Electronic Nose Research Articles

Copper Corrole Electropolymers

The electrochemical polymerization of the copper complex of 5,10,15-tris-(4-aminophenyl) corrole leads to the formation of conductive films, which have been deposited onto interdigitated electrodes to develop chemoresistive sensors. These devices are of interest because of their facile miniaturization and integration into low-cost electronic circuits. It is interesting to note that the properties of these films, such as morphology, semiconducting nature and sensitivity, can be tuned by modifying the synthetic electropolymerization protocols. This opportunity can allow the preparation of cross-selective sensors that can be integrated into miniaturized conductometric electronic nose platforms.

Effects of Wickerhamomyces anomalus Co-Fermented with Saccharomyces cerevisiae on Volatile Flavor Profiles during Steamed Bread Making Using Electronic Nose and HS-SPME-GC-MS.

Steamed bread is a traditional staple food in China, and it has gradually become loved by people all over the world because of its healthy production methods. With the improvement in people's living standards, the light flavor of steamed bread fermented by single yeast cannot meet people's needs. Multi-strain co-fermentation is a feasible way to improve the flavor of steamed bread. Here, the dynamic change profiles of volatile substances in steamed bread co-fermented by Saccharomyces cerevisiae SQJ20 and Wickerhamomyces anomalus GZJ2 were analyzed using the electronic nose (E-nose) and headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The five detectors of the E-nose rapidly detected the changes in volatile substances in different dough or steamed bread with the highest response value in co-fermented dough. A total of 236 volatile substances were detected in all the samples using HS-SPME-GC-MS, and alcohols were the most variable component, especially Phenylethyl alcohol. Significantly, more alcohols and esters were upregulated in co-fermented dough, and the addition of W. anomalus GZJ2 improved the key volatile aroma compounds of steamed bread using the relative odor activity value method (ROAV), especially the aldehydes and alcohols. Moreover, these key volatile aroma compounds can be quickly distinguished using the W2S detector of the E-nose, which can be used for the rapid detection of aroma components in steamed bread.

Dynamic changes in volatile profiles and bacterial communities during natural fermentation of Mei yu, traditional Chinese fermented fish pieces

Microbial metabolism is important for the unique flavor formation of Mei yu, a kind of traditional Chinese fermented fish pieces. However, the interactive relationship between microorganisms and flavor components during fermentation is still unclear. In this study, electronic nose and headspace-solid-phase microextraction-gas chromatography-mass spectrometry analysis were performed to identify flavor components in Mei yu during the fermentation, and the absolute microbial quantification was conducted to identify the diversity and succession of microbial communities. During fermentation, there was an increase in the types of volatile compounds. Alcohols, aldehydes, aromatics and esters were the main flavor compounds and significantly increased in Mei yu, while hydrocarbon and aldehydes significantly decreased. The absolute abundances of Lactobacillus, Lactococcus and Weissella increased significantly after 3 days’ fermentation, which were closely associated with the productions of 1-nonanol, 2-methoxy-4-vinylphenol, guaiacol, ethyl palmitate and ethyl caprylate that might though pathways related to fatty acid biosynthesis and amino acid metabolism. However, these genera were negatively correlated with the production of indole. Additionally, the total volatile basic nitrogen (TVB-N) levels of Mei yu fermented during 3 days were within the limits of 25 mg TVB-N/100 g fish, with the contents of free amino acids and lipoxygenase activities were significant lower than that of 4 days’ fermentation. In view of food safety and flavor, it suggested that the natural fermented Mei yu at room temperature should be controlled within 3 days. This study highlights the application of absolute quantification to microbiome analysis in traditional fermented Mei yu and provides new insights into the roles of microorganisms in flavor formation during fermentation.

An integrated approach for discrimination of Magnoliae officinalis cortex before and after being processed by ginger juice combining LC/MS, GC/MS, intelligent sensors, and chemometrics.

Magnoliae officinalis cortex (MOC) is an important traditional Chinese medicine (TCM), and both raw and stir-fried MOC were commonly used in clinic. This study aimed to discriminate MOC and MOC stir-fried with ginger juice (MOCG) using an integrated approach combining liquid chromatography/mass spectrometry (LC/MS), gas chromatography/mass spectrometry (GC/MS), intelligent sensors, and chemometrics. The sensory characters of the samples were digitalized using intelligent sensors, i.e., colorimeter, electronic nose, and electronic tongue. Meanwhile, the chemical profiles of the samples were analyzed using LC/MS and GC/MS methods. Chemometric models were constructed to discriminate samples of MOC and MOCG based on not only the sensory data but also the chemical data. The differential sensory characters (L* and b* from colorimeter, ANS from electronic tongue, W1S and W2S from electronic nose) and the differential chemical compounds (26 and 11 compounds from LC/MS and GC/MS, respectively) were discovered between MOC and MOCG. Furthermore, twelve differential compounds showed good relations with differential sensory characters. Finally, artificial neural network models were established to discriminate samples of MOC and MOCG, in which W1S, W2S, ANS, b*, and 10 differential compounds were among the top 10 important variables, respectively. Samples of MOC and MOCG can be discriminated not only by the digitalized data of color, taste, and scent detected by intelligent sensors but also by chemical information obtained from LC/MS and GC/MS using chemometrics. The variations in sensory characters and chemical compounds between MOC and MOCG partially resulted from the Maillard reaction products and the oxidation of some compounds in the stir-frying process.

Physicochemical properties and flavor substances analyses of refined beef tallow with dry fractionation treatment

Dry fractionation represents a significant technique for separation of diverse fractions from beef tallow. The objective of this study was to undertake a systematic investigation of alterations in physicochemical properties, crystallization behavior, thermal properties, and flavor compounds that occur during the beef tallow dry fractionation process. The solid component yielded at 40, 30, and 15 °C were 44.88%, 33.72%, and 13.04% respectively, with an 8.36% liquid content at 15 °C, which was consistent with the characteristics of saturated fatty acids content. The β – β’ transformation in the dry fractionation process was clearly revealed by X-ray diffraction. Differential scanning calorimetry curves exhibited alterations in exothermic and endothermic peak, as well as enthalpy. Electronic nose identified short-chain compounds, aldehydes, ketones, and nitrogen-containing substances as flavor compounds. Volatile compounds were quantified using HS-SPME-GC–MS. Overall, dry fractionation produces beef tallow fractionated compounds with diverse physicochemical properties and aromatic-active substances, thereby expanding its potential utilization.

Hydrogen Gas-Grilling in Meat: Impact on Odor Profile and Contents of Polycyclic Aromatic Hydrocarbons and Volatile Organic Compounds.

The effect of fuel (hydrogen vs. butane) on the formation of volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) was evaluated for grilled horse meat (very low-fat and low-fat) cooking vertically. Gas chromatography-mass spectrometry was used to analyze PAHs and VOCs. An electronic nose was used to evaluate the odor profile. Total high-molecular-weight PAHs ranged from 19.59 to 28.65 µg/kg with butane and from 1.83 to 1.61 µg/kg with hydrogen. Conversely, total low-molecular-weight PAHs went from 184.41 to 286.03 µg/kg with butane and from 36.88 to 41.63 µg/kg with hydrogen. Aldehydes and alkanes were the predominant family in a total of 59 VOCs. Hydrogen gas-grilling reduced significantly (p < 0.05) the generation of VOCs related to lipid oxidation. The odor profile was not modified significantly despite the change of PAHs and VOCs. The findings indicate that hydrogen is a viable alternative to butane for grilling horse meat. Hydrogen gas-grilling may be regarded as a safe cooking procedure of meat from a PAH contamination point and perhaps sustainable environmentally compared to a conventional technique. The present study provides the basis for the use of hydrogen gas in grilled meat.

Facile E-nose based on single antenna and graphene oxide for sensing volatile organic compound gases with ultrahigh selectivity and accuracy

Volatile organic compounds (VOCs) gases exist as important indicators in various processes such as human breath, food spoilage, plant disease, and industrial production, which makes VOC sensing a promising nondestructive detection method. Nanomaterial enabled electronic noses (e-noses) are gaining increasing attention for their exceptional ability to distinguish between multiple VOCs. To achieve this, multiple sensors with distinct nanomaterials are crucial for providing enough diversity for successful discrimination. Apparently if e-nose can be implemented solely on single sensor with single common nanomaterial operating at room temperature, the complexity and power consumption can be greatly decreased. In this study, a significant milestone is achieved as, for the first time, a single antenna sensor coated with commercial graphene oxide, demonstrates comparable performance to array-based e-noses, and even outperforms them in terms of the ultimate ambition of selectivity—isomeric VOC classification. An impressive classification accuracy of 96.7 % is attained for multiple VOC gases, including isomers. Additionally, the concentration of each component in VOC isomer mixtures is accurately determined. Unlike conventional antenna sensors, the sensor maintains stable communication during sensing operations. Finally, the practical feasibility of the antenna e-nose (Ant-nose) is successfully demonstrated with a series of food quality assessments.

Microbial community dynamics and flavor formation mechanisms during fermentation of fermented chilies from different regions based on macro-genome sequencing, SPME-GC-MS, electronic nose, and electronic tongue

Microbial community dynamics and flavor formation mechanisms during fermentation of fermented chilies from different regions based on macro-genome sequencing, SPME-GC-MS, electronic nose, and electronic tongue

Preserving the Aromatic Profile of Aged Toma Piemontese PDO Cheese with Gaseous Ozone Technology: A Quality Assessment via SPME-GC–MS/E-Nose

The efficacy of low gaseous ozone concentrations (300 ppb and 400 ppb) in controlling spoilage microflora and preserving the quality of the aged Toma Piemontese PDO cheese was explored. The research integrates consumer tests, Gas Chromatography-Mass Spectrometry (GC-MS) with Solid phase Microextraction (SPME) fiber and Electronic Nose (e-nose) analysis to conduct a detailed assessment of the cheese's aromatic composition. Results indicate that low ozone concentrations significantly affected spoilage microflora, preserving the overall quality. Through GC-FID (Flame Ionization Detection) analysis, 22 of all identified compounds by GC-MS were quantified, including ethyl acetate (sweety), diacetyl and acetoin (buttery). Compared with the untreated sample, ozone treatments maintained the distinctive characteristics of Toma Piemontese PDO cheese, reducing the formation of off-flavors-related compounds (i.e., ethanol). Moreover, ozone-treated samples correlated with positive aroma scores given by consumers. However, sensory perception involves complex interactions among aroma compounds, highlighting the importance of advanced approaches. The utilization of a 12-sensor Quartz Microbalance (QMB) e-nose played a crucial role in identifying subtle differences in aroma, contributing to a more nuanced understanding of ozone treatments on the cheese's sensory profile. In conclusion, this research demonstrates the potential of ozone technology as a viable and effective method for improving the quality of aged Toma Piemontese PDO cheese.

Authenticity Verification of Alcoholic Beverages in IoT Environment through Fluorescence Hyperspectral Technology and Machine Learning

In the Internet of Things (IoT) environment, verifying the authenticity of beverages is an important issue. Baijiu, a unique Chinese distilled liquor, has gained popularity among people due to its distinctive flavor. In recent years, there has been increasing concern about the adulteration of baijiu. Many studies in this field have attempted to combine chemometrics, electronic nose, Raman spectroscopy, and other detection technologies with machine learning for baijiu detection. However, no research has yet applied the mature detection technology of fluorescence hyperspectral technology to this field. Additionally, existing studies have mainly focused on branded baijiu, without addressing the problem of adulteration in bulk baijiu, which remains a long-standing issue. In this study, a combination of fluorescence hyperspectral and machine learning was used for rapid and nondestructive adulteration detection of bulk liquor in Chengdu. The fluorescence hyperspectral data of three types of samples, namely, original liquor, adulterated liquor with water, and adulterated liquor with industrial alcohol, were obtained, and the raw data were preprocessed using Savitzky–Golay (SG), baseline correction (BL), multivariate scatter correction (MSC), and the combination of Savitzky–Golay and baseline correction (SG-BL). Finally, PCA and MDS are also used to extract spectral data characteristics, respectively. For model selection, three models were chosen: back-propagation artificial neural network (BP-ANN), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). For model evaluation, we choose accuracy, recall, precision, F1-score, memory occupied, and time consumed by the model to evaluate. The experiments show that the combination of fluorescence hyperspectral technology and machine learning can provide a new method, MSC-PCA-SVM, for bulk liquor detection, with a final classification accuracy, recall, precision, and F1-score all reaching 100%, The effectiveness and application potential of this method in the IoT environment have been demonstrated. The results of this study can not only be applied to liquor detection but also have the value of reference for the adulthood and detection of the entire food industry. It is of great significance to standardize the market and protect consumer rights.

Explosives Detection Using Octanethiol Gold Nanoparticle Inkjet Printed Devices

This study focuses on implementing 1-octanethiol gold nanoparticles (OT-AuNPs) as chemiresistive sensors and comparing fabrication by inkjet-printing with conventional drop-casting to detect volatile organic compounds (VOCs) and explosives including HMTD, PETN, KClO3, TATP, RDX, TNT, and UN. Inkjet-printing technology potentially offers more controlled deposition of OT-AuNPs and more uniform sensor properties compared to a drop-casting process. Using inkjet-printing, we found that a minimum of 600 OT-AuNPs printed layers with 2.4[Formula: see text]pL drop volume, 9 drops per layer, and 60[Formula: see text][Formula: see text]m drop spacing was sufficient to reduce chemiresistor device baseline resistances to around [Formula: see text] and obtain more consistent responses. By contrast, 12[Formula: see text][Formula: see text]L OT-AuNPs drop-casting yielded device resistances spread over a range of [Formula: see text] due to uneven deposition. For inkjet-printed devices, higher response magnitudes with lower variability were achieved for explosive vapor detection whereas a larger spread was observed for drop casting. The improved uniformity of baseline resistances and sensor responses indicates inkjet-printed devices were more reproducible and repeatable than drop-casting. In addition, inkjet-printing consumes lower amounts of OT-AuNPs for material and cost savings. The results demonstrate that inkjet-printed devices are promising for use in sensor arrays to fabricate electronic noses for VOCs and explosives detection.

Sweat-based stress screening with gas chromatography–ion mobility spectrometry and electronic nose

BackgroundDiagnosis of stress generally involves uses of questionnaires which can provide biased results. The more reliable approach relies on observation of individual symptoms by psychiatrists which is time consuming and could not be applicable for massive scale screening tests. This research established alternative approaches with gas chromatography–ion mobility spectrometry (GC–IMS) and electronic nose (e-nose) to perform fast stress screening based on fingerprinting of highly volatile compounds in headspaces of sweat. The investigated samples were obtained from 154 female nurse volunteers who also provided the data of questionnaire-based mental health scores with the high stress cases confirmed by psychiatrists. ResultsThe interviews by psychiatrists revealed 14 volunteers with high stress. Their axillary sweat samples and that from 32 nurses with low/moderate stress (controls) were collected onto cotton rods and analysed with GC–IMS. The possible marker peaks were selected based on the accuracy data. They were tentatively identified as ammonia, diethyl ether, methanol, octane, pentane, acetone and dimethylamine which could involve different endogenous mechanisms or the relationships with the local microbiomes. The data were further analysed using partial least squares discriminant analysis with the receiver operating characteristic curves showing the optimum accuracy, sensitivity and selectivity of 87%, 86% and 88%, respectively. Providing that the samples were obtained from the nurses without deodorant uses, the high stress cases could be screened using e-nose sensors with the accuracy of 89%. The sensor responses could be correlated with the marker peak area data in GC–IMS with the coefficients ranging from −0.70 to 0.80. SignificanceThis represents the first investigation of highly volatile compound markers in sweat for high stress screening. The established methods were simple, reliable, rapid and non-invasive, which could be further adapted into the portable platform of e-nose sensors with the practical application to perform the screening tests for nurses in Phra Nakorn Si Ayutthaya hospital, Thailand.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and “segmentation-extraction-regression” deep learning assisted quantification of flavonoids

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3′,5,5′-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a “segmentation−extraction−regression” deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 × 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multi-ingredient quantitative analysis of flavonoids.

Fish Fillet Analogue Using Formulation Based on Mushroom (Pleurotus ostreatus) and Enzymatic Treatment: Texture, Sensory, Aromatic Profile and Physicochemical Characterization.

The growing demand for alternative sources of non-animal proteins has stimulated research in this area. Mushrooms show potential in the innovation of plant-based food products. In this study, the aim was to develop prototype fish fillets analogues from Pleurotus ostreatus mushrooms applying enzymatic treatment (β-glucanase and transglutaminase-TG). A Plackett-Burman 20 experimental design was used to optimize forty variables. Oat flour (OF) exerted a positive effect on the hardness and gumminess texture parameters but a negative effect on cohesiveness and resilience. Soy protein isolate (SPI) exhibited a positive effect on elasticity, gumminess and chewiness, while acacia gum had a negative effect on elasticity, cohesiveness and resilience. After sensory analysis the assay with 1% cassava starch, 5% OF, 5% SPI, 0.1% transglutaminase (240 min/5 °C), 1% coconut oil, 1% soybean oil, 0.2% sodium tripolyphosphate, 0.6% β-glucanase (80 °C/10 min) and without β-glucanase inactivation was found to exhibit greater similarity to fish fillet. The classes hydrocarbons, alcohols and aldehydes are the predominant ones in aromatic profile analysis by chromatography and electronic nose. It is concluded that a mushroom-based analogue of fish fillet can be prepared using enzymatic treatment with TG.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses.

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing.

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.

Monitoring of meat quality and change-point detection by a sensor array and profiling of bacterial communities

BackgroundReal-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in the food matrices, and hence it requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. The existing analytical methods are too complicated for rapid in situ screening of foodstuff. ResultsWe have studied the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, comparing the results with changes in the microbiome composition of the spoiling meat samples. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and to identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition such as increase in the proportion of Brochothrix and Pseudomonas spp. and disappearance of Mycoplasma spp., and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R2 values of 0.98, 0.93, and 0.91, respectively. Integration of e-nose and computer vision into a single analytical panel improved the meat state identification accuracy up to 0.85, allowing for more reliable meat state assessment. SignificanceAccurate identification of the change-point in the meat state achieved by digitalizing volatile spoilage markers from the e-nose unit holds promises for application of smart miniaturized devices in food industry.

Olfactory-chemical establishment using odour wheels and fingerprints to manage odor pollution for the petrochemical industry

The odor nuisance may cause major environmental issues, so identifying the odor characteristics is important for odor management and traceability in the petrochemical industry. In this study, odour wheels and fingerprints were applied to manage odor pollution in the petrochemical industry. First, the odor profile method and portable electronic nose evaluation were applied to analyze the flavour sensory profiles of five upstream and downstream petrochemical plants, in order to grasp the odor natures of plants. Second, 375 organized and unorganized samples of 5 plants were analyzed by gas chromatography-mass spectrometry (GC–MS) technology, and a total of 583 odorants were obtained, including sulfur compounds, alcohols, esters, aldehydes, ketones, acids, alkanes, alkenes, halogenated hydrocarbons, aromatic hydrocarbons, nitrogen compounds, and other compounds. Third, the key odorants were identified by gas chromatography-olfactory (GC-O) and odor activity value (OAV) analysis. Fourth, the fingerprint spectras were developed by the identification of key odorants and process sources through principal component analysis. For plant A, 21 odorants were confirmed, including naphthalene, 1-methylnaphthalene, 2-methylnaphthalene, 1-methylindene, and 2-methylindene. Methyl methacrylate, acetophenone, isobutene, 4-vinyl-1-cyclohexene, acrylonitrile, and styrene were key element for plant B. The fingerprint factor of plant C had 20 odorants, including methanol, butanol, 2-ethylhexanol, acetic acid, and ammonia. And there were 21 odorants for plant D and 14 odorants for plant E, respectively, with similar substance types, including 2-ethyl-2-hexenal, 3-methyl-4-heptanone, isobutyl butyrate, and 1,2,3,5-tetraethylbenzene. Finally, the odor wheels were established by the correlation analysis between odorants and malodor, and compounds in fingerprint spectra weaker related to malodor of plants were removed. Hence, the characteristic odorants of each plant were further clarified, and the odors were associated with compounds, achieving rapid diagnosis and traceability of odor pollution in the petrochemical park. This provided new ideas and technical methods for odor pollution monitoring and identification.

A strategy to distinguish similar traditional Chinese medicines by liquid chromatography-mass spectrometry, electronic senses, and gas chromatography-ion mobility spectrometry: Marsdeniae tenacissimae Caulis and Paederiae scandens Caulis as examples.

Marsdeniae tenacissimae Caulis (MTC), a popular traditional Chinese medicine, has been widely used in the treatment of tumor diseases. Paederiae scandens Caulis (PSC), which is similar in appearance to MTC, is a common counterfeit product. It is difficult for traditional methods to effectively distinguish between MTC and PSC. Therefore, there is an urgent need for a rapid and accurate method to identify MTC and PSC. The aim is to distinguish between MTC and PSC by analyzing the differences in nonvolatile organic compounds (NVOCs), taste, odor, and volatile organic compounds (VOCs). Liquid chromatography-mass spectrometry (LC-MS) was utilized to analyze the NVOCs of MTC and PSC. Electronic tongue (E-tongue) and electronic nose (E-nose) were used to analyze their taste and odor respectively. Gas chromatography-ion mobility spectrometry (GC-IMS) was applied to analyze VOCs. Finally, multivariate statistical analyses were conducted to further investigate the differences between MTC and PSC, including principal component analysis, orthogonal partial least squares discriminant analysis, discriminant factor analysis, and soft independent modeling of class analysis. The results of this study indicate that the integrated strategy of LC-MS, E-tongue, E-nose, GC-IMS, and multivariate statistical analysis can be effectively applied to distinguish between MTC and PSC. Using LC-MS, 25 NVOCs were identified in MTC, while 18 NVOCs were identified in PSC. The major compounds in MTC are steroids, while the major compounds in PSC are iridoid glycosides. Similarly, the distinct taste difference between MTC and PSC was precisely revealed by the E-tongue. Specifically, the pronounced bitterness in PSC was proven to stem from iridoid glycosides, whereas the bitterness evident in MTC was intimately tied to steroids. The E-nose detected eight odor components in MTC and six in PSC, respectively. The subsequent statistical analysis uncovered notable differences in their odor profiles. GC-IMS provided a visual representation of the differences in VOCs between MTC and PSC. The results indicated a relatively high relative content of 82 VOCs in MTC, contrasted with 32 VOCs exhibiting a similarly high relative content in PSC. In this study, for the first time, the combined use of LC-MS, E-tongue, E-nose, GC-IMS, and multivariate statistical analysis has proven to be an effective method for distinguishing between MTC and PSC from multiple perspectives. This approach provides a valuable reference for the identification of other visually similar traditional Chinese medicines.

Screening of lactic acid bacteria with the ability to reduce goaty flavor related fatty acids in goat milk

In this study, we conducted sensory evaluation and gas chromatography–mass spectrometry analysis on fermented goat milk samples prepared by 12 strains of lactic acid bacteria (LAB) isolated from goat milk to screen for strains with the ability to reduce the goaty flavor. The bacterial counts of fermented goat milk was 7.07–9.01 log CFU/mL. The electronic nose distinguished fresh goat milk (FGM) and fermented goat milk, and the electronic tongue results showed that Leuconostoc citreum 1, 4, 20, 22, 32, and 57, Latilactobacillus curvatus 144 and 147 imparted fermented goat milk a taste different from FGM. Overall, Leuconostoc citreum 57, Leuconostoc citreum 126, Latilactobacillus curvatus 142, Latilactobacillus curvatus 143, and Latilactobacillus curvatus 147 were screened with the ability to improve the flavor of goat milk. They gave fermented goat milk a goat flavor score lower than or equal to FGM. And the fermented goat milk samples 57, 126, 142, 143, and 147 contained 25, 22, 15, 24, and 17 volatile flavor compounds, respectively, with a greater variety and content of ketones and aldehydes and lower levels of hexanoic acid, octanoic acid, and decanoic acid than FGM. However, the pH and WHC results indicated that the application of these strains as secondary cultures is necessary. Our finding provides basic research data to improve the flavor of goat milk products.