Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications.
CMOS
PAT
Raspberry Pi
d-ROM Fast
horse blood
imaging
oxidative stress
photometric analysis
raw data
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
20 May 2021
20 May 2021
Historique:
received:
31
03
2021
revised:
10
05
2021
accepted:
16
05
2021
entrez:
2
6
2021
pubmed:
3
6
2021
medline:
3
6
2021
Statut:
epublish
Résumé
Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.
Identifiants
pubmed: 34065190
pii: s21103552
doi: 10.3390/s21103552
pmc: PMC8160707
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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