info:eu-repo/semantics/article
Computer-vision based second-order (kinetic-color) data generation: arsenic quantitation in natural waters
Fecha
2020-09Registro en:
Belén, Federico; Vallese, Federico Danilo; Leist, Lisa G.T.; Ferrão Flores, Marco; de Araújo Gomes, Adriano; et al.; Computer-vision based second-order (kinetic-color) data generation: arsenic quantitation in natural waters; Elsevier Science; Microchemical Journal; 157; 9-2020; 1-8; 104916
0026-265X
CONICET Digital
CONICET
Autor
Belén, Federico
Vallese, Federico Danilo
Leist, Lisa G.T.
Ferrão Flores, Marco
de Araújo Gomes, Adriano
Pistonesi, Marcelo Fabian
Resumen
Computer vision-based analytical methods have gained popularity in the literature, digital images and/or movies have been used to build univariate (traditional analytical line) and multivariate models. This paper describes, for the first time to the best of our knowledge, second-order data processing obtained with a computer vision-based analytical device. Therefore, a flow batch assembly coupled whit a drop system to determining arsenic in water samples without chemical/external pretreatment was employed. Arsenic is extracted from the water samples as arsine to react with a drop of silver diethyldithiocarbamate producing a colored complex. The entire reaction is recorded with a digital microscope to obtain videos as a function of time, generating second order data that is subsequently treated with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). The proposed low-cost method exhibits good performance, satisfactory detection limit (0.07 µg L−1) and linear response from 0.05 to 1.00 µg L−1 of As in water samples.