Regression Models to Estimate Photosynthetic Rate and Leaf Pigment Content of Kaffir Lime (Citrus hystrix DC)

Abstract

Kaffir lime (Citrus hystrix DC) is a minor citrus that originated from Southeast Asia with its leaf as the main economic part of food flavoring additives and essential oil. Numerous agricultural intensifications are arranged on kaffir lime considering morpho-physiological trait observation, especially leaf photosynthetic. This study aimed to estimate the photosynthetic rate and leaf pigment content of kaffir lime by using regression analysis. Mathematical equation and coefficient determination values of 28 regression models were constructed to estimate chlorophyll-a, chlorophyll-b, total chlorophyll, and carotenoid content, based on its photosynthetic rate. Seven types of tested regression models were linear, zero intercept linear, exponential, logarithmic, polynomial, zero intercept polynomial, and power. The result showed the zero intercept linear regression model as the most powerful with the highest R² in all leaf pigment variables. The present study provided several models, i.e., y = 0.0302x (R² 0.83) for chlorophyll-a estimation, y = 0.0111x (R² 0.85) for chlorophyll-b estimation, y = 0.0413x (R² 0.84) for total chlorophyll estimation, y = 0.0108x (R² 0.92) for carotenoid estimation, by using its photosynthetic rate as the x value.