Firmness prediction in ‘Rojo Brillante’ persimmon using hyperspectral imaging technology

Visual evaluation of external colour is the most used maturity index for harvesting of persimmon fruit, since skin colour evolution from green to red during the fruit maturity process is closely linked to internal physicochemical changes.
The decline of firmness that takes place as persimmon mature is of special importance from a commercial point of view, as fruit firmness is determinant to decide the appropriate postharvest conditions in order to preserve fruit quality.
Hyperspectral imaging has been shown as a powerful fruit inspection system to assess ripeness features or to detect damages or contaminants in different fruits.
In the present study, we evaluate the potential use of hyperspectral imaging system to predict persimmon firmness.
To this end, ‘Rojo Brillante’ persimmons were harvested at three different maturity stages based on visual evaluation of external colour.
A standard hyperspectral imaging system in reflectance mode in the range 420-1080 nm was used to capture the images of the fruit.
Then, external colour was determined with a colorimeter (Hunter Lab parameters) and fruit firmness was determined with a texturometer and was expressed as force (N) to break the flesh.
Study of correlations between flesh firmness and hyperspectral imaging data and between flesh firmness and Hunter Lab parameters were performed by using partial least square regression (PLS-R). After selecting the three wavelengths that showed the highest correlation with fruit firmness, the firmness prediction by hyperspectral imaging was better (R²=0.79 and a standard error of prediction (SEP) = 4.33(N)) than that obtained by colorimetry (R²=0.71 and SEP=5.10(N)). Therefore, hyperspectral imaging is presented as a potential non-destructive tool to acutely predict firmness of persimmon.

VIII International Postharvest Symposium: Enhancing Supply Chain and Consumer Benefits – Ethical and Technological Issues

S. Munera, C. Besada, J. Blasco, S. Cubero, A. Salvador, P. Talens, N. Aleixos

Diospyros kaki, firmness, quality, computer vision, image, Hunter Lab

https://doi.org/10.17660/ActaHortic.2018.1194.108