Strawberry Freshness Assessment by Hyperspectral Imaging
Abstract
Background. Strawberry is a highly valued and perishable food item. The freshness of these fruits plays a crucial role in their quality, as it determines their shelf life, nutritional content, visual appeal, and safety for human consumption. Traditional methods of assessing fruit freshness are subjective, labor-intensive, and have low productivity. This study aims to develop a methodology for quantitatively assessing the freshness of strawberries using hyperspectral imaging, which can provide objective and accurate measurements of fruit quality.
Materials and method. During the research, we evaluated the spectral properties of the outer surface and internal structure of strawberries from "Remontant Elizabeth II" over a period of 26 days after harvesting. The measuring instrument used was an acousto-optical Vis-NIR imaging spectrometer. Digital data processing involved preprocessing spectral images, morphological analysis, and calculating a quantitative metric for spectral reflectance at the most informative wavelengths. Statistical analysis was based on constructing regression models to determine the post-harvest period for strawberries. Model evaluation was done using the coefficients of determination (R2), relative error (RE), and root mean squared error (RMSE).
Results. The methodology for assessing the freshness of strawberries using hyperspectral imaging has been proposed. Mathematical models for determining the post-harvest period of "Remontant Elizaveta II" strawberries were obtained using hyperspectral images of the surface and internal structure of the samples. Analysis of the spectral properties of the external surface of fruits showed higher accuracy in determining the postharvest period, with , and .. Regression models with different polynomial orders were assessed, and the cubic polynomial showed the greatest effectiveness. A set of the most informative wavelengths was determined, based on which multiple regression analysis was performed, demonstrating the highest accuracy.
Conclusion. The developed methodology for quantitative analysis of strawberry freshness stands out for its precision, objectivity, efficiency, and automation. Assessment of individual stages, including sample preparation, hyperspectral imaging, digital data processing, and statistical analysis will be beneficial to advance methods for spectral diagnostics of food products. Proposed approach could supplement traditional methods of food quality control. Research could be used to develop optimal strategies for transportation, processing, storage and marketing of strawberry batches.
EDN: EMYEPG
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Список литературы
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Devassy, B. M., & George, S. (2021). Comparison of Regression Models for Estimating Strawberry Firmness Using Hyperspectral Imaging: Spectral Preprocessing to Compensate for Packaging Film. Journal of Spectral Imaging, 10, 55–69. https://doi.org/10.1255/jsi.2021.a3
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Gao, Z., et al. (2020). Real-time hyperspectral imaging for estimating strawberry ripeness in the field using deep learning. Artificial Intelligence in Agriculture, 4, 31–38. https://doi.org/10.1016/j.aiia.2020.04.003
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Jha, S. K., et al. (2010). Firmness characteristics of mango hybrids under ambient storage. Journal of Food Engineering, 97(2), 208–212. https://doi.org/10.1016/j.jfoodeng.2009.10.011
Katrašnik, J., Pernuš, F., & Likar, B. (2013). Radiometric calibration and noise estimation of acousto-optic tunable filter hyperspectral imaging systems. Applied Optics, 52(15), 3526–3537. https://doi.org/10.1364/AO.52.003526
Ktenioudaki, A., et al. (2022). Decision support tool for determining shelf-life of strawberries using hyperspectral imaging technology. Biosystems Engineering, 221, 105–117. https://doi.org/10.1016/j.biosystemseng.2022.06.013
Lu, R., & Peng, Y. (2006). Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering, 93(2), 161–171. https://doi.org/10.1016/j.biosystemseng.2005.11.004
Mendoza, F., et al. (2011). Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 62(2), 149–160. https://doi.org/10.1016/j.postharvbio.2011.05.009
Nunes, C. N., & Emond, J.-P. (2007). Relationship between weight loss and visual quality of fruits and vegetables. Proceedings of Florida State Horticultural Society, 120, 235–245.
Omar, A. F. (2013). Spectroscopic profiling of soluble solids content and acidity of intact grapes, limes, and starfruit. Sensor Review, 33(3), 238–245. https://doi.org/10.1108/02602281311324690
Sánchez, M. T., et al. (2012). Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. Journal of Food Engineering, 110(1), 102–108. https://doi.org/10.1016/j.jfoodeng.2011.12.003
Seki, H., et al. (2023). Visualization of sugar content distribution in white strawberries using near-infrared hyperspectral imaging. Foods, 12(5), 122–136. https://doi.org/10.3390/foods12050931
Shao, Y., & He, Y. (2008). Nondestructive measurement of acidity of strawberries using visible and near infrared spectroscopy. International Journal of Food Properties, 11(1), 102–111. https://doi.org/10.1080/10942910701257057
Wang, H., et al. (2015). Fruit quality evaluation using spectroscopy technology: A review. Sensors (Switzerland), 15(5), 11889–11927. https://doi.org/10.3390/s150511889
Zhang, C., et al. (2016). Hyperspectral imaging analysis for ripeness evaluation of strawberries using support vector machine. Journal of Food Engineering, 179, 11–18. https://doi.org/10.1016/j.jfoodeng.2016.01.002
Zhang, D., et al. (2018). Rapid prediction of sugar content in Dangshan pear (Pyrus spp.) using hyperspectral imagery data. Food Analytical Methods, 11(8), 2336–2345. https://doi.org/10.1007/s12161-018-1212-3
Zhang, Y., et al. (2015). Predicting apple sugar content based on spectral characteristics of apple tree leaves in different phenological stages. Computers and Electronics in Agriculture, 112, 20–27. https://doi.org/10.1016/j.compag.2015.01.006
Copyright (c) 2025 Georgiy V. Nesterov, Anastasia V. Guryleva, Milana O. Sharikova, Svetlana A. Sukhanova, Alexander S. Machikhin
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