica, Echium and Lavandula as predominant pollens). With linear discriminant analysis, the honey samples yielded about 84% classification accuracy and 72% for crossed validation. In this study, the honey samples correctly classified for the different types of the dominant pollen were: 53% for Lavandula, 83% for Erica and 78% for Echium pollen

Microbiological assessment, nutritional characterization and phenolic compounds of bee pollen from Mellipona mandacaia Smith, 1983

This study aims to assess the microbiological parameters and the chemical composition of 21 samples of stingless bee pollen (Melipona mandacaia) from two regions of Bahia, Brazil (João Dourado and Uibaí), with particular emphasis on the nutritional value, total phenols and flavonoids and fatty acids composition. Regarding the microbiological quality, the studied microorganisms (moulds and yeasts, coliforms, Escherichia coli, Staphyl ococcus aureus, Salmonella sp., psychrotrophic and sulfite-reducing Clostridia) were absent in all samples. On the other hand, the values obtained for the aerobic mesophilic microorganism ranged from 11.0 ± 1.0 to 1.32 ± 1.2 cfu·g -1 (U samples). The nutritional parameters (moisture, ash, water activity, pH, total acidity, protein, fiber, total phenolic, flavonoids and reducing sugars) were within the stipulated by law, except for pH and moisture content, which presented superior and inferior values, respectively. Polyunsaturated fatty acids (54.1%) were significantly higher than saturated (42.18%) and monounsaturated (3.71%). It was found that the bee pollen is safe from the microbiological point of view and has a good nutritional quality. The influence of the geographical origin on the assessed parameters was evident, especially concerning the fatty acid profile.info:eu-repo/semantics/publishedVersio

Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?

This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.This research was funded by “BisOlive: Use of olive pomace in the feeding of Bísaro swine. Evaluation of the effect on meat quality” project. NORTE-01-0247-FEDER-072234. Financial support under the CIMO project (UIDB/00690/2020).info:eu-repo/semantics/publishedVersio

Kinematical Test of Large Extra Dimension in Beta Decay Experiments

The forthcoming experiments on neutrino mass measurement using beta decay, open a new window to explore the Large Extra Dimension model. The Kaluza-Klein tower of neutrinos in Large Extra Dimension contribute to the Kurie function of beta decay that can be tested kinematically. In addition to providing an alternative approach using just the kinematical properties, we show that KATRIN can probe the compactification radius of extra dimensions down to 0.2 \mu m which is better, at least by a factor of two, than the upper limits from neutrino oscillation experiments.Comment: 4 pages, 4 figures; v2: discussion improved, matches the version published at PL