Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types

Sensory quality control of alcoholic beverages using fast chemical sensors

Control de calidad sensorial de bebidas alcohólicas utilizando rápidos sensores químicosEn la presente tesis Doctoral, han sido aplicados dos sensores artificiales para el análisis debebidas alcohólicas: la nariz electrónica basada en la espectrometría de masas (MS) y la lenguaelectrónica basada en la espectroscopía infrarroja con transformada de Fourier (FTIR). Elpropósito fue desarrollar nuevas estrategias para analizar la autenticidad de estos productos,desde un punto de vista sensorial, por medio de técnicas las espectrales antes mencionadas.Adicionalmente, ha sido utilizado un espectrofotómetro UV-visible como ojo electrónico. Eltrabajo presentado pretende ser un avance significativo hacia el desarrollo de un catadorelectrónico mediante la fusión de los tres sensores químicos: nariz electrónica, lenguaelectrónica y ojo electrónico.Sensory quality control of alcoholic beverages using fast chemical sensorsIn the present Doctoral Thesis, two chemical artificial sensors are applied to the analysis ofalcoholic beverages: the Mass Spectrometry (MS)-based electronic-noses and Fouriertransform infrared (FTIR)-based electronic-tongue. The aim was developing new strategies totest the authenticity of these products, from a sensory point of view, by means of the spectraltechniques above mentioned. Additionally, has been used an UV-visible spectrophotometer aselectronic eye. The work presented wants to be a significant advance towards the developmentof an electronic taster through the fusion of three chemical sensors: electronic nose, electronictongue and electronic eye

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.This work was co-financed by FCT/MEC and FEDER under Program PT2020 (Project UID/EQU/50020/2013); by Fundacao para a Ciencia e Tecnologia under the strategic funding of UID/BIO/04469/2013 unit; and by Project POCTEP through Project RED/AGROTEC-Experimentation network and transfer for development of agricultural and agro industrial sectors between Spain and Portugal

Comparison of supervised learning statistical methods for classifying commercial beers and identifying patterns

In this study, 13 properties (alcohol-, real extract-, flavonoid-, anthocyanin, glucose, fructose, maltose, sucrose content, EBC [European Brewery Convention] and L*a*b* color, bitterness) of 21 beers (alcohol-free pale lagers, alcohol-free beer-based mixed drinks, beer-based mixed drinks, international lagers, wheat beers, stouts, fruit beers) were determined. In the first step, multiple factor analysis (MFA) was performed for the whole data and five clusters (target classes) were determined; then, a bootstrapping was applied to establish a balanced data so as every cluster should contain 100 samples and the total sample size is 500. In the second step, 12 supervised learning algorithms (random trees [RND], Quinlan's C4.5 decision tree algorithm [C4.5], Iterative Dichotomiser 3 algorithm [ID3], cost-sensitive decision tree algorithm [CSMC4], cost-sensitive classification tree [CSCRT], k-nearest neighbors algorithm [KNN], radial basis function [RBF], multilayer perceptron neural network [MLP], prototype nearest neighbor [PNN], linear discriminant analysis [LDA], naïve Bayes with continuous variables [NBC], partial least squares discriminant analysis [PLS-DA]) were applied to classify each brand into the target classes. Furthermore, several error rates were calculated: re-substitution error rate (RER), cross-validated error rate (CV), bootsrap error (BOOT), leaveone-out (LOO), and train-test error rate (TRAIN). The MFA could discriminate five groups, which can be characterized by some analytical parameters, and the other multivariate methods performed similarly. The methods can be discriminated best based on the BOOT, CV, and LOO. The best estimation methods are the C4.5, CSMC4, and CSCRT; these performed best along the flavonoid content and EBC color. It identified that the methods most sensitive to the properties are the NBC. The classification ability fluctuated greatly in the case of three properties (glucose, maltose, sucrose). A remarkable fluctuation has been experienced in the case of L*a*b* color parameters, flavonoid content, EBC color, and bitterness by NBC method

Beer classification by means of a potentiometric electronic tongue

In this work, an Electronic Tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) is presented for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, Principal Component Analysis (PCA) and Linear Discriminant Analysis(LDA) in order to achieve the correct recognition of samples variety. In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. Finally, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show a quantitative application, alcohol content was predicted from the array data employing an Artificial Neural Network model

Data fusion approaches for the characterization of musts and wines based on biogenic amine and elemental composition

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures

Potential use of electronic noses, electronic tongues and biosensors, as multisensor systems for spoilage examination in foods

Development and use of reliable and precise detecting systems in the food supply chain must be taken into account to ensure the maximum level of food safety and quality for consumers. Spoilage is a challenging concern in food safety considerations as it is a threat to public health and is seriously considered in food hygiene issues accordingly. Although some procedures and detection methods are already available for the determination ofspoilage in food products, these traditional methods have some limitations and drawbacks as they are time-consuming,labour intensive and relatively expensive. Therefore, there is an urgent need for the development of rapid, reliable, precise and non-expensive systems to be used in the food supply and production chain as monitoring devices to detect metabolic alterations in foodstuff. Attention to instrumental detection systems such as electronic noses, electronic tongues and biosensors coupled with chemometric approaches has greatly increased because they have been demonstrated as a promising alternative for the purpose of detecting and monitoring food spoilage. This paper mainly focuses on the recent developments and the application of such multisensor systems in the food industry. Furthermore, the most traditionally methods for food spoilage detection are introduced in this context as well. The challenges and future trends of the potential use of the systems are also discussed. Based on the published literature, encouraging reports demonstrate that such systems are indeed the most promising candidates for the detection and monitoring of spoilage microorganisms in different foodstuff

A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10-20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation.This work was co-financed by FCT/MEC and FEDER under Programme PT2020 (Project UID/EQU/50020/2013); and under the strategic funding of UID/BIO/04469/2013 unit

Sensory intensity assessment of olive oils using an electronic tongue

Olive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14±2 to 25±6 mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.This study was supported by Fundação para a Ciência e a Tecnologia (FCT),Portugal and the European Community fund FEDER, under the Program PT2020 (Project UID/EQU/50020/2013); under the strategic funding of UID/BIO/04469/2013 unit; and by Project POCTEP through Project RED/AGROTEC – Experimentation network and transfer for development of agricultural and agroindustrial sectors between Spain and Portugal

Nonlinear feature extraction through manifold learning in an electronic tongue classification task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.Peer ReviewedPostprint (published version