Introduction: Currently, the food industry emphasizes customer-centric innovations in food production. Regarding dried or processed fruits and vegetables, as well as fresh-cut fruit, consumers prefer the use of health-safe PPO inhibitors. In this case, the use of cold plasma, which has found a new path in the food industry in the last decade, can be an alternative to sulfurization without leaving chemical residues and with acceptable effectiveness. Also, since color quality analysis via image processing is a non-destructive method for assessing fresh fruit quality, this study aimed to evaluate the effectiveness of specific treatments in extending the shelf life of apricots through color analysis, as well as to model and predict shelf life using image processing data.
Materials and Methods: Fresh apricots were subjected to cold plasma treatment (60 W power, 20 kV voltage) at 11 duration levels (ranging from 0 to 20 minutes) with five replicates. Samples were stored at refrigeration temperatures until the onset of mold or rot, and daily images of all treatments were captured using a Redmi Note 13 Pro mobile phone camera. Image analysis and the extraction of L*, a*, and b* values were performed using ImageJ software, followed by the calculation of the Browning Index (BI), Chroma (C*), and Hue angle. Various regression models were examined to determine the relationship between cold plasma treatment time, storage time, and image-based characteristics. The models evaluated included linear models, models incorporating interaction effects, and models containing quadratic terms. For each response variable, the different models were compared based on regression coefficients, taking into account the influence of model parameters on the assessment of fit. The equation for the selected model was reported for each characteristic. Data were analyzed using SPSS software (version 25) to assess the significance of treatments via Analysis of Variance (ANOVA) and the Tukey test at the 5% significance level, and graphs were generated using SPSS and Excel. Multivariate linear and nonlinear regression models (second- and third-order polynomial, and exponential) were developed to predict quality indices based on image-derived features.
Results and Discussion: The results indicated that, among the factors investigated, storage duration contributed most significantly to the observed changes in many of the characteristics. When analyzing the effects of cold plasma and storage duration, categorical models demonstrated a superior ability to explain data variability compared to simple models that treated these factors merely as continuous variables. Given that visual changes in apricots do not follow a random pattern—with storage duration accounting for a substantial portion of the variability—modeling these changes is of significant importance. The models evaluated included linear models, models incorporating interaction effects, and models featuring quadratic components. Regarding shelf-life prediction using non-destructive image processing, it was found that a combination of color features—and geometric features, if necessary—can provide information on the sample's storage stage. Significant differences in many of the studied visual attributes across various cold plasma treatment durations—along with the significant interaction between treatment and storage time—indicate that cold plasma treatment does not merely control changes during the initial stage of storage but can also alter the trajectory of changes in visual attributes over time. This is particularly evident for L*, a*, BI, C*, and Hue.
Conclusion: Overall, the results demonstrated that digital imaging serves as a suitable tool for the non-destructive monitoring of quality changes in apricots during storage. A key advantage of this method is that data acquisition and subsequent modeling were performed without damaging the samples. Given that the study aims to enhance product safety and shelf life without compromising visual and sensory quality, the combined use of cold plasma and image processing offers a practical solution for extending the shelf life of fresh apricots and predicting their storage longevity.