Frontiers in Chemistry
·
2025
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IF 4.2
Pan-Pan Zhang,Xin-Jing Gui,Xue-Hua Fan,Han-Li,Hai-Yang Li,Xiao-Peng Li,Feng-Yu Dong,Yan-Li Wang,Jing-Yao,Jun-Han Shi,Rui-Xin Liu
Amomi fructus (AF) has been used for both medicinal and food purposes for centuries. However, issues such as source mixing, substandard quality, and product adulteration often affect its efficacy. This study used E-nose (EN) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to determine and analyze the volatile organic compounds (VOCs) in AF and its counterfeit products. A total of 111 VOCs were detected by HS-GC-IMS, with 101 tentatively identified. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) identified 47 VOCs as differential markers for distinguishing authentic AF from counterfeits (VIP value >1 and P < 0.05). Based on the E-nose sensor response value and the peak volumes of the 111 VOCs, the unguided Principal Component Analysis (PCA), guided Principal Component Analysis-Discriminant Analysis (PCA-DA), and Partial Least Squares-Discriminant Analysis (PLS-DA) models were established to differentiate AF by authenticity, origin, and provenance. The authenticity identification model achieved 100.00% accuracy after PCA analysis, while the origin identification model and the provenance identification model were 95.65% (HS-GC-IMS: PLS-DA) and 98.18% (HS-GC-IMS: PCA-DA/PLS-DA), respectively. Further data-level fusion of E-nose and HS-GC-IMS significantly improved the accuracy of the origin identification model to 97.96% (PLS-DA), outperforming single-source data modeling. In conclusion, the intelligent data fusion algorithm based on E-nose and HS-GC-IMS data effectively identifies the authenticity, origin, and provenance of AF, providing a rapid and accurate method for quality evaluation.
研究领域:
中药学
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分析化学
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DOI
PubMed
高影响因子
Current Research in Food Science
·
2025
·
IF 7
Xuexue Zheng,He Xie,Wenjie Fu,Chao Wang,Tengfei Hu,Xingchang Ou,Jian'an Huang,Zhonghua Liu,Qin Li
While extensive research has focused on the flavor of Fu brick tea (FBT), the perceptual interactions among its aromatic components remain unclear. To address this, twenty representative aroma compounds were selected from FBT to determine their thresholds and evaluate their interactions by analyzing threshold changes before and after mixing. The results revealed discrepancies between the determined thresholds and literature values, particularly for α -terpineol, methyl heptenone, and geranylacetone, which demonstrated ratios of 73.54, 6.62, and 6.41, respectively. Mixtures with similar aromas or structures exhibited synergy, whereas those with differing aromas or structures displayed masking effects. Additionally, the odor compound ( E )-nerolidol was added to the tea infusion at a concentration below its threshold level to investigate its impact on the overall aroma perception of FBT through sensory analysis and electronic nose assessments. The findings indicated that sub-threshold compounds could significantly influence the overall aroma profile of FBT.
研究领域:
食品科学
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感官分析
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DOI
PubMed
高影响因子
Food Chemistry-X
·
2026
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IF 9.2
Guili Huang,Yu Shi,Xinyao Quan,Qianshuo Shao,Su Yan,Siyao Sui,Jiajia Ma,Jiayu Qian,Weiming Chai,Yuning Wang
Volatile Organic Compounds (VOCs) are critical determinants of loquat flavor quality and cultivar differentiation, but comprehensive characterization and rapid detection remain challenging. This study aimed to establish a robust approach for discriminating three loquat cultivars and evaluating their flavor quality by integrating electronic nose (E-nose) technology and Comprehensive Two-Dimensional Gas Chromatography–Time-of-Flight Mass Spectrometry (GC × GC-TOF-MS). GC × GC-TOF-MS was used for VOC profiling, while E-nose was applied for rapid fragrance characterization, with orthogonal partial least squares-discriminant analysis (OPLS-DA) and correlation analysis performed to identify discriminatory compounds and sensor-VOC relationships. GC × GC-TOF-MS identified over 2000 volatile compounds across three loquat cultivars. OPLS-DA further screened 22 discriminatory VOCs with variable importance in projection (VIP) values >1, which contributed to aroma hierarchy shifts among cultivars. E-nose sensors W1W and W1S showed high sensitivity to floral-scent-related VOCs, serving as key factors for distinguishing fragrance quality. Notably, E-nose detection results exhibited good consistency with GC × GC-TOF-MS data, and W1W/W1S sensors were significantly positively correlated with key VOCs. These findings demonstrate that the integration of E-nose and GC × GC-TOF-MS offers complementary advantages for comprehensive VOC characterization and cultivar discrimination. The established approach provides a scientific basis for optimizing loquat quality standards by incorporating VOC-driven biochemical authenticity and a potential efficient method for rapid, non-destructive assessment of postharvest flavor quality in loquat cultivars. These results facilitate quality control and cultivar authentication in the fruit industry.
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DOI
PubMed
高影响因子
Advanced Science
·
2024
·
IF 14.3
Xuemei Liu,Qiaofen Chen,Shiyuan Xu,Jiaying Wu,Jingwen Zhao,Zhengfu He,Aiwu Pan,Jianmin Wu
Helicobacter pylori (HP), a common microanaerobic bacteria that lives in the human mouth and stomach, is reported to infect ≈50% of the global population. The current diagnostic methods for HP are either invasive, time-consuming, or harmful. Therefore, a noninvasive and label-free HP diagnostic method needs to be developed urgently. Herein, reduced graphene oxide (rGO) is composited with different metal-based materials to construct a graphene-based electronic nose (e-nose), which exhibits excellent sensitivity and cross-reactive response to several gases in exhaled breath (EB). Principal component analysis (PCA) shows that four typical types of gases in EB can be well discriminated. Additionally, the potential of the e-nose in label-free detection of HP infection is demonstrated through the measurement and analysis of EB samples. Furthermore, a prototype of an e-nose device is designed and constructed for automatic EB detection and HP diagnosis. The accuracy of the prototype machine integrated with the graphene-based e-nose can reach 92% and 91% in the training and validation sets, respectively. These results demonstrate that the highly sensitive graphene-based e-nose has great potential for the label-free diagnosis of HP and may become a novel tool for non-invasive disease screening and diagnosis.
研究领域:
医学诊断
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材料科学
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生物传感
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DOI
PubMed
高影响因子
Foods
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2024
·Vol. 13
·期 12
·1865
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IF 4.7
Jiayu Lu,Zheng Jiang,Jingjie Dang,Dishuai Li,Daixin Yu,Cheng Qu,Qinan Wu
Chamomile has become one of the world’s most popular herbal teas due to its unique properties. Chamomile is widely used in dietary supplements, cosmetics, and herbal products. This study aimed to investigate the volatile aromatic components in chamomile. Two analytical techniques, gas chromatography–mass spectrometry (GC-MS) and an ultra-fast gas chromatography electronic nose, were employed to examine samples from Xinjiang (XJ), Shandong (SD), and Hebei (HB) in China, and imported samples from Germany (GER). The results revealed that all chamomile samples contained specific sesquiterpene compounds, including α-bisabolol, bisabolol oxide, bisabolone oxide, and chamazulene. Additionally, forty potential aroma components were identified by the electronic nose. The primary odor components of chamomile were characterized by fruity and spicy notes. The primary differences in the components of chamomile oil were identified as (E)-β-farnesene, chamazulene,α-bisabolol oxide B, spathulenol andα-bisabolone oxide A. Significant differences in aroma compounds included geosmin, butanoic acid, 2-butene, norfuraneol,γ-terpinene. This study demonstrates that GC–MS and the ultra-fast gas chromatography electronic nose can preliminarily distinguish chamomile from different areas, providing a method and guidance for the selection of origin and sensory evaluation of chamomile. The current study is limited by the sample size and it provides preliminary conclusions. Future studies with a larger sample size are warranted to further improve these findings.
研究领域:
植物学
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分析化学
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食品科学
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DOI
PubMed
高影响因子
Algal Research-Biomass Biofuels and Bioproducts
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2026
·
IF 5
Jikang Sui,Jinku Zhang,Xiangning Lu,Kangning Wang,Mingkai Bai,Adewale Olusegun Obadina,Shiyang Li,Guangxin Feng,Haohao Wu
Microalgae are a sustainable and nutrient-dense food source, but their strong off-flavors significantly limit broader consumption. This study evaluates the effectiveness of a laccase-catechin mediator in improving the odor profiles of Spirulina, Chlorella, and Chlamydomonas reinhardtii . Mediators were applied ex situ (pre-formed) or in situ (generated within the suspension). Ex-situ mediators, optimized at 40 °C for 2 h, achieved better deodorization compared to in-situ mediators, with optimal catechin dosages of 28, 16, and 8 mg/g biomass for Spirulina, Chlorella, and C. reinhardtii , respectively. Sensory evaluation showed reduced grassy, earthy, and fatty odors, and enhanced floral and fruity notes, with the ex-situ approach leading to higher overall acceptability. GC–MS analysis confirmed that mediators decreased the diversity and abundance of key odor-active volatiles in three microalgae species. Laccase-catalyzed quinone-type mediators effectively neutralize undesirable compounds, offering a promising enzymatic strategy for improving the sensory quality of microalgae-based foods, especially for sustainable food applications.
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DOI
高影响因子
Small
·
2025
·
IF 12.1
Mianyi Xiang,Yamin Liu,Ziyang Yang,Jinlei Jiang,Weicheng Wang,Yao Hu,Daxiang Cui,Qichao Li
Electronic noses (e-noses) have become indispensable analytical platforms for gas detection. However, conventional e-nose systems face significant limitations in portable and wearable implementations due to their bulk and high-power consumption. Herein, a single-sensor-based multifunctional e-nose system is reported by integrating a micro-electromechanical system (MEMS) gas sensor with a flexible printed circuit board (FPCB). Specifically, the ZnO-ZnSnO 3 raspberry-like microspheres (ZZSRM) are utilized as the gas-sensitive materials, and the gas selectivity of the sensor is enhanced through a dual-temperature modulation strategy. Additionally, a gas classification model based on the MiniRocket algorithm has been developed, enabling efficient feature extraction and low-complexity classification of response signals, thereby satisfying the real-time processing demands of embedded devices. Moreover, a silent communication method is proposed, which maps breathing frequency to Morse code for information transmission in specific scenarios. Experimental results demonstrate that the wearable system achieves high-precision classification and concentration prediction for eight volatile organic compounds (VOCs), while simultaneously enabling robust recognition of exhaled signals and instantaneous conversion of Morse code into legible alphabetic characters. By combining the gas sensor with artificial intelligence (AI) technology, this work establishes a multifunctional flexible e-nose that merges portable gas detection and silent communication, offering a novel technological framework for environmental monitoring and human-machine interaction.
研究领域:
生物医学工程
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微机电系统
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气体检测
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人工智能
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可穿戴设备
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DOI
PubMed
高影响因子
FOOD RESEARCH INTERNATIONAL
·
2025
·
IF 8
Jiaying Jin,Xiaogang Wang,Yu Zhang,Liangjie Ba,Su Xu,Lingshuai Meng,Xiaohong Kou,Sen Cao
Blueberry aroma is a key determinant of fruit quality and market value. However, the mechanisms underlying its formation during ripening remain poorly understood. This study utilized the highbush blueberry cultivar ‘ Legacy ’ and employed an integrated approach combining E -nose, GC–MS, GC-IMS, and transcriptomics to systematically analyze the formation and changes in VOCs as well as the underlying molecular mechanisms throughout fruit ripening. The results revealed that during the entire fruit ripening process, VOCs were predominantly composed of alcohols, aldehydes, and terpenoids. Key compounds, including hexanol, linalool and geraniol, progressively accumulated. Furthermore, the combination of GC–MS and GC-IMS provided a more comprehensive profile of the volatile composition in blueberry fruit. Transcriptomic analysis further elucidated its metabolic regulatory mechanisms: 1) Terpenoid biosynthesis is co-regulated by the MAV and MEP pathways, with the MEP pathway playing a dominant role in monoterpene biosynthesis; 2) Fatty acid metabolism generates C6 aldehydes via LOX2S, while upregulation of ADH gene expression significantly enhances the conversion of aldehydes to alcohols. This study provides valuable theoretical foundations for flavor development during blueberry ripening.
研究领域:
植物生物学
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食品科学
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分子生物学
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代谢组学
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农业生物技术
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DOI
PubMed
高影响因子
CHEMICAL ENGINEERING JOURNAL
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2025
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IF 13.2
Wenbin Li,Gaocan Qi,Haihui Ding,Weibin Yan,Xijun Liu,Zhihao Yuan
The safe use of fuel gases in industrial production and daily life requires prompt and accurate detection of their concentrations in the environment. However, due to the strong reducing properties of the flammable gases used as fuel, traditional chemoresistive gas sensors face challenges in simultaneously detecting multiple flammable gases. To address this issue, we have developed a metal oxide-based electronic nose capable of detecting common fuel gases, including CH 4 , H 2 , and CO. By controlling the composition and structure of metal oxides, as well as introducing noble metal modifications, we optimized the response differences of these gas sensitive materials to CH 4 , H 2 , and CO. Four sensitive materials were selected from 8 candidates using principal component analysis to construct gas sensor array on a compact four-channel circuit board. The response sensitivities from the sensor array to various concentrations of CH 4 , H 2 , CO, and their mixtures were utilized to train a backpropagation artificial neural network. A new component-identification outlet layer was integrated with the traditional concentration-recognition to improve the training speed and enable simultaneous identification of gas components and concentrations. Through combined material optimization and dual-output training, the backpropagation artificial neural network achieved 90% training accuracy within just 50 iterations, with both sparse cross-entropy loss and mean squared error reduced below 0.005 after 300 training epochs. The resulting E -nose can accurately identify gas components and determine their concentrations in single-, binary-, and ternary-component mixtures of CH 4 , H 2 , and CO, with detection accuracies exceeding 91%. This study provides a practical approach for E -nose construction, enabling reliable monitoring of flammable gases.
研究领域:
化学传感
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人工智能
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气体检测
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传感器技术
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材料科学
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产品关联
DOI
高影响因子
ACS Omega
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2023
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IF 4.1
Yuzhu Li,Kangzhuo Yang,Zhanglan He,Zhipeng Liu,Jialing Lu,Dong Zhao,Jia Zheng,Michael C. Qian
Electronic nose (E-nose) technology is frequently attempted to simulate the human olfactory system to recognize complex odors. Metal oxide semiconductors (MOSs) are E-noses’ most popular sensor materials. However, these sensor responses to different scents were poorly understood. This study investigated the characteristic responses of sensors to volatile compounds in a MOS-based E-nose platform, using baijiu as an evaluation system. The results showed that the sensor array had distinctive responses for different volatile compounds, and the response intensities varied depending on the sensors and the volatile compounds. Some sensors had dose–response relationships in a specific concentration range. Among all the volatiles investigated in this study, fatty acid esters had the greatest contribution to the overall sensor response of baijiu. Different aroma types of Chinese baijiu and different brands of strong aroma-type baijiu were successfully classified using the E-nose. This study provided an understanding of detailed MOS sensor response with volatile compounds, which could be further applied to improve the E-nose technology and its practical application in food and beverages.
研究领域:
食品科学
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分析化学
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DOI
PubMed
高影响因子
EUROPEAN FOOD RESEARCH AND TECHNOLOGY
·
2023
·Vol. 249
·期 7
·1749-1759
·
IF 3.3
Liu Ying,Liu Xin,Gao Ying,Dong Shijian,Xiang Xiaole,Ma Lulu,Li Shugang
Egg white powder (EWP) is a kind of food processing ingredient with high quality, low price and superior processing performance. However, its application in food industry is seriously affected by the problems such as strong off-flavor and its unknown volatile compounds. In this study, EWP was taken as the research object, and the off-flavor difference of EWP under different drying methods (spray drying, vacuum drying and freeze drying) was taken as the breakthrough point. With the help of gas chromatograph-mass spectrometer (GC–MS), electronic nose (E-nose) and sensory evaluation, it was found that the off-flavor of EWP was described as fishy and green, and high temperatures made the off-flavor worse, especially SD-EWP presented the strongest odor. A total of 13 key flavor compounds and 7 potential off-flavor compounds in SD-EWP were identified by gas chromatograph–mass spectrometer–olfactometry (GC-MS-O) and aroma reconstruction experiments. The relationship model of key flavor compounds, E-nose and off-flavor was established by multivariate analysis. It was verified that octanal, nonanal, 1-octen-3-ol and geranyl acetone were the off-flavor compounds of EWP. This study provided theoretical guidance and technical reference for the flavor research and sensory quality improvement of EWP.
研究领域:
食品科学
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感官科学
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化学分析
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产品关联
DOI
高影响因子
FOOD CHEMISTRY
·
2019
·
IF 5.399
Min Xu,Jun Wang,Luyi Zhu
Electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) combined with chemometrics methods were applied for qualitative identification and quantitative prediction of tea quality. Main chemical components, such as amino acids, catechins, polyphenols and caffeine were measured by traditional methods. Feature-level fusion strategy for the integration of the signals was introduced to integrate the E-nose, E-tongue and E-eye signals, aiming at improving the performances of identification and prediction models. Perfect results with an accuracy of 100% were obtained for qualitative identification of tea quality grades, based on fusion signals by support vector machine and random forest. Quantitative models were established for predicting the contents of the chemical components based on independent electronic signals and fusion signals by partial least squares regression, support vector machine and random forest. Random forest based on the fusion signals achieved the best performance in predicting the concentration of those chemical components.
研究领域:
食品科学
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化学计量学
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分析化学
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机器学习
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DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）