Jove
Visualize
Contact Us

Related Experiment Videos

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays.

Keith J Albert1, Daljeet S Gill, Tim C Pearce

  • 1The Max Tishler Laboratory for Organic Chemistry, Department of Chemistry, Tufts University, Medford, MA 02155, USA.

Analytical and Bioanalytical Chemistry
|August 24, 2002
PubMed
Summary

This study demonstrates automatic identification of sensor classes in randomized arrays using optical decoding. This method accurately classifies hundreds of sensors without prior location knowledge, enabling efficient sensor array analysis.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Blinded 2-Year Longitudinal Evaluation of SARS-CoV-2 Antigenemia in Long COVID.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026
Same author

Digital seed amplification assay for TDP-43 aggregate quantification in CSF.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Differences in SARS-CoV-2 Antigen Persistence in Individuals With Systemic Autoimmune Rheumatic Diseases Compared to the General Population: A RECOVER-Adult Cohort Study.

Arthritis & rheumatology (Hoboken, N.J.)·2026
Same author

High-throughput single-vesicle imaging platform for direct extracellular vesicle profiling of human plasma.

Nature communications·2026
Same author

Endovascular profiles linked to neutrophil activation in children and young adults with long COVID.

Pediatric research·2026
Same author

Attomolar fecal cytokine profiling reveals gut immune dynamics and disease states.

bioRxiv : the preprint server for biology·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Area of Science:

  • Analytical Chemistry
  • Materials Science

Background:

  • High-density sensor arrays are crucial for chemical sensing.
  • Identifying individual sensors in randomized arrays without prior knowledge is challenging.

Purpose of the Study:

  • To demonstrate automatic sensor identification and classification within a high-density randomized optical imaging fiber array.
  • To introduce and compare computational decoding methods for sensor classification.

Main Methods:

  • Randomly distributed two types of fluorescence-based vapor-sensitive microspheres (Nile Red dye) into an optical imaging fiber array.
  • Employed supervised and unsupervised computational decoding methods based on sensor spectral changes (sensor-response profiles).
  • Validated classification accuracy by comparing responses from randomized arrays to known control arrays.

Related Experiment Videos

Main Results:

  • Achieved 99.32% accuracy in correctly classifying 587 sensors.
  • Both supervised and unsupervised decoding methods yielded equal classification rates.
  • The unsupervised method, focusing on odor-response profiles, is identified as a superior decoding model for these vapor-sensitive arrays.

Conclusions:

  • Automatic sensor identification and classification are feasible in high-density randomized arrays.
  • Optical decoding techniques enable positional registration of sensors without increased time or cost.
  • The developed methods are applicable to various multiplexed fluorescence-based arrays and assays.