Abstract:
Separating fruits into groups according to ripeness, hardness or chemical properties before storing them for fresh consumption or processing them into a product can provide great advantages. Thus, in this study, research on determining the ripeness and chemical properties of persimmons, which determine their hardness and astringency character, using spectral techniques and artificial intelligence, which can be used in the industry, has been compiled. In this way, it is hoped that higher and standard quality persimmons will be delivered to the consumer and added value will be provided. In general, the appropriate harvest time is determined and all persimmons in the orchards are harvested. However, while some of these fruits are at the desired ripeness, some may be less ripe and some may be over ripe. Therefore, grouping persimmons with the use of spectral techniques and artificial intelligence will provide advantages for using more suitable storage conditions and periods. Although the separation of persimmons according to hardness can be done by personnel, this requires both extra labour and the personnel's hand-squeezing and checking the fruit can inevitably damage the fruit and shorten the shelf life of the fruit. As in the grouping of fruits according to size, it is possible to make groupings such as early ripeness, ripe and overripe with these innovative techniques. Among these fruits, those harvested at early ripeness can be stored longer, while those harvested at excessive ripeness can be stored for a shorter period. Determining the parameters according to the ripeness status of persimmons in the storage or ripening process is important for obtaining standard quality products. It is thought that separating the fruits to be sold according to their tissue hardness can increase product standardization and consumer satisfaction. Packaging of persimmons with tissue hardness between certain limits and removal of persimmons outside these limits from the production line can be done with spectral techniques and artificial intelligence modelling. In studies conducted on this subject, it has been reported that sugar content, firmness, and pH values of persimmons are determined accurately with computer vision and artificial intelligence applications. These values can provide information about the ripeness status and sensory properties of the fruit. With these innovative techniques, it is possible to determine the tannin component which cause astringent taste in persimmons and to remove those fruits with high tannin content from the production line. For this purpose, correlations between spectral measurements and tannin content should be established with R&D studies conducted under industrial conditions and without damaging the fruits. Each fruit can be examined in real time with spectral techniques. In this way, persimmons with high tannin content can be removed from the production line. Separation of astringent and de-astringed characteristics and tannin contents of persimmon fruit by using visible and near infrared and chemometrics were reported. Thanks to the use of non-destructive methods and artificial intelligence such as hyperspectral imaging system and spectroscopy, for quality assessment of persimmon and fruits were reported. Because of cheap spectral techniques and the development of artificial intelligence, there is now the potential to examine each fruit in real time and instantly under industrial conditions. Thus, it is thought that these new methods can be used in the near future for persimmon to be stored, processed and offered to the consumer in a more standard and high quality.
