The best performing color space and machine learning regression algorithm for the accurate estimation of chromium (VI) and iron (III) in aqueous samples using low-cost and portable flatbed scanner colorimetry

The study utilizes the colorimetric method (involving 1,5-diphenylcarbazide and potassium thiocyanate as complexing agents), computer vision, and machine learning (ML) regression algorithms to determine the content of Cr (VI) and Fe (III) in water samples. To process digital images of water samples,...

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Vydané v:	Journal of the Iranian Chemical Society Ročník 21; číslo 9; s. 2335 - 2349
Hlavní autori:	Ichsan, Chairul, Rodiah, Siti
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024 Springer Nature B.V
Predmet:	Accuracy Algorithms Analytical Chemistry Artificial intelligence Biochemistry Calibration Chemistry Chemistry and Materials Science Chromium Color Colorimetry Computer vision Datasets Diphenylcarbazide Ecosystems Environmental impact Errors Estimation Flatbed Inorganic Chemistry Iron Machine learning Measurement techniques Organic Chemistry Original Paper Physical Chemistry Pollutants Potassium Python Quality assessment Regression Scanners Spectrometry Thiocyanates Water quality Water sampling Color spaces Machine learning regression algorithm Chromium (VI) Iron (III) Flatbed scanner colorimetry Environmental monitoring
ISSN:	1735-207X, 1735-2428
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