Rapid characterization of biomass using near infrared spectroscopy coupled with multivariate data analysis: Part 1 yellow-poplar (Liriodendron tulipifera L.)

2010 ◽  
Vol 101 (12) ◽  
pp. 4570-4576 ◽  
Author(s):  
Kofi Nkansah ◽  
Benjamin Dawson-Andoh ◽  
Jeffrey Slahor
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Liriodendron Tulipifera ◽  
Yellow Poplar ◽  
Rapid Characterization

Classification of Stevia rebaudiana Using Near Infrared Spectroscopy and Multivariate Data Analysis

2017 ◽  
Vol 901 ◽  
pp. 103-109 ◽  
Author(s):  
Yohanes Martono ◽  
Ferdy S. Rondonuwu ◽  
Suryasatriya Trihandaru
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Geographical Area ◽  
Stevia Rebaudiana ◽  
Classification Method ◽  
Rebaudioside A

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.

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