(176e) Integrating Hyperspectral and Physicochemical Analyses for Monitoring Refrigerated Strawberry Chemometrics

Refrigeration is a standard method for extending the shelf life of perishable fruits such as strawberries. However, variations in storage conditions can alter the physicochemical properties of fruits, impacting their overall quality. Traditional methods for evaluating fruit quality often involve labor-intensive and time-consuming analyses. In recent years, hyperspectral imaging has emerged as a promising non-destructive tool for quality assessment in the food industry. Strawberries are known for their high perishability due to their moisture content and susceptibility to spoilage. Refrigeration is commonly employed to prolong their freshness and maintain quality during storage and transportation. Understanding how storage temperatures affect strawberries' physicochemical properties is essential for optimizing storage protocols and ensuring product quality. This study focuses on evaluating the quality of refrigerated strawberries through a combination of traditional physicochemical analyses and hyperspectral imaging. This research is motivated by the need for efficient and reliable methods to evaluate fruit quality during storage. Conventional techniques often involve destructive sampling and lengthy analyses, which may not be suitable for real-time monitoring in industrial settings. Hyperspectral imaging offers a non-destructive approach that can provide rapid and comprehensive insights into fruit quality and composition. By integrating this technique with conventional physicochemical analyses, we aim to develop a robust method for quality assessment that can be applied in the food industry. Fresh strawberries were stored at 4°C and 9°C for a week in a refrigeration workbench with controlled temperature and air velocity and monitored relative and absolute humidity. Samples were prepared upon arrival according to freshness, color, and size. The laboratory was kept at a controlled temperature of 17±2ºC. Physicochemical analyses included total soluble solids content, moisture content, water activity, pH, color, and weight variation and were performed on days 1, 2, 3, 4, and 7. Additionally, hyperspectral images were acquired using a visible/near-infrared (Vis/NIR) camera. The images were processed to extract spectral information corresponding to the changes in physicochemical parameters. The results revealed significant changes in the physicochemical properties of strawberries during storage. Moisture content and weight decreased gradually, while total soluble solids content, pH, water activity, and color presented no statistically significant difference, remaining relatively stable. Hyperspectral imaging captured subtle changes in strawberry surface reflectance, corresponding to variations in physicochemical properties correlated to weight loss, and through machine learning, it was possible to predict all physicochemical parameters using only the acquired images. In conclusion, this study demonstrates the feasibility of integrating hyperspectral imaging with traditional physicochemical analyses to monitor refrigerated strawberries' quality. The observed correlations between spectral data and physicochemical parameters provide insights into non-destructive quality assessment methods. The results indicate significant changes in moisture content and weight variation over the storage period, with no significant difference between temperatures. Linear regression models were developed to assess the relationship between these parameters, highlighting the importance of controlling variables across the production chain. Furthermore, the study highlights the potential of using the software for analyzing hyperspectral images and predicting physicochemical parameters. The spectral data showed consistency across wavelengths and storage temperatures, indicating its potential for determining storage days through regression. In summary, the integrated approach of hyperspectral imaging and physicochemical analyses offers a comprehensive method for evaluating strawberry quality during refrigerated storage. Further research is warranted to validate these findings across different fruit types and storage conditions and refine modeling techniques for predicting fruit quality attributes. Overall, this study contributes to advancing quality control practices in the food industry, offering potential applications for enhancing efficiency and product quality.