Classification of <i>Stevia rebaudiana</i> Using Near Infrared Spectroscopy and Multivariate Data Analysis

Abstract

Stevia rebaudiana leaf contains stevioside and rebaudioside A as main diterpene glycosides. These compounds are used as natural sweetener and potentially as drug candidate of diabetes type 2. Rapid and nondestructive method for S. rebaudiana leaves (n = 23) classification based on geographical area and main diterpene glycosides content was carried out using near infrared spectroscopy combined with multivariate data analysis. Linear discriminant analysis (LDA) was applied to discriminate S. rebaudiana leaves based on geographical area. Principal component analysis (PCA) was established to classify S. rebaudiana leaves based on main diterpene glycosides content. HPLC analysis was used as reference data to divide PCA result into groups. LDA model correctly classified 95% of the S. rebaudiana leaves based on geographical area. PCA model correctly classified 95% and 90% of S. rebaudiana leaves based on rebaudioside A and stevioside content, respectively. The classification method using near infrared spectroscopy combined with multivariate data analysis demonstrate potential use of the classification method established as quality control technique of S. rebaudiana leaves.