Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

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

Summary

This summary is machine-generated.

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

Same journal

Single-cell mass spectrometry imaging: platform advances for multimodal spatial omics.

Analytical and bioanalytical chemistry·2026

Same journal

Advancing total uronic acid quantification using a stable isotope dilution approach: validation and application to plant- and algal-derived polysaccharides.

Analytical and bioanalytical chemistry·2026

Same journal

Electroanalytical method development for the receptor tyrosine kinase inhibitor lenvatinib using a Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>-MXene based molecularly imprinted polymer modified carbon electrode.

Analytical and bioanalytical chemistry·2026

Same journal

Impact of blood contamination on hydrophilic metabolomics in human meningioma tissue.

Analytical and bioanalytical chemistry·2026

Same journal

A UiO-66@MR paper-based colorimetric sensor for sensitive detection of food spoilage volatile organic compounds and visual freshness monitoring.

Analytical and bioanalytical chemistry·2026

Same journal

An electrochemical sensing platform based on UiO@TATF COF/CB composites for the detection of nitrofurazone.

Analytical and bioanalytical chemistry·2026

See all related articles

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.

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.