Artificial Intelligence Applications and Innovations: Day-to-Day Life Impact

Rodrigues, João M. F.; Cardoso, Pedro J. S.; Chinnici, Marta

doi:10.3390/app132312742

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…On the other hand, very recently, the use of machine learning models like artificial neural networks (ANN) have been implemented to solve problems in very diverse areas, like engineering and science [21], because ANNs are considered tools that can help us to analyze variable relationships in complex systems from large datasets [22]. However, the performance of conventional ANNs depends strongly on the size of the input data and large variables in the data, which may contain redundant information, which would increase the complexity of the computation.…”

Section: Introductionmentioning

confidence: 99%

Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures

Estrada-Moreno,

Rendon-Lara,

Jiménez-Núñez

2024

Applied Sciences

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Dyes are organic compounds capable of transmitting their color to materials, which is why they are widely used, for example, in textile fibers, leather, paper, plastic, and the food industry. In the dying process, measuring the dye’s content is extremely important to evaluate the process efficiency and minimize the dye’s discharge in wastewater, but most of the time, dyes are present in multi-component mixtures; hence, quantification by spectrophotometric methods presents a great challenge because the signal obtained in the measurement overlaps the components in the mixture. In order to overcome this issue, the use of the high-performance liquid chromatography (HPLC) method is recommended; however, it has the disadvantage of being an expensive technique, complex, and requiring excessive sample preparation. In recent years, some direct spectrophotometric methods based on multivariate regression algorithms for the quantification of dyes in bicomponent mixtures have been reported. This study presents a new framework that uses a combined ANN and principal component analysis (PCA) model for the determination of the concentration of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. The PCA–ANN model was trained and validated with ternary mixture samples of TZ, AR, and B1, and with known different compositions, spectra absorbance samples were measured in a UV-Vis spectrophotometer at wavelengths between 350–700 nm with intervals of 1 nm. The PCA–ANN model showed a mean absolute prediction error and correlation coefficient (r2) of less than 1% and greater than 0.99, respectively. The results demonstrate that the PCA–ANN model is a quick and highly accurate alternative in the simultaneous determination of dyes in ternary aqueous mixtures.

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Section: Introductionmentioning

confidence: 99%