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Determination of the astringency of persimmon cv ¿Rojo Brillante¿ using hyperspectral imaging

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Determination of the astringency of persimmon cv ¿Rojo Brillante¿ using hyperspectral imaging

Abstract

The persimmon cv. ¿Rojo Brillante¿ is astringent at harvest due to the high soluble tannin content and therefore it must be submitted to postharvest deastringency treatment before commercialising the fruit. An established common practice to remove astringency in this cultivar is to expose the fruit under anaerobic conditions, at 95-98% CO2 for 24-36 h. This treatment allows commercialization of non-astringent fruit with a crispy texture. The level of astringency must be measured to ensure the treatment effectiveness. The current methods used for this purpose are based on the soluble tannin content determination; however, these methods are slow, subjective, and destructive. In this work, the use of hyperspectral imaging has been studied as a potential objective method to determine different level of astringency in persimmon. Samples of persimmon fruit were exposed to different treatments with CO2 in order to obtain fruit with different level of astringency. Hyperspectral images of intact fruits and flesh slices were acquired in the range of 420-1080 nm in reflectance and transmittance modes respectively, and the data were processed using a Partial Least Square (PLS) analysis. For all the hyperspectral images, the average spectrums obtained from two regions of interest were used as independent variables in the model. The dependent variables were the tannin content estimated using a destructive method based on the reaction of the tannins in contact with a ferric chloride solution, and the data of astringency sensory evaluation. The best predictive capability obtained for the model was 74.4% for the sensory evaluation in the intact fruits, and 88.5% for the content of tannins in the flesh slices. The obtained results point to hyperspectral images is a very promising technique for the evaluation of astringency in persimmons.