Abstract
This work explores the effects of embedded software-driven measurements on a sensory target when using a LED as a photodetector. Water turbidity is used as the sensory target in this study to explore these effects using a practical and important water quality parameter. Impacts on turbidity measurements are examined by adopting the Paired Emitter Detector Diode (PEDD) capacitive discharge technique and comparing common embedded software/firmware implementations. The findings show that the chosen software method can (a) affect the detection performance by up to 67%, (b) result in a variable sampling frequency/period, and (c) lead to an disagreement of the photo capacitance by up to 23%. Optimized code is offered to correct for these issues and its effectiveness is shown through comparative analyses, with the disagreement reduced significantly from 23% to 0.18%. Overall, this work demonstrates that the embedded software is a key and critical factor for PEDD capacitive discharge measurements and must be considered carefully for future measurements in sensor related studies.
Highlights
LED photometry using the Paired Emitter Detector Diode (PEDD) capacitive discharge technique was originally established as a bi-directional communications method [1] and later explored as a viable colorimetric chemical sensor [2]
In the discharge time domain it can be seen that the corrected Ts approach yields more appealing characteristics with higher sensitivities, lower limits of detection and a greater agreeable range compared to the uncorrected Ts approach, which is in line with previous discussions
As the work in this study has shown, there is no ubiquitous procedure when it comes to PEDD implementations with the choice of methodology and timing demonstrated to have a considerable effect on the response characteristics
Summary
LED photometry using the PEDD capacitive discharge technique was originally established as a bi-directional communications method [1] and later explored as a viable colorimetric chemical sensor [2]. Examples include sweat [15,16,17], hemoglobin [18], human serum [19,20,21], proteins [22,23], glucose [24], dissolved organic substances [25], creatinine in physiological fluids [20], urine [26,27,28], liver function screening [29] and saliva [30] Throughout this literature, there has been no study related to the effects of embedded software implementations [31]. This can be significant for the sensing fields requiring quantitative measurements, e.g., the physical/bio/chemical sensing domains
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
