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.
| Original language | English |
|---|---|
| Pages (from-to) | 281-293 |
| Number of pages | 13 |
| Journal | Analytical Letters |
| Volume | 40 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Jan 2007 |
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Keywords
- Artificial neural network
- Back propagation
- Fluoral-P
- Formaldehyde
- Spectrometric
ASJC Scopus subject areas
- Analytical Chemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
Cite this
Quantitative analysis of formaldehyde using UV-VIS spectrophotometer pattern recognition and artificial neural networks. / Loh, Han Chern; Chong, Kok Wai; Ahmad, Musa.
In: Analytical Letters, Vol. 40, No. 2, 01.2007, p. 281-293.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantitative analysis of formaldehyde using UV-VIS spectrophotometer pattern recognition and artificial neural networks
AU - Loh, Han Chern
AU - Chong, Kok Wai
AU - Ahmad, Musa
PY - 2007/1
Y1 - 2007/1
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Back propagation
KW - Fluoral-P
KW - Formaldehyde
KW - Spectrometric
UR - http://www.scopus.com/inward/record.url?scp=34547921658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547921658&partnerID=8YFLogxK
U2 - 10.1080/00032710600867606
DO - 10.1080/00032710600867606
M3 - Article
AN - SCOPUS:34547921658
VL - 40
SP - 281
EP - 293
JO - Analytical Letters
JF - Analytical Letters
SN - 0003-2719
IS - 2
ER -