"Reference sample comparison method": A new voltammetric electronic tongue method and its application in assessing the shelf life of fresh milk.

Shelf life is a critical comprehensive indicator of food quality. Voltammetric electronic tongue (V-Et), is well-suited for assessing food shelf life, due to its capable of capturing food overall fingerprints. This study designed a "reference sample comparison method" for V-Et to assess the shelf life of fresh milk. Quality differences between milk samples of different shelf lives and reference samples were quantified by differential degree (Dd) values. A new "one-to-one" model of milk shelf life was established based on Dd values, and significantly improved predictive accuracy by 11.14 %-17.17 % and 14.86 %-44.47 % in overall quality shelf life assessment compared to "many-to-one" models based on SVM and DFA. Even in the more sophisticated evaluation of microbial safety and sensory quality shelf life, it attained relative errors of 13.57 % and 7.68 %, respectively. All these findings showed the significant potential of the "reference sample comparison method" in assessing food shelf life with V-Et.

Early Diagnosis of Bronchopulmonary Dysplasia with E-Nose: A Pilot Study in Preterm Infants.

Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in preterm and is still associated with increased mortality and morbidity. The great interest lies in identifying early biomarkers that can predict the development of BPD. This pilot study explores the potential of e-nose for the early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath condensate (EBC). Fourteen mechanically ventilated very preterm infants were included in this study. The clinical parameters and EBC were collected within the first 24 h of life. The discriminative ability of breath prints between preterms who did and did not develop BPD was investigated using pattern recognition, a machine learning algorithm, and standard statistical methods. We found that e-nose probes can significantly predict the outcome of "no-BPD" vs. "BPD". Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8, and p = 0.019, 0.009, 0.043, 0.047, respectively. The e-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms.

Non-Invasive Diagnostic Approaches for Kidney Disease: The Role of Electronic Nose Systems.

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease's presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field.

Intelligent Evaluation and Dynamic Prediction of Oysters Freshness with Electronic Nose Non-Destructive Monitoring and Machine Learning.

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster.

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.

Electronic Nose Analysis of Exhaled Breath Volatile Organic Compound Profiles during Normoxia, Hypoxia, and Hyperoxia.

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.

Machine Learning-Based Nanozyme Sensor Array as an Electronic Tongue for the Discrimination of Endogenous Phenolic Compounds in Food.

The detection of endogenous phenolic compounds (EPs) in food is of great significance in elucidating their bioactivity and health effects. Here, a novel bifunctional vanillic acid-Cu (VA-Cu) nanozyme with peroxidase-like and laccase-like activities was successfully prepared. The peroxidase mimic behavior of VA-Cu nanozyme can catalyze 3,3',5,5'-tetramethylbenzidine (TMB) to generate oxidized TMB (oxTMB). Owing to the high reducing power of EPs, this process can be inhibited, and the degree of inhibition increases with the increase of reaction time. Additionally, owing to the outstanding laccase mimic behavior of the VA-Cu, it can facilitate the oxidation of various EPs, resulting in the formation of colored quinone imines, and the degree of catalysis increases with the increase of reaction time. Based on the interesting experimental phenomena mentioned above, a six-channel nanozyme sensor array (2 enzyme-mimic activities × 3 time points = 6 sensing channels) was constructed, successfully achieving discriminant analysis of nine EPs. In addition, the combination of artificial neural network (ANN) algorithms and sensor arrays has successfully achieved accurate identification and prediction of nine EPs in black tea, honey, and grape juice. Finally, a portable method for identifying EPs in food has been proposed by combining it with a smartphone.

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers.

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Data set for Gambung green tea aroma using on electronic nose.

OBJECTIVES: In recent years, there has been much discussion and research on electronic nose (e-nose). This topic has developed mainly in the medical and food fields. Typically, e-nose is combined with machine learning algorithms to predict or detect multiple sensory classes in each tea sample. Therefore, in e-nose systems, e-nose signal processing is an important part. In many situations, a comprehensive set of experiments is required to ensure the prediction model can be generalized well. This data set specifically focuses on two main goals such as classification of green tea quality and prediction of organoleptic score. In this experiment, Gambung dry green tea samples were used. The challenge is that dry tea does not emit as strong an aroma as tea infusions, making it more difficult for the e-nose system to detect and identify the aromas. This data set offers a valuable resource for researchers and developers to conduct investigations and experiments by classifying and detecting organoleptic scores that aim to categorize and identify organoleptic ratings. This enables a deeper understanding of the quality of dry green tea and encourages further integration of e-nose technology in the tea industry. DATA DESCRIPTION: This experiment focused on analyzing green tea aroma using six gas sensors. Seventy-eight green tea samples were tested, each observed three times, using a tea chamber connected to a sensor chamber via a hose and an intake micro air pump. Air flowed from the tea chamber to the sensor chamber for 60 s, followed by 60 s of aroma data recording. This data was saved into CSV files and labeled according to the Indonesian National Standard (SNI) 3945:2016, which includes special and general requirements for green tea quality. An organoleptic test by a tea tester further labeled the data set into "good" or "quality defect" for classification and provided organoleptic scores based on dry appearance, brew color, taste, aroma, and dregs of brewing for continuous label.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Comparison and identification of aroma components in 21 kinds of frankincense with variety and region based on the odor intensity characteristic spectrum constructed by HS-SPME-GC-MS combined with E-nose.

Frankincense is an important seasoning and spice known for its distinctive and intricate flavor profile. Considering the considerable variation in the aromatic quality of frankincense due to geographical origin, species diversity and cultivation conditions, frankincense from major global origins was characterized holistically for the first time. The electronic nose (E-nose) with headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and sensory evaluation were implemented to characterize the aroma components of 21 commercial varieties of frankincense from around the world. The results showed that a total of 149 volatile organic compounds (VOCs) of 10 categories were identified in frankincense, among which the numbers of alcohols, terpenes and esters compounds accounted for 22.15 %, 18.79 % and 15.44 % of the total VOCs of frankincense, respectively. The PLS-DA model effectively distinguished frankincense from Oman/Somalia and other origins. Furthermore, the study identified two differential VOCs with VIP > 1 in three Asian countries and five in six African countries. The total VOCs content and sensory characteristic score of "Lemon/Citrus" in Oman frankincense is significantly higher than other regions. The OAV results showed that 61 substances (e.g., Diacety, alpha-Pinene, Camphene, Myrcene) as key aroma compounds and OICS model indicated that p-Cymenol was found to contribute significantly to the citrus aroma in frankincense. This study identified the fundamental components of frankincense flavor and revealed different flavor descriptors of frankincense, which are crucial for reconstructing frankincense flavor and improving flavor quality.

Combining with E-nose, GC-MS, GC-IMS and chemometrics to explore volatile characteristics during the different stages of Zanthoxylum bungeanum maxim fruits.

To explore the volatile characteristics of Z. bungeanum fruits during different developmental stages, the dynamical changes of volatile organic compounds (VOCs) were detected by E-nose, GC-MS and GC-IMS, respectively. The results showed that terpenes, alcohols, esters and aldehydes played the important roles in the aroma formation of Z. bungeanum. Meanwhile, these VOCs also exhibited the high abundance levels among five growth stages of Z. bungeanum. According to the analysis of odor activity value (OAV) and relative odor activity value (ROAV), 37 VOCs can be recognized as the important aroma compounds. Thereinto, ß-myrcene and linalool were the most key aroma compounds. Multi-factor analysis exhibited that the combination of GC-MS and GC-IMS was a better strategy to clarify the volatile characteristics comprehensively. Using the above combined VOC datasets, six positively correlated modules and 32 hub VOCs were finally identified by weighted correlation network analysis among five growth stages of Z. bungeanum.

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.

Change in volatile profiles of wheat flour during maturation.

The volatile profiles of wheat flour during maturation were examined through headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with electronic nose (E-nose) and electronic tongue (E-tongue) analyses. The wheat flour underwent maturation under three distinct conditions for predetermined durations. While GC/MS coupled with E-tongue exhibited discernment capability among wheat flour samples subjected to varying maturation conditions, E-nose analysis solely relying on principal component analysis failed to achieve discrimination. 83 volatile compounds were identified in wheat flour, with the highest abundance observed in samples matured for 50 d at 25 °C. Notably, trans-2-Nonenal, decanal, and nonanal were the main contributors to the characteristic flavor profile of wheat flour. Integration of HS-SPME-GC/MS with E-tongue indicated superior flavor development and practical viability in wheat flour matured for 50 d at 25 °C. This study furnishes a theoretical groundwork for enhancing the flavor profiles of wheat flour and its derivative products.

Imaging of Volatile Organic Compounds Using a Single Nanowire-Based Electronic Nose for Future Biomedical Applications.

This study introduces an array of semiconductor oxide single nanowires fabricated using advanced semiconductor processing techniques, including electron beam lithography and thin-film deposition, which is well-suited for large-scale nanowire integration. A four-channel nanowire array consisting of tin oxide (SnO2), indium oxide (In2O3), ferric oxide (Fe3O4), and titanium oxide (TiO2) was developed. As a proof of concept, we converted the response curves of the sensor array to heat maps, enabling comprehensive feature representation. The fabricated electronic nose (E-nose) was utilized to detect three types of volatile organic compounds (VOCs), with the results visualized in a heat map format. Additionally, the performance of each individual sensor was quantitatively studied, highlighting the array's potential for enhanced gas detection and analysis. To further illustrate the interaction between gas molecules and the nanowires, we visualized the gas response results by mapping the sensor's signal changes. These visualizations provide a clear representation of how different gas molecules interact with specific nanowires. For example, the heat maps reveal distinct response patterns for each type of VOC, allowing for the identification and differentiation of gases based on their unique signatures. This visualization technique not only enhances the understanding of gas-nanowire interactions but also demonstrates the effectiveness of the E-nose in distinguishing between various VOCs. The SnO2 nanowire gas sensor showed enhanced gas response compared to other materials. The SnO2 and TiO2 gas sensors showed enhanced response (62 and 56 s) and recovery times (100 and 37 s).

Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring.

INTRODUCTION: Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. MATERIALS AND METHODS: Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. RESULTS: A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. CONCLUSIONS: The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

Discriminative analysis of aroma profiles in diverse cigar products varieties through integrated sensory evaluation, GC-IMS and E-nose.

Cigars, treasured for their rich aromatic profiles, occupy a notable segment in the global consumer market. The objective of this study was to characterize the volatile aroma compounds that shape the flavor profiles of six distinct varieties of Great Wall cigars, contributing to the understanding of cigar aroma analysis. Utilizing HS-GC-IMS and sensory evaluation, the study discerned the aroma profiles of GJ No. 6 (GJ), Animal from the Chinese zodiac (SX), Range Rover No. 3 Classic (JD), Miracle 132 (QJ), Sheng Shi No. 5 (SS), and Red 132 (HS) cigars. The analysis uncovered a spectrum of characteristic aromas, including tobacco, creaminess, cocoa, leather, baking, herbaceous, leathery, woodsy, and fruity notes. A total of 88 compounds were identified, categorized into 11 chemical classes, with their quantities varying among the cigars in a descending order of QJ, JD, GJ, SS, HS, and SX. 24 compounds, such as 2-heptanone, n-butanol, 2,6-dimethylpyrazine and 2-furfuryl methyl sulfide were considered as key differential components. The volatile components were effectively differentiated using principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and cluster analysis, revealing correlations between sensory attributes, key components, and electronic nose (E-nose). This research introduces a novel method for analyzing volatile aroma components in cigars, offering insights to enhance cigar quality and to foster the development of new products with unique aroma profiles.

Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review.

Electronic noses (eNoses) are electronic bionic olfactory systems that use sensor arrays to produce response patterns to different odors, thereby enabling the identification of various scents. Gastrointestinal diseases have a high incidence rate and occur in 9 out of 10 people in China. Gastrointestinal diseases are characterized by a long course of symptoms and are associated with treatment difficulties and recurrence. This review offers a comprehensive overview of volatile organic compounds, with a specific emphasis on those detected via the eNose system. Furthermore, this review describes the application of bionic eNose technology in the diagnosis and screening of gastrointestinal diseases based on recent local and international research progress and advancements. Moreover, the prospects of bionic eNose technology in the field of gastrointestinal disease diagnostics are discussed.

A comprehensive analysis of aroma quality and perception mechanism in ginger-infused stewed beef using instrumental analysis, sensory evaluation and molecular docking.

The ginger-infused stewed beef exhibited a satisfactory odor in Chinese cooking meat. This study aimed to reveal its aroma quality and perception mechanism through electronic nose, sensory evaluation and gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS) coupled with chemometric methods and molecular docking. Sensory evaluation and electronic nose analysis indicated ginger could greatly modify aroma profile of beef. Most C6-C10 aldehydes significantly decreased and terpenes increased in ginger-infused stewed beef. Orthogonal partial least squares-discriminant analysis (OPLS-DA) found 7 key markers for distinguishing stewed beef with or without ginger. Ginger additions could reduce fatty acids consumption. Moreover, the key contributors of fatty, bloody, meaty, ginger and mint aroma attributes, namely (E)-2-octenal, 1-octen-3-ol, 2-acetylthiazole, zingiberene and γ-elemene, respectively, selected by partial least squares regression (PLSR) analysis were docked with the olfactory receptor. Hydrogen bonds and hydrophobic interactions were the main interaction forces between olfactory receptor and the five compounds.

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates.

Identifying the aging time of Liupao Tea (LPT) presents a persistent challenge. We utilized an AI-Multimodal fusion method combining FTIR, E-nose, and E-tongue to discern LPT's aging years. Compared to single-source and two-source fusion methods, the three-source fusion significantly enhanced identifying accuracy across all four machine learning algorithms (Decision tree, Random forest, K-nearest neighbor, and Partial least squares Discriminant Analysis), achieving optimal accuracy of 98-100 %. Physicochemical analysis revealed monotonic variations in tea polysaccharide (TPS) conjugates with aging, observed through SEM imaging as a transition from lamellar to granular TPS conjugate structures. These quality changes were reflected in FTIR spectral characteristics. Two-dimensional correlation spectroscopy (2D-COS) identified sensitive wavelength regions of FTIR from LPT and TPS conjugates, indicating a high similarity in spectral changes between TPS conjugates and LPT with aging years, highlighting the significant role of TPS conjugates variation in LPT quality. Additionally, we established an index for evaluating quality of aging, which is sum of three fingerprint peaks (1029 cm-1, 1635 cm-1, 2920 cm-1) intensities. The index could effectively signify the changes in aging years on macro-scale (R2 = 0.94) and micro-scale (R2 = 0.88). These findings demonstrate FTIR's effectiveness in identifying aging time, providing robust evidence for quality assessment.