NON-DESTRUCTIVE QUALITY ASSESSMENT OF ORGANIC AND PROTECTED GEOGRAPHICAL INDICATION (PGI) SWEET ORANGE CV. BIONDO DEL GARGANO USING NIR SPECTROSCOPY

The citrus industry has grown exponentially due to increasing demand for its consumption. However, it is a non-climacteric fruit that must be harvested only at an optimal edible ripening stage. Hence, to determine its internal quality and ripening stage, producers and packinghouses typically collect fruit sets throughout ripening and use them to predict quality attributes using traditional and, in many cases, destructive and time-consuming methods. This strategy leads to an increase in fruit waste because it does not conform to the expected quality and doesn’t allow repeated measurements of the same fruit over time [1], which has resulted in a growing demand to develop rapid, accurate, economical and non-destructive technologies for determining food-produce quality, such as Near Infrared spectroscopy (NIR) [2,3]. This study was carried out in the framework of the European project InnOBreed (2022-2026), which aims to promote the breeding of organic crops in the fruit sector and improve the performance of the fruit. The objective of one specific task of this project is to deliver highthroughput, efficient, and robust tools to evaluate the fruit organoleptic and nutritional quality of fruits by NIR spectroscopy. In this context, the application of NIR spectroscopy was evaluated to monitor and eventually predict the quality evolution of an ancient orange variety Biondo del Gargano, certified organic and PGI, during ripening (~6 weeks) and post-harvest (~6 weeks) under two storage conditions, room temperature at 20°C (RT) and cold temperature at 5°C (CT), by Partial Least Squares (PLS) regression. In both cases, the full dataset was split into the calibration set (70% of the total number of samples), used to build the regression model, and a validation set (the remaining 30%), used to test the model. In addition, for the post-harvest dataset, ANOVA-Simultaneous Component Analysis (ASCA) [4] was used to understand and highlight the importance of storage period and temperature on the fruit quality based on their NIR spectral signature. The model to predict pH showed promising prediction performance in post-harvest, with RMSEP of 0.10 and a relative error close to 3%. ASCA highlighted that both studied factors were significant, although time exerted the main effect on the NIR signature and fruit quality change during storage. NIR spectroscopy and ASCA have proven to be a powerful combination for understanding the impact of different sources of variability on fruit quality attributes (time and temperature). This valuable approach could be used for further studies considering the influence of other factors, such as variety, climate, or geographical position. [1] L.S. Magwaza, U.L. Opara, H. Nieuwoudt, P.J. Cronje, W. Saeys, B. Nicolaï, Food and Bioprocess Technology, 2012, 5, 425-444 [2] A.M. Cavaco, R. Pires, M.D. Antunes, T. Panagopoulos, A. Brázio, A.M. Afonso, R. Guerra, Postharvest Biology and Technology, 2018, 141, 86-97 [3] R. Fakhlaei, A. Babadi, C. Sun, N. Ariffin, A. Khatib, J. Selamat, Z. Xiaobo, Food Chemistry, 2024, 441, 138402 [4] A.K. Smilde, J.J. Jansen, H.C.J. Hoefsloot, R.J.A.N. Lamers, J. van der Greef, M.E. Timmerman, Bioinformatics, 2005, 21, 3043-3048