Discrimination of mineral waters by electronic tongue, sensory evaluation and chemical analysis

Development of Taste Sensor with Lipid/Polymer Membranes for Detection of Umami Substances Using Surface Modification

A taste sensor employs various lipid/polymer membranes with specific physicochemical properties for taste classification and evaluation. However, phosphoric acid di(2-ethylhexyl) ester (PAEE), employed as one of the lipids for the taste sensors, exhibits insufficient selectivity for umami substances. The pH of sample solutions impacts the dissociation of lipids to influence the membrane potential, and the response to astringent substances makes accurate measurement of umami taste difficult. This study aims to develop a novel taste sensor for detecting umami substances like monosodium L-glutamate (MSG) through surface modification, i.e., a methodology previously applied to taste sensors for non-charged bitter substance measurement. Four kinds of modifiers were tested as membrane-modifying materials. By comparing the results obtained from these modifiers, the modifier structure suitable for measuring umami substances was identified. The findings revealed that the presence of carboxyl groups at para-position of the benzene ring, as well as intramolecular H-bonds between the carboxyl group and hydroxyl group, significantly affect the effectiveness of a modifier in the umami substance measurement. The taste sensor treated with this type of modifier showed excellent selectivity for umami substances.

Analysis of umami taste and their contributing compounds in edible fungi based on electronic tongue, sensory evaluation, and chemical analysis

Edible fungi are rich in nutrients and have unique umami taste, which varies with genotypes, growth conditions, and harvest time. In this study, umami compounds in 12 species of edible fungi are analyzed and identified by electronic tongue. Through principal component analysis and discriminant factor analysis, these 2 methods could be successfully distinguished the variety of 12 edible fungi. Besides, the umami intensity of edible fungi soup is also evaluated by sensory and chemical analysis methods, for example, Tricholoma matsutake is 5.60 ± 0.34 and 5.17 ± 0.38, Coprinus comatus is 7.70 ± 0.23 and 9.83 ± 0.34 through sensory evaluation and electronic tongue respectively, followed by establishing the correlation from the response data by PLS (partial least squares analysis). According to the PLS model, with a correlation coefficients of calibration models greater than 0.7 and the low root mean square error of calibration and root mean square error of prediction values, the results correlate well with each other. Therefore, we can indicate that the electronic tongue is able to analyze and evaluate the umami intensity of edible fungi to some extent.

Electronic tongue for the simple and rapid determination of taste and odor compounds in water

Taste and odor (T&O) compounds present in natural water bodies could originate from algae. In this study, alga-generated compounds that can cause T&O issues in water, such as geosmin (GE), 2-Methylisoborneol (MIB), 2,4,6-Trichloroanisole (TCA), 2-Methylbenzofuran (MB), 2-Isopropyl-3-methoxypyrazine (IPMP), 2-Isobutyl-3-methoxypyrazine (IBMP), cis-3-Hexenyl acetate (HA), trans,trans-2,4-Heptadienal (HD), trans,cis-2,6-Nonadienal (ND), and trans-2-Decenal (DN), were determined through solid-phase microextraction coupled with gas chromatography/mass spectrometry (HS-SPME GC/MS) and electronic tongue (E-tongue), and the results from the two techniques were compared. Although HS-SPME GC/MS facilitates the detection and quantification of T&O compounds with high precision and accuracy, the sample preparation and handling is difficult and the analysis time (1 h) is longer than those of other analytical methods. E-tongue can be used as an alternative analytical method for water quality analysis and risk management because it enables controlled and rapid analysis (3 min) of T&O compounds in water at a low cost. Notably, principal component analysis indicated that E-tongue can discriminate and quantify eight T&O compounds at as low as 0.02 μg L-1 concentration. Further, partial least squares analysis confirmed that the sensor exhibits high sensitivity to concentration changes. The sensors with the highest variable importance in projection scores were determined to be SCS (1.39 and 1.38) for GE and MIB, CTS (1.34) for IPMP, CPS (1.33) for IBMP, AHS (1.42) for HA, ANS (1.22) for HD, and NMS (1.14 and 1.19) for ND and DN.

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors

Integrating machine learning with surface-enhanced Raman scattering (SERS) accelerates the development of practical sensing devices. Such integration, in combination with direct detection or indirect analyte capturing strategies, is key to achieving high predictive accuracies even in complex matrices. However, in-depth understanding of spectral variations arising from specific chemical interactions is essential to prevent model overfit. Herein, we design a machine-learning-driven "SERS taster" to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at parts-per-million levels. Our receptors employ numerous noncovalent interactions to capture chemical functionalities within flavor molecules. By strategically combining all receptor-flavor SERS spectra, we construct comprehensive "SERS superprofiles" for predictive analytics using chemometrics. We elucidate crucial molecular-level interactions in flavor identification and further demonstrate the differentiation of primary, secondary, and tertiary alcohol functionalities. Our SERS taster also achieves perfect accuracies in multiplex flavor quantification in an artificial wine matrix.

Human breast milk, infant formula, and follow-up milks comparison by electronic tongue

Human breast milk, infant formula, and follow-up milks were tested by a commercial electronic tongue (αAstree, Alpha MOS) with the aim to determine taste diversity, since it has been recently shown that infants exposed to different tastes early in life, develop different food preference at a later age. Human milk (36 samples) were obtained from 13 lactating women, while 12 samples of infant formula and 14 samples of follow-up milk were obtained from the Croatian market and opened prior to analysis. Human breast milk samples showed a much higher diversity than both infant formulae and follow-up milks. These results suggest that breast-fed infants are exposed to a broader sensory experience, while formula fed infants are exposed to less diverse taste. Future studies will probably answer how this influences later food choice, taste preferences, and consequently, risk of obesity and other chronic diseases.

Water discrimination based on the kinetic variations of AgNP spectrum

The assessment of water quality and its classification have considerable importance on public health. This requires monitoring of a wide range of physical, chemical and biological parameters. Here, an array of sensors composed of absorbances in different wavelengths in a kinetic process was used for classification. The data were obtained in the kinetic absorbance variations of silver nanoparticles (AgNPs) in the presence of different mineral waters. Spectral variations with time for each water sample were vectorized, and the matrix composed of these vectors was analyzed using principal component analysis (PCA) and hierarchical cluster analysis (HCA) as unsupervised clustering methods. The distinct clusters of nine different water samples were obtained using PCA and clustering by HCA resulted in an error rate of only 14.8%, which corresponds to misclassification of 4 water samples out of 27. The ability of the method for the discrimination of water samples using AgNP as the sole reagent can be attributed to the high dimensionality of data and the influence of the chemical environment in each water sample on the absorbance variations of AgNPs.

Water samples can be classified by AgNPs.

Organoleptic Analysis of Drinking Water Using an Electronic Tongue Based on Electrochemical Microsensors

The standards that establish water’s quality criteria for human consumption include organoleptic analysis. These analyses are performed by taste panels that are not available to all water supply companies with the required frequency. In this work, we propose the use of an electronic tongue to perform organoleptic tests in drinking water. The aim is to automate the whole process of these tests, making them more economical, simple, and accessible. The system is composed by an array of electrochemical microsensors and chemometric tools for multivariable processing to extract the useful chemical information. The array of sensors is composed of six Ion-Sensitive Field Effect Transistors (ISFET)-based sensors, one conductivity sensor, one redox potential sensor, and two amperometric electrodes, one gold microelectrode for chlorine detection, and one nanocomposite planar electrode for sensing electrochemical oxygen demand. A previous study addressed to classify water samples according to taste/smell descriptors (sweet, acidic, salty, bitter, medicinal, chlorinous, mouldy, and earthy) was performed. A second study comparing the results of two organoleptic tests (hedonic evaluation and ranking test) with the electronic tongue, using Partial Least Squares regression, was conducted. The results show that the proposed electronic tongue is capable of analyzing water samples according to their organoleptic characteristics, which can be used as an alternative method to the taste panel.

Analysis and Evaluation of the Characteristic Taste Components in Portobello Mushroom

To identify the characteristic taste components of the common cultivated mushroom (brown; Portobello), Agaricus bisporus, taste components in the stipe and pileus of Portobello mushroom harvested at different growth stages were extracted and identified, and principal component analysis (PCA) and taste active value (TAV) were used to reveal the characteristic taste components during the each of the growth stages of Portobello mushroom. In the stipe and pileus, 20 and 14 different principal taste components were identified, respectively, and they were considered as the principal taste components of Portobello mushroom fruit bodies, which included most amino acids and 5'-nucleotides. Some taste components that were found at high levels, such as lactic acid and citric acid, were not detected as Portobello mushroom principal taste components through PCA. However, due to their high content, Portobello mushroom could be used as a source of organic acids. The PCA and TAV results revealed that 5'-GMP, glutamic acid, malic acid, alanine, proline, leucine, and aspartic acid were the characteristic taste components of Portobello mushroom fruit bodies. Portobello mushroom was also found to be rich in protein and amino acids, so it might also be useful in the formulation of nutraceuticals and functional food.

PRACTICAL APPLICATION

The results in this article could provide a theoretical basis for understanding and regulating the characteristic flavor components synthesis process of Portobello mushroom.

Quantitative Determination of Spring Water Quality Parameters via Electronic Tongue

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in spring water is proposed here. The electronic voltammetric tongue consisted of a set of four noble electrodes (iridium, rhodium, platinum, and gold) housed inside a stainless steel cylinder. These noble metals have a high durability and are not demanding for maintenance, features required for the development of future automated equipment. A pulse voltammetry study was conducted in 83 spring water samples to determine concentrations of nitrate (range: 6.9–115 mg/L), sulfate (32–472 mg/L), fluoride (0.08–0.26 mg/L), chloride (17–190 mg/L), and sodium (11–94 mg/L) as well as pH (7.3–7.8). These parameters were also determined by routine analytical methods in spring water samples. A partial least squares (PLS) analysis was run to obtain a model to predict these parameter. Orthogonal signal correction (OSC) was applied in the preprocessing step. Calibration (67%) and validation (33%) sets were selected randomly. The electronic tongue showed good predictive power to determine the concentrations of nitrate, sulfate, chloride, and sodium as well as pH and displayed a lower R² and slope in the validation set for fluoride. Nitrate and fluoride concentrations were estimated with errors lower than 15%, whereas chloride, sulfate, and sodium concentrations as well as pH were estimated with errors below 10%.

Use of JAR-Based Analysis for Improvement of Product Acceptance: A Case Study on Flavored Kefirs

A common question of dairy product developments is the possible success of the new product. Several publications reported successful results using just-about-right (JAR) scales; although there is some debate about their advantages/disadvantages. This study highlights the limitations and opportunities of JAR scales and penalty analysis of fruit flavored kefirs. The first question is whether penalty analysis results help to improve the product and thus its overall liking (OAL)? The second question is what happens to those who rated the products "ideal" (JAR) before product development when evaluating the new products? Fruit flavored live-flora stirred-type kefir samples were formulated and evaluated by 92 consumers before and after the JAR-based product development. The OAL of two products significantly increased after product development. A new visualization tool is introduced, which shows what happens to those who rated the attribute as JAR but the attribute has been modified. A general product development scheme is also introduced for JAR-based kefir product development.

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits

In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry.

PRACTICAL APPLICATION

Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods for characterizing food flavors. Based on those results, one can draw a conclusion that the fusion system composed of E-tongue and E-nose could guarantee a satisfying result in the prediction of sensory attributes and volatile components for fruit quality profile.

Determination of rice sensory quality with similarity analysis-artificial neural network method in electronic tongue system

A novel similarity analysis-artificial neural network method was developed in electronic tongue system for predicting rice sensory quality.

Characterization of conventional, biodynamic, and organic purple grape juices by chemical markers, antioxidant capacity, and instrumental taste profile

The objectives of this study were to characterize organic, biodynamic, and conventional purple grape juices (n = 31) produced in Europe based on instrumental taste profile, antioxidant activity, and some chemical markers and to propose a multivariate statistical model to analyze their quality and try to classify the samples from the 3 different crop systems. Results were subjected to ANOVA, correlation, and regression analysis, principal component analysis (PCA), hierarchical cluster analysis (HCA), soft independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLSDA). No statistical significant differences (P > 0.05) were observed among juices from the 3 crop systems. Using PCA and HCA, no clear separation among crop systems was observed, corroborating the ANOVA data. However, PCA showed that the producing region highly affects the chemical composition, electronic tongue parameters, and bioactivity of grape juices. In this sense, when organic and biodynamic were grouped as "nonconventional" juices, SIMCA model was able to discriminate 12 out of 13 organic/biodynamic juices and 17 out of 18 conventional juices, presenting an efficiency of 93.5%, while 11 out of 13 non-conventional and 100% conventional grape juices were correctly classified using PLSDA. The use of electronic tongue and the determination of antioxidant properties and major phenolic compounds have shown to be a quick and accurate analytical approach to assess the quality of grape juices.

Applying parallel factor analysis and Tucker-3 methods on sensory and instrumental data to establish preference maps: case study on sweet corn varieties

BACKGROUND

Traditional internal and external preference mapping methods are based on principal component analysis (PCA). However, parallel factor analysis (PARAFAC) and Tucker-3 methods could be a better choice. To evaluate the methods, preference maps of sweet corn varieties will be introduced.

RESULTS

A preference map of eight sweet corn varieties was established using PARAFAC and Tucker-3 methods. Instrumental data were also integrated into the maps. The triplot created by the PARAFAC model explains better how odour is separated from texture or appearance, and how some varieties are separated from others.

CONCLUSION

Internal and external preference maps were created using parallel factor analysis (PARAFAC) and Tucker-3 models employing both sensory (trained panel and consumers) and instrumental parameters simultaneously. Triplots of the applied three-way models have a competitive advantage compared to the traditional biplots of the PCA-based external preference maps. The solution of PARAFAC and Tucker-3 is very similar regarding the interpretation of the first and third factors. The main difference is due to the second factor as it differentiated the attributes better. Consumers who prefer 'super sweet' varieties (they place great emphasis especially on taste) are much younger and have significantly higher incomes, and buy sweet corn products rarely (once a month). Consumers who consume sweet corn products mainly because of their texture and appearance are significantly older and include a higher ratio of men.

Descriptive sensory analysis of Aceto Balsamico Tradizionale di Modena DOP and Aceto Balsamico Tradizionale di Reggio Emilia DOP

BACKGROUND

Aceto Balsamico Tradizionale (ABT) is a typical Italian vinegar available in two different forms: Aceto Balsamico Tradizionale di Modena DOP (ABTM) and Aceto Balsamico Tradizionale di Reggio Emilia DOP (ABTRE). ABT is obtained by alcoholic fermentation and acetic bio-oxidation of cooked grape must and aged at least 12 years in wooden casks and is known and sold around the world. Despite this widespread recognition, data on sensory characteristics of these products are very scarce. Therefore a descriptive analysis was conducted to define a lexicon for the ABT sensory profile and to create a simple, stable and reproducible synthetic ABT for training panellists.

RESULTS

A lexicon of 20 sensory parameters was defined and validated and a synthetic ABT was prepared as standard reference. Simple standards for panellist training were also defined and the sensory profiles of ABTM and ABTRE were obtained.

CONCLUSION

The obtained results confirm that descriptive analysis can be used for the sensory characterisation of ABT and that the sensory profiles of ABTM and ABTRE are very different. Furthermore, the results demonstrate that a lexicon and proper standard references are essential for describing the sensory qualities of ABT both for technical purposes and to protect the product from commercial fraud.

Combining electronic tongue array and chemometrics for discriminating the specific geographical origins of green tea

The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing.

Training of panellists for the sensory control of bottled natural mineral water in connection with water chemical properties

As bottled mineral water market is increasing in the world (especially in emergent and developed countries), the development of a simple protocol to train a panel to evaluate sensory properties would be a useful tool for natural drinking water industry. A sensory protocol was developed to evaluate bottled natural mineral water (17 still and 10 carbonated trademarks). The tasting questionnaire included 13 attributes for still water plus overall impression and they were sorted by: colour hues, transparency and brightness, odour/aroma and taste/flavour/texture and 2 more for carbonated waters (bubbles and effervescence). The training lasted two months with, at least, 10 sessions, was adequate to evaluate bottled natural mineral water. To confirm the efficiency of the sensory training procedure two sensory groups formed the whole panel. One trained panel (6 persons) and one professional panel (6 sommeliers) and both participated simultaneously in the water tasting evaluation of 3 sample lots. Similar average scores obtained from trained and professional judges, with the same water trademarks, confirmed the usefulness of the training protocol. The differences obtained for trained panel in the first lot confirm the necessity to train always before a sensory procedure. A sensory water wheel is proposed to guide the training in bottled mineral water used for drinking, in connection with their chemical mineral content.