The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays
Abstract
A comprehensive review on the development and state of the art of colorimetric and fluorometric sensor arrays is presented. Chemical sensing aims to detect subtle changes in the chemical environment by transforming relevant chemical or physical properties of molecular or ionic species (i.e., analytes) into an analytically useful output. Optical arrays based on chemoresponsive colorants (dyes and nanoporous pigments) probe the chemical reactivity of analytes, rather than their physical properties (e.g., mass). The chemical specificity of the olfactory system does not come from specific receptors for specific analytes (e.g., the traditional lock-and-key model of substrate-enzyme interactions), but rather olfaction makes use of pattern recognition of the combined response of several hundred olfactory receptors. In a similar fashion, arrays of chemoresponsive colorants provide high-dimensional data from the color or fluorescence changes of the dyes in these arrays as they are exposed to analytes. This provides chemical sensing with high sensitivity (often down to parts per billion levels), impressive discrimination among very similar analytes, and exquisite fingerprinting of extremely similar mixtures over a wide range of analyte types, in both the gas and liquid phases. Design of both sensor arrays and instrumentation for their analysis are discussed. In addition, the various chemometric and statistical analyses of high-dimensional data (including hierarchical cluster analysis (HCA), principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs)) are presented and critiqued in reference to their use in chemical sensing. A variety of applications are also discussed, including personal dosimetry of toxic industrial chemical, detection of explosives or accelerants, quality control of foods and beverages, biosensing intracellularly, identification of bacteria and fungi, and detection of cancer and disease biomarkers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Aggregation-Induced Emission: Together We Shine, United We Soar!
Mei, J., Leung, N. L. C., Kwok, R. T. K., Lam, J. W. Y., & Tang, B. Z. (2015) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00263 · Cited by 8,270
A novel multigene family may encode odorant receptors: A molecular basis for odor recognition
Buck, L., & Axel, R. (1991) · Cell · doi:10.1016/0092-8674(91)90418-x · Cited by 4,987
Nanostructures in Biodiagnostics
Rosi, N. L., & Mirkin, C. A. (2005) · Chemical Reviews · doi:10.1021/cr030067f · Cited by 4,603
An overview of nanoparticles commonly used in fluorescent bioimaging
Wolfbeis, O. S. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00392f · Cited by 1,631
Surface-Enhanced Raman Spectroscopy
Haynes, C. L., McFarland, A. D., & Van Duyne, R. P. (2005) · Analytical Chemistry · doi:10.1021/ac053456d · Cited by 1,144
Sensors for Breath Testing: From Nanomaterials to Comprehensive Disease Detection
Konvalina, G., & Haick, H. (2014) · Accounts of Chemical Research · doi:10.1021/ar400070m · Cited by 561
Smartphone-based biosensors: A critical review and perspectives
Roda, A., Michelini, E., Zangheri, M., Di Fusco, M., Calabria, D., & Simoni, P. (2016) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2015.10.019 · Cited by 520
Cited by
Bio-Integrated Wearable Systems: A Comprehensive Review
Ray, T. R., Choi, J., Bandodkar, A. J., Krishnan, S., Gutruf, P., Tian, L., Ghaffari, R., & Rogers, J. A. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00573 · Cited by 1,350
Artificial intelligence biosensors: Challenges and prospects
Jin, X., Liu, C., Xu, T., Su, L., & Zhang, X. (2020) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112412 · Cited by 399
Chemiresistive Sensor Arrays for Gas/Volatile Organic Compounds Monitoring: A Review
Rath, R. J., Farajikhah, S., Oveissi, F., Dehghani, F., & Naficy, S. (2023) · Advanced Engineering Materials · doi:10.1002/adem.202200830 · Cited by 183
A Colorimetric Artificial Olfactory System for Airborne Improvised Explosive Identification
Wang, G., Li, Y., Cai, Z., & Dou, X. (2020) · Advanced Materials · doi:10.1002/adma.201907043 · Cited by 98
Li, F., Chao, S., Wang, X., Hu, Z., Wang, D., Tang, C., Gong, Z., Zhang, H., & Fan, M. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c07025 · Cited by 8
Metadata
- Authors
- Li, Z., Askim, J. R., & Suslick, K. S.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-09-12
- Volume
- 119
- Issue
- 1
- Pages
- 231-292
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2889675990
- Cited by
- 1,191
- References
- 13
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays. https://app.cochat.ai/papers/10.1021/acs.chemrev.8b00226
- The paper
- Li, Z., Askim, J. R., & Suslick, K. S. (2019). The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.8b00226
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.