Optimization of Raman Spectra Processing to Identify Thermal Treatment Markers in Milk

Introduction: Heat treatment of milk causes a partial transformation of its components that intensifies with increasing thermal load and may impair quality. Using labile components and their transformation products as markers of thermal load is hindered by their high variability. Among spectrometric methods, Raman scattering spectroscopy is the most promising, yet its application is limited both by spectral noise, for the suppression of which no formalized criteria for selecting smoothing parameters exist, and by the absence of a formalized approach to identifying stable spectral markers of thermal load.

Purpose: To develop a formalized approach to establishing the spectral regions that correspond to thermal load during heat treatment of milk.

Materials and Methods: Samples of fresh skim milk were subjected to four thermal load variants: thermization at 50 ℃ (control) and thermization followed by pasteurization at 70, 80, and 90 ℃ for 30 s. For each sample, Raman spectra were recorded on an EnSpectr R532 spectrometer over the 240–4400 cm⁻¹ range with a step of 0.7–1.8 cm⁻¹ at an excitation wavelength of 532 nm. Smoothing was carried out using the simple moving average method.

Results: The spectra were noisy and required smoothing. A formalized system was developed for determining the number of moving-average smoothing stages and the window half-width at each stage, based on a set of key factors and boundary conditions. Four to five smoothing stages proved sufficient; across the stages the window half-width first increased from 5 to 13–14 points and then narrowed sharply to 2–6 points. Using formalized statistical procedures, two spectral regions were identified (1289–1452 and 1475–2764 cm⁻¹) in which spectral intensity corresponded to the degree of thermal load.

Conclusion: The proposed formalized processing complex provides a reproducible selection of smoothing parameters and, on this basis, delineates the probable location of thermal-load markers in milk. The two identified regions should be treated as provisional boundaries of this location, subject to refinement as the dataset expands.

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