Quantitative Analysis of Formaldehyde Using UV‐VIS Spectrophotometer Pattern Recognition and Artificial Neural Networks

Abstract

This study is focused on the quantitative analysis of formaldehyde in aqueous solution using a Fluoral‐P reagent and describes the characterization of the reaction, including the effect of reagent concentrations, pH, response time, dynamic range, reproducibility, photostability, and selectivity by using an ultraviolet‐visible (UV‐VIS) spectrophotometer. The relative standard deviation value was 1.79 to 2.12%. The dynamic range of the complex gives a linear stimulation of 0.00 to 3.60 ppm for the concentration of formaldehyde. The reproducibility of this study is high, with 1.79 and 2.12% for 20 and 40 ppm of formaldehyde, respectively. The interference from acetaldehyde (formaldehyde: acetaldehyde=1:100) was lower than 2.10%. In addition, the application of artificial neural networks to quantitative analysis for formaldehyde has also been done in this study to optimize the dynamic range of formaldehyde involved in the formation of Fluoral‐P–formaldehyde complex. A three‐layer feed‐forward network and the back propagation algorithm‐operated training process were used in this study. For quantitative analysis of formaldehyde, artificial neural networks, networking with 23 hidden neurons and 40,000 cycle numbers with 0.001% learning rate, produce the best training results, with sum‐squared error value 0.5847.