Correlation and Linear Regression of Physico-biochemical Attributes of Ber (Ziziphus mauritiana) cv. Umran During Ambient Storage

Aim: The objective of the study was to investigate the correlation and linear regression between the physical and biochemical characteristics of Indian jujube (Ziziphus mauritiana Lamk.) cv. Umran, which have been influenced by post-harvest treatments during storage under ordinary room conditions. Place and Duration: The experiment was carried out at the Department of Horticulture, Sri Karan Narendra Agriculture University, Jobner, Rajasthan from February 2019-20. Methodology: Calcium chloride (CaCl2) (0.5, 1.0 and 2.0%) and gibberellic acid (GA3) (20, 40 and 60 ppm) were used to treat the fruits after harvesting. The treated fruits were then stored under normal or ambient room conditions. In present study evaluated physical attributes, such as physiological weight loss, decay loss, marketability, pulp content and biochemical parameters i.e., total soluble solids, titratable acidity, ascorbic acid content and total sugars content. The Pearson correlation coefficient (r) and simple linear regression were used to calculate the relationship between fruit weight and various physico-chemical attributes. Results: The correlation analysis showed a significant positive correlation between the weight of fruit and quality attributes, including marketability (r = 0.953), pulp content (r = 0.847), total soluble solids (r = 0.931), titratable acidity (r = 0.961), ascorbic acid (r = 0.984), and total sugars (r = 0.961) at 9th day after storage. Whereas, a significant negative correlation was found between the weight of the fruit and physiological loss in weight (r = -0.943) and decay loss (r = -0.953). Linear regression models provided more support for these correlations, showing that variations in fruit weight accounted for a significant proportion of the variability in the assessed quality parameters. The coefficients of determination (r²) ranged from 0.718 to 0.968. Conclusion: The robust correlations and regression models inform evidence-based storage techniques and reducing fruit quality losses during ambient storage.