Pilot Aquaphotomic Study of the Effects of Audible Sound on Water Molecular Structure

Sound affects the medium it propagates through and studies on biological systems have shown various properties arising from this phenomenon. As a compressible media and a “collective mirror”, water is influenced by all internal and external influences, changing its molecular structure accordingly. The water molecular structure and its changes can be observed as a whole by measuring its electromagnetic (EMG) spectrum. Using near-infrared spectroscopy and aquaphotomics, this pilot study aimed to better describe and understand the sound-water interaction. Results on purified and mineral waters reported similar effects from the applied 432 Hz and 440 Hz frequency sound, where significant reduction in spectral variations and increased stability in water were shown after the sound perturbation. In general, the sound rearranged the initial water molecular conformations, changing the samples’ properties by increasing strongly bound, ice-like water and decreasing small water clusters and solvation shells. Even though there was only 8 Hz difference in applied sound frequencies, the change of absorbance at water absorbance bands was specific for each frequency and also water-type-dependent. This also means that sound could be effectively used as a perturbation tool together with spectroscopy to identify the type of bio, or aqueous, samples being tested, as well as to identify and even change water functionality.


Introduction
Sounds are mechanical waves of pressure that propagate through a transmission medium. Being a wave of pressure, the sound acts as a mechanical stimulus and has an influence on the medium through which it propagates. In physics, sound waves and their properties are widely investigated and generally well-understood, however in life sciences the investigation of sound has not yet received full attention.
There are many records documenting how sound affects biomolecules in water solutions, single-cells and even whole organisms, plants especially [1][2][3][4][5][6][7]. One study showed that audible sound in the form of music  was able to affect growth, metabolism and antibiotic susceptibility of prokaryotic as well as eukaryotic microbes [8,9]. A study about the influence of sound on chrysanthemum plants, discovered that sound wave accelerated the synthesis of RNA and soluble protein, indicating that some stress-induced genes might be switched on under sound stimulation. Further, another study showed that survival rate in the conditions of water deprivation is significantly higher in sound-treated Arabidopsis adult plants compared to plants kept in silence [2]. This study revealed significant upregulation of 87 genes, the majority of which are responsible for abiotic stress

Reference Measurements
Near-infrared spectroscopy (NIRS) in the range of 900-1700 nm, in parallel with monitoring of several physico-chemical parameters, was used to characterize pure water (hereinafter PW) and a chosen mineral water (hereinafter MW), both measured before and after sound perturbation at the frequencies of 432 Hz and 440 Hz. The mean values and standard deviations of measured electrical conductivity, pH, salinity and temperature of the samples through 10 min of sound are presented in Table 1. pH values were observed to be slightly more resilient to change in samples after the perturbation was applied, where the waters had an average of 0.81% and 0.38% more stable pH, respectively, after being perturbed by the sound with 432 Hz and 440 Hz frequencies. The difference in variance between samples that were perturbed and the control ones was not found to be statically significant (data not shown), however, statistical significance is not the same as practical importance [25]. Increased changes in values were seen for electrical conductivity of more than 18% on average after sound. These parameters are hard to measure precisely for waters, especially for PW. As soon as the vial is opened there is an exchange with the atmosphere of the environment; some evaporation may occur, gases from the atmosphere, such as CO 2 and O 2 , may influence the resulting measurements of pH and electroconductivity. The changes in pH are, for example, very small, and due to the inherent probe instability [26] may not reflect accurate values. This all points out the difficulties of reference methods usually used to describe the effects of perturbations that may be very delicate, but nevertheless genuine and consistent, which requires devising other means and more controlled measurements which can completely eliminate possible influences of the environment. There is also a possibility that these physico-chemical parameters may simply be inadequate to capture all of the information relevant for the thorough description of water as a complex system and its dynamics in response to some perturbations [27]. Table 1. Measured water parameters, each presented with its mean and standard deviation (SD), with increase in value stability for the perturbed waters, compared to the control ones in general also having higher standard deviations. A similar trend in changes was seen through all the samples. However, despite this, the current results pointed to some degree of stability of water properties in the presence of sound. One possible way of interpretation this is that sound as a mechanical wave-inducing constant perturbation on the samples, reduces the susceptibility to influences from other factors. This phenomenon is further investigated in the following analyses.
The mean temperature values showed the tendency to decrease after the sound application, irrespective of the water type or the frequency of the applied sound, while the salinity did not change at all, evidencing that sound does not change the chemical compo- nents in the water. The change in temperature might be a result of absorbed heat generated by the sound perturbation. Previous works have shown that at least part of the audible sound energy is transformed into heat that can be absorbed by water, and spent on the reorganization of water molecular structure, causing the change in its temperature [6,10] The raw water absorbance spectra generated during the experiments are presented in Figure 1a. The spectra show absorbance higher than OD = 1 a.u. and are rather flat except for the large, broad peak at lower wavelengths.
However, on closer inspection, several spectral features could be observed: large absorbance band centered around 957-963 nm corresponds to the 2nd overtone of water OH stretching vibrations, smaller band corresponding to the overtone of the combination band of free water molecules can be detected around 1143-1155 nm, and a broad peak corresponding to the 1st water overtone of water stretching vibrations can be seen located in the region 1300-1600 nm. The spectral profiles of PW show a generally higher spectral profile compared to the MW. This baseline shift is generated partially from the unfixed environmental conditions (temperature, humidity, water layer on the surface of the sample's vials, etc.), that may influence scattering of light and lead to differences in the real pathlength of light, but the higher spectral profile of PW can also be due to the higher actual water content since MW contain lots of minerals. When applying SNV preprocessing on the raw data, the baseline effects were removed (Figure 1b). Calculating difference spectra (Figure 1c-f) yielded more information on the effects of sound. This method requires the subtraction of a sample spectrum, usually of the control group (in this case the waters before sound perturbation), from the spectra of the other samples (waters after sound perturbation). The calculated difference spectra presented in Figure 1c,d, respectively, display 432 Hz and 440 Hz datasets with averaged waters' spectra after subtraction of averaged spectrum of the control.
After averaging the whole spectra of PW and MW (Figure 1e), and subtracting their controls, a common increase in light absorption was observed at 1335-1403 nm and 1515-1600 nm, regions related to free, trapped and strongly-bounded water, respectively, while the absorbance in the in-between region (~1416-1490 nm) showed a decrease after the sound perturbation. The absorbance in the spectral region of absorbance of small water clusters with one hydrogen bond (1440-1444 nm), two hydrogen bonds (1462-1468 nm), three hydrogen bonds (1476-1482 nm) and four hydrogen bonds (1488-1494 nm) [15] showed differences between the two examined waters, but the common feature for both was decreased light absorption in general. The absorbance at absorbance bands corresponding to water solvation shells (1360-1366 nm and 1380-1388 nm) [15], hydronium ions (1434-1438 nm) [15], protonated and hydroxylated water clusters (1564 nm, 1583 nm and 1589 nm) [29][30][31], as well as water superoxide ions [31], increased after the sound perturbations. Although PW and MW are both waters and exhibited similar spectral curvatures, a contrast emerged describing unique characteristics for each sample type. As a mineral water, having minerals, after sound stimulus, MW was seen with much higher absorption at the bands related to water ions (water solvation shells (1380-1388 nm) [15] and trapped water (1391-1397 nm) [32]).
When calculating average spectra according to the applied sound frequency (Figure 1f), water samples after 432 Hz sound stimulus were characterized by a higher increase in ionic water species, while displaying less free water molecules and small water clusters compared to the molecular structure of water after 440 Hz sound stimulus. This finding suggests that a particular tuning even with only 8 Hz difference could generate different effects, which by themselves are dependent on the type of media/water being perturbed (sample-dependent) and are related to respective changes of water functionalities [15,16,33,34]. ergy is transformed into heat that can be absorbed by water, and spent on the reorganization of water molecular structure, causing the change in its temperature [6,10].

Aquaphotomic Spectral Analysis
2.2.1. Initial Exploration of Spectra: Raw Spectra of Water, Preprocessed Spectra and Difference Spectra The raw water absorbance spectra generated during the experiments are presented in Figure 1a. The spectra show absorbance higher than OD = 1 a.u. and are rather flat except for the large, broad peak at lower wavelengths.

Figure 1.
Near-infrared spectra of tested pure and mineral waters and difference spectra analyses (DSA) on defined datasets, preprocessed with SNV: (a) Raw spectra, separated into the water overtones; (b) raw spectra at the 1st water overtone (1300-1600 nm) transformed with SNV and smoothed with Savitzky-Golay filter using 2nd order polynomial (9 points), showing decreased baseline offset; (c) difference spectra analysis of 432 Hz data, with specific spectral patterns for each water; (d) difference spectra analysis of 440 Hz data with different spectral patterns than those at 432 Hz; (e) difference spectra analysis of averaged water types, displaying similarities in the spectral curves and differences in regions related to small water clusters and water ions; (f) difference spectra analysis of averaged frequencies, showing how the gap of 8 Hz can affect water differently. Near-infrared spectra of tested pure and mineral waters and difference spectra analyses (DSA) on defined datasets, preprocessed with SNV: (a) Raw spectra, separated into the water overtones; (b) raw spectra at the 1st water overtone (1300-1600 nm) transformed with SNV and smoothed with Savitzky-Golay filter using 2nd order polynomial (9 points), showing decreased baseline offset; (c) difference spectra analysis of 432 Hz data, with specific spectral patterns for each water; (d) difference spectra analysis of 440 Hz data with different spectral patterns than those at 432 Hz; (e) difference spectra analysis of averaged water types, displaying similarities in the spectral curves and differences in regions related to small water clusters and water ions; (f) difference spectra analysis of averaged frequencies, showing how the gap of 8 Hz can affect water differently.

Exploratory Analysis-Principal Component Analysis (PCA)
In order to better understand the effects of sound and confirm the initial findings, further analysis was performed-PCA (principal component analysis) with mean-centering and SNV preprocessing applied on spectral data. The experimental dataset was split into four datasets, according to the water type and sound frequency (PW-432 Hz, PW-440 Hz, MW-432 Hz, MW-440 Hz) and then PCA was applied to explore if there are any patterns or groupings in the spectra that can be related to the applied sound perturbation ( Figure 2). The score plot of PC1 vs. PC2 from each PCA analysis are presented in Figure 2a and show repeatable patterns where generally scores of waters after sound perturbation showed diminished dispersion (inter-class variation) in the PC1-PC2 defined space. This implied that the sound-perturbed waters were more stable, becoming less prone to environmental influences, such as temperature, humidity, light, etc., which is consistent with the previous findings based on water parameters. The separation of the scores before and after sound perturbation was also a common property for all datasets and could be observed in the score space of PC1 vs. PC2. The loadings of PC1 and PC2 are given in Figure 2b and show that the absorbance bands that explain this separation of scores according to the sound perturbation are the same wavelengths already observed in the analysis of the subtracted spectra. The shape of PCA loadings was similar to the spectral profiles of subtracted spectra shown in Figure 1c,d, and again, but here in PC1 loadings, MW showed smaller differences compared to the PW for both 432 Hz and 440 Hz frequencies. On the other hand, PW being more receptive of changes, displayed opposing peaks in the 1366-1490 nm region, with direction shift different for the two frequencies.
Looking at the score plots of PCA analysis of PW ( Figure 2a) it can be observed that in both cases, after the sound perturbation, the scores of PW are located in the positive part of PC1. The loading of PC1 for the dataset of sound perturbation by 432 Hz, shows the most prominent spectral feature as a negative peak at 1416 nm, which means that absorbance at this band increases under the sound influence.
For the 440 Hz dataset, the most prominent feature is the peak at 1397 nm, which can be interpreted as an increase in absorbance at this band under the influence of sound. The absorbance band at 1416 nm can be assigned to the free water, but it is more likely that this band can be attributed to so-called hydration water molecules [15], while 1397 nm is assigned to quasi-free water molecules, the single water molecules trapped in the local field of ions [28]. Similarly, for the score plots of PCA analysis of MW, the scores in the case of the 432 Hz dataset are mostly in the negative part of PC1, while in the case of 440 Hz they are in the positive part. In this case, in order to be able to compare the shape and sign of all loadings (Figure 2b), due to the arbitrary assignment of loadings in PCA, the loading vector of PC1 in the case of the MW dataset for 432 Hz frequency had to be multiplied by −1; therefore, the interpretation of the meaning of the scores of the MW dataset for 432 Hz is actually reversed. Having this in mind, it can be observed from the loadings plot ( Figure 2b) that most distinctive features are a large negative peak at 1422 nm for the 432 Hz dataset PC1 loading, and a positive peak at 1391 nm in the case of the 440 Hz dataset PC1 loading. In the first case, this result means that sound of 432 Hz leads to an increase of absorbance at 1391 nm (trapped water band), while sound of 440 Hz leads to an increase in absorbance at 1422 nm (hydration band). From this, it can be concluded that the sound of 432 Hz frequency shows common effect for both waters, the increased absorbance of trapped water molecules, while 440 Hz sound increases the absorbance of hydration water. In both cases, this increase comes at the expense of reorganization of hydrogen-bonded water (absorbance bands at wavelengths longer than 1440 nm).
There is also, one more interesting observation. Looking into the percentage of explained variation, it can be seen that PC1 for all datasets explains more than 90% of variation in the spectral data, except in the case of the MW-440 Hz dataset, where PC1 explains only 56.8%, which means that there are additional spectral pattern variations specific for 440 Hz with high variations.
Next, PCA was repeated but this time on two datasets, separated into water type (either only PW or MW) in order to find out what the influence of sound is on waters in general ( Figure 3).
In the cases of both waters, the PCA score plots revealed the pattern of separation of scores in groups of "before sound" and "after sound" along the PC2 axis (Figure 3a), whereas the scores corresponding to the "after sound" group were located only on one side of PC2. For the PW dataset, the variance explained by PC2 was 6.8%, while in the case of the MW dataset, a higher percentage of variance (25.2%) was described by the same factor ( Figure 3b). Looking at the loadings of PC2 for both PW and MW PCA analysis, familiar absorbance bands similar to the ones from Figure 2b can be seen. This further showed the importance of the already observed bands, as the ones where sound influences the absorbance of water, more precisely, influences particular water molecular structures, especially trapped water (1391-1403 nm) and hydration water (1416-1422 nm). In this analysis, looking at the loadings of PC2, the effects of sound can also be better observed at the region of hydrogen bonded water (1440-1496 nm) where it seems that the small water clusters with 1-4 hydrogen bonds are affected, also, by the sound influence, as well as the strongly bound water (absorbance bands at wavelengths longer than 1508 nm, in particular 1539 nm and 1558 nm).
Molecules 2022, 27, 6332 6 of 21 2a and show repeatable patterns where generally scores of waters after sound perturbation showed diminished dispersion (inter-class variation) in the PC1-PC2 defined space. This implied that the sound-perturbed waters were more stable, becoming less prone to environmental influences, such as temperature, humidity, light, etc., which is consistent with the previous findings based on water parameters. The separation of the scores before and after sound perturbation was also a common property for all datasets and could be observed in the score space of PC1 vs. PC2. The loadings of PC1 and PC2 are given in Figure 2b and show that the absorbance bands that explain this separation of scores according to the sound perturbation are the same wavelengths already observed in the analysis of the subtracted spectra. The shape of PCA loadings was similar to the spectral profiles of subtracted spectra shown in Figure 1c,d, and again, but here in PC1 loadings, MW showed smaller differences compared to the PW for both 432 Hz and 440 Hz frequencies.
On the other hand, PW being more receptive of changes, displayed opposing peaks in the 1366-1490 nm region, with direction shift different for the two frequencies. Figure 2. PCA scores and loadings separating samples before and after sound: (a) Scores of 1st and 2nd principal components for the 4 datasets; (b) loadings of 1st principal components for the 4 datasets, separating before from after samples (it should be noted that the sign of PC loadings is assigned arbitrarily, and due to this, the loading of PC1 for MW at 432 Hz was multiplied by −1).

Figure 2.
PCA scores and loadings separating samples before and after sound: (a) Scores of 1st and 2nd principal components for the 4 datasets; (b) loadings of 1st principal components for the 4 datasets, separating before from after samples (it should be noted that the sign of PC loadings is assigned arbitrarily, and due to this, the loading of PC1 for MW at 432 Hz was multiplied by −1).

Discriminating Analysis-Soft Independent Modeling of Class Analogies (SIMCA)
Further, classification analysis, SIMCA, was performed on the already SNV-preprocessed data with 5% significance (95% confidence interval). Three different groups of classes were assigned, such as water type (Class1: PW/Class2: MW), frequency (Class1: 432 Hz/Class2: 440 Hz) and sound perturbation (Class1: before sound perturbation/Class2: after sound perturbation) were performed and their distinction from one another was investigated separately.
First, differentiation between PW and MW was performed on four datasets (waters before 432 Hz, after 432 Hz, before 440 Hz and after 440 Hz) with 100% classification accuracy. It was observed that after the sound the Mahalanobis distances (interclass distances) between the waters decreased by 52% for 432 Hz and 46% for 440 Hz. This pointed

Discriminating Analysis-Soft Independent Modeling of Class Analogies (SIMCA)
Further, classification analysis, SIMCA, was performed on the already SNV-preprocessed data with 5% significance (95% confidence interval). Three different groups of classes were assigned, such as water type (Class1: PW/Class2: MW), frequency (Class1: 432 Hz/Class2: 440 Hz) and sound perturbation (Class1: before sound perturbation/Class2: after sound perturbation) were performed and their distinction from one another was investigated separately.
First, differentiation between PW and MW was performed on four datasets (waters before 432 Hz, after 432 Hz, before 440 Hz and after 440 Hz) with 100% classification accuracy. It was observed that after the sound the Mahalanobis distances (interclass distances) between the waters decreased by 52% for 432 Hz and 46% for 440 Hz. This pointed out the waters displaying similar properties when stimulated by sound, a tendency seen in previous analysis.
Second, the SIMCA analysis performed with the aim of discriminating the samples before and after sound perturbation showed that the classes are different with an interclass distance of 0.76. When the datasets were separated according to the water type, the discrimination accuracy was 98.75% (Figure 4a), but the interclass distances were larger compared to the previous analysis when the waters were put into the same dataset. The values of interclass distances were 1.21 for PW and 1.55 for MW. When the datasets were split into four according to the water type and sound application, and SIMCA analysis was performed once again, the largest value of interclass distance was observed and it was 3.72 between MW before sound and PW after sound. As can be seen from the Cooman's plot in Figure 4a, the spreading of the scores of samples before sound was more pronounced compared to the scores corresponding to samples after sound perturbation. In order to quantify and compare this "spreading", the individual PCA models created by SIMCA for each dataset were examined, and the differences between the scores of the same samples were explained in each case by PC2. The percentage of explained variance by PC2 was 1.45% and 1.28%, respectively for PW and MW before sound, decreasing to 0.72% and 0.55% for the respective waters after sound. This was again consistent with previous analyses and pointed towards the influence of sound that can be described as "equalizing", i.e., reducing the influences of other factors present in the experiment, thus that the measured spectra of the waters showed lower variation among measured replicates.
Molecules 2022, 27, 6332 9 of 21 out the waters displaying similar properties when stimulated by sound, a tendency seen in previous analysis. Second, the SIMCA analysis performed with the aim of discriminating the samples before and after sound perturbation showed that the classes are different with an interclass distance of 0.76. When the datasets were separated according to the water type, the discrimination accuracy was 98.75% (Figure 4a), but the interclass distances were larger compared to the previous analysis when the waters were put into the same dataset. The values of interclass distances were 1.21 for PW and 1.55 for MW. When the datasets were split into four according to the water type and sound application, and SIMCA analysis was performed once again, the largest value of interclass distance was observed and it was 3.72 between MW before sound and PW after sound. As can be seen from the Cooman's plot in Figure 4a, the spreading of the scores of samples before sound was more pronounced compared to the scores corresponding to samples after sound perturbation. In order to quantify and compare this "spreading", the individual PCA models created by SIMCA for each dataset were examined, and the differences between the scores of the same samples were explained in each case by PC2. The percentage of explained variance by PC2 was 1.45% and 1.28%, respectively for PW and MW before sound, decreasing to 0.72% and 0.55% for the respective waters after sound. This was again consistent with previous analyses and pointed towards the influence of sound that can be described as "equalizing", i.e., reducing the influences of other factors present in the experiment, thus that the measured spectra of the waters showed lower variation among measured replicates. In the last step, SIMCA was performed on the same four datasets used in PCA analysis (PW at 432 Hz, PW at 440 Hz, MW at 432 Hz and MW at 440 Hz) to explore the differences before and after sound. The generated discriminating powers show the variables with the highest contribution to the separation of the classes (before sound/after sound). Due to these discriminating powers having different magnitudes unique for the analyzed group of spectra, they were adjusted using feature scaling (unity-based normal- In the last step, SIMCA was performed on the same four datasets used in PCA analysis (PW at 432 Hz, PW at 440 Hz, MW at 432 Hz and MW at 440 Hz) to explore the differences before and after sound. The generated discriminating powers show the variables with the highest contribution to the separation of the classes (before sound/after sound). Due to these discriminating powers having different magnitudes unique for the analyzed group of spectra, they were adjusted using feature scaling (unity-based normalization that brings all values into the range of 0-1) for easier comparison, where their recalculated normalized values are presented in Figure 4b. Similar to difference spectra and PCA loadings, the water absorbance bands (WABs) important for discrimination between "before sound" and "after sound" groups, were shown to be dependent on the sample type and located in the same wavelength regions. When the discriminating powers' prominent peaks (marked with arrows in Figure 4b) were summarized for comparison (Table 2), there was a repeating pattern of variables that were important for discrimination between before and after sound perturbation, and included the following absorbance bands: asymmetric stretching vibrations (1335-1347 nm) [15], H 2 O symmetric stretching proton hydrates H+·(H 2 O) 4 (1372 nm) [29,30], trapped and free water molecules (1391-1409 nm), water molecules with four hydrogen bonds (1490-1496 nm) [15] and protonated water pentamer (1552-1564 nm) [35]. These water bands were consistent through the four datasets, pointing to a similar effect generated from the perturbation by sound. Small water clusters with three and less hydrogen bonds were also repeatedly seen to be prominent (1440-1477 nm), together with several other wavelength coordinates related to water solvation shells and hydroxylated water clusters (1360-1366 nm) [36,37], hydration band (1422 nm), proton hydrates at 1329-1335 nm [29,35] and at 1583-1589 nm [29]. Table 2. Water absorbance bands (WABs) prominent for distinguishing samples perturbed by sound from the ones used as a control (before sound perturbation). From yellow to red, repeatability of the same band activation increases in the 4 displayed datasets, where most consistent and notable water matrix coordinates (WAMACS) were shown in darker color. PW-432 Hz  1304  1347  1354  1372  1385  1398  1409  1422  1440  1447  1465  1496  1515  1528  1546  1564  1583  PW-440 Hz  1304  1316  1335  1347  1372  1398  1416  1434  1447  1465  1477  1496  1508  1552  1564  1583  MW-432 Hz  1310  1335  1354  1366  1372  1385  1398  1403  1422  1440  1459  1477  1496  1515  1552  1564  MW-440 Hz  1304  1316  1329  1347  1360  1372  1391  1409  1422  1453  1477  1490  1502  1515  1533  1552  1564  1577  1589 2.2.4. Aquagrams As an important visualization tool of aquaphotomics, aquagrams [15,34,38]-radial graphs presenting the WASP (water spectral pattern) of samples at chosen WABs (water absorbance bands)-were prepared in several ways to emphasize different aspects of the investigation (Figures 5 and 6). ization that brings all values into the range of 0-1) for easier comparison, where their recalculated normalized values are presented in Figure 4b. Similar to difference spectra and PCA loadings, the water absorbance bands (WABs) important for discrimination between "before sound" and "after sound" groups, were shown to be dependent on the sample type and located in the same wavelength regions. When the discriminating powers' prominent peaks (marked with arrows in Figure 4b) were summarized for comparison (Table  2), there was a repeating pattern of variables that were important for discrimination between before and after sound perturbation, and included the following absorbance bands: asymmetric stretching vibrations (1335-1347 nm) [15], H2O symmetric stretching proton hydrates H+·(H2O)4 (1372 nm) [30], trapped and free water molecules (1391-1409 nm), water molecules with four hydrogen bonds (1490-1496 nm) [15] and protonated water pentamer (1552-1564 nm) [35]. These water bands were consistent through the four datasets, pointing to a similar effect generated from the perturbation by sound. Small water clusters with three and less hydrogen bonds were also repeatedly seen to be prominent (1440-1477 nm), together with several other wavelength coordinates related to water solvation shells and hydroxylated water clusters (1360-1366 nm) [36,37], hydration band (1422 nm), proton hydrates at 1329-1335 nm [35] and at 1583-1589 nm [30].

Aquagrams
As an important visualization tool of aquaphotomics, aquagrams [15,34,38]-radial graphs presenting the WASP (water spectral pattern) of samples at chosen WABs (water absorbance bands)-were prepared in several ways to emphasize different aspects of the investigation (Figures 5 and 6).  increase in absorbance bands related to aqueous protons and strongly-bounded water; (b) 440 Hz PW and MW at 1300-1600 nm with a differently-consistent WASP characterized by higher concentration of water solvation shells, trapped and free water molecules. In the first step, spectral absorbance values were standardized using the mean and standard deviation for each wavelength, followed by averaging of PW and MW separately for samples corresponding to before and after sound perturbation. This process was performed for datasets obtained using different sound frequencies, 432 Hz and 440 Hz, in order to present in the aquagram, what the difference in effects of sound is depending on the applied frequency. This result is presented in Figure 5 and distinction between the frequencies was observed at regions related to "ice-like" (strongly-bonded) and "vaporlike" (less bonded) water, respectively [33].
Even though the absorbance in the region of small water clusters (1434-1492 nm) decreased after sound perturbation, some specific bands corresponding to the absorbance of water dimer (1440 nm), trimer (1465 nm), tetramer (1472 nm) and pentamer (1490 nm), were still prominent in the case of 440 Hz, but only for the PW. In this case, absorbance was also high at the hydration water band (1422-1428 nm). One more difference between frequencies was observed at the band of proton hydrates (1342 nm), where in the case of 432 Hz, the absorbance was very high, in contrast to the 440 Hz spectral pattern. For 432 Hz samples (Figure 5a), both PW and MW waters shared similar spectral patterns in the 1546-1563 nm region, where the absorbance of hydrogen bonded water and protonated water clusters was increased, while intermediate water species, such as water trimer (1464 nm) and water tetramer (1474 nm) [15], showed tendency of decreased absorbance. Water solvation shells (1364 nm, 1387 nm) and trapped water molecules (1398 nm) did not show changes in MW at that frequency, possibly due to the presence of minerals resulting in the stability of these water species. For PW, proton hydrates (1344 nm) and strongly bound In the first step, spectral absorbance values were standardized using the mean and standard deviation for each wavelength, followed by averaging of PW and MW separately for samples corresponding to before and after sound perturbation. This process was performed for datasets obtained using different sound frequencies, 432 Hz and 440 Hz, in order to present in the aquagram, what the difference in effects of sound is depending on the applied frequency. This result is presented in Figure 5 and distinction between the frequencies was observed at regions related to "ice-like" (strongly-bonded) and "vapor-like" (less bonded) water, respectively [33].
Even though the absorbance in the region of small water clusters (1434-1492 nm) decreased after sound perturbation, some specific bands corresponding to the absorbance of water dimer (1440 nm), trimer (1465 nm), tetramer (1472 nm) and pentamer (1490 nm), were still prominent in the case of 440 Hz, but only for the PW. In this case, absorbance was also high at the hydration water band (1422-1428 nm). One more difference between frequencies was observed at the band of proton hydrates (1342 nm), where in the case of 432 Hz, the absorbance was very high, in contrast to the 440 Hz spectral pattern. For 432 Hz samples (Figure 5a), both PW and MW waters shared similar spectral patterns in the 1546-1563 nm region, where the absorbance of hydrogen bonded water and protonated water clusters was increased, while intermediate water species, such as water trimer (1464 nm) and water tetramer (1474 nm) [15], showed tendency of decreased absorbance. Water solvation shells (1364 nm, 1387 nm) and trapped water molecules (1398 nm) did not show changes in MW at that frequency, possibly due to the presence of minerals resulting in the stability of these water species. For PW, proton hydrates (1344 nm) and strongly bound water (1518-1563 nm) were seen to drastically increase, suggesting stronger rearrangements in response to sound. These characteristics, however, were unique for the 432 Hz frequency. At 440 Hz, PW displayed contrasting properties, with a decrease in strongly bound water, while increasing all small water clusters and "vapor-like" water structures (1344-1410 nm). MW, with slightly different pattern, was seen to preserve strongly bound water, while increasing the absorbance of proton hydrates and solvation shells (1364-1387 nm) which may indicate an increase in solvation ability. There was also an increase in the absorbance of water molecules confined between ions (1398 nm).
In the next step, the aquagrams were calculated with both waters' spectra taken together to try to obtain more general water spectral patterns, irrespective of the water type, and to see what the difference between water spectral patterns is depending on the frequency of the applied sound, and what exactly sound does to water molecular structure independent of sound frequency ( Figure 6). The specific WASPs for 432 Hz and 440 Hz were generated when the averaged spectra of controls were subtracted from their corresponding datasets and averaging according to the frequency was performed.
Even though there were differences at specific water absorbance bands depending on the sound frequency, in general it can be concluded that the effect of sound on water resulted in increased absorbance of strongly bound, ice-like water. In another words, if the differences between the waters are not considered, on average, sounds at 432 Hz and 440 Hz frequency promoted crystallization of water. However, the aquagrams in Figure 6 can be somewhat misleading as they are calculated using averaged spectra of both waters together and may look contradictory to the previously presented aquagrams, especially the ones given in Figure 5b, which display WASPs of waters perturbed by 440 Hz sound. This apparent contradiction is exactly the result of averaging, where the much stronger influence of sound of 432 Hz masked the less influential effects of 440 Hz. This just emphasizes the importance of the finding that the effects of the sound on water molecular network are sample-and frequency-dependent.

PW at 432 Hz
Our results showed that despite the small difference of only 8 Hz, perturbation by sound using frequencies of 432 Hz and 440 Hz produced large and consistent differences in water samples as multiple analyses confirmed and established that they are waterdependent. In other words, it was determined that sound affected pure and mineral water in a different way. This has two implications. First, it provides basis for the future use of sound in perturbation spectroscopy to differentiate between the samples, and second, it implies that other water-based systems may be affected in different ways by the sound of the same frequency. When it comes to the effect of sound on water, in general, it was observed that, on average it led to the reduction in the samples' variability during the measurements and the samples became more stable against environmental influences. This was observed not only in the spectral analysis, but also in the measurements of physicochemical parameters. This stability can be explained, as it was succinctly presented in aquagrams, by the effect of sound on the molecular network of water which promotes crystallization. In simpler words, the water which is strongly hydrogen bonded is not easily changed, and therefore it is more stable against influences from the environment. The temperature measurements also support this explanation, as the temperature of all the samples was shown to decrease after sound perturbation. However, if the effects of sounds of different frequencies are considered separately, which was shown to be the most correct, the effect of sound with 432 Hz frequency promotes crystallization, and the effects are much stronger compared to the effects of 440 Hz sound, which are actually opposite and could be said to enhance evaporation and solubilization.
Our findings, even though based on investigation of sound effects on the simplest aqueous systems and only two frequencies, may prove to have wider implications, especially considering the major role water plays as a matrix for biological systems. Recent scientific reports showed that audible sound promotes crystallization of proteins, which is frequency-dependent, and shows some variation based on protein type; this was found to be connected with the change of temperature and evaporation of protein solution [6,10]. Although the mentioned studies were performed using different frequencies and even variable-frequency sound perturbation, there is a common link with our study-sound affects water-based systems and changes their molecular structure in a frequency-and sample-type-dependent manner.
In conclusion, this study successfully applied NIR spectroscopy for rapid and noninvasive characterization of sound effects on water, and aquaphotomics inquiry allowed for the interpretation of the molecular dynamics after the applied perturbation, giving a better understanding of the sound-water interaction. Future research efforts will be directed towards exploration of the effects of additional frequencies on specific water systems, including biological.

Experimental Setup
As water samples, purified water (PW) (Organo, Purelite-α, Tokyo, Japan) and Yunosato Gold mineral water (MW) (Yunosato Onsen, Hashimoto, Japan) were used. The content of the mineral water is described in the following table (Table 4): Both waters were kept in similar containers (plastic bottles and glass vials, depending on the experiment) and conditions (at room temperature). Music tuned separately at 432 Hz and 440 Hz was played by the Japanese pianist and composer Acoon Hibino on a YAMAHA MOTIF XF8 synthesizer, sounded by a pair of BOSE L1 Compact stereo speakers.
The PW and MW samples were placed a meter away from the sound source inside glass vials, specifically made for measurements with a MicroNIR spectrophotometer by VIAVI Solutions (Scottsdale, AZ, USA). Each water type was prepared with 2 replicates in a total of 4 vials per frequency, each measured 5 consecutive times. Sample replicates at every frequency were measured in random order, first spectra were taken at 432 Hz, then at 440 Hz, for a total of 80 spectra. Several sample parameters were measured before and after sound by LAQUA Horiba F-74BW meter, such as pH, electrical conductivity, electrical resistivity, salinity and sample temperature (Figure 7).
Yunosato Gold mineral water (MW) (Yunosato Onsen, Hashimoto, Japan) were used. The content of the mineral water is described in the following table (Table 4): Both waters were kept in similar containers (plastic bottles and glass vials, depending on the experiment) and conditions (at room temperature). Music tuned separately at 432 Hz and 440 Hz was played by the Japanese pianist and composer Acoon Hibino on a YAMAHA MOTIF XF8 synthesizer, sounded by a pair of BOSE L1 Compact stereo speakers. The PW and MW samples were placed a meter away from the sound source inside glass vials, specifically made for measurements with a MicroNIR spectrophotometer by VIAVI Solutions (Scottsdale, AZ, USA). Each water type was prepared with 2 replicates in a total of 4 vials per frequency, each measured 5 consecutive times. Sample replicates at every frequency were measured in random order, first spectra were taken at 432 Hz, then at 440 Hz, for a total of 80 spectra.
Several sample parameters were measured before and after sound by LAQUA Horiba F-74BW meter, such as pH, electrical conductivity, electrical resistivity, salinity and sample temperature (Figure 7).

Near-Infrared Spectroscopy
Near-infrared spectroscopy was selected as it is a rapid, non-destructive and noninvasive measurement technique, that requires very little or no sample preparation at all, and it can also be used for real-time monitoring. In the near-infrared spectral region (780-2500 nm) there are four main water absorbance maxima located at around 970 nm, 1190 nm, 1450 nm and 1940 nm, due to the 2nd overtone of the OH stretching band, the combination of the first overtone of the OH stretching and OH bending band, 1st overtone of the OH stretching bands and combination of the OH stretching band and OH bending band, respectively [40]. Following the aquaphotomics findings and systematization of the knowledge about water, these main bands are even further resolved and currently there are more than 500 known absorbance bands in this region [41], which is why this technique is specifically chosen to investigate the molecular structure of water under influence of sound perturbation.
The spectrophotometer used for this study was a MicroNIR 1700-ES (Viavi Solutions, Scottsdale, AZ, USA), capable of acquiring spectra in the wavelength range of 908.1-1676.2 nm, with a wavelength step of approximately 6 nm. The device was set on reflectance mode and used with its vial-holder attachment and a 3D-printed light-shutter cap (Figure 8a). During the experiments, samples were measured before and after the played sounds, where each vial had its spectra taken 5 times consecutively and represented as 1 sample.
knowledge about water, these main bands are even further resolved and currently there are more than 500 known absorbance bands in this region [41], which is why this technique is specifically chosen to investigate the molecular structure of water under influence of sound perturbation.
The spectrophotometer used for this study was a MicroNIR 1700-ES (Viavi Solutions, Scottsdale, AZ, USA), capable of acquiring spectra in the wavelength range of 908.1-1676.2 nm, with a wavelength step of approximately 6 nm. The device was set on reflectance mode and used with its vial-holder attachment and a 3D-printed light-shutter cap (Figure 8a). During the experiments, samples were measured before and after the played sounds, where each vial had its spectra taken 5 times consecutively and represented as 1 sample. The entire experimental flow is given schematically in Figure 9. Steps 1-3 were first performed for the sound perturbation using 432 Hz music, then the entire experiment was repeated, using the 440 Hz frequency. The entire experimental flow is given schematically in Figure 9. Steps 1-3 were first performed for the sound perturbation using 432 Hz music, then the entire experiment was repeated, using the 440 Hz frequency.

Aquaphotomics Spectral Analysis
In this study, the main focus of the analysis was placed on the 1st water overtone (1300-1600 nm), where several analytical methods were applied to the acquired spectral data, such as calculation of difference spectra and MVA (multi-variate analysis), including PCA (principal component analysis) [42] and SIMCA (soft independent modeling by class analogy) [43]. This particular wavelength region was chosen since it is by far the best studied in aquaphotomics; there is almost no overlap with the absorbance of other functional groups and it provides the most information about the molecular structure of water [15].
Difference spectra were calculated by subtracting the values of a control's selected wavelength λ control (subtrahend) from the same wavelength of the sample in focus λ (minuend), where the difference is represented as λ DSA : With this calculation applied to all spectra, fundamental and environmental effects are reduced, being left only with other perturbations that have occurred and are not present equally in all samples (such as sound).

Aquaphotomics Spectral Analysis
In this study, the main focus of the analysis was placed on the 1st water overtone (1300-1600 nm), where several analytical methods were applied to the acquired spectral data, such as calculation of difference spectra and MVA (multi-variate analysis), including PCA (principal component analysis) [42] and SIMCA (soft independent modeling by class As preprocessing, SNV (standard normal variate) [44] was performed in this and all other analyses to reduce spectral baseline offset. In addition to SNV, the spectral data were smoothed using the Savitzky-Golay 2nd order polynomial filter (9 points) [45].
As an exploratory analysis, PCA (principal component analysis) was used, a statistical procedure that forms the basis of all used MVA (multi-variate analysis). It allows summarizing of information content in large data tables by means of a smaller set of "summary indices" that can be more easily visualized and analyzed; in the form of scores and loading plots in this study. Scores are projections of original spectra in the pattern spaces defined by principal components, while loadings show the weight coefficients of original variables. Mean-centering was applied only in PCA calculations as an additive transformation, after which each sample became relative to the global mean and analyses were of the variance around the global mean. Another statistical method used in this study is SIMCA (soft independent modeling by class analogy), applied for supervised classification of data, with confidence interval at 95%. Classification of spectra is based on a comparison of Mahalanobis distance, which is the distance between the spectrum and the centroid of each class. As an additional transformation, specifically used in recalculating presented SIMCA discriminating powers, feature scaling (unity-based normalization) was applied, a technique that brings all values within the range from 0 to 1, where the smallest value becomes 0, the biggest becomes 1, and all other values are spread in between, calculated by the following formula: where X is the selected value, X is the resulting normalized value, X min is the smallest (minimum) and X max is the biggest (maximum) values within each discriminating power table. All MVA analyses were performed using commercially available multivariate analysis software, Pirouette (version 4.5, Infometrix, Bothell, WA, USA). After conducting the listed MVA above, prominent WABs were chosen for distinguishing the effects from sound and aquagrams were generated for illustrating the spectral patterns of the averaged datasets in a simple manner: where λ is the selected wavelength, A λ is the resulting wavelength value in the aquagram, A λ is the absorbance after applying SNV preprocessing, µ λ is the mean for all values at the specific wavelength and σ λ is the standard deviation of the same wavelength. Assignments for the main WAMACS of the 1st water overtone are provided in Table 5 and used for characterization and interpretation of the changes in water molecular structure of the tested samples. Self-organization [29], water activity [46], germination [47].

Data Availability Statement:
The data presented in this study are available on request from the corresponding author. The data are currently not publicly available due to the organization of the local repository that will be open to public in future.