Maintaining cherry quality after harvest requires rapid and effective interventions to limit fruit metabolic activity and slow down degradation processes during storage.
In this context, hydrocooling is an interesting technique for rapidly removing field heat, reducing respiration rates and limiting fruit quality losses.
However, assessing cooling effectiveness by considering individual chemical, physical and microbiological parameters may provide only a partial picture of product condition.
A recent study from Ukraine addressed this issue by developing a mathematical model based on so-called factor areas, designed to integrate multiple indicators into a quantitative and visually interpretable assessment of sweet and sour cherry quality.

Hydrocooling and experimental design
The study examined three sweet cherry cultivars, ‘Valeriy Chkalov’, ‘Krupnoplidna’ and ‘Melitopolska Chorna’, and one sour cherry cultivar, ‘Vstrecha’.
The fruit underwent cooling in water at approximately 1 °C, supplemented with lactic and acetic acids at experimentally determined concentrations.
The treatment continued until the temperature inside the fruit, near the stone, reached approximately 2 °C.
The assessment considered 15 indicators, divided into ten negative parameters, whose increase indicates a deterioration in quality, and five positive parameters associated with the preservation of nutritional and commercial value.
Indicators and quality profile
The former included weight loss, respiration rate, heat production, microbial growth and enzyme activity; the latter included sugar content, soluble solids, titratable acidity, ascorbic acid and phenolic compounds.
The measured values were normalised against predefined reference values and plotted on polar charts, in which the resulting area summarises the product’s quality profile.
Cooling results
The results indicate that hydrocooling helped preserve the positive indicators: the area of favourable factors (Spos) ranged from 2.35 to 2.37 conventional square units, while the quality maintenance coefficient ranged from 0.99 to 1.00.
At the same time, the main negative indicators decreased.
In three of the four cultivars, respiration rate and heat production fell to less than half their initial levels; the abundance of mesophilic microorganisms and fungi was also generally lower after treatment.
The coefficient representing the reduction in negative impacts ranged from 44.20% to 49.73%, indicating a beneficial effect under the experimental conditions.
Cultivar differences
The integrated assessment also revealed differences among cultivars. ‘Krupnoplidna’ showed the most favourable overall profile, with the smallest negative-factor area and the highest coefficient for reducing negative impacts.
Conversely, the results for ‘Melitopolska Chorna’ suggested a less favourable response under the conditions investigated.
Applications and limitations of the model
The main contribution of the study lies in the possibility of comparing cultivars and treatments using a composite indicator while retaining visibility of the individual factors contributing to the overall result.
The model could therefore support the assessment of postharvest technologies and the optimisation of cooling and storage protocols.
However, it is important to note that the cooling solution contained organic acids and that separate controls using water alone or no treatment were not included. Consequently, the effects of cooling cannot be distinguished from those of the acids.
Furthermore, the model was tested on only four cultivars and requires validation across a wider range of genotypes, growing environments and storage periods.
This approach therefore provides a promising basis for complementing conventional analyses, but its ability to predict actual storage life and commercial losses requires further investigation.
Source: Palamarchuk, I., Ivanova, І., Bal-Prylypko, L., Palamarchuk, V., Mushtruk, M., Drobitko, A., Lialyk, A., & Pyurko, O. (2026). Factor-area modelling of sweet cherry and sour cherry quality after hydrocooling treatment. Scifood, 20, 599-620. https://doi.org/10.5219/scifood.162
Image source: Stefano Lugli
Andrea Giovannini
PhD in Agricultural, Environmental and Food Science and Technology - Arboriculture and Fruitculture, University of Bologna, IT
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