Sensors that learn: The evolution from taste fingerprints to patterns of early disease detection

Nicolaos Christodoulides, Michael P. McRae, Glennon W. Simmons, Sayli S. Modak, John McDevitt

Research output: Contribution to journalReview article

Abstract

The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.

Original languageEnglish (US)
Article number251
JournalMicromachines
Volume10
Issue number4
DOIs
StatePublished - Apr 1 2019

Fingerprint

Biomarkers
Health
Sensors
Artificial intelligence
Learning systems
Data acquisition
Big data

Keywords

  • Biomarkers
  • Biosensors
  • Early disease detection
  • Electronic taste chip
  • Electronic tongue
  • Point-of-care
  • Programmable bio-nano-chip (p-BNC)
  • Saliva
  • Serum

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Cite this

Sensors that learn : The evolution from taste fingerprints to patterns of early disease detection. / Christodoulides, Nicolaos; McRae, Michael P.; Simmons, Glennon W.; Modak, Sayli S.; McDevitt, John.

In: Micromachines, Vol. 10, No. 4, 251, 01.04.2019.

Research output: Contribution to journalReview article

Christodoulides, Nicolaos ; McRae, Michael P. ; Simmons, Glennon W. ; Modak, Sayli S. ; McDevitt, John. / Sensors that learn : The evolution from taste fingerprints to patterns of early disease detection. In: Micromachines. 2019 ; Vol. 10, No. 4.
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