Determination of Harvest Maturity for Mango (<i>Mangifera indica</i>L.) Fruit by Non-Destructive Criteria

Haden, Kent, Palmer, and Keitt mango varieties were studied to establish the relationship of harvest time to 1) seasonal accumulated day-degrees or heat units (˚C), 2) daily rainfall amount (mm), and 3) physical fruit development attributes in order to fix maturity standards for export and local markets. Randomized Complete Block Design with four replications was used. In each case of Haden, Kent, Palmer, and Keitt varieties, physical fruit development attributes established as standard harvest maturity values were: weight (640 g, 836 g, 837 g, and 1104 g, respectively), length (16.31 cm, 16.19 cm, 21.22 cm, and 19 cm, respectively), width (30.97 cm, 33.47 cm, 30.86 cm, and 35.91 cm, respectively), volume (598 cm3, 807 cm3, 772 cm3, and 959 cm3, respectively), density (1.147 g/cm3, 1.076 g/cm3, 1.084 g/cm3, and 1.189 g/cm3, respectively), and indentation (0.25 cm, 0.49 cm, and 0.50 cm, respectively). The intensity of grooves around the stylar-scar end of Palmer fruits was studied and used as maturity index. Index values of 0.075 mls, 0.150 mls, 0.425 mls, and 0.116 mls, respectively, for Haden, Kent, Palmer, and Keitt varieties were recorded as latex exuded at harvest since these values tallied with the other physical harvest maturity index values, and also with those of rain fall and temperature values. Temperature, rainfall, and physical characteristics are therefore important non-destructive criteria for fixing maturity index values for mango fruits.

Prediction method for nutritional quality of Korla pear during storage

It is difficult to control the quality of Korla pear with different degrees of maturity during storage.Here,a method was proposed for predicting the effects of harvest maturity and cold storage time on the quality indices(soluble solid content(SSC)and Vitamin C(Vc)content)of Korla pear.The generalized regression neural network(GRNN)and adaptive neuro-fuzzy inference system(ANFIS)were employed to predict the quality changes of Korla fragrant pear fruit during storage.The results demonstrated that during cold storage the SSC in pears with 10%-70%harvest maturity showed continuous increases in the first 90 d of storage and then a slight decline thereafter,while that in pears with 80%and 90%harvest maturity exhibited slow decreases throughout the storage process.With the extension of storage time,the Vc content of pears with 10%-90%harvest maturity showed continuous decreases.The harvest maturity of Korla pear was extremely positively correlated with SSC and Vc content(p<0.01)in a given storage period.Storage time showed an extremely significant negative correlation with the Vc content(p<0.01)at the 40%-90%harvest maturity and an significant negative correlation with the Vc content(p<0.05)at the 10%-30%harvest maturity.At the 10%-70%harvest maturity,storage time showed a significant positive correlation with the SSC(p<0.05).The trained model could well predict the variation trend of quality indices of pear fruit during storage.The ANFIS with the input membership function of gbellmf had the best performance in predicting the SSC(RMSE=0.175;R2=0.98),and that with the input membership function of trimf exhibited the best performance in predicting Vc content(RMSE=0.075;R2=0.99).The research findings can provide reference for predicting the fruit nutritional quality at delivery and decision-making on the storage time of Korla fragrant pear.