Introduction: In scholarly research, the reference list serves not an auxiliary but an evidential function: citations to prior studies are the primary means by which readers can verify the grounds for a study design, analytical procedures, regulatory safety criteria, and the interpretation of results. A disconnect between an article’s claims and its cited sources, caused by irrelevant citations and the artificial inflation of reference lists, undermines both research reproducibility and trust in scientific knowledge.

Purpose: To provide authors with a practice-oriented standard for working with scholarly sources that ensures the traceability of argumentation and the verifiability of key research claims.

Results: The article synthesizes international publication-ethics requirements and editorial guidance on citation accuracy and authors’ accountability for ensuring that references substantively support the statements to which they are attached. It proposes applied rules and self-check procedures for assessing the functionality and relevance of cited sources, aligned with the typical rhetorical nodes of a technological manuscript (problem framing, methodological justification, results interpretation, conclusions, and practical recommendations). The paper advances the principle that every strong claim must be supported by a verifiable source and operationalizes it for technology-oriented articles through: (1) functional linking of citations to claims that determine reproducibility (processing regimes, process parameters, analytical methods, and safety/quality criteria); (2) a clear distinction between acceptable review citations used to document consensus and mandatory primary sources for numerical regimes, methods, and regulatory requirements; (3) a list of typical citation problems in studies on storage and processing of raw agricultural materials (substituting a primary source with a review, transferring parameters across non-comparable matrices, citing regulations without the current version, and “chain-citing” methods); (4) a pre-submission reference-audit protocol that verifies whether the source has been read, whether the claim–source match is semantically accurate, and whether the citation is functionally necessary; and (5) recommendations for post-publication correction of identified bibliographic errors through contacting the editorial office.

Conclusion: The proposed framework shifts source work from a formal “formatting” step to a research quality-control procedure. Its application reduces the risk of irrelevant citations and bibliographic negligence, increases the transparency of technological decisions, and strengthens the reproducibility of results as an essential requirement for applied research in the storage and processing of raw agricultural materials.

Introduction: Prebiotic oligosaccharides are increasingly attracting the attention of researchers due to their numerous nutraceutical and health-promoting properties and are of significant interest for research aimed at their implementation in the food industry. In the Russian Federation, the main source of raw material for industrial inulin production is the tuberous sunflower (Helianthus tuberosus L.), which is distinguished by its high protein content. This fact presents technological difficulties during its processing. Traditionally, enzymatic hydrolysis has been the predominant method for producing inulin from plant materials to break down pectin and cellulose compounds. Of interest is the identification of the most effective enzymatic preparation that demonstrates the best performance under optimal processing parameters for inulin-containing raw material substrates.

Purpose:  To evaluate the effectiveness of various types of enzymatic preparations for modifying the carbohydrate composition of inulin-containing raw material extracts and to determine the optimal parameters for pre-processing.

Materials and Methods: The enzymatic complexes Pectolux and Cellolux BGK were used as a standard in the study; the effect of the enzymatic complex Biozyme Plus was investigated as an alternative. The object of the study was an aqueous extract from dried and crushed Jerusalem artichoke tubers of the Omskiy Bely variety, obtained from a substrate pre-treated with enzyme preparations. The research methods included determining the mass fraction of dry matter and refractometric analysis of the extracts. The carbohydrate composition of the extracts was analyzed by high-performance liquid chromatography using a standard sample of a carbohydrate mixture (glucose, fructose, sucrose) for calibration and evaluation of the obtained values. A comparison of the quality indicators of the obtained extracts was carried out through organoleptic evaluation according to three characteristics: color, transparency, and opalescence. The kinetics of the extraction process were studied with varying enzyme dosages and hydromodule, at temperatures ranging from 40 to 80°C, and extraction times ranging from 20 to 60 minutes.

Results: A comprehensive analysis of the physicochemical characteristics and carbohydrate composition of the control and test extracts revealed significant differences in inulin content and quality indicators. Samples treated with Cellolux BGK and Biozyme Plus differed in their degree of opalescence but had a lighter, yellowish hue. The extract treated with Pectolux had a straw color and high transparency. The volume of the extract doubled with Pectolux, and 1.6 times with the Cellolux BGK complex, compared to the control sample. At temperatures up to 50°C, the inulin content in the extract increases linearly, with no significant increase thereafter. However, at temperatures above 70°C, inulin hydrolysis occurs, breaking it down into di- and monosaccharides.

Conclusion: A laboratory experiment under controlled conditions was used to conduct a comparative analysis and evaluate the effectiveness of enzyme preparations of varying activity on the degree of hydrolysis and oligosaccharide formation from pectin and cellulose compounds in inulin-containing raw materials. The inulin concentration in the resulting extracts was 78–79%, indicating the high quality of the product. The results of the study, using Jerusalem artichoke tubers as an example, confirm the effectiveness of enzyme preparations. The obtained data open new prospects for the development of biotechnological processes and their economic efficiency.

Introduction: The need for a comprehensive flour quality assessment system, including the presence of impurities, additives, and improvers, involves the use of machine vision and machine learning, an advanced field of artificial intelligence. An important area of obtaining data for analysis is the use of optical spectral methods.

Purpose: To study the optical photoluminescent properties of various types of flour in order to develop methods for automatic identification of the composition of mixtures during storage and production of bakery products.

Materials and Methods: For spectral measurements, flour samples from wheat, rye, oats, rice, peas, buckwheat and chickpeas were used. Optical spectral measurements of the obtained flour samples were carried out in the extended spectral range of 200–500 nm on a CM 2203 diffraction spectrofluorimeter. 

Results: All spectral characteristics contain peaks at the following wavelengths: 290nm, 272 nm, 286 nm, 362 nm, as well as a weak maximum of 424 nm. Rice flour has the strongest excitement, and buckwheat flour has the least. For chickpea flour, strong absorption occurs in the short-wavelength (260–290 nm) and long-wavelength (420 nm or more) regions. The other types of flour studied have approximately similar characteristics. The integral parameters H in the entire studied range of 220–500 nm are determined with an error of up to 10.9 % (for oat flour) and differ by 4.3 times for all the studied types of flour. However, for the design of machine vision systems, it is advisable to identify flour types by integral parameters in the narrower areas of λ1-λ2 and by their ratios. Based on the selected features, classification models were built that showed accuracy of up to 88.9 % during testing, while problematic pairs of classes such as pea and buckwheat flour were identified.

Conclusion: The integral and statistical parameters of the spectra have a high separation ability. The ratio H220-500/H470-500 (85.3 %), kurtosis (84.8 %) and mathematical expectation (84.6 %) are the most complex estimates, which are recommended to be used as the basis for constructing classification algorithms. Practical testing on machine learning models has confirmed the possibility of automatic identification of flour types with accuracy that meets the requirements of industrial control.

Introduction: Research into lactic acid bacteria, which are widely used in the food industry, has revealed a lack of information regarding the intricate characteristics of individual probiotic strains, including their identification, elucidation of biochemical properties, and evaluation of antagonistic activity.

Purpose: To isolate and comprehensively characterize Lactococcus lactis strain No. 15 in terms of its morphological, physiological, and biochemical properties, as well as to assess its probiotic potential and stress tolerance, in order to identify a novel strain with optimal functional characteristics for application in the production of fermented dairy products.

Materials and Methods: To achieve these goals, we used MALDI-MS to identify the strain, then evaluated its functional characteristics through a series of biochemical tests including API testing, enzyme activity assessment, and antagonist activity against pathogenic microorganisms. We also conducted in vitro modeling of stress resistance under simulated gastrointestinal conditions.

Results: The strain demonstrated remarkable viability and exhibited robust beta-galactosidase and other important hydrolytic enzyme activities. Additionally, it showed a strong antagonistic effect against Staphylococcus aureus VKPM No. 6646 and Klebsiella aerogenes VKMP No. 13214, as well as resistance to low pH and bile salts.

Conclusion: Lactococcus lactis subsp. lactis № 15 has potential for use in the production of fermented dairy products with enhanced functional and technological properties. The data collected can serve as a basis for the development of new bacterial starters with high safety and efficacy in food production.

Introduction: Spray drying is a critical unit operation in milk powder production, strongly affecting product quality, storage stability, and process energy efficiency. Although computational fluid dynamics (CFD) has been widely applied to drying analysis, many existing models do not adequately capture internal droplet structural transformations or the effect of combined convective and radiative heating on heat and mass transfer kinetics. As a result, their predictive capability and usefulness for process optimization remain limited.

Purpose: To develop and numerically implement a CFD-oriented mathematical model of milk spray drying that describes the evolution of droplet temperature and moisture content under combined convective and radiative heating and identifies rational operating conditions.

Materials and Methods: The model was formulated as a system of differential equations describing heat conduction, moisture diffusion, and the kinetics of structural transformations within the particle. Numerical simulations were performed in Python (PyCharm environment) using the SciPy, NumPy, and Matplotlib libraries. Calculations were carried out for a single milk droplet under different specific heat input levels ranging from 0.000156 to 0.000273 J over a treatment time of 120 s. Structural and morphological changes were incorporated through a correction coefficient, K₁.

Results: The model generated temperature and moisture profiles as functions of energy input. Increasing the heat input raised the droplet surface temperature from 331 to 360 K, but did not result in a proportional increase in drying intensity. In all tested regimes, the final moisture content remained within a narrow range of 4.9-5.1%. Incorporation of the structural coefficient K₁ made it possible to adequately describe the reduction in moisture diffusivity at the final stage of drying associated with crust formation at the particle surface.

Conclusion: Higher energy input levels appear unjustified from an energy-efficiency perspective, as they may impose excessive thermal stress without providing a substantial increase in drying rate. The proposed model can be used to optimize spray-drying conditions, support dryer design, and develop digital twins of dehydration processes for food emulsions and suspensions.

Introduction: The process of boiling massecuite in batch vacuum pans (BVPs) is a key stage in sugar production, determining its quality and energy efficiency. Despite a significant amount of research, the relationship between the pan fill level (L) and the dynamics of the dry solids content (Bx) remains insufficiently studied, which limits the possibilities for precise process control.

Purpose: To establish a quantitative relationship between the fill level of a BVP, the Bx dynamics, and the parameters of the boiling curve for the development of an optimized control strategy for the massecuite boiling process.

Materials and Methods: Experimental methods of online monitoring (Bx, level, temperature, pressure) on UVA-60S type pans were used, along with mathematical modeling of the boiling curve based on a sigmoid function and an adaptive MPC algorithm. Statistical data processing included correlation and regression analysis.

Results: It was found that the optimal pan fill level (60–70 %) ensures maximum process efficiency. The implementation of the MPC algorithm significantly improved crystal quality (reduction in the size variation coefficient) and reduced energy consumption. A strong correlation was revealed between the supersaturation gradient and crystal quality (r = 0.82).

Conclusion: The research results contribute to the international agenda on energy efficiency management and the development of intelligent control systems in the food industry. The proposed method demonstrates potential for integration with digital twins, ensuring precise control and adaptation to changing production conditions. The practical significance of the work is confirmed by successful testing in industrial conditions, making it applicable for optimizing processes at sugar plants. A promising direction is the further development of hybrid control systems using artificial intelligence methods.

Introduction: Current trends in the bakery market necessitate the expansion of the product range, including organic products. Research on the quality of organically grown grain crops in Russia is limited due to the low prevalence of organic farming and lack of data, but it is becoming relevant in the context of the Russian Federation's state policy on the production of organic products aimed at expanding this market segment.

Purpose: To analyze organically grown crops, as well as their processed products, followed by a comparative analysis of the data obtained with the regulatory documentation in force in Russia to assess their suitability in the production of functional bakery products and expand the range of organic products on the Russian market.

Materials and methods: The objects of the study were organic spring wheat of the Tulaykovskaya-10 variety, organic rye of the Saratovskaya-7 variety and products of their processing: flour with a grinding diameter of 1.0 and 0.5 mm. The quality assessment was carried out in accordance with generally accepted methods for determining the organoleptic and physico-chemical parameters of grain crops. A comparative analysis of the data obtained was carried out with regulatory documentation for these types of products. 

Results: The analysis revealed an excess of the proportion of spoiled grains in the sample of organic wheat (3.8 % at a rate of < 3.0 %) and fusarium grains in the sample of organic rye (1.2 % at a rate of < 1.0 %), the remaining indicators are within acceptable values. The relationship between the degree of grinding and flour parameters has been established: when the particle diameter decreases from 1.0 to 0.5 mm, humidity decreases by 0.5 % (11.2 → 10.7), the number of drops by 15 seconds (215.0 → 200.0), an increase in the acid number of fat by 1.13 mg KOH/1 g (16.83 → 17.96) for organic wheat flour; a decrease in the number of drops by 18 c (226.0 → 208.0), an increase in the acid number of fat by 10 mg KOH/ 1 g (57.33 → 67.33) for organic rye flour.

Conclusion: The scientific novelty of the work lies in a comprehensive assessment of the quality of Russian organic grain crops and their processed products. The practical significance of the study is to substantiate the possibility of using organic grain crops in baking. The results obtained create a scientific basis for expanding the production and processing of organic grain crops in Russia and contribute to the development of the domestic market of organic bakery products. 

Introduction: Monitoring the freshness of refrigerated fish remains one of the persistent difficulties in the fish processing industry. Existing reference methods for assessing  refrigerated fish freshness are destructive and inherently unable to reflect the spatial distribution of spoilage markers. Hyperspectral imaging (HSI) has emerged as a powerful non-destructive tool that captures both spatial and spectral data at the pixel scale.

Purpose: The aim of this study is to evaluate the effectiveness of hyperspectral imaging for the binary classification of refrigerated rainbow trout (Oncorhynchus mykiss) fillets into early (≤48 h) and late (>48 h) storage stages.

Materials and Methods: The study was conducted on rainbow trout fillets stored at +2 ± 2 °C for 16 days. Hyperspectral images were acquired using a FigSpec FS-23 camera (spectral range 400–1000 nm). Principal component analysis (PCA) was performed, and a neural network model for binary classification of samples was developed using the TensorFlow framework and the high-level Keras API.

Results: A characteristic nonlinear dynamics of reflectance was observed during storage. PCA showed that the first principal component accounted for 93.8 % of the data variance. The neural network model achieved 90 % accuracy in binary classification of the samples.

Conclusion: The results demonstrate the potential of hyperspectral imaging as a non-destructive tool for assessing fish freshness. The developed method provides accurate discrimination between fresh and non-fresh samples and can be recommended for adoption in industrial incoming inspection protocols.

Introduction: Blackcurrant pomace is utilized only to a limited extent in the food industry. One accessible method for its processing is infrared (IR) drying; however, prolonged thermal exposure promotes oxidation and degradation of biologically active compounds. This necessitates the determination of optimal IR drying parameters that enable the maximum possible preservation of bioactive constituents in blackcurrant pomace.

Purpose: To develop a mathematical model and determine the optimal operational parameters of the IR drying process for blackcurrant pomace that ensure maximal retention of bioactive compounds within the proposed modeling framework.

Materials and Methods: To identify the optimal parameter values that maximize the preservation of biologically active compounds in blackcurrant pomace, a three-level, three-factor response surface methodology (RSM) was applied using a Box–Behnken experimental design. Drying was carried out in a universal infrared drying chamber of the “Universal-SD-2P” series, which implements a combined radiation–convection drying mechanism.

Results: Based on the experimental data analyzed using response surface methodology and the Box–Behnken design, mathematical models were developed to describe the influence of IR drying parameters on the retention of ascorbic acid, catechins, anthocyanins, and on the yield of dry matter in blackcurrant pomace. Using these models, the optimal drying parameters were calculated as follows: drying time — 4 h, temperature — 60 °C, and layer thickness — 6.2 mm. Under these optimal conditions, the predicted concentrations of bioactive compounds in the dried pomace were: ascorbic acid — 122.47 mg/100 g, catechins — 2568.1 mg/100 g, and anthocyanins — 540.65 mg/100 g. With a dry matter content of 93.6%, the resulting product exhibits sufficient microbiological stability and is suitable for long-term storage. A comparison between the calculated optimal drying parameters (4 h, 60 °C, layer thickness 6.2 mm) and previously obtained experimental data from drying performed at 4 h, 60 °C, and a layer thickness of 3.8 mm indicates partial agreement between the model predictions and empirical observations.

Conclusion: The mathematical models developed in this study, together with the optimized drying parameters (time, temperature, and layer thickness), enable the targeted production of dried blackcurrant pomace with a specified chemical composition. This creates opportunities for its subsequent use as a functional food enrichment ingredient.