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Vol. 186, Issue 3, March 2015, pp. 104-111




Non-Destructive Classification of Assam Black Tea Using Ultra-fast Gas Chromatography (UFGC) Coupled
with Soft Independent Modeling of Class Analogy (SIMCA)

1 Santosh Kumar, 1 P. C. Panchariya, 1 P. Bhanu Prasad, 2 A. L. Sharma

1 Digital Systems Group, CSIR-Central Electronics Engineering Research Institute, Pilani-333031, India
Tel.: +91-1596-252267, fax: +91-1596-242294 2 School of Instrumentation, Devi Ahilya University, Takshila Campus, Khandwa Road, Indore-452001, India

E-mail: ceerisk@gmail.com


Received: 9 December 2014 /Accepted: 27 February 2015 /Published: 31 March 2015

Digital Sensors and Sensor Sysstems


Abstract: This paper presents the non destructive and qualitative discrimination, classification and identification of Assam black tea (Camellia Sinesis (L.) O. Kuntze) with their aroma compounds using a new and ultra fast gas chromatograph (UFGC) analyzer. Eight different varieties of Assam black tea were discriminated by using their aroma profile by recording the frequency spectra of surface acoustic wave (SAW) sensor. The result demonstrates the power of gas chromatography to discriminate the seasonal variety tea samples of same origin. Principal Component Analysis is used to visualize the data variation. Standard normal variate (SNV) transformation is applied as a preprocessing method, to remove the slope variation of the data. The multivariate data analysis method, Soft Independent Modeling of Class Analogy (SIMCA) is used to construct the classification models, which works with the individual PCA model for each group of tea samples. SIMCA provides the samples belong to the same class or more than one classes. The classification Accuracy with SIMCA model is 100 % except for the samples E and F, which shows some overlapping in Cooman’s plot. The overall result demonstrates that, frequency spectra by zNose, incorporated with suitable pattern recognition methods can be applied as a rapid and efficient method to identify the Assam black tea varieties of same origin.


Keywords: Assam Black Tea, Classification, Nondestructive, Pattern Recognition, SIMCA.


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