[Application and development of electronic sensing technologies represented by electronic nose in modern traditional Chinese medicine preparations].

Odor is one of the important indicators evaluating the quality of traditional Chinese medicines. Research data has shown that there are increasing methods available for evaluating the odors of traditional Chinese medicines. Compared with conventional odor sensing techniques, electronic noses stand out for their convenience, high speed, and objectivity. The progress in the pharmaceutical technology of traditional Chinese medicines has provided new formulas and dosage forms for the innovative development in this field. The electronic nose with versatility can be customized to be equipped with a variety of cross-sensors, which can well satisfy the needs of the traditional Chinese medicine preparation technology. This study summarizes the characteristics, application status, and representative products of the current electronic nose, and analyzes the application and feasibility of electronic nose in the production of traditional Chinese medicine preparations based on the current status of odor evaluation. This review is expected to provide new methods, techno-logies, and ideas for electronic nose to play its unique role in the whole-process quality control and pharmaceutical process of traditional Chinese medicine preparations.

Modulating peppermint oil flavor release properties of emulsion-filled protein gels: Impact of cross-linking method and matrix composition.

For some food applications, it is desirable to control the flavor release profiles of volatile flavor compounds. In this study, the effects of crosslinking method and protein composition on the flavor release properties of emulsion-filled protein hydrogels were explored, using peppermint essential oil as a model volatile compound. Emulsion-filled protein gels with different properties were prepared using different crosslinking methods and gelatin concentrations. Flavor release from the emulsion gels was then monitored using an electronic nose, gas chromatography-mass spectrometry (GC-MS), and sensory evaluation. Enzyme-crosslinked gels had greater hardness and storage modulus than heat-crosslinked ones. The hardness and storage modulus of the gels increased with increasing gelatin concentration. For similar gel compositions, flavor release and sensory perception were faster from the heat-crosslinked gels than the enzyme-crosslinked ones. For the same crosslinking method, flavor release and perception decreased with increasing gelatin concentration, which was attributed to retardation of flavor diffusion through the hydrogel matrix. Overall, this study shows that the release of hydrophobic aromatic substances can be modulated by controlling the composition and crosslinking of protein hydrogels, which may be useful for certain food applications.

Flavor characteristics of garlic fish cakes using electronic nose and tongue analyses.

This study investigated the utility of garlic powder as a functional ingredient. The aim was to develop fish cakes with improved functionality and sensory preference based on the antioxidant activity and quality characteristics. Increasing amounts of garlic powder in the prepared fish cakes were associated with increasing total polyphenol and flavonoid contents, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazolin-6-sulfonic acid) (ABTS+) radical scavenging activity, and reducing power. Furthermore, electronic tongue and electronic nose analyses showed an increased the intensity of umami and sourness and increased the levels of volatile compounds. The lowest trimethylamine peak corresponded to the highest amount of garlic powder. Sensory evaluation indicated that 3% garlic powder had the highest score for all criteria. Fishy odor decreased as the proportion of garlic powder increased. These findings suggest that the addition of 3% garlic powder improves quality characteristics, sensory preference, and antioxidant activity of fish cakes.

Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis.

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.

Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion.

This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.

Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion.

For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.

Effects of pile-fermentation on the aroma quality of dark tea from a single large-leaf tea variety by GC × GC-QTOFMS and electronic nose.

Aroma is one of the significant quality factors of dark tea (DT). However, for a single large-leaf tea variety, there are few studies analyzing the effect of pile-fermentation on the aroma quality of DT. The GC × GC-QTOFMS, electronic nose (E-nose) and GC-olfactometry (GC-O) techniques were employed to analysis the difference of tea products before and after pile-fermentation. A total of 149 volatile metabolites (VMs) were identified, with 92 VMs exhibiting differential characteristics. Among these, 31 VMs with OAV > 1.0 were found to be correlated with E-nose results (|r| > 0.8). Additionally, GC-O analysis validated seven major differential metabolites. Notably, naphthalene, 2-methylnaphthalene, and dibenzofuran were found to enhance the woody aroma, while (Z)-4-heptenal, 2-nonenal and 1-hexanol were associated with an increase in mushroom, fatty and sweet odors, respectively. Moreover, 1-octen-3-ol was linked to reducing pungent fishy smell. These findings could provide a certain theoretical basis for understanding the influence of pile-fermentation on the aroma quality of dark tea.

Characterization of different grades of Jiuqu hongmei tea based on flavor profiles using HS-SPME-GC-MS combined with E-nose and E-tongue.

In order to distinguish different grades of Jiuqu hongmei tea (black tea), four different grades of Jiuqu hongmei tea were used as materials in this study: Super Grade (SuG), First Grade (FG), Second Grade (SG), and Third Grade (TG). HS-SPME-GC-MS combined with electronic nose (E-nose) and electronic tongue (E-tongue) technology was used to detect and analyze tea samples. The results showed that 162 volatile substances were identified, mainly alcohols, followed by hydrocarbons, aldehydes, ketones and esters. Twenty-nine volatile compounds were found in all grades of tea samples. The results of heat map analysis showed that the relative contents of five volatile compounds in different grades of Jiuqu hongmei tea were positively correlated with the grades of Jiuqu hongmei tea. By orthogonal partial least squares discriminant analysis (OPLS-DA), 35 different compounds of SuG and FG, 30 different compounds of SG and TG, 34 different compounds of FG and SG were found. Overall, the results indicated that there were significant differences in volatile compounds among different grades of Jiuqu hongmei tea, and the use of HS-SPME-GC-MS combined with E-nose and E-tongue could provide a scientific reference method as an effective tool for detecting flavor characteristics of other types of black tea grades.

Characterization of the key odorants in floral aroma green tea based on GC-E-Nose, GC-IMS, GC-MS and aroma recombination and investigation of the dynamic changes and aroma formation during processing.

To characterize the key odorants of floral aroma green tea (FAGT) and reveal its dynamic evolution during processing, the volatile metabolites in FAGT during the whole processing were analyzed by integrated volatolomics techniques, relative odor activity value (rOAV), aroma recombination, and multivariate statistical analysis. The volatile profiles undergone significant changes during processing, especially in the withering and fixation stages. A total of 184 volatile compounds were identified (∼53.26% by GC-MS). Among them, 7 volatiles with rOAV > 1 were identified as characteristic odorants of FAGT, and most of these compounds reached the highest in withering stage. According to the formation pathways, these key odorants could be divided into four categories: fatty acid-derived volatiles, glycoside-derived volatiles, amino acid-derived volatiles, and carotenoid-derived volatiles. Our study provides a comprehensive strategy to elucidate changes in volatile profiles during processing and lays a theoretical foundation for the targeted processing of high-quality green tea.

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil.

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

[Establishment of a fast discriminant model with electronic nose for Polygonati Rhizoma mildew based on odor variation].

The odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees was analyzed and the relationship between the odor variation and the mildewing degree was explored. A fast discriminant model was established according to the response intensity of electronic nose. The α-FOX3000 electronic nose was applied to analyze the odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees and the radar map was used to analyze the main contributors among the volatile organic compounds. The feature data were processed and analyzed by partial least squares discriminant analysis(PLS-DA), K-nearest neighbor(KNN), sequential minimal optimization(SMO), random forest(RF) and naive Bayes(NB), respectively. According to the radar map of the electronic nose, the response values of three sensors, namely T70/2, T30/1, and P10/2, increased with the mildewing, indicating that the Pollygonati Rhizoma produced alkanes and aromatic compounds after the mildewing. According to PLS-DA model, Pollygonati Rhizoma samples of three mildewing degrees could be well distinguished in three areas. Afterwards, the variable importance analysis of the sensors was carried out and then five sensors that contributed a lot to the classification were screened out: T70/2, T30/1, PA/2, P10/1 and P40/1. The classification accuracy of all the four models(KNN, SMO, RF, and NB) was above 90%, and KNN was most accurate(accuracy: 97.2%). Different volatile organic compounds were produced after the mildewing of Pollygonati Rhizoma, and they could be detected by electronic nose, which laid a foundation for the establishment of a rapid discrimination model for mildewed Pollygonati Rhizoma. This paper shed lights on further research on change pattern and quick detection of volatile organic compounds in moldy Chinese herbal medicines.

Interaction between major catechins and umami amino acids in green tea based on electronic tongue technology.

Umami amino acids inhibit the bitter and astringent taste presentation of catechins, which is essential for the taste regulation of green tea. In this study, the concentration-intensity trends and taste threshold properties of major catechin monomers were investigated using an electronic tongue. The taste and chemical structure interactions between the ester-type catechins and theanine, glutamic acid (Glu), and aspartic acid (Asp) were further analyzed by in vitro simulation and analysis of their reciprocal chemical structures. The results showed that the bitterness and astringency of the major catechin monomers increased with increasing concentration, and their bitterness thresholds and their electron tongue response values were higher than those of the astringent values, while the bitterness and astringency of the ester-type catechins were higher than those of the nonester type. The three amino acids inhibited the bitterness intensity of ester catechins (epigallocatechin gallate, epicatechin gallate, and gallocatechin gallate) at different concentrations, and the effects on the astringency intensity of ester catechins were complicated. Ester catechins significantly enhanced the umami intensity of theanine, Glu, and Asp at different concentrations. Their reciprocal chemical structures showed that hydrogen bonding was the main interaction force between the three ester-type catechins and the umami amino acids, with theanine and Glu interacting more strongly with ester-type catechins than Asp, and Glu having a lower binding energy to ester-type catechins, which bonded more easily.

[Rapid identification and differential markers of Curcumae Radix decoction pieces of different sources based on Heracles Neo ultra-fast gas phase electronic nose].

Since Curcumae Radix decoction pieces have multiple sources, it is difficult to distinguish depending on traditional cha-racters, and the mixed use of multi-source Curcumae Radix will affect its clinical efficacy. Heracles Neo ultra-fast gas phase electronic nose was used in this study to quickly identify and analyze the odor components of 40 batches of Curcumae Radix samples from Sichuan, Zhejiang, and Guangxi. Based on the odor fingerprints established for Curcumae Radix decoction pieces of multiple sources, the odor components was identified and analyzed, and the chromatographic peaks were processed and analyzed to establish a rapid identification method. Principal component analysis(PCA), discriminant factor analysis(DFA), and soft independent modeling cluster analysis(SIMCA) were constructed for verification. At the same time, one-way analysis of variance(ANOVA) combined with variable importance in projection(VIP) was employed to screen out the odor components with P<0.05 and VIP>1, and 13 odor components such as ß-caryophyllene and limonene were hypothesized as the odor differential markers of Curcumae Radix decoction pieces of diffe-rent sources. The results showed that Heracles Neo ultra-fast gas phase electronic nose can well analyze the odor characteristics and rapidly and accurately discriminate Curcumae Radix decoction pieces of different sources. It can be applied to the quality control(e.g., online detection) in the production of Curcumae Radix decoction pieces. This study provides a new method and idea for the rapid identification and quality control of Curcumae Radix decoction pieces.

Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning.

The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes.

Monitoring the baking quality of Tieguanyin via electronic nose combined with GC-MS.

Roasting is extremely important for Tieguanyin oolong tea production because it strongly affects its chemical composition and sensory quality. In addition, there were significant differences in the preference for roasted tea among different people. However, the effect of roasting degree on the aroma characteristics and flavor quality of Tieguanyin tea is still unclear. To further study this, an electronic nose combined with gas chromatography-mass spectrometry (GC-MS) was used to monitor the baking process of Tieguanyin. The physicochemical indexes, sensory quality, and odor characteristics of the tea leaves subjected to different roasting conditions were measured. The increase in the roasting degree caused a decrease in the amount of taste substances such as tea polyphenols, catechins, and amino acids and a sharp increase in the phenol to ammonia ratio. Sensory evaluation results showed that moderate roasting could help improve the quality of the tea leaves. The results obtained using the electronic nose and GC-MS showed that there were substantial differences in the volatile substances, and 103 flavor compounds were highly correlated with the aroma characteristics of roasted tea with different roasting degrees. In addition, the electronic nose combined with various classification models could better distinguish tea leaves with different roasting degrees. Among them, the accuracy of the RF training set and prediction set reached>98.44%. The results of this study will aid in comprehensively monitoring the effects of the baking process on the flavor, chemical composition, and aroma of Tieguanyin as well as in distinguishing Tieguanyin tea leaves with different qualities.

Chemical Composition Analysis and Discrimination of Essential Oils of Artemisia Argyi Folium from Different Germplasm Resources Based on Electronic Nose and GC/MS Combined with Chemometrics.

In this study, the electronic nose and GC/MS were used to analyze the chemical components of essential oils from different germplasm resources of Artemisia argyi Folium (A. argyi), in order to quickly identify essential oils of A. argyi from different germplasm resources and clarify the differences among different A. argyi samples. The essential oils of A. argyi were extracted by steam distillation. This article describes for the first time that electronic nose combined with chemometrics can distinguish the essential oils of A. argyi from different germplasm, which proves the reliability and potential of this technology. GC/MS was used to identify 134 volatile components from the essential oil of A. argyi. The main bioactive components were cineole, thujarone, artemisia ketone, ß-caryophyllene, (-)-4-terpinol, 3,3,6-trimethyl-1,5-heptadien-4-ol, (-)-α-thujone, camphor, borneol. In addition, the results of principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that there were significant differences in the essential oils of A. argyi from different germplasm resources, terpenes, alcohols and ketones played an important role in identifying the essential oils of A. argyi from different germplasm resources. This indicates that electronic nose and GC/MS combined with chemometrics can be used as reliable techniques to identify different germplasm resources of A. argyi, and provide certain reference value for quality evaluation, selection of high-quality varieties and rational development of resources of A. argyi.

Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors.

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

A review on electronic nose for diagnosis and monitoring treatment response in lung cancer.

Lung cancer is one of the common malignancies with high mortality rate and a poor prognosis. Most lung cancer cases are diagnosed at an advanced stage either due to limited resources of infrastructure, trained human resources, or delay in clinical suspicion. Low-dose computed tomography has emerged as a screening tool for lung cancer detection but this may not be a feasible option for most developing countries. Electronic nose is a unique non-invasive device that has been developed for lung cancer diagnosis and monitoring response by exhaled breath analysis of volatile organic compounds. The breath-print have been shown to differ not only among lung cancer and other respiratory diseases, but also between various types of lung cancer. Hence, we postulate that the breath-print analysis by electronic nose could be a potential biomarker for the early detection of lung cancer along with monitoring treatment response in a resource-limited setting. In this review, we have consolidated the current published literature suggesting the use of an electronic nose in the diagnosis and monitoring treatment response of lung cancer.

Analysis of thermal oxidation of different multi-element oleogels based on carnauba wax, ß-sitosterol/lecithin, and ethyl cellulose by classical oxidation determination method combined with the electronic nose.

Thermal oxidation of different multi-element oleogels (DMEOs) was assessed by measuring the peroxide, p-anisidine, and total antioxidant values. Moreover, a rapid discriminant model for determining oleogel oxidation was established using an electronic nose combined with cluster analysis (CA), principal component analysis (PCA), discriminant factor analysis (DFA), and linear discriminant analysis (LDA). The oxidation degree of the oleogels was sensitive to their gelation temperature. The oleogels prepared with ethyl cellulose showed the highest oxidation value compared with those prepared with ß-sitosterol and lecithin, or carnauba wax. Loading resveratrol and adding surfactants can effectively reduce the oxidation of DMEOs. During the thermal oxidation, the DMEOs were sensitive to nitrogen oxides and methane species. The accuracy rates of the discriminant analyses by CA, PCA, DFA, and LDA were 95.00%, 97.25%, 96.25%, and 100%, respectively, which were consistent with that of the American Oil Chemists' Society Official Method.

Metabolite profiling and identification of novel umami compounds in the chaya leaves of two species using multiplatform metabolomics.

Chaya (Cnidoscolus chayamansa and C. aconitifolius) is a fast-growing medicinal plant, and its leaves exhibit a strong umami taste. Here metabolite variation and umami-related compounds in the leaves of two chaya species were determined using a multiplatform untargeted-metabolomics approach, electronic tongue, and in silico screening. Metabolite profiles varied between the leaves of the two species and among leaf maturation stages. Young leaves exhibited the highest umami taste intensity, followed by mature and old leaves. Partial least square regression and computational molecular docking analyses revealed five potent umami substances (quinic acid, trigonelline, alanyl-tyrosine, leucyl-glycyl-proline, and leucyl-aspartyl-glutamine) and three known umami compounds (l-glutamic acid, pyroglutamic acid, and 5'-adenosine monophosphate). The five substances were validated as novel umami compounds using electronic tongue assay; leucyl-glycyl-proline exhibited synergism with monosodium glutamate, thereby enhancing the umami taste. Thus, substances contributing to the taste of chaya leaves were successfully identified.