Search Results (700)

It is well known that different muscles of yak exhibit distinctive characteristics, such as muscle fibers and metabolomic profiles. We hypothesized that different muscles could alter the flavor profile of yak jerky. Therefore, the objective of this study was to investigate the differences in flavor profiles of yak jerky produced by longissimus thoracis (LT), triceps brachii (TB) and biceps femoris (BF) through electronic nose (E-nose), electronic tongue (E-tongue), gas chromatography–mass spectrometry (GC-MS) and gas chromatography–ion mobility spectrometry (GC-IMS). The results indicated that different muscles played an important role on the flavor profile of yak jerky. And E-nose and E-tongue could effectively discriminate between yak jerky produced by LT, TB and BF from aroma and taste points of view, respectively. In particular, the LT group exhibited significantly higher response values for ANS (sweetness) and NMS (umami) compared to the BF and TB groups. A total of 65 and 47 volatile compounds were characterized in yak jerky by GC-MS and GC-IMS, respectively. A principal component analysis (PCA) model and robust principal component analysis (rPCA) model could effectively discriminate between the aroma profiles of the LT, TB and BF groups. Ten molecules could be considered potential markers for yak jerky produced by different muscles, filtered based on the criteria of relative odor activity values (ROAV) > 1, p < 0.05, and VIP > 1, namely 1-octen-3-ol, eucalyptol, isovaleraldehyde, 3-carene, D-limonene, γ-terpinene, hexanal-D, hexanal-M, 3-hydroxy-2-butanone-M and ethyl formate. Sensory evaluation demonstrated that the yak jerky produced by LT exhibited superior quality in comparison to that produced by BF and TB, mainly pertaining to lower levels of tenderness and higher color, taste and aroma levels. This study could help to understand the specific contribution of different muscles to the aroma profile of yak jerky and provide a scientific basis for improving the quality of yak jerky. Full article

This study investigates volatile organic compound (VOC) profiles in the exhaled breath of normal subjects under different oxygenation conditions—normoxia (FiO2 21%), hypoxia (FiO2 11%), and hyperoxia (FiO2 35%)—using an electronic nose (e-nose). We aim to identify significant differences in VOC profiles among the three conditions utilizing principal component analysis (PCA) and canonical discriminant analysis (CDA). Our results indicate distinct VOC patterns corresponding to each oxygenation state, demonstrating the potential of e-nose technology in detecting physiological changes in breath composition (cross-validated accuracy values: FiO2 21% vs. FiO2 11% = 63%, FiO2 11% vs. FiO2 35% = 65%, FiO2 21% vs. FiO2 35% = 71%, and p < 0.05 for all). This research underscores the viability of breathomics in the non-invasive monitoring and diagnostics of various respiratory and systemic conditions. Full article

There has been a recent increase in the frequency of mass disaster events. Following these events, the rapid location of victims is paramount. Currently, the most reliable search method is scent detection dogs, which use their sense of smell to locate victims accurately and efficiently. Despite their efficacy, they have limited working times, can give false positive responses, and involve high costs. Therefore, alternative methods for detecting volatile compounds are needed, such as using electronic noses (e-noses). An e-nose named the ‘NOS.E’ was developed and has been used successfully to detect VOCs released from human remains in an open-air environment. However, the system’s full capabilities are currently unknown, and therefore, this work aimed to evaluate the NOS.E to determine the efficacy of detection and expected sensor response. This was achieved using analytical standards representative of known human ante-mortem and decomposition VOCs. Standards were air diluted in Tedlar gas sampling bags and sampled using the NOS.E. This study concluded that the e-nose could detect and differentiate a range of VOCs prevalent in ante-mortem and decomposition VOC profiles, with an average LOD of 7.9 ppm, across a range of different chemical classes. The NOS.E was then utilized in a simulated mass disaster scenario using donated human cadavers, where the system showed a significant difference between the known human donor and control samples from day 3 post-mortem. Overall, the NOS.E was advantageous: the system had low detection limits while offering portability, shorter sampling times, and lower costs than dogs and benchtop analytical instruments. Full article

In this study, we present an electronic nose (e-nose) humidity compensation system based on rapid detection to solve the issue of humidity drift’s potential negative impact on the performance of electronic noses. First, we chose the first ten seconds of non-steady state (rapid detection mode) sensor data as the dataset, rather than waiting for the electronic nose to stabilize during the detection process. This was carried out in the hope of improving the detection efficiency of the e-nose and to demonstrate that the e-nose can collect gasses efficiently in rapid detection mode. The random forest approach is then used to optimize and reduce the dataset’s dimensionality, filtering critical features and improving the electronic nose’s classification capacity. Finally, this study builds an electronic nose humidity compensation system to compensate for the datasets generated via rapid real-time detection, efficiently correcting the deviation of the sensor response caused by humidity variations. This method enhanced the average resolution of the electronic nose in this trial from 87.7% to 99.3%, a 12.4% improvement, demonstrating the efficacy of the humidity compensation system based on rapid detection for the electronic nose. This strategy not only improves the electronic nose’s anti-drift and classification capabilities but also extends its service life, presenting a new solution for the electronic nose in practical detecting applications. Full article

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products. Full article

Tibetan butter and Indian ghee are both fat products derived from cow’s milk or other dairy products that are rich in nutrients. Although both Tibetan butter and Indian ghee are primarily produced by filtering, heating, separating, cooling, and molding, there are differences in their production processes. Tibetan butter is produced in a process similar to that of butter, while Indian ghee is clarified butter obtained by further extraction based on the obtained butter. Both types of ghee are susceptible to adulteration; Indian ghee is primarily adulterated with vegetable oils, animal fats, and other fats or non-fats, while Tibetan butter is typically adulterated with animal body fat and non-fats, including mashed potatoes. There are numerous research reports on the detection techniques for adulteration in Indian ghee, while there are very few reports on the detection technology for adulteration of Tibetan butter. Studies have shown that techniques such as gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR), and electronic nose (E-nose), either individually or in combination, are efficient in distinguishing adulterated Indian ghee. These findings could serve as a reference for the detection of adulteration in Tibetan butter in the future. Full article

Fish swim bladder (FSB) is a type of traditional nutraceutical, but the lack of high-quality drying methods limits its premium market development. In order to obtain optimal-quality dried FSBs from Chinese longsnout catfish, the effects of liquid nitrogen pre-freezing (LNF) and drying on the physical properties and flavor of FSB were evaluated. Four methods were used for FSB drying, including natural air-drying (ND), hot-air-drying (HD), LNF combined with freeze-drying (LN-FD), and LNF combined with HD (LN-HD). Color, collagen content, rehydration ratio, textural properties, and flavor characteristics (by GC-IMS, E-nose, and E-tongue) were measured to clarify the differences among four dried FSBs. The results showed that ND cannot effectively remove moisture from FSB as the final product showed a stronger sourness in taste. HD led to a decrease in the collagen content and the collapse of the fiber structure in FSB. Compared to HD, LN-HD showed a higher collagen content (0.56 g/g) and a different flavor fingerprint. FSB treated by LN-FD had better physical qualities in terms of an attractive color, a high collagen content (0.79 g/g), low shrinkage, a higher rehydration ratio (2.85), and a soft texture, while also possessing richer characteristic flavors. The application of LN-FD may help the optimization of the nutrition level, rehydration ability, mouthfeel, and flavor of dried FSB. Full article

This study investigates the application of an eNose (electrochemical sensory array) device as a rapid and cost-effective screening tool to detect increasingly prevalent counterfeit electronic cigarettes, and those to which potentially hazardous excipients such as vitamin E acetate (VEA) have been added, without the need to generate and test the aerosol such products are intended to emit. A portable, in-field screening tool would also allow government officials to swiftly identify adulterated electronic cigarette e-liquids containing illicit flavorings such as menthol. Our approach involved developing canonical discriminant analysis (CDA) models to differentiate formulation components, including e-liquid bases and nicotine, which the eNose accurately identified. Additionally, models were created using e-liquid bases adulterated with menthol and VEA. The eNose and CDA model correctly identified menthol-containing e-liquids in all instances but were only able to identify VEA in 66.6% of cases. To demonstrate the applicability of this model to a commercial product, a Virginia Tobacco JUUL product was adulterated with menthol and VEA. A CDA model was constructed and, when tested against the prediction set, it was able to identify samples adulterated with menthol 91.6% of the time and those containing VEA in 75% of attempts. To test the ability of this approach to distinguish commercial e-liquid brands, a model using six commercial products was generated and tested against randomized samples on the same day as model creation. The CDA model had a cross-validation of 91.7%. When randomized samples were presented to the model on different days, cross-validation fell to 41.7%, suggesting that interday variability was problematic. However, a subsequently developed support vector machine (SVM) identification algorithm was deployed, increasing the cross-validation to 84.7%. A prediction set was challenged against this model, yielding an accuracy of 94.4%. Altered Elf Bar and Hyde IQ formulations were used to simulate counterfeit products, and in all cases, the brand identification model did not classify these samples as their reference product. This study demonstrates the eNose’s capability to distinguish between various odors emitted from e-liquids, highlighting its potential to identify counterfeit and adulterated products in the field without the need to generate and test the aerosol emitted from an electronic cigarette. Full article

Background: For optimal clinical outcomes in full mouth rehabilitations, it is vital to determine the optimal jaw relations and confirm the appropriate vertical dimension of occlusion (VDO). The current study aims to evaluate various facial measurements as an adjunct in determining VDO in dentate individuals. Methods: A total of one hundred and twenty subjects, sixty males and sixty females, of the age group 19-30 were selected for the study. VDO (chin–nose distance) and other facial measurements like the glabella to subnasion (G-S) distance, both right and left pupil to rima oris (P-R) distance, both right and left corner of mouth to outer canthus of eye (M-E) distance, and both right and left ear to eye (E-e) distance were measured using a Vernier caliper. Results: The mean ± standard deviation of the C-N distance, G-S distance, right P-R distance, right M-E distance, left M-E distance, right E-e distance, and left E-e distance were 67.70 mm ± 3.22 mm, 60.29 mm ± 3.67 mm, 65.99 mm ± 3.72 mm, 66.00 mm ± 3.91 mm, 69.51 mm ± 3.71 mm, 69.48 mm ± 3.68 mm, 69.59 mm ± 3.98 mm, and 69.51 mm ± 3.95 mm, respectively. Pearson’s correlation coefficient between the C-N distance and M-E distance was found to be 0.739 (right), 0.730 (left); that between the C-N distance and E-e distance was found to be 0.738 (right), 0.732 (left); that between the C-N distance and P-R distance was found to be 0.660(right), 0.670(left); and that between the C-N distance and G-s distance was found to be 0.417. Conclusions: The present study reported a high positive correlation between the chin to nose distance and the distance between both the right and left lateral corner of the mouth to the outer canthus of the eye, and the distance between both the right and left ear to the eye. Hence, these measurements can be used as an adjunct for establishing VDO in the edentulous patient. Full article

Changes in the flavor and taste profiles of Paddy Field Carp after deodorization with perilla juice (PJ), cooking wine (CW) and a mixture of the two (PJ-CW) were analyzed using the E-nose, E-tongue, gas chromatography–ion mobility spectrometry (GC-IMS), free amino acid analysis and taste nucleotide analysis. The E-nose and E-tongue revealed that deodorization reduced the content of sulfur-containing compounds, enhanced umami, bitterness, sourness and astringency, and decreased saltiness. PCA and OPLS-DA analysis successfully distinguished between the effects of the treatments. Free amino acids increased from 8777.67 to 11,125.98 mg/100 g and umami amino acids increased from 128.24 to 150.37 mg/100 g after PJ-CW deodorization (p < 0.05). Equivalent umami concentration (EUC) comparisons showed that PJ-CW treatment produced the greatest synergistic umami enhancement (to 3.15 g MSG equiv./100 g). GC-IMS detected 52 aroma compounds; PJ treatment produced the greatest diversity of aldehydes, including heptanal, nonanal, hexanal, 3-methylbutanal, (E)-2-heptenal and (E,E)-2,4-heptadienal. The total content of volatile flavor compounds was the highest after PJ-CW treatment, and the content of many characteristic flavor substances (3-hydroxy-2-butanone, benzaldehyde, 5-methyl-2(3H)-furanone) increased. These findings provided a theoretical basis for the further development of deodorization methods for Paddy Field Carp. Full article

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO₂ gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0–11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO₂ gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO₂ (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves. Full article

Port wine vinegar, a product of the esteemed Port wine, is renowned for its intricate blend of flavors and aromas, a result of complex microbial interactions. This study delves into the fascinating world of yeast and acetic acid bacteria (AAB) interactions during fermentation, which significantly influence the vinegar’s chemical composition and sensory properties. We specifically investigate the role of yeasts in fermenting sugars into ethanol, a process that AAB then converts into acetic acid. The impact of these interactions on the production of secondary metabolites, such as gluconic acid, ketones, aldehydes, and esters, which contribute to the vinegar’s unique sensory profile, is thoroughly examined. Advanced analytical techniques, including GC-MS and e-nose technology, alongside sensory evaluation, are employed to assess these effects. The research underscores the significance of ethanol tolerance in AAB and other production challenges in determining vinegar quality and underscores the importance of optimizing fermentation conditions and sustainable practices. The findings of this study underscore the importance of strain interactions and production techniques, which can significantly enhance the quality and market appeal of Port wine vinegar, providing valuable insights for the industry. This review also identifies exciting and critical areas for future research, inspiring further exploration and proposing strategies for advancing production and application in culinary, health, and industrial contexts. Full article

Consumers often face a lack of information regarding the quality of apples available in supermarkets. General appearance factors, such as color, mechanical damage, or microbial attack, influence consumer decisions on whether to purchase or reject the apples. Recently, devices known as electronic noses provide an easy-to-use and non-destructive assessment of ripening stages based on Volatile Organic Compounds (VOCs) emitted by the fruit. In this study, the ‘Golden Delicious’ apples, stored and monitored at the ambient temperature, were analyzed in the years 2022 and 2023 to collect data from four Metal Oxide Semiconductor (MOS) sensors (MQ3, MQ135, MQ136, and MQ138). Three ripening stages (less ripe, ripe, and overripe) were identified using Principal Component Analysis (PCA) and the K-means clustering approach from various datasets based on sensor measurements in four experiments. After applying the K-Nearest Neighbors (KNN) model, the results showed successful classification of apples for specific datasets, achieving an accuracy higher than 75%. For the dataset with measurements from all experiments, an impressive accuracy of 100% was achieved on specific test sets and on the evaluation set from new, completely independent experiments. Additionally, correlation and PCA analysis showed that choosing two or three sensors can provide equally successful results. Overall, the e-nose results highlight the importance of analyzing data from several experiments performed over a longer period after the harvest of apples. There are similarities and differences in investigated VOC parameters (ethylene, esters, alcohols, and aldehydes) for less or more mature apples analyzed during autumn or spring, which can improve the determination of the ripening stage with higher predicting success for apples investigated in the spring. Full article

This study was conducted to investigate the effect of inoculation with Exiguobacterium profundum FELA1 isolated from traditional shrimp paste and koji on the taste, flavor characteristics, and bacterial community of rapidly fermented shrimp paste. E-nose and e-tongue results showed higher levels of alcohols, aldehydes, and ketones, enhanced umami and richness, and reduced bitterness and astringency in samples of shrimp paste inoculated with fermentation (p < 0.05). Eighty-two volatile compounds were determined using headspace solid-phase microextraction and gas chromatography–mass spectrometry (HS-SPEM-GC-MS). The contents of 3-methyl-1-butanol, phenylethanol, isovaleraldehyde, and 2-nonanone in the inoculated samples were significantly increased (p < 0.05), resulting in pleasant odors such as almond, floral, and fruity. High-throughput sequencing results showed that the addition of koji and FELA1 changed the composition and abundance of bacteria and reduced the abundance of harmful bacteria. Spearman’s correlation coefficient indicated that the alcohols, aldehydes, and ketones of the inoculated fermented samples showed a strong correlation (|ρ| > 0.6) with Virgibacillus and Exiguobacterium, which contributed to the formation of good flavor in the fast fermented shrimp paste. This study may offer new insights into the production of rapidly fermented shrimp paste with better taste and flavor. Full article