The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Smartphone-Based Food Diagnostic Technologies: A Review
Open accessAbstract
A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies.
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
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
Crocombe, R. A. (2018) · Applied Spectroscopy · doi:10.1177/0003702818809719 · Cited by 532
Smartphone-based analytical biosensors
Huang, X., Xu, D., Chen, J., Liu, J., Li, Y., Song, J., Ma, X., & Guo, J. (2018) · The Analyst · doi:10.1039/c8an01269e · Cited by 305
Mayer, M., & Baeumner, A. J. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00719 · Cited by 300
Chromium Ion Pair Luminescence: A Strategy in Broadband Near-Infrared Light-Emitting Diode Design
Rajendran, V., Fang, M.-H., Huang, W.-T., Majewska, N., Lesniewski, T., Mahlik, S., Leniec, G., Kaczmarek, S. M., Pang, W. K., Peterson, V. K., et al. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c08334 · Cited by 283
Automatic smartphone-based microfluidic biosensor system at the point of care
Xu, D., Huang, X., Guo, J., & Ma, X. (2018) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2018.03.018 · Cited by 264
Metadata
- Authors
- Rateni, G., Dario, P., & Cavallo, F.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-06-20
- Volume
- 17
- Issue
- 6
- Pages
- 1453
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2677169684
- Cited by
- 333
- References
- 1
- FWCI
- 13.15
- Type
- journal article
- Language
- en
- Open access
- gold
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: Smartphone-Based Food Diagnostic Technologies: A Review. https://app.cochat.ai/papers/10.3390/s17061453
- The paper
- Rateni, G., Dario, P., & Cavallo, F. (2017). Smartphone-Based Food Diagnostic Technologies: A Review. Sensors. https://doi.org/10.3390/s17061453
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.