Electronic Tongue System Research Articles

Metabolite Profiling and Identification of Sweet/Bitter Taste Compounds in the Growth of Cyclocarya Paliurus Leaves Using Multiplatform Metabolomics.

Cyclocarya paliurus tea, also known as "sweet tea", an herbal tea with Cyclocarya paliurus leaves as raw material, is famous for its unique nutritional benefits and flavor. However, due to the unique "bittersweet" of Cyclocarya paliurus tea, it is still unable to fully satisfy consumers' high-quality taste experience and satisfaction. Therefore, this study aimed to explore metabolites in Cyclocarya paliurus leaves during their growth period, particularly composition and variation of sweet and bitter taste compounds, by combining multi-platform metabolomics analysis with an electronic tongue system and molecular docking simulation technology. The results indicated that there were significant differences in the contents of total phenols, flavonoids, polysaccharides, and saponins in C. paliurus leaves in different growing months. A total of 575 secondary metabolites were identified as potential active metabolites related to sweet/bitter taste using nontargeted metabolomics based on UHPLC-MS/MS analysis. Moreover, molecular docking technology was utilized to study interactions between the candidate metabolites and the sweet receptors T1R2/T1R3 and the bitter receptors T2R4/T2R14. Six key compounds with high sweetness and low bitterness were successfully identified by using computational simulation analysis, including cis-anethole, gluconic acid, beta-D-Sedoheptulose, asparagine, proline, and citrulline, which may serve as candidates for taste modification in Cyclocarya paliurus leaves. These findings provide a new perspective for understanding the sweet and bitter taste characteristics that contribute to the distinctive sensory quality of Cyclocarya paliurus leaves.

An Integration of UPLC-Q-TOF-MS, GC-MS, Electronic Nose, Electronic Tongue, and Molecular Docking for the Study of the Chemical Properties and Flavor Profiles of Moringa oleifera Leaves

Moringa oleifera leaves (MOLs) have gained significant attention due to their nutritional and biological activity. Therefore, this study aimed to examine its flavor characteristics and underlying compositions. In this study, we used ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), gas chromatography-mass spectrometry (GC-MS), electronic nose, electronic tongue, and molecular docking to comprehensively investigate the chemical properties and flavor profiles of MOLs. UPLC-Q-TOF-MS and GC-MS were instrumental in identifying the 20 non-volatile and 19 volatile constituents of MOLs, respectively. The electronic nose and electronic tongue systems provided an objective evaluation of the sweet, bitter, and spicy attributes and flavor characteristics of MOLs. Concurrently, molecular docking was employed to elucidate the material basis of flavor profiles. It revealed that glucosinolates and flavonoids are probably the key components for the bitter taste of MOLs. The sweet taste may be attributed to glucosinolates and flavonoids. The spicy scent appears to be linked to the presence of glucosinolates and alkaloids. The integration of these techniques confers a thorough understanding of the chemical composition and sensory properties of MOLs. These findings have significant implications for innovative applications in the food industry as well as pharmaceuticals and agriculture sectors; furthermore, they contribute towards enhancing the perception of Moringa oleifera as a valuable natural resource.

Orodispersible Dosage Forms with Rhinacanthin-Rich Extract as a Convenient Formulation Dedicated to Pediatric Patients.

Rhinacanthins, derived from Rhinacanthus nasutus, widely used in traditional medicine, exhibit antifungal, anticancer, antiviral, antibacterial, and antiplatelet aggregation effects. Recently, their anti-diabetic activity was confirmed, which makes them an interesting natural alternative in the therapy of the early stage of diabetes mellitus. The aim of this study was to demonstrate the possibility of formulating orodispersible tablets (ODTs) and orodispersible films (ODFs) containing rhinacanthin-rich extract (RRE). Tablets with 50 mg or 100 mg of RRE were produced by direct compression. ODFs were manufactured by casting of Lycoat RS 720 or polyvinyl alcohol solution with RRE and additional excipients. The mechanical properties and disintegration times of the prepared formulations were studied. The effectiveness of taste masking was analyzed with an electronic tongue system. Six months simplified stability studies were performed in conditions complying to ICH guidelines. Appropriate friability of ODTs was achieved, despite low tensile strength (0.45-0.62 MPa). All prepared ODFs successfully met the acceptance criteria regarding Young's modulus, tensile strength, and elongation at break. The observed variations in their mechanical properties were dependent on the type and quantity of polymers and plasticizers used. Disintegration time of ODTs ranged from 38.7 s to 54.2 s, while for ODFs from 24.2 to 40 s in the pharmacopoeial apparatus. Analyses made with the electronic tongue showed the significant taste-masking effect in both formulations. The addition of sucralose as a sweetener and menthol with mint flavor as a taste-masking agent was sufficient to mask an RRE's taste in the case of ODTs and ODFs. Stability studies of ODTs packed in the PVC/Alu blisters showed a decrease in the RRE content below 90% after 6 months. However, ODFs with PVA were physicochemically stable for 6 months while being stored in Alu/Alu sachets. Our study proved for the first time the possibility of the formulation of orodispersible dosage forms with RRE, characterized by good mechanical properties, disintegration time, and appropriate taste masking.

Distinct Changes in Metabolic Profile and Sensory Quality with Different Varieties of Chrysanthemum (Juhua) Tea Measured by LC-MS-Based Untargeted Metabolomics and Electronic Tongue.

Chrysanthemum tea, a typical health tea with the same origin as medicine and food, is famous for its unique health benefits and flavor. The taste and sensory quality of chrysanthemum (Juhua) tea are mainly determined by secondary metabolites. Therefore, the present research adopted untargeted metabolomics combined with an electronic tongue system to analyze the correlation between the metabolite profiles and taste characteristics of different varieties of chrysanthemum tea. The results of sensory evaluation showed that there were significant differences in the sensory qualities of five different varieties of chrysanthemum tea, especially bitterness and astringency. The results of principal component analysis (PCA) indicated that there were significant metabolic differences among the five chrysanthemum teas. A total of 1775 metabolites were identified by using untargeted metabolomics based on UPLC-Q-TOF/MS analysis. According to the variable importance in projection (VIP) values of the orthogonal projections to latent structures discriminant analysis (OPLS-DA), 143 VIP metabolites were found to be responsible for metabolic changes between Huangju and Jinsi Huangju tea; among them, 13 metabolites were identified as the key metabolites of the differences in sensory quality between them. Kaempferol, luteolin, genistein, and some quinic acid derivatives were correlated with the "astringency" attributes. In contrast, l-(-)-3 phenyllactic acid and L-malic acid were found to be responsible for the "bitterness" and "umami" attributes in chrysanthemum tea. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the flavonoid and flavonol biosynthesis pathways had important effects on the sensory quality of chrysanthemum tea. These findings provide the theoretical basis for understanding the characteristic metabolites that contribute to the distinctive sensory qualities of chrysanthemum tea.

Electrochemical fingerprinting of complex solutions using boron-doped diamond electrodes: Advanced classifications by machine learning

Boron-doped diamond (BDD) electrochemical sensors exhibit high sensitivity and a wide-ranging voltammogram scan capability, facilitating the generation of unique fingerprinting information. These attributes are invaluable for the rigorous quality control and real-time monitoring of intricate liquid compositions. In this study, we present an advanced approach for the authentication of complex liquid using BDD electrochemical sensors, in conjunction with Principal Component Analysis (PCA) and Support Vector Machine (SVM) algorithms. Leveraging the heightened sensitivity of BDD sensor signals, it was demonstrated 96 % accuracy rate in authenticity assessments from 21 distinct varieties of red wines. Additionally, high accuracy of classification by grape varieties was demonstrated using electrochemical fingerprints combined with machine learning. Our novel non-specific electronic tongue system offers multimodal electrochemical fingerprinting, ensuring fast and user-friendly measurements without relying on sensor arrays.

Taste Bud-Inspired Single-Drop Multitaste Sensing for Comprehensive Flavor Analysis with Deep Learning Algorithms.

The electronic tongue (E-tongue) system has emerged as a significant innovation, aiming to replicate the complexity of human taste perception. In spite of the advancements in E-tongue technologies, two primary challenges remain to be addressed. First, evaluating the actual taste is complex due to interactions between taste and substances, such as synergistic and suppressive effects. Second, ensuring reliable outcomes in dynamic conditions, particularly when faced with high deviation error data, presents a significant challenge. The present study introduces a bioinspired artificial E-tongue system that mimics the gustatory system by integrating multiple arrays of taste sensors to emulate taste buds in the human tongue and incorporating a customized deep-learning algorithm for taste interpretation. The developed E-tongue system is capable of detecting four distinct tastes in a single drop of dietary compounds, such as saltiness, sourness, astringency, and sweetness, demonstrating notable reversibility and selectivity. The taste profiles of six different wines are obtained by the E-tongue system and demonstrated similarities in taste trends between the E-tongue system and user reviews from online, although some disparities still exist. To mitigate these disparities, a prototype-based classifier with soft voting is devised and implemented for the artificial E-tongue system. The artificial E-tongue system achieved a high classification accuracy of ∼95% in distinguishing among six different wines and ∼90% accuracy even in an environment where more than 1/3 of the data contained errors. Moreover, by harnessing the capabilities of deep learning technology, a recommendation system was demonstrated to enhance the user experience.

Evaluation of umami taste in Hanwoo with different feed sources by chemical analysis, electronic tongue analysis, and sensory evaluation

This study aimed to evaluate umami taste in Hanwoo with different feed by chemical analysis, sensory evaluation and an electronic tongue system. Hanwoo cattle were divided into three groups: control group (fed only total mixed ration [TMR]), T1 (fed soybean meal + TMR), and T2 (fed soybean meal + corn-dried distiller’s grain with solubles [Corn DDGS] + TMR). The three most abundant fatty acids (C18:1n-9, C16:0, and C18:0) in the T1, T2, and control groups accounted for 83.63%, 86.07%, and 85.52% of the total fatty acid content, respectively. Umami taste-related glutamic acid levels were significantly high in T1 (109.89 mg/kg), followed by T2 (66.66 mg/kg) and control (47.27 mg/kg). Fatty acid levels showed a high correlation with umami taste. The results of this study showed that the amino acid and fatty acid levels had been affected by feed types and soybean- or Corn DDGS-based feed potentially enhanced Hanwoo’s umami flavor.

Comparison of taste characteristics between koji mold–ripened cheese and Camembert cheese using an electronic tongue system

Koji mold, classified in the genus Aspergillus, is used to produce traditional Japanese fermented foods such as miso, soy sauce, and sake. In recent years, the application of koji mold to cheese ripening has attracted attention, and cheese surface-ripened with koji mold (koji cheese) has been studied. In this study, to evaluate the taste characteristics of koji cheese, an electronic tongue system was employed to measure the taste values of cheese samples ripened using 5 strains of koji mold in comparison with commercial Camembert cheese. All koji cheese samples exhibited lower sourness and greater bitterness, astringency, saltiness, and umami richness than the Camembert cheese samples. The intensity of each taste characteristic differed depending on the koji mold strain. These results indicate that koji cheese has a different taste value than conventional mold-ripened cheese. Furthermore, the results also indicate that various taste characteristics can be achieved by selecting different koji molds.

Characterization of selected commercially available grilled lamb shashliks based on flavor profiles using GC-MS, GC × GC-TOF-MS, GC-IMS, E-nose and E-tongue combined with chemometrics

HS-SPME-GC-MS, SPME-Arrow-GC × GC-TOF-MS, HS-GC-IMS, Electronic-nose, and Electronic-tongue systems were applied in a feasibility study of the flavor characterization of five commercially available Chinese grilled lamb shashliks. A total of 198 volatile organic compounds (VOCs) were identified (∼71% by GC × GC-TOF-MS). Using data fusion strategies, five predictive models were applied to the composition of VOCs and brand identification of the lamb shashliks. Compared with partial least squares regression, support vector machine, deep neural network, and RegBoost modeling, a momentum deep belief network model performed best in predicting VOCs content and identifying shashlik brands (R2 above 0.96, and RMSE below 0.1). Intelligent sensory technology combined with chemometrics is a promising approach to the flavor characterization of shashliks and other food matrices.

Intelligent electronic tongue system for the classification of genuine and false honeys

ABSTRACT Honey quality is a global concern since this product is highly susceptible to adulteration, given its competitive price. As a reliable strategy for honey authenticity determination, this work introduces an intelligent classification system that considers the pattern recognition point of view to develop an economical and quick analytical method to identify and differentiate genuine from adulterated honey. This work used an electronic tongue composed of three working electrodes of carbon, platinum, and gold. The system used Cyclic voltammetry to obtain data from 50 genuine and 50 adulterated honey samples. Subsequently, the system used multivariate data analysis using a pattern recognition methodology composed of three big stages, including data organization and normalization, dimensionality reduction, and k-Nearest Neighbors (k-NN) as a classification method. The process was validated with the Leave One Out Cross Validation technique (LOOCV), reaching a classification accuracy performance of 100%. The results show that it was possible the development of a combined methodology between analytical tools and chemometrics for an in-situ, quick and efficient authenticity honey evaluation.

Electrolyte drink analysis by electronic tongue using carbon nanotube paste electrodes

Electrolyte drink becomes very popular nowadays that drinking aid in rehydration which mostly contain water, sugar, and electrolytes (Na, K, Mg, Ca ions). In this work, several electrolyte drinks including M sport, M-plus, Sponsor, Sponsor citrus, Gatorade were analyzed by electronic tongue comparing to mineral and tap water. Non-specified electrode array used in electronic tongue was fabricated by mixing carbon nanotube with copper oxide or several types of metal-phthalocyanine (MPc) including MnPc, FePc, CoPc and ZnPc. The electronic tongue system was developed by using automatic electrochemical measurement setup with rotating eight beaker tray together with electrode holder slider in up and down position. This system was controlled by Arduino with developed python protocol. The cyclic voltammetry measurement indicated an ion current flow between electrodes with peaks in some cases. These cyclic graphs were converted to the specific line graphs for further analysis. Principle component analysis was used to classify the signal into several distingue groups. It was found that with the first two principal components, the mineral water and tap water can be totally separate from the other electrolyte drinks with total variation of about 77%.

Taste Compound Generation and Variation in Chinese Water Chestnut (Eleocharis dulcis (Burm.f.) Trin. ex Hensch.) Processed with Different Methods by UPLC-MS/MS and Electronic Tongue System.

Chinese water chestnut (CWC) is popular among consumers due to its unique flavor and crisp and sweet taste. Thus far, the key substances affecting the taste compound of CWC are still unclear. In this study, we used UPLC-MS/MS and an electronic tongue system to study the effects of four typical steaming and cooking methods, cooking without peel for 10 min (PC), steaming without peel for 15 min (PS), cooking with peel for 30 min (WPC), steaming with peel for 30 min (WPS), on the taste compound generation and variation of CWC, and revealed the secret of its crisp and sweet taste. The results show that the electronic tongue can effectively identify the taste profile of CWC, and the effective tastes of CWC were umami, bitterness, saltiness, and sweetness. We screened 371 differential compounds from 640 metabolic species. Among them, nucleotides and their derivatives, carbohydrates, organic acids and their derivatives, and amino acids and their derivatives are closely related to the key taste of CWC, and these compounds affected the taste of CWC through six related metabolic pathways: oxidative phosphorylation and purine metabolism; alanine, aspartate, and glutamate; bile secretion; amino sugar and nucleotide sugar metabolism; the phenylpropane pathway; and toluene degradation. This study reveals the potential metabolic causes of taste compound generation and variation in the taste of CWC.

Yogurt classification using an electronic tongue system and machine learning techniques

• The development of a pattern recognition methodology for the classification of yogurts. • The use of a dataset of yogurts from the region of Boyacá-Colombia. • The development of an electronic tongue system for the classification of yogurt Electronic tongue sensor arrays allow the analysis of liquid substances and provide information for classification tasks to artificially emulate a taste recognition system. These kinds of devices are needed for the alimentary industry because they ensure their products’ quality; however, they are still in the development phase because of the components required for their robustness. This paper presents an electronic tongue system for yogurt classification to contribute to developing automatic systems for this industry. The developed system uses a sensor array with Screen-Printed Electrode sensors, a potentiostat, and a multiplexer to handle eight different sensors. A pattern recognition methodology for the processing of multi-step amperometry signals is defined. It comprises the following stages: First, a data unfolding process to build a two-dimensional matrix with the information from the sensors is applied. Next, raw data is normalized using the mean-centered group scaling method. Then, a dimensionality reduction stage is applied. Two methods are evaluated, the t-distributed stochastic neighbor embedding ( t -SNE) and Principal Component Analysis (PCA). Subsequently, a supervised machine learning classification algorithm is used. Five methods were evaluated to determine the best classifier: LDA, classification trees(C4.5), naive Bayes, k -NN, and SVM. A dataset of 151 samples of 8 different yogurts is used for the methodology validation. This validation is executed through a Leave One Out Cross Validation (LOOCV) method. Final classification accuracy of 100% was obtained When PCA and LDA or t -SNE and k -NN were used; however, from the computational point of view, a shorter execution time was evidenced for PCA and LDA, showing the convenience in its use in the methodology.

Synergistic effect of lactoferrin and red yeast rice on the quality characteristics of novel plant-based meat analog patties

This study reports physicochemical, textural characteristics, sensory properties, and fatty acid profile of plant-based meat analog (PBMA) patties formulated with lactoferrin (LTF), red yeast rice (RYR), and the combination of LTF and RYR compared to control (beef). The comparison revealed that the raw patties in the treated groups were lighter (42–46) in color (p < 0.05) and displayed moderate yellowness (17–20) than control. Conversely, in cooked patties, all color indices decreased meaningfully (p < 0.05) compared to control. The proximate composition validated higher moisture (60–61%), protein (22–25%), and fat (6–7%) in comparison to control. Whereas, shrinkage percentage (2.35–5.33%) and cooking yield (CY%) (2.24–2.11%) reduced considerably (p < 0.05) in treated samples than in control. The electronic tongue system (ETS) taste traits of sourness and saltiness were negatively affected by the addition of LTF and RYR, while the umami taste attribute displayed a positive tendency in the treated samples compared to the control. Collectively, the incorporation of natural colorants substantially affected the two major clusters of fatty acid profiles. The principal component analysis model showed clear separation (Dim1 and Dim2) in taste and textural attributes, which necessitates a further investigation to develop the superior quality of plant-based meat products.

A Novel Voltammetric Electronic Tongue Based on Nanocomposites Modified Electrodes for the Discrimination of Red Wines from Different Geographical Origins

A novel voltammetric electronic tongue (VE-tongue) system based on three nanocomposites modified working electrodes was used for the discrimination of red wine from different geographical origins. The three types of modified working electrodes were fabricated to detect glucose (Glu), tartaric acid (TA), and non-specific flavor information in a red wine sample, respectively. The electrochemical properties of three electrodes were tested by cyclic voltammetric method, and pH, accumulation time, and scan rates were optimized for Glu and TA sensors. Scanning electron microscopy (SEM), X-ray proton spectrum (XPS), and X-ray diffraction (XRD) were used for the characterization of modified materials. This sensor array was then applied to identify four kinds of red wines from different geographical origins, and the multi-frequency and potential steps (STEP) method was used to obtain flavor information regarding rice wines. The classification ability of this VE-tongue system was evaluated by using partial least squares (PLS) regression and principal component analysis (PCA), while back propagation neural network (BPNN), random forest (RF), support vector machines (SVM), deep neural network (DNN), and K-nearest neighbor (KNN) were used for the prediction. The results showed that PCA could explain about the 95.7% of the total variance, and BPNN performed best in the prediction work (the prediction accuracy was 95.8%). Therefore, the VE-tongue system with BPNN was chosen to effectively discriminate red wines from different geographical origins, and the novel VE-tongue aiming at red wine discrimination with high accuracy and lower cost was established.

An Olfactory-Taste Synesthesia Model Combined With Electronic Nose and Electronic Tongue to Identify Flavor Substances

The main task of the electronic nose (e-nose) and electronic tongue (e-tongue) is to identify flavour substances. The human olfactory-taste nerve mechanisms affect the identification of flavour substances by the e-nose and e-tongue systems. Therefore, this study proposed an olfactory-taste synesthesia model built on the human olfactory-taste nerve pathways to process the e-nose and e-tongue data for flavour substance identification. Next, this study simulated the power spectral density and synchronization for the synesthesia model nodes and validated the bionic performance of the synesthesia model. The results suggested that, first, the synesthesia model characterized the dynamic characteristics of the human olfactory-taste nerve conduction pathways. Second, during stimulation, the power spectral density for the synesthesia model nodes exhibited 1/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> characteristics. The synesthesia model nodes exhibited synchronization upon stimulation. The 1/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> characteristics and synchronization indicated the bionic performance of the synesthesia model. Finally, this study chose beer and apple samples as flavour substances and input the olfactory and taste information for flavour substances into the synesthesia model for identification. Compared with the identification results of a single e-nose, single e-tongue, and the combined electronic nose and tongue systems under the multipattern recognition models, the synesthesia model acquired best accuracy, Kappa coefficients, and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -scores in the identification for the beer and apple samples. In conclusion, the effective identification results for flavour substances can be achieved by the synesthesia model.

Electronic Nose and Tongue for Assessing Human Microbiota

The technological developments of recent times have allowed the use of innovative approaches to support the diagnosis of various diseases. Many of such clinical conditions are often associated with metabolic unbalance, in turn producing an alteration of the gut microbiota even during asymptomatic stages. As such, studies regarding the microbiota composition in biological fluids obtained by humans are continuously growing, and the methodologies for their investigation are rapidly changing, making it less invasive and more affordable. To this extent, Electronic Nose and Electronic Tongue tools are gaining importance in the relevant field, making them a useful alternative—or support—to traditional analytical methods. In light of this, the present manuscript seeks to investigate the development and use of such tools in the gut microbiota assessment according to the current literature. Significant gaps are still present, particularly concerning the Electronic Tongue systems, however the current evidence highlights the strong potential such tools own to enter the daily clinical practice, with significant advancement concerning the patients’ acceptability and cost saving for healthcare providers.

Investigation of Chloroquine Resinate Feasibility and In Vitro Taste Masking Evaluation for Pediatric Formulations.

In this study, chloroquine resinates were prepared at a 1:1 (w:w) drug-to-resin ratio using the batch method with polacrilex (PC), sodium polystyrene sulfonate (SPS), and polacrilin potassium (PP) ion exchange resins (IER). The influence of drug/resin ratio and pH of the medium on drug loading efficiency was explored. UV-VIS spectrophotometric analysis showed that SPS resin had high loading efficiency for chloroquine diphosphate (CLP), above 89%, regardless of the pH. PP resin was more effective at pH 5.0 (90.68%) than at pH 1.0 (2.09%), and PC resin had only 27.63% of CLP loading efficiency. CLP complexation with IER yielded amorphous mixtures according to results from differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD), thus indicating drug-resin interaction. The taste masking efficiency was evaluated with in vitro methods using an adapted dissolution test and an electronic tongue system. During dissolution tests, SPS released only 1.0% of CLP after 300 s, while PP released over 10% after 90 s in simulated saliva solution. The electronic tongue distinguished the samples containing CLP, resins, and resinates by using multidimensional projection techniques that indicated an effective drug taste masking. In an accelerated stability study, the drug contents did not decrease in chloroquine resinates, and there was no physical degradation of the resinates after 60 days. Using chloroquine resinates therefore represents a novel way to evaluate taste masking in vitro which is relevant for the early formulation development process.

Electronic tongue as innovative instrument for detection of crocin concentration in saffron (Crocus sativus L.).

Electronic tongue is a new approach for simple and fast detection, classification, and quantification of the solved compounds. Crocin is the main source of color of saffron(Crocus sativus L.). An electronic tongue system was used to predict the concentration of saffron crocin in the present study. The measurement system included an electrochemical sensor array based on voltammetry electrodes, a three-electrode cell, a potentiostat, a personal computer. Aqueous analyte were provided by blending pure crocin and different saffron samples from Iran and Spain with distilled water. Output signals of the electronic tongue system were analyzed by principal component analysis and artificial neural networks. Based on principal component analysis, the total variance among pure crocin was 99% and that of saffron samples was 100%. The accuracy of artificial neural network model was 98.80%. The results indicated that the developed electronic tongue system and artificial neural network model can successfully predict crocin concentration in saffron.

A Comparative Study on the Taste Characteristics of Satellite Cell Cultured Meat Derived from Chicken and Cattle Muscles.

This study investigated the amino acid and nucleotide-related compound composition and taste characteristics of cultured muscle tissue (CMT) obtained by culturing satellite cells isolated from chicken and cattle and compared them to those of traditional meat (TM). The content of all amino acids except valine and tyrosine was significantly different between CMT and TM (p<0.05). The amount of glutamic acid was not significantly different between CMT and TM in cattle, but the glutamic acid in chicken CMT was lower than that of TM (p<0.05). Among the nucleotide-related compounds, only the content of inosine-5'-monophosphate (IMP) was significant, and the amount of IMP in CMT derived from chicken and cattle was significantly lower than that of TM (p<0.05). There were significant differences in the taste characteristics assessed by an electronic tongue system, and the umami, bitterness, and sourness values of CMT were significantly lower than those of TM from both chicken and cattle (p<0.05). The results of the present study suggest that it is necessary to develop a satellite cell culture method that could increase the umami and bitterness intensity of CMT and adjust the composition of the growth medium to produce cultured meat with a taste similar to that of TM.