This article presents a method that efficiently enables us to identify differences in the composition of water clusters. The Fourier transform infrared (FTIR) spectra in the O–H stretching region 2500 cm⁻¹ to 4000 cm⁻¹ of apple juice, raspberry juice, lyophilized water from apples (apple water), and lyophilized water from raspberry juice (raspberry water) were measured at a controlled temperature applying the Attenuated Total Reflection (ATR) sampling technique. The dependence of the IR spectra of ultra-pure water on temperature was also studied. Deconvolution to five Gaussians was used to demonstrate the absorption maxima of the spectra. The analysis of these deconvoluted spectra allows us to reveal the differences in the water clusters of different origin. The comparison of the deconvoluted FTIR-ATR spectra of apple and raspberry juices demonstrates that the application of such a method enables us to successfully evaluate the impact of different soluble solids occurred in juices on the composition of water clusters. The comparison of differences in the deconvoluted FTIR-ATR spectra of “apple water” and “raspberry water” with the deconvoluted spectra of ultra-pure water shows the suitability of such a method in the identification of changes in water clusters, where the total concentration of additives is as low as 20 ppm.

Choosing an excellent method to compare the aim characteristics of a large number of cultivars simultaneously in a sufficiently comprehensive way is often an essential challenge for breeders. This article presents the results of a comprehensive assessment of the Estonian conditions of 58 strawberry (Fragaria × ananassa) cultivars of various geographical origins according to the main economically useful markers. Based on two years of data, cultivars were clustered separately for each marker using the K-means clustering method. Considering the data on the aim characteristics, including high yield, high fruit weight, high total soluble solids TSS (°Brix) content, optimal titratable acidity TA (%) content, and a high °Brix/acidity ratio, the groups of similar cultivars formed clearly. The validation of cultivars into similar groups is important for further breeding decisions. A K-means clustering method is ideal for clustering strawberry cultivars according to their characteristics.

Finding the ideal statistical method for grouping phenological data is always an important step for breeders to draw correct conclusions from it possibly. In this paper, K-cluster analysis is presented as a perfect tool for grouping phenological data. The present research was performed based on the phenological data of 61 strawberries (Fragaria × ananassa) cultivars of different geo-graphical origins grown in Estonian conditions. Groups of strawberry cultivars were deter-mined according to flowering and ripening time: early, middle and late, based on the sum of ef-fective temperatures above +5°C. The result of the K-cluster analysis carried out in this way makes it possible to precisely plan the ripening time of berries of different strawberry cultivars. Using such analysis data, it is possible to combine with different early, mid or late strawberry cultivars to extend the picking period. Also, this technique can be used to study the effect of cli-matic changes occurring over the years on the phenology of strawberry cultivars grown in the region.