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Fabrication of optical fiber salicylic acid (SA) sensors based on immobilization of ferric(III) nitrate and copper(II) acetate on Dowex‐50x8 is presented in this paper. The SA forms a stable purple complex with immobilized Fe 3+ at pH 2.1 with a response time of 10 min while it forms a stable yellowish green complex with immobilized Cu 2+ at pH 6.5 with a response time of 8 min. The reflectance spectra of the sensors were measured by using an optical fiber spectrophotometer. The results showed these SA sensors have maximum reflectance at 786 nm and 725 nm for SAFe complex and SACu complex, respectively. The useful dynamic response ranges are 0.02–0.50 g/L (SAFe) and 0.40–1.40 g/L (SACu). These complexes are stable for more than 24 hours. A good reproducibility (0.90%— SAFe; 0.86%— SACu) of measurement was obtained with these sensors. [ABSTRACT FROM AUTHOR]

Abstract: Immobilized salicylic acid onto XAD-2 (styrene–divinylbenzene cross-linked copolymer) has been attempted in this study as a reagent phase for the development of an optical fibre copper (II) sensor. The measurements were carried out at a given wavelength of 690.27nm since it yielded the largest divergence different in reflectance spectra before and after reaction with the analyte element. The optimum response was obtained at pH 5.0. The linear dynamic range of Cu(II) was found within the concentration range of 1.0–2.0mmolL−1 with its LOD of 0.5mmolL−1. The sensor response from different probes (n =9) gave an R.S.D. of 8.4% at 0.55mmolL−1 Cu(II). The effect of interfered ions at 1:1 molar ratio of Cu(II):foreign ion was also studied in this work. [Copyright &y& Elsevier]

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back-propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Optical fibre salicylic acid (SA) sensor is fabricated based on immobilisation of ferric(III) nitrate on Dowex-50x8. SA forms a stable purple complex with immobilised Fe3+ at pH 2.1 with a response time of 10min. The reflectance spectra of the sensor were measured by using an optical fibre spectrophotometer. The results showed this SA sensor has a maximum reflectance at 786nm. The useful dynamic response range from 2.0×10−2 to 5.0×10−1 g/L. The complex is stable for more than 24h. A good reproducibility (0.9%) of measurement was obtained with this probe. [Copyright &y& Elsevier]

A simple and rapid spectrophotometric method for salicylic acid (SA) determination has been carried out based on complexation. In this study, copper(II) acetate was used as a reagent. SA form a stable yellowish green complex with copper(II) acetate at pH 7. The useful dynamic range is 0.02g/l-0.30g/l. It has a maximum absorption at 397 nm and is stable for more than 50 h. The results were used to train up artificial neural networks (ANNs) for data optimisation. For training and validation, the Bayesian Regularization (BR) method and leave one out cross-validation method were used, respectively. The model scores a correlation and summation of squared error (SSE) skill of 0.9993 and 1.5322 x 10-4 g/l, respectively, for two hidden neurons. The average predicted error is small (3.77 x 10-3 g/L). [ABSTRACT FROM AUTHOR]

The application of an artificial neural network (ANN) in optimizing the response of an optical fiber salicylic acid (SA) sensor is presented in this paper. This sensor is fabricated based on immobilization of ferric(III) nitrate on Dowex-50 x 8. The reflectance spectra of the sensor were measured by using an optical fiber spectrophotometer. A backpropagation (BP) ANN was used to analyze the response of the sensor developed. The results showed that the ANN technique was effective and useful in broadening the limited dynamic response range of the SA sensor (0.02 - 0.50 g/L) to an extensive calibration response (0.02-2.00 g/L). It was found that a network with 15 hidden neurons was highly accurate in predicting the response of the optical fiber SA sensor. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.014 g/L and 0.032 g/L, respectively. [ABSTRACT FROM AUTHOR]