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1. Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications

Author

Alessandro Tonelli, Veronica Mangia, Alessandro Candiani, Francesco Pasquali, Tiziana Jessica Mangiaracina, Alessandro Grazioli, Michele Sozzi, Davide Gorni, Simona Bussolati, Annamaria Cucinotta, Giuseppina Basini, and Stefano Selleri

Subjects
Raspberry Pi, CMOS, raw data, photometric analysis, imaging, oxidative stress, Chemical technology, TP1-1185
Abstract

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.

Published
2021
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2. Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications

Author

Veronica Mangia, Stefano Selleri, Tiziana Jessica Mangiaracina, Michele Sozzi, Giuseppina Basini, Annamaria Cucinotta, Alessandro Tonelli, Francesco Pasquali, Alessandro Candiani, Davide Gorni, Alessandro Grazioli, and Simona Bussolati

Subjects
Computer science, Raspberry Pi, TP1-1185, 01 natural sciences, Biochemistry, Article, Analytical Chemistry, Raspberry pi, photometric analysis, 03 medical and health sciences, horse blood, Horse blood, d-ROM Fast, oxidative stress, Electrical and Electronic Engineering, Adaptation (computer science), Instrumentation, raw data, 030304 developmental biology, 0303 health sciences, business.industry, Chemical technology, 010401 analytical chemistry, CMOS, imaging, Atomic and Molecular Physics, and Optics, 0104 chemical sciences, Microcontroller, business, Raw data, Computer hardware, PAT
Abstract

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.

Published
2021

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