Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms

García Díaz, Pilar; Utrilla Manso, Manuel; Alpuente Hermosilla, Jesús; Martínez Rojas, Juan A.

doi:10.3390/app11167301

Open AccessArticle

Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms

by

Pilar García Díaz

^*

,

Manuel Utrilla Manso

,

Jesús Alpuente Hermosilla

and

Juan A. Martínez Rojas

Department of Signal Theory and Communications, Polytechnic School, University of Alcalá, 28871 Alcalá de Henares, Spain

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(16), 7301; https://doi.org/10.3390/app11167301

Submission received: 8 July 2021 / Revised: 2 August 2021 / Accepted: 6 August 2021 / Published: 9 August 2021

(This article belongs to the Special Issue Novel Spectroscopy Applications in Food Detection)

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Abstract

Acoustic analysis of materials is a common non-destructive technique, but most efforts are focused on the ultrasonic range. In the audible range, such studies are generally devoted to audio engineering applications. Ultrasonic sound has evident advantages, but also severe limitations, like penetration depth and the use of coupling gels. We propose a biomimetic approach in the audible range to overcome some of these limitations. A total of 364 samples of water and fructose solutions with 28 concentrations between 0 g/L and 9 g/L have been analyzed inside an anechoic chamber using audible sound configurations. The spectral information from the scattered sound is used to identify and discriminate the concentration with the help of an improved grouping genetic algorithm that extracts a set of frequencies as a classifier. The fitness function of the optimization algorithm implements an extreme learning machine. The classifier obtained with this new technique is composed only by nine frequencies in the (3–15) kHz range. The results have been obtained over 20,000 independent random iterations, achieving an average classification accuracy of 98.65% for concentrations with a difference of ±0.01 g/L.

Keywords: acoustic chemical analysis; non-destructive analysis; feature extraction; automatic classification

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MDPI and ACS Style

García Díaz, P.; Utrilla Manso, M.; Alpuente Hermosilla, J.; Martínez Rojas, J.A. Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms. Appl. Sci. 2021, 11, 7301. https://doi.org/10.3390/app11167301

AMA Style

García Díaz P, Utrilla Manso M, Alpuente Hermosilla J, Martínez Rojas JA. Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms. Applied Sciences. 2021; 11(16):7301. https://doi.org/10.3390/app11167301

Chicago/Turabian Style

García Díaz, Pilar, Manuel Utrilla Manso, Jesús Alpuente Hermosilla, and Juan A. Martínez Rojas. 2021. "Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms" Applied Sciences 11, no. 16: 7301. https://doi.org/10.3390/app11167301

APA Style

García Díaz, P., Utrilla Manso, M., Alpuente Hermosilla, J., & Martínez Rojas, J. A. (2021). Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms. Applied Sciences, 11(16), 7301. https://doi.org/10.3390/app11167301

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Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms

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