Impact of the Covering Vegetable Oil on the Sensory Profile of Canned Tuna of Katsuwonus pelamis Species and Tuna’s Taste Evaluation Using an Electronic Tongue

The impact of the covering vegetable oil (sunflower oil, refined olive oil and extra virgin olive oil, EVOO) on the physicochemical and sensory profiles of canned tuna (Katsuwonus pelamis species) was evaluated, using analytical techniques and a sensory panel. The results showed that canned tuna covered with EVOO possesses a higher content of total phenols and an enhanced antioxidant capacity. This covering medium also increased the appreciated redness-yellowness color of the canned tuna, which showed a higher chromatic and intense color. Olfactory and kinesthetic sensations were significantly dependent on the type of oil used as covering medium. Tuna succulence and adhesiveness were promoted by the use of EVOO, which also contributed to decreasing the tuna-related aroma sensations. The tuna sensory data could be successfully used to identify the type of vegetable oil used. Moreover, a potentiometric electronic tongue allowed discriminating between the canned tuna samples according to the vegetable oil used (mean sensitivity of 96 ± 8%; repeated K-fold cross-validation) and the fruity intensity of the EVOO (mean sensitivity of 100%; repeated K-fold cross-validation). Thus, the taste sensor device could be a practical tool to verify the authenticity of the declared covering medium in canned tuna and to perceive the differences in consumers' taste.

Comparative flavor analysis of four kinds of sweet fermented grains by sensory analysis combined with GC-MS

Four types of cereals (glutinous rice, purple rice, red rice, yellow millet) were selected to produce sweet fermented grains. Flavor profiles of sweet fermented grains are comparatively studied to distinguish various flavor types by using GC-MS, electronic nose (E-nose), and sensory analysis, and the amino acid composition and physicochemical properties of sweet fermented grains were analyzed. The results showed that the volatile compounds of sweet fermented grains were significantly different. Esters and alcohols were the major volatile compounds in sweet fermented grains. The electronic nose, electronic tongue and sensory analysis jointly verified that the volatile components of sweet fermented grains had differences between them. The sweet fermented grains could be classified based on differences in volatile compounds. In the amino acids analysis, Glu, Pro, Asp and Leu were the most abundant. The difference in physicochemical properties is more helpful to distinguish different types of sweet fermented grains. Correlation analysis between antioxidant active substances and color value showed a positive correlation between with a* value, and a negative correlation with L*, b* value. Our results suggested that there were differences in the flavor characteristics of sweet fermented grains fermented from different types of cereals. The results of the study will provide valuable information for the selection of raw materials for sweet fermented grains.

Use of an electronic tongue in the assessment of highly diluted systems

Introduction: “Eletronic tongue” is a device commonly used in the analysis of tastants, heavy metal ions, fruit juice, wines and also in the development of biosensors [1-3]. Briefly, the e-tongue is constituted by sensing units formed by ultrathin films of distinct materials deposited on gold interdigitated electrodes, which are immersed in liquid samples, followed by impedance spectroscopy measurements [1]. The e-tongue sensor is based on the global selectivity concept, i.e., the materials forming the sensing units are not selective to any substance in the samples, therefore, it allows the grouping of information into distinct patterns of response, enabling the distinction of complex liquid systems [1]. Aim: Our aim was to use e-tongue system for the assessment the homeopathic medicine Belladonna at different degrees of dilution, in attempt to differentiate highly diluted systems. Methods: Ultrathin films forming the sensing units were prepared by the layer-by-layer technique [4], using conventional polyelectrolytes such as poly(sodium styene sulfonate) (PSS) and poly(allylamine) hydrochloride (PAH), chitosan and poly(3,4-ethylenedioxythiophene) (PEDOT). Homeopathic medicines (Belladonna 1cH, 6cH, 12cH and 30cH) were prepared by dilution and agitation according to Hahnemann´s method [5], using ethanol at 30% (w/w) as vehicle. Experimental data acquisition was conducted by blind tests measurements involving Belladonna samples and the vehicle used in the dilutions. Five independent and consecutive measurements were taken for each solution at 1 kHz, which were further analysed by Principal Component Analysis (PCA), a statistical method largely employed to reduce the dimensionality of the original data without losing information in the correlation of the samples [3]. Results: Figure 1 shows that the five independent measurements are grouped quite closed each other for each solution analysed, with a clear distinction of them. Therefore, it was noticed a change in the observed pattern measured at different days, indicating a reduced reproducibility, although the groups of data could still be identified. Discussion: PCA is a powerful tool highly employed to extract relevant information in the correlation of data analysis of e-tongue systems. PCA plots showed a good statistical correlation of the systems (PC1 + PC2 ³ 90%), with the solutions being straightforwardly distinguished each other and also from the vehicle used. Conclusion: Despite the differences of data obtained along distinct days of analysis, the e-tongue could detect differences among the samples tested, even considering the highly diluted cases studied.

Characterization, Classification, and Authentication of Polygonatum sibiricum Samples by Volatile Profiles and Flavor Properties

The importance of monitoring key aroma compounds as food characteristics to solve sample classification and authentication is increasing. The rhizome of Polygonatum sibiricum (PR, Huangjing in Chinese) has great potential to serve as an ingredient of functional foods owing to its tonic effect and flavor properties. In this study, we aimed to characterize and classify PR samples obtained from different processing levels through their volatile profiles and flavor properties by using electronic nose, electronic tongue, and headspace gas chromatography-mass spectrometry. Nine flavor indicators (four odor indicators and five taste indicators) had a strong influence on the classification ability, and a total of 54 volatile compounds were identified in all samples. The traditional Chinese processing method significantly decreased the contents of aldehydes and alkanes, while more ketones, nitrogen heterocycles, alcohols, terpenoids, sulfides, and furans/pyrans were generated in the processing cycle. The results confirmed the potential applicability of volatile profiles and flavor properties for classification of PR samples, and this study provided new insights for determining the processing level in food and pharmaceutical industries based on samples with specific flavor characteristics.

Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue

Plums are one of the commercially important stone fruits that are available on the market in both fresh and processed form and the most sought-after products are prunes, cans, jams, and juices. Maturity, harvest, and post-harvest technologies fundamentally determine the relatively short shelf life of plums which is often threatened by Monilinia spp. Causing brown rot worldwide. The aim of the present research was to use advanced analytical techniques, such as hand-held near infrared spectroscopy (NIRS) and electronic tongue (e-tongue) to detect M. fructigena fungal infection on plums and quantify this fungal contamination in raw plum juices. For this purpose, plums were inoculated with fungal mycelia in different ways (control, intact, and through injury) and stored under different conditions (5 °C, and 24 °C) for eight days. The results obtained with the two instruments were analyzed with chemometric methods, such as linear discriminant analysis (LDA) and partial least squares regression (PLSR). The NIRS-based method proved successful when detectability before the appearance of visible signs of the infection was studied. E-tongue was able to detect and quantify the concentration of juice derived from plum developed with M. fructigena with RMSECV lower than 5% w/w. Overall, the two methods proved to be suitable for discriminating between the treatment groups, however, the classification accuracy was higher for samples stored at 24 °C. The research results show both NIRS and e-tongue are beneficial methods to reduce food waste by providing rapid determination of fruit quality.

The Fungal Communities and Flavor Profiles in Different Types of High-Temperature Daqu as Revealed by High-Throughput Sequencing and Electronic Senses

Polymicrobial co-fermentation is among the distinct character of high-temperature Daqu. However, fungal communities in the three types of high-temperature Daqu, namely, white high-temperature Daqu, black high-temperature Daqu, and yellow high-temperature Daqu, are yet to be characterized. In this study, the fungal diversity, taste, and aroma profiles in the three types of high-temperature Daqu were investigated by Illumina MiSeq high-throughput sequencing, electronic tongue, and electronic nose, respectively. Ascomycota and Basidiomycota were detected as the absolute dominant fungal phylum in all types of high-temperature Daqu samples, whereas Thermomyces, Thermoascus, Aspergillus, Rasamsonia, Byssochlamys, and Trichomonascus were identified as the dominant fungal genera. The fungal communities of the three types of high-temperature Daqu differed significantly (p < 0.05), and Thermomyces, Thermoascus, and Monascus could serve as the biomarkers in white high-temperature Daqu, black high-temperature Daqu, and yellow high-temperature Daqu, respectively. The three types of high-temperature Daqu had an extremely significant difference (p < 0.01) in flavor: white high-temperature Daqu was characterized by sourness, bitterness, astringency, richness, methane, alcohols, ketones, nitrogen oxides, and sulfur organic compounds; black high-temperature Daqu was characterized by aftertaste-A, aftertaste-B, methane-aliph, hydrogen, and aromatic compounds; and yellow high-temperature Daqu was characterized by saltiness, umami, methane, alcohols, ketones, nitrogen oxides, and sulfur organic compounds. The fungal communities in the three types of high-temperature Daqu were significantly correlated with taste but not with aroma, and the aroma of high-temperature Daqu was mainly influenced by the dominant fungal genera including Trichomonascus, Aspergillus, Thermoascus, and Thermomyces. The result of the present study enriched and refined our knowledge of high-temperature Daqu, which had positive implications for the development of traditional brewing technique.

An Overview of Applications of Electronic Nose and Electronic Tongue in Food and Dairy Industry

The dairy and food industries aims to achieve targeted productivity and very keen on product quality and consumer demand. Due to increasing technological advances, these food and dairy industries gained numerous technological support. Among the diverse development, the usages of e-nose and e-tongue paved the way to handle the production without any kind of deviation. In the food industries product perfection, uniform taste and aroma plays a supreme role because these parameters determine the marketing strategy. The electronic nose (e-nose) is a non-destructive intelligent electronic sensing instrument, which mimics the human olfactory system to detect, discriminate and classify odour samples. e-nose and e-tongue is an electronic tool used as a fast screening method to provide information about the product quality which is not easily done by manual methods as it is a time-consuming process. In this paper, the principle of e-nose and e-tongue and their applications in the dairy and food industries are explained.

Characteristics of Beef Patties Substituted by Different Levels of Textured Vegetable Protein and Taste Traits Assessed by Electronic Tongue System

The main objective of this study was to incorporate soy-based textured vegetable protein (TVP) into beef patties in different quantities (10–40%) and compare various characteristics of these innovative formulations with a regular beef patty as a control. Incorporation of 10–40% TVP resulted in significantly lower (p < 0.05) moisture and fat contents, while higher crude fiber contents were detected compared to beef as the control. In addition, cooked patties showed higher pH levels (p < 0.05), with color coordinates expressing lighter, yellowish, and slightly redder indices than raw patties. Similarly, a plant protein that includes TVP minimizes (p < 0.05) WHC (water holding capacity), both RW% (release water) and CL% (cooking loss). Furthermore, hardness, cohesiveness, and thickness were reduced significantly (p < 0.05), while gumminess and chewiness increased (p < 0.05) considerably with the substitution of TVP (10–40%) compared to the control. Patties made without TVP received higher scores for sourness, bitterness, umami, and richness than the rest of the formulations. However, a higher tendency was detected for sourness, astringency, umami, and saltiness values with increasing additions of TVP. Nevertheless, hierarchical clustering revealed that the largest group of fatty acid profiles, including palmitoleic acid (C16:1), stearic acid (C18:0), and palmitic acid (C16:0), was slightly reduced with the addition of TVP, while arachidic acid (C20:0), lauric acid (C12:0), and oleic acid (C18:1) increased moderately with increasing levels of TVP. Meanwhile, the second-largest cluster that included linoleic acid (C18:2), arachidonic acid (C20:4), and linolenic acid (C18:3) increased enormously with higher levels of TVP incorporation. Taken together, it is suggested that incorporation of TVP up to 10–40% in beef patties shows promising results.

Predicting Vodka Adulteration: A Combination of Electronic Tongue and Artificial Neural Networks

An artificial neural network was used to build models caple of predicting and quantifying vodka adulteration with methanol and/or tap water. A voltammetric electronic tongue based on gold and copper microelectrodes was used, and 310 analyses were performed. Vodkas were adulterated with tap water (5 to 50% (v/v)), methanol (1 to 13% (v/v)), and with a fixed addition of 5% methanol and tap water varying from 5 to 50% (v/v). The classification model showed 99.5% precision, and it correctly predicted the type of adulterant in all samples. Regarding the regression model, the root mean squared error was 3.464% and 0.535% for the water and methanol addition, respectively, and the prediction of the adulterant content presented an R2 0.9511 for methanol and 0.9831 for water adulteration.