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Cited by 16Advanced Chemical Sensor TechnologiesBiosensors and Analytical DetectionWound Healing and Treatments

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Volatile organic compounds (VOCs) detection for the identification of bacterial infections in clinical wound samples

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Azimzadeh, Mostafa, Khashayar, Patricia, Mousazadeh, Marziyeh, Daneshpour, Maryam, Rostami, Mohammad, Goodlett, David R., Manji, Karim, Fardindoost, Somayeh, Akbari, Mohsen, Hoorfar, Mina·2025·Talanta
DOI 10.1016/j.talanta.2025.127991
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Abstract

Early detection of wound infections is critical for timely intervention and prevention of possible complications since prompt treatment can help lower pathogen spread and enhance faster healing. Early detection also helps reduce the risk of serious infections requiring extensive medical interventions or life-threatening diseases such as sepsis. Culture-based approaches currently used for bacterial identification have limited sensitivity and specificity. At the same time, they are time-consuming, resulting in delays in therapy and, therefore, having a negative impact on the treatment outcomes. Quantifying the volatile organic compounds (VOCs) released by bacteria residing in wounds is a promising, non-invasive option for detecting infections at early stages. This method allows for continuous monitoring without requiring invasive procedures, thereby reducing patient discomfort and the risk of further complications. Spectroscopy methods and sensors are the primary VOC detection and quantification approaches, but sensors are more rapid, cost-effective, non-invasive, and precise. This review highlights the significance of the early detection of wound infection to enable timely intervention and prevent complications, emphasizing the limitations of culture-based approaches. It also explores the potential of quantifying VOCs using different methods and discusses the correlation between their levels and the rate of bacterial infections in wounds. Additionally, the review evaluates current VOC-based monitoring methods for wound management, identifies gaps in the field, and advocates for further research to advance wound care and enhance patient outcomes. • Early wound infection detection aids prompt treatment and faster healing. • VOC quantification offers a non-invasive, continuous infection monitoring option. • Sensors for VOC detection are rapid, affordable, precise, and non-invasive. • The review covers VOC quantification methods for wound monitoring. • The main focus is on array-based, nano, micro, and point-of-care sensors.

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Research works printed but not linked: 155

  1. Reeves, 2022, Volatile organic compounds in the early diagnosis of non-healing surgical wounds: a systematic review, World J. Surg., 46, 1669, 10.1007/s00268-022-06548-3 · DOI
  2. Daulton, 2020, The detection of wound infection by ion mobility chemical analysis, Biosensors, 10, 10.3390/bios10030019 · DOI
  3. Zhang, 2023, Biophotonics technologies for the detection of VOCs in healthcare applications: are we there yet?, Appl. Phys. Rev., 10, 10.1063/5.0145194 · DOI
  4. Ratiu, 2020, An optimistic vision of future: diagnosis of bacterial infections by sensing their associated volatile organic compounds, Crit. Rev. Anal. Chem., 50, 501, 10.1080/10408347.2019.1663147 · DOI
  5. Kunze, 2013, Detection and validation of volatile metabolic patterns over different strains of two human pathogenic bacteria during their growth in a complex medium using multi-capillary column-ion mobility spectrometry (MCC-IMS), Appl. Microbiol. Biotechnol., 97, 3665, 10.1007/s00253-013-4762-8 · DOI
  6. Kunze-Szikszay, 2021, Identification of volatile compounds from bacteria by spectrometric methods in medicine diagnostic and other areas: current state and perspectives, Appl. Microbiol. Biotechnol., 105, 6245, 10.1007/s00253-021-11469-7 · DOI
  7. Johnson, 2022, Wound infection: a review of qualitative and quantitative assessment modalities, J Plast Reconstr Aesthet Surg, 75, 1287, 10.1016/j.bjps.2021.11.060 · DOI
  8. Sethi, 2013, Clinical application of volatile organic compound analysis for detecting infectious diseases, Clin. Microbiol. Rev., 26, 462, 10.1128/CMR.00020-13 · DOI
  9. Mitra, 2022, The human skin volatolome: a systematic review of untargeted mass spectrometry analysis, Metabolites, 12, 824, 10.3390/metabo12090824 · DOI
  10. Duffy, 2019, Endogenous and microbial volatile organic compounds in cutaneous health and disease, TrAC, Trends Anal. Chem., 111, 163, 10.1016/j.trac.2018.12.012 · DOI
  11. Reeves, 2022, Volatile organic compounds in the early diagnosis of non-healing surgical wounds: a systematic review, World J. Surg., 46, 1669, 10.1007/s00268-022-06548-3 · DOI
  12. Almeida, 2021, Smart dressings for wound healing: a review, Adv. Skin Wound Care, 34, 1, 10.1097/01.ASW.0000725188.95109.68 · DOI
  13. Mehmood, 2014, Applications of modern sensors and wireless technology in effective wound management, J. Biomed. Mater. Res. B Appl. Biomater., 102, 885, 10.1002/jbm.b.33063 · DOI
  14. Tanaka, 2022, Detecting bacterial infections in wounds: a review of biosensors and wearable sensors in comparison with conventional laboratory methods, Analyst, 147, 1756, 10.1039/D2AN00157H · DOI
  15. Kruse, 2015, The external microenvironment of healing skin wounds, Wound Repair Regen., 23, 456, 10.1111/wrr.12303 · DOI
  16. Jahanshahi, 2020, An engineered infected epidermis model for in vitro study of the skin's pro-inflammatory response, Micromachines, 11, 227, 10.3390/mi11020227 · DOI
  17. Mirani, 2023, Smart dual-sensor wound dressing for monitoring cutaneous wounds, Adv Healthc Mater, 12, 10.1002/adhm.202203233 · DOI
  18. Bessa, 2015, Bacterial isolates from infected wounds and their antibiotic susceptibility pattern: some remarks about wound infection, Int. Wound J., 12, 47, 10.1111/iwj.12049 · DOI
  19. Yang, 2023, The trends in wound management: sensing, therapeutic treatment, and “theranostics”, J. Sci.: Advanced Materials and Devices, 8
  20. Ramirez-Acuña, 2019, Diabetic foot ulcers: current advances in antimicrobial therapies and emerging treatments, Antibiotics, 8, 193, 10.3390/antibiotics8040193 · DOI
  21. Barbier, 2023, Rapid diagnostics for skin and soft tissue infections: the current landscape and future potential, Curr. Opin. Infect. Dis., 36, 57, 10.1097/QCO.0000000000000901 · DOI
  22. Mirani, 2023, Smart dual-sensor wound dressing for monitoring cutaneous wounds, Adv. Healthcare Mater., 12, 10.1002/adhm.202203233 · DOI
  23. Hadisi, 2020, Hyaluronic acid (HA)-Based silk fibroin/zinc oxide core–shell electrospun dressing for burn wound management, Macromol. Biosci., 20, 10.1002/mabi.201900328 · DOI
  24. Mirani, 2017, An advanced multifunctional hydrogel-based dressing for wound monitoring and drug delivery, Adv. Healthcare Mater., 6, 10.1002/adhm.201700718 · DOI
  25. Hasanpour, 2022, A hybrid thread-based temperature and humidity sensor for continuous wound monitoring, Sensor. Actuator. B Chem., 370, 10.1016/j.snb.2022.132414 · DOI
  26. Mota, 2024, Comparative analysis of electrochemical and optical sensors for detection of chronic wounds biomarkers: a review, Biosens. Bioelectron., 251, 10.1016/j.bios.2024.116095 · DOI
  27. Uberoi, 2024, The wound microbiota: microbial mechanisms of impaired wound healing and infection, Nat. Rev. Microbiol., 22, 507, 10.1038/s41579-024-01035-z · DOI
  28. Caldwell, 2020, Bacteria and antibiotics in wound healing, Surgical Clinics, 100, 757
  29. Slade, 2022, Real-time detection of volatile metabolites enabling species-level discrimination of bacterial biofilms associated with wound infection, J. Appl. Microbiol., 132, 1558, 10.1111/jam.15313 · DOI
  30. Yin, 2023, A smartphone-based fluorescent sensor for rapid detection of multiple pathogenic bacteria, Biosens. Bioelectron., 242, 10.1016/j.bios.2023.115744 · DOI
  31. Bakshi, 2023, Nanophotonic and hydrogel-based diagnostic system for the monitoring of chronic wounds, Biosens. Bioelectron., 242, 10.1016/j.bios.2023.115743 · DOI
  32. Mota, 2024, Comparative analysis of electrochemical and optical sensors for detection of chronic wounds biomarkers: a review, Biosens. Bioelectron., 10.1016/j.bios.2024.116095 · DOI
  33. Wang, 2016, Microbial volatile organic compounds and their application in microorganism identification in foodstuff, TrAC, Trends Anal. Chem., 78, 1, 10.1016/j.trac.2015.08.010 · DOI
  34. Kahremanoğlu, 2022, Recent progress in wearable extractive sampling technology, TrAC, Trends Anal. Chem., 157, 10.1016/j.trac.2022.116740 · DOI
  35. Ashrafi, 2018, Validation of biofilm formation on human skin wound models and demonstration of clinically translatable bacteria-specific volatile signatures, Sci. Rep., 8, 9431, 10.1038/s41598-018-27504-z · DOI
  36. Thomas, 2010, Novel noninvasive identification of biomarkers by analytical profiling of chronic wounds using volatile organic compounds, Wound Repair Regen., 18, 391, 10.1111/j.1524-475X.2010.00592.x · DOI
  37. Mousazadeh, 2024, Nanomaterials in electrochemical nanobiosensors of miRNAs, Nanoscale, 16, 4974, 10.1039/D3NR03940D · DOI
  38. Mousavi, 2023, Recent advances in smart wearable sensors as electronic skin, J. Mater. Chem. B, 11, 10332, 10.1039/D3TB01373A · DOI
  39. Wörner, 2023, Supporting wound infection diagnosis: advancements and challenges with electronic noses, Frontiers in Sensors, 4, 10.3389/fsens.2023.1250756 · DOI · Paper page
  40. Brown, 2018, Wearable technology for chronic wound monitoring: current dressings, advancements, and future prospects, Front. Bioeng. Biotechnol., 6, 47, 10.3389/fbioe.2018.00047 · DOI
  41. Mohamed Salleh, 2022, Detecting bacterial infections in wounds: a review of biosensors and wearable sensors in comparison with conventional laboratory methods, 1756
  42. Galstyan, 2021, Detection of volatile organic compounds: from chemical gas sensors to terahertz spectroscopy, Rev. Anal. Chem., 40, 10.1515/revac-2021-0127 · DOI
  43. Ratiu, 2019, GC-MS application in determination of volatile profiles emitted by infected and uninfected human tissue, J. Breath Res., 13, 10.1088/1752-7163/aaf708 · DOI
  44. Daulton, 2020, The detection of wound infection by ion mobility chemical analysis, Biosensors, 10, 10.3390/bios10030019 · DOI
  45. Ashrafi, 2018, Validation of biofilm formation on human skin wound models and demonstration of clinically translatable bacteria-specific volatile signatures, Sci. Rep., 8, 10.1038/s41598-018-27504-z · DOI
  46. Elmassry, 2020, Frontiers | volatilomes of bacterial infections in humans, Front. Neurosci., 14
  47. Roodt, 2018, Human skin volatiles: passive sampling and GC × GC-ToFMS analysis as a tool to investigate the skin microbiome and interactions with anthropophilic mosquito disease vectors, J. Chromatogr. B, 1097–1098, 83, 10.1016/j.jchromb.2018.09.002 · DOI
  48. Gallagher, 2008, Analyses of volatile organic compounds from human skin, Br. J. Dermatol., 159, 780, 10.1111/j.1365-2133.2008.08748.x · DOI
  49. Monedeiro, 2021, Metabolic profiling of VOCs emitted by bacteria isolated from pressure ulcers and treated with different concentrations of bio-AgNPs, Int. J. Mol. Sci., 22, 10.3390/ijms22094696 · DOI
  50. Dormont, 2013, Human skin volatiles: a review, J. Chem. Ecol., 39, 569, 10.1007/s10886-013-0286-z · DOI · Paper page
  51. Elmassry, 2020, Volatilomes of bacterial infections in humans, Front. Neurosci., 14, 257, 10.3389/fnins.2020.00257 · DOI
  52. Fitzgerald, 2024, Volatilomes of human infection, Anal. Bioanal. Chem., 416, 37, 10.1007/s00216-023-04986-z · DOI
  53. Lu, 2022, Use of GC-IMS for detection of volatile organic compounds to identify mixed bacterial culture medium, AMB Express, 12, 10.1186/s13568-022-01367-0 · DOI
  54. Kunze, 2013, Detection and validation of volatile metabolic patterns over different strains of two human pathogenic bacteria during their growth in a complex medium using multi-capillary column-ion mobility spectrometry (MCC-IMS), Appl. Microbiol. Biotechnol., 97, 3665, 10.1007/s00253-013-4762-8 · DOI
  55. Spanel, 2020, Quantification of volatile metabolites in exhaled breath by selected ion flow tube mass spectrometry, SIFT-MS, Clin Mass Spectrom, 16, 18, 10.1016/j.clinms.2020.02.001 · DOI
  56. Allardyce, 2006, Detection of volatile metabolites produced by bacterial growth in blood culture media by selected ion flow tube mass spectrometry (SIFT-MS), J. Microbiol. Methods, 65, 361, 10.1016/j.mimet.2005.09.003 · DOI
  57. Storer, 2011, Detection of volatile compounds produced by microbial growth in urine by selected ion flow tube mass spectrometry (SIFT-MS), J. Microbiol. Methods, 87, 111, 10.1016/j.mimet.2011.06.012 · DOI
  58. Chippendale, 2014, Quantification by SIFT-MS of volatile compounds emitted by in vitro cultures of S. aureus, S. pneumoniae and H. influenzae isolated from patients with respiratory diseases, Anal. Methods, 6, 10.1039/c4ay00209a · DOI
  59. Ilbeigi, 2022, Solid phase microextraction–multicapillary column–ion mobility spectrometry (SPME–MCC–IMS) for detection of methyl salicylate in tomato leaves, J. Agric. Food Chem., 70
  60. Gao, 2024, Gas chromatography-ion mobility spectrometry for the detection of human disease: a review, Anal. Methods, 16, 10.1039/D4AY01452A · DOI
  61. Khatib, 2022, Sensors for volatile organic compounds, ACS Nano, 16, 7080, 10.1021/acsnano.1c10827 · DOI
  62. Yan, 2022, Electrical sensing of volatile organic compounds in exhaled breath for disease diagnosis, Curr. Opin. Electrochem., 33
  63. Salinas Alvarez, 2019, Detection of volatile compounds emitted by bacteria in wounds using gas sensors, Sensors (Basel), 19, 10.3390/s19071523 · DOI · Paper page
  64. Bruins, 2009, Device-independent, real-time identification of bacterial pathogens with a metal oxide-based olfactory sensor, Eur. J. Clin. Microbiol. Infect. Dis., 28, 775, 10.1007/s10096-009-0700-1 · DOI
  65. Haalboom, 2019, Differentiation between infected and non-infected wounds using an electronic nose, Clin. Microbiol. Infect., 25, 10.1016/j.cmi.2019.03.018 · DOI
  66. Qian, 2022, Study on sensor array optimization of medical electronic nose for wound infection detection, IEEE Transactions on Circuits and Systems II: Express Briefs, 69, 1867
  67. Šetkus, 2006, Featuring of bacterial contamination of wounds by dynamic response of SnO2 gas sensor array, Sensor. Actuator. B Chem., 115, 412, 10.1016/j.snb.2005.10.003 · DOI
  68. Sun, 2020, Collaborative detection for wound infections using electronic nose and FAIMS technology based on a rat wound model, Sensor. Actuator. B Chem., 320, 10.1016/j.snb.2020.128595 · DOI
  69. Venkatachalaiah, 2020, PANI/TiO(2) nanocomposite-based chemiresistive gas sensor for the detection of E. Coli bacteria, IET Nanobiotechnol., 14, 761, 10.1049/iet-nbt.2020.0046 · DOI
  70. Seesaard, 2020, A hybrid electronic nose system for discrimination of pathogenic bacterial volatile compounds, Anal. Methods, 12, 5671, 10.1039/D0AY01255F · DOI
  71. Shauloff, 2021, Sniffing bacteria with a carbon-dot artificial nose, Nano-Micro Lett., 13, 112, 10.1007/s40820-021-00610-w · DOI
  72. Teixeira, 2024, An odorant-binding protein based electrical sensor to detect volatile organic compounds, Sensor. Actuator. B Chem., 411, 10.1016/j.snb.2024.135726 · DOI
  73. Liu, 2017, Smartphone-based sensing system using ZnO and graphene modified electrodes for VOCs detection, Biosens. Bioelectron., 93, 94, 10.1016/j.bios.2016.09.084 · DOI
  74. Chen, 2023, AI‐Driven wearable mask‐inspired self‐healing sensor array for detection and identification of volatile organic compounds, Adv. Funct. Mater., 34
  75. Paknahad, 2019, Selective detection of volatile organic compounds in microfluidic gas detectors based on "like dissolves like", Sci. Rep., 9, 161, 10.1038/s41598-018-36615-6 · DOI
  76. Ghazi, 2022, Selective detection of VOCs using microfluidic gas sensor with embedded cylindrical microfeatures coated with graphene oxide, J. Hazard Mater., 424
  77. Sivaperuman, 2024, Binary and ternary metal oxide semiconductor thin films for effective gas sensing applications: a comprehensive review and future prospects, Prog. Mater. Sci., 142
  78. Ji, 2019, Gas sensing mechanisms of metal oxide semiconductors: a focus review, Nanoscale, 11, 22664, 10.1039/C9NR07699A · DOI
  79. Cho, 2016, High-resolution p-type metal oxide semiconductor nanowire array as an ultrasensitive sensor for volatile organic compounds, Nano Lett., 16, 4508, 10.1021/acs.nanolett.6b01713 · DOI
  80. Marikutsa, 2021, Comparison of Au-functionalized semiconductor metal oxides in sensitivity to VOC, Sensor. Actuator. B Chem., 326, 10.1016/j.snb.2020.128980 · DOI
  81. Liu, 2008, Towards development of chemosensors and biosensors with metal-oxide-based nanowires or nanotubes, Biosens. Bioelectron., 24, 167, 10.1016/j.bios.2008.04.014 · DOI
  82. Saviauk, 2018, Electronic nose in the detection of wound infection bacteria from bacterial cultures: a proof-of-principle study, Eur. Surg. Res., 59, 1, 10.1159/000485461 · DOI
  83. Tomic, 2021, VOCs sensing by metal oxides, conductive polymers, and carbon-based materials, Nanomaterials, 11, 10.3390/nano11020552 · DOI
  84. Duncan, 2017, Sensing by smell: Nanoparticle-enzyme sensors for rapid and sensitive detection of bacteria with olfactory output, ACS Nano, 11, 5339, 10.1021/acsnano.7b00822 · DOI
  85. Lachinov, 2023, Prospects for the application of sensor elements for detecting the presence of bacteria in real time, Appl. Biochem. Microbiol., 59, 373, 10.1134/S0003683823030122 · DOI
  86. Saviauk, 2018, Electronic nose in the detection of wound infection bacteria from bacterial cultures: a proof-of-principle study, Eur. Surg. Res., 59
  87. Wörner, 2023, Frontiers | Supporting wound infection diagnosis: advancements and challenges with electronic noses, Frontiers in Sensors, 4
  88. Kuchmenko, 2022, Portable electronic nose system for fast gynecological-conditions diagnosis in consulting room: a case study, Sensor. Actuator. B Chem., 358, 10.1016/j.snb.2022.131538 · DOI
  89. Wilson, 2018, Application of electronic-nose technologies and VOC-biomarkers for the noninvasive early diagnosis of gastrointestinal diseases (dagger), Sensors (Basel), 18, 10.3390/s18082613 · DOI
  90. Yusuf, 2015, In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology, BMC Bioinf., 16, 158, 10.1186/s12859-015-0601-5 · DOI
  91. Byun, 2010, Wound-state monitoring for burn patients using E-nose/SPME system, ETRI J., 32, 440, 10.4218/etrij.10.0109.0300 · DOI
  92. Sun, 2017, Sensor array optimization of electronic nose for detection of bacteria in wound infection, IEEE Trans. Ind. Electron., 64, 7350, 10.1109/TIE.2017.2694353 · DOI
  93. Farmand, 2022, Carbon nanostructures: a comprehensive review of potential applications and toxic effects, 3 Biotech, 12, 159, 10.1007/s13205-022-03175-6 · DOI
  94. Liu, 2008, Towards development of chemosensors and biosensors with metal-oxide-based nanowires or nanotubes, Biosens. Bioelectron., 24, 167, 10.1016/j.bios.2008.04.014 · DOI
  95. Gan, 2023, Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis, Biosens. Bioelectron., 237, 10.1016/j.bios.2023.115540 · DOI
  96. Rath, 2024, Ammonia detection: a pathway towards potential point-of-care diagnostics, Biosens. Bioelectron., 251, 10.1016/j.bios.2024.116100 · DOI
  97. Bordbar, 2020, Ultrafast detection of infectious bacteria using optoelectronic nose based on metallic nanoparticles, Sensor. Actuator. B Chem., 319, 10.1016/j.snb.2020.128262 · DOI
  98. Binabaji, 2024, Innovative wearable sweat sensor array for real-time volatile organic compound detection in noninvasive diabetes monitoring, Anal. Chem., 96, 10.1021/acs.analchem.4c02034 · DOI
  99. Luka, 2015, Microfluidics integrated biosensors: a leading technology towards lab-on-a-chip and sensing applications, Sensors, 15, 30011, 10.3390/s151229783 · DOI
  100. Yeganegi, 2023, Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review, Front. Chem., 11, 10.3389/fchem.2023.1267187 · DOI
  101. Azimzadeh, 2021, CRISPR-powered microfluidics in diagnostics: a review of main applications, Chemosensors, 10, 3, 10.3390/chemosensors10010003 · DOI
  102. Khosravi, 2023, Ultrasensitive electrochemical miR-155 nanocomposite biosensor based on functionalized/conjugated graphene materials and gold nanostars, Sensor. Actuator. B Chem., 375, 10.1016/j.snb.2022.132877 · DOI
  103. Sadrabadi, 2024, Novel electrochemical biosensor for breast cancer detection, based on a nanocomposite of carbon nanofiber, metal–organic framework, and magnetic graphene oxide, Bioelectrochemistry, 155, 10.1016/j.bioelechem.2023.108558 · DOI
  104. Gross, 2018, Cyclic-voltammetry-based solid-state gas sensor for methane and other VOC detection, Anal. Chem., 90, 6102, 10.1021/acs.analchem.8b00184 · DOI
  105. Yavarinasab, 2020, Graphene/poly (methyl methacrylate) electrochemical impedance-transduced chemiresistor for detection of volatile organic compounds in aqueous medium, Anal. Chim. Acta, 1109, 27, 10.1016/j.aca.2020.02.065 · DOI
  106. Zhang, 2014, Identification of volatile biomarkers of gastric cancer cells and ultrasensitive electrochemical detection based on sensing interface of Au-Ag alloy coated MWCNTs, Theranostics, 4, 154, 10.7150/thno.7560 · DOI
  107. Kakı, 2013, Synthesis, characterization, electrochemistry and VOC sensing properties of novel metallophthalocyanines with four cyclohexyl-phenoxyphthalonitrile groups, Sensor. Actuator. B Chem., 188, 1033, 10.1016/j.snb.2013.07.103 · DOI
  108. Çimen, 2014, Synthesis, characterization, electrochemistry and VOC sensing properties of novel ball-type dinuclear metallophthalocyanines, Sensor. Actuator. B Chem., 202, 1137, 10.1016/j.snb.2014.06.066 · DOI
  109. Zetola, 2016, Identification of a large pool of microorganisms with an array of porphyrin based gas sensors, Sensors (Basel), 16, 466, 10.3390/s16040466 · DOI
  110. Gębicki, 2013, Prototype of electrochemical sensor for measurements of volatile organic compounds in gases, Sensor. Actuator. B Chem., 177, 1173, 10.1016/j.snb.2012.12.025 · DOI
  111. Cao, 2024, Hydrophobic MOF/PDMS-Based QCM sensors for VOCs identification and quantitative detection in high-humidity environments, ACS Appl. Mater. Interfaces, 30, 16
  112. Bhattacharjee, 2021, Study of multichannel QCM prospects in VOC detection, J. Phys. Conf., 1900, 10.1088/1742-6596/1900/1/012020 · DOI
  113. Das, 2025, Fabrication of surface functionalized QCM sensor for BTX detection at ambient conditions, Talanta, 283, 10.1016/j.talanta.2024.127081 · DOI
  114. Temel, 2016, Calix[4]arene coated QCM sensors for detection of VOC emissions: Methylene chloride sensing studies, Talanta, 153, 10.1016/j.talanta.2016.03.026 · DOI
  115. Matsuguchi, 2006, Molecular imprinting strategy for solvent molecules and its application for QCM-based VOC vapor sensing, Sensor. Actuator. B Chem., 113, 94, 10.1016/j.snb.2005.02.028 · DOI
  116. Wang, 2020, Metal-organic frameworks for QCM-based gas sensors: a review, Sensor Actuator Phys., 307, 10.1016/j.sna.2020.111984 · DOI
  117. Milne, 2016, A wearable wound moisture sensor as an indicator for wound dressing change: an observational study of wound moisture and status, Int. Wound J., 13, 1309, 10.1111/iwj.12521 · DOI
  118. Öztürk, 2016, Electrochemically growth of Pd doped ZnO nanorods on QCM for room temperature VOC sensors, Sensor. Actuator. B Chem., 222, 280, 10.1016/j.snb.2015.08.083 · DOI
  119. Khashayar, 2022, New frontiers in microfluidics devices for miRNA analysis, TrAC, Trends Anal. Chem., 156, 10.1016/j.trac.2022.116706 · DOI
  120. Azimzadeh, 2021, Microfluidic-based oxygen (O2) sensors for on-chip monitoring of cell, tissue and organ metabolism, Biosensors, 12, 6, 10.3390/bios12010006 · DOI
  121. Alwarappan, 2022, 2D metal carbides and nitrides (MXenes) for sensors and biosensors, Biosens. Bioelectron., 205, 10.1016/j.bios.2021.113943 · DOI
  122. Bhardwaj, 2022, Advances in MXenes-based optical biosensors: a review, Biosens. Bioelectron., 202, 10.1016/j.bios.2022.113995 · DOI
  123. Crunaire, 2014, Discriminating bacteria with optical sensors based on functionalized nanoporous xerogels, Chemosensors, 2, 171, 10.3390/chemosensors2020171 · DOI
  124. Kladsomboon, 2018, Development of organic-inorganic hybrid optical gas sensors for the non-invasive monitoring of pathogenic bacteria, Sensors (Basel), 18, 10.3390/s18103189 · DOI
  125. Tait, 2015, Analysis of pathogenic bacteria using exogenous volatile organic compound metabolites and optical sensor detection, RSC Adv., 5, 15494, 10.1039/C4RA13914C · DOI
  126. Lim, 2016, Bacterial culture detection and identification in blood agar plates with an optoelectronic nose, Analyst, 141, 918, 10.1039/C5AN01990G · DOI
  127. Lonsdale, 2013, The use of colorimetric sensor arrays to discriminate between pathogenic bacteria, PLoS One, 8, 10.1371/journal.pone.0062726 · DOI
  128. Carey, 2011, Rapid identification of bacteria with a disposable colorimetric sensing array, J. Am. Chem. Soc., 133, 7571, 10.1021/ja201634d · DOI
  129. Zheng, 2023, Battery-free and AI-enabled multiplexed sensor patches for wound monitoring, Sci. Adv., 9, eadg6670, 10.1126/sciadv.adg6670 · DOI
  130. DeJong, 2017, Bacterial detection and differentiation via direct volatile organic compound sensing with surface enhanced Raman spectroscopy, ChemistrySelect, 2, 8431, 10.1002/slct.201701669 · DOI
  131. Qu, 2024, Artificial nose of scalable plasmonic array gas sensor for Multi-Dimensional SERS recognition of volatile organic compounds, Chem. Eng. J., 482, 10.1016/j.cej.2024.148773 · DOI
  132. Tjandra, 2022, Polydiacetylene-based sensors to detect volatile organic compounds, Chem. Mater., 34, 2853, 10.1021/acs.chemmater.1c04318 · DOI
  133. Sun, 2020, Colorimetric sensor array based on gold nanoparticles: design principles and recent advances, TrAC, Trends Anal. Chem., 122, 10.1016/j.trac.2019.115754 · DOI
  134. Lim, 2014, Colorimetric sensor array allows fast detection and simultaneous identification of sepsis-causing bacteria in spiked blood culture, J. Clin. Microbiol., 52, 592, 10.1128/JCM.02377-13 · DOI · Paper page
  135. Pathak, 2023, Recent advances in sensing materials targeting clinical volatile organic compound (VOC) biomarkers: a review, Biosensors, 13, 10.3390/bios13010114 · DOI
  136. Yang, 2023, Machine learning-assisted optical nano-sensor arrays in microorganism analysis, TrAC, Trends Anal. Chem., 159, 10.1016/j.trac.2023.116945 · DOI
  137. Qin, 2022, Metasurface micro/nano-optical sensors: principles and applications, ACS Nano, 16, 11598, 10.1021/acsnano.2c03310 · DOI
  138. Yu, 2018, Artificial intelligence in healthcare, Nat. Biomed. Eng., 2, 719, 10.1038/s41551-018-0305-z · DOI · Paper page
  139. Urbina, 2022, Dual use of artificial-intelligence-powered drug discovery, Nat. Mach. Intell., 4, 189, 10.1038/s42256-022-00465-9 · DOI · Paper page
  140. Ahmed, 2022, Enabling artificial intelligence for Genome sequence analysis of COVID-19 and alike viruses, Interdiscipl. Sci. Comput. Life Sci., 14, 504, 10.1007/s12539-021-00465-0 · DOI
  141. Al Saiegh, 2022, Patient and procedure selection for mechanical thrombectomy: toward personalized medicine and the role of artificial intelligence, J. Neuroimaging, 32, 798, 10.1111/jon.13003 · DOI
  142. Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 · DOI · Paper page
  143. Javaid, 2016, Saliva as a diagnostic tool for oral and systemic diseases, J Oral Biol Craniofac Res, 6, 66
  144. Dinges, 2019, Cancer metabolomic markers in urine: evidence, techniques and recommendations, Nat. Rev. Urol., 16, 339, 10.1038/s41585-019-0185-3 · DOI
  145. Kwiatkowski, 2022, Clinical studies of detecting COVID-19 from exhaled breath with electronic nose, Sci. Rep., 12, 10.1038/s41598-022-20534-8 · DOI
  146. Hu, 2019, Electronic noses: from advanced materials to sensors aided with data processing, Advanced Materials Technologies, 4, 10.1002/admt.201800488 · DOI
  147. Parrilla, 2022, Wearable wristband-based electrochemical sensor for the detection of phenylalanine in biofluids, Biosens. Bioelectron., 197, 10.1016/j.bios.2021.113764 · DOI
  148. Juang, 2022, Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care, PLoS One, 17, 10.1371/journal.pone.0276501 · DOI
  149. Vitense, 2021, Detection of Mycobacterium avium ssp. paratuberculosis in cultures from fecal and tissue samples using VOC analysis and machine learning tools, Front. Vet. Sci., 8, 10.3389/fvets.2021.620327 · DOI
  150. Arora, 2022, Machine learning approaches to identify discriminative signatures of volatile organic compounds (VOCs) from bacteria and fungi using SPME-DART-MS, Metabolites, 12, 10.3390/metabo12030232 · DOI
  151. Zhu, 2021, Toward healthcare diagnoses by machine-learning-enabled volatile organic compound identification, ACS Nano, 15, 894, 10.1021/acsnano.0c07464 · DOI
  152. Robin, 2021, High-performance VOC quantification for IAQ monitoring using advanced sensor systems and deep learning, Atmosphere, 12, 1487, 10.3390/atmos12111487 · DOI
  153. Chowdhury, 2022, VOC-net: a deep learning model for the automated classification of rotational THz spectra of volatile organic compounds, Appl. Sci., 12, 8447, 10.3390/app12178447 · DOI
  154. Vieira, 2023, CHEIC: chemical Image Classificator. An intelligent system for identification of volatiles compounds with potential for respiratory diseases using Deep Learning, Expert Syst. Appl., 234, 10.1016/j.eswa.2023.121178 · DOI
  155. Zhang, 2023, Using transfer-learning-based algorithms as data reduction strategies for volatile organic compounds classification using plasma spectroscopy, J. Phys. Appl. Phys., 56, 10.1088/1361-6463/accbcc · DOI

Metadata

Authors
Azimzadeh, M., Khashayar, P., Mousazadeh, M., Daneshpour, M., Rostami, M., Goodlett, D. R., Manji, K., Fardindoost, S., Akbari, M., & Hoorfar, M.
Affiliations
  • University of Victoria (CA)
  • University of Minnesota (US)
  • University of Southern California (US)
  • University of Calgary (CA)
  • University of British Columbia (CA)
Corresponding author
  • Akbari, Mohsen
  • Hoorfar, Mina
ORCID
Venue
Talanta
Publisher
Elsevier BV
Published
2025-09
Volume
292
Pages
127991
ISSN
0039-9140 (ISSN-L)
DOI
10.1016/j.talanta.2025.127991
OpenAlex
W4408595922
Cited by
16
References
2
Citations by year
2026: 12 · 2025: 4
FWCI
1.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Talanta (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • UVic’s Research and Learning Repository (University of Victoria) (submittedVersion, cc-by-nc-nd)
Funders
  • Genome British Columbia (RLA005, 265MET)
  • Canadian Institutes of Health Research (202303PJT-496230-CMZ-CABA-275073)
  • NSERC (RGPIN-2022-04433, RGPIN-2024-04869)
  • Metabolomics Innovation Centre
  • Genome Canada
  • NIH NIAID (1R01AI147314)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
  • Physical Sciences › Engineering › Biomedical Engineering › Biosensors and Analytical Detection
  • Health Sciences › Medicine › Surgery › Wound Healing and Treatments
Keywords
volatile organic compounds, wound infection, bacterial identification, early infection detection, non-invasive monitoring, VOC sensors, nanosensors, microsensors, sepsis prevention, wound care
MeSH terms
Bacteria, Bacterial Infections, Humans, Wound Infection, Volatile Organic Compounds

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