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Aquaphotomics utilizes water-light interaction for in-depth exploration of water, its structure and role in aqueous and biologic systems. The aquagram, a major analytical tool of aquaphotomics, allows comparison of water molecular structures of different samples by comparing their respective absorbance spectral patterns. Temperature is the strongest perturbation of water changing almost all water species. To better interpret and understand spectral patterns, the objective of this work was to develop a novel, temperature-scaled aquagram that provides standardized information about changes in water molecular structure caused by solutes, with its effects translated to those which would have been caused by respective temperature changes. NIR spectra of Milli-Q water in the temperature range of 20-70 °C and aqueous solutions of potassium chloride in concentration range of 1 to 1000 mM were recorded to demonstrate the applicability of the proposed novel tool. The obtained results presented the influence of salt on the water molecular structure expressed as the equivalent effect of temperature in degrees of Celsius. The temperature-based aquagrams showed the well-known structure breaking and structure making effects of salts on water spectral pattern, for the first time presented in the terms of temperature influence on pure water. This new method enables comparison of spectral patterns providing a universal tool for evaluation of various bio-aqueous systems which can provide better insight into the system's functionality.

Aquaphotomics is a novel scientific discipline involving the study of water and aqueous systems. Using light-water interaction, it aims to extract information about the structure of water, composed of many different water molecular conformations using their absorbance bands. In aquaphotomics analysis, specific water structures (presented as water absorbance patterns) are related to their resulting functions in the aqueous systems studied, thereby building an aquaphotome-a database of water absorbance bands and patterns correlating specific water structures to their specific functions. Light-water interaction spectroscopic methods produce complex multidimensional spectral data, which require data processing and analysis to extract hidden information about the structure of water presented by its absorbance bands. The process of extracting information from water spectra in aquaphotomics requires a field-specific approach. It starts with an appropriate experimental design and execution to ensure high-quality spectral signals, followed by a multitude of spectral analysis, preprocessing and chemometrics methods to remove unwanted influences and extract water absorbance spectral pattern related to the perturbation of interest through the identification of activated water absorbance bands found among the common, consistently repeating and highly influential variables in all analytical models. The objective of this paper is to introduce the field of aquaphotomics and describe aquaphotomics multivariate analysis methodology developed during the last decade. Through a worked-out example of analysis of potassium chloride solutions supported by similar approaches from the existing aquaphotomics literature, the provided instruction should give enough information about aquaphotomics analysis i.e. to design and perform the experiment and data analysis as well as to represent water absorbance spectral pattern using various forms of aquagrams-specifically designed aquaphotomics graph

Nowadays a quick and inexpensive method, which allows rapid, in vivo comprehensive probiotic bacteria identification, is needed. To elucidate a new concept to evaluate probiotic bacteria, near infrared spectroscopy with aquaphotomics were applied to monitor the growth of eight Lactobacillus bulgaricus and one Lactobacillus gasseri bacteria strains. Their resistance to low pH (1.8) in the presence of pepsin and bile were measured and further used as reference data for analysis of the simultaneously acquired spectral data. The acquired spectral data in the region of 1100–1300 nm were subjected to various methods for multivariate data analyses—principal component analysis, linear discriminant analysis, soft independent modeling of class analogy, and partial least squares regression. The results showed high accuracy of bacteria strains classification according to their resistance and the potential of the tested wavelength region for rapid selection and prediction of some basic phenotypic characteristics of probiotic candidates. Results of the current study also revealed different suitability of each growth stage when using near infrared spectra for the classification of bacteria strains.