## 1 Introduction
In spite of this, we show that audible acoustic spectroscopy can achieve impressive accuracy in the determination of fructose concentration in water. Another interesting application of ultrasound spectroscopy is the monitoring of bio-catalysis in solutions and complex dispersions, even in real-time, reviewed in detail by [PERSON] and [PERSON] in [21]. The information that can be extracted from ultrasound data is impressive: substrate concentrations along the entire course of the reaction, time profile analysis of the degree of polymerization, reaction rate evolutions, kinetic mechanism evaluation, kinetic and equilibrium constant measurements, and real-time traceability of structural changes in the medium associated with chemical reactions, among others. Finally, an interesting and fascinating application of audible acoustic measurements can be found in [22; 23]. Both deal on the determination of Martian rock properties using the microphone of the recent NASA Perseverance rover. This microphone is used to record the sounds associated with the microcrater-forming laser induced breakdown spectroscopy device shots. Additionally, artificial intelligence (AI) algorithms have been incorporated into many engineering applications in recent years. They are integrated in research always providing a remarkable improvement in performance and efficiency. The use of these algorithms is enhanced by continuous and increasing computing power and massive data collection. Although they do not always offer the optimal solution, they approach it with a very acceptable balance of cost and accuracy. Moreover, in many applications there is no unique solution, but rather several solutions under conflicting criteria. Recent studies on the application of IA in different fields of engineering can be found in: computer engineering [24; 25; 26; 27], electrical engineering [28; 29], petroleum engineering [30], fluid mechanic engineering [31; 32], energy engineering [33; 34; 35; 36], and acoustic engineering [37]. In this work a direct application of audible acoustic spectroscopy to the determination of fructose concentrations in distilled water is presented. It is shown that no data conversion to speeds of sound is necessary, hence eliminating the source of some uncertainty, and most importantly, accuracies of the order of 1 part in 100,000 (0.001%) in weight can be achieved. The use of audible sound has some advantages over ultrasound, mainly the low cost of the measuring equipment and the noncontact nature of the measurements. In order to optimize the technique, the results from a series of different pulses and noises were previously compared and the best sound was selected for the final determination. This is a clear improvement over our previous technique based on resonant vibrations of the sample, which involved direct contact [38]. In this work, 364 samples of different concentrations of high purity fructose in distilled water were used for the study of the best pulse characteristics for acoustic chemical analysis. A constant volume of 150 mL for all samples was selected. The container was a simple cylindrical glass. A small anechoic chamber was used to place the samples and to make the sound recordings. The microphone was placed vertically over the surface of the liquid. The sound source was one earpiece placed parallel to the microphone over the liquid surface. Different sound configurations were explored: chirp, square pulses, white noise, and maximum length sequence (MLS). In the end, MLS produced the best results in our preliminary studies and was selected for the final analysis. The samples were excited by these sounds during 30 s intervals and the reflected sound was recorded. These recordings were divided into 2-s samples whose spectra were calculated by means of the Praat program [39]. The resulting spectra were processed by means of a grouping genetic algorithm (GGA) taking a training set of 80% and a test set of 20%.