On-line Magazine 'Sensors & Transducers' (S&T e-Digest)

    (ISSN 1726- 5479)

Vol. 52, Issue 2, February 2005, pp. 300-309

Potentiometric Fingerprint Profiling of Eurycoma longifolia Extracts (Tongkat Ali)

Using Multichannel Artificial Lipid-Polymer Membrane Sensor

Ismail ZHARI¹, Ahmad MOHD NOOR², Oon – Sim CHEW¹, Shafiqual Islam A.K.M¹

¹School of Pharmaceutical Sciences, Universiti Sains Malaysia,

11800 Minden Penang, Malaysia

²Northen Malaysia University College of Engineering,

02600 Arau, Perlis, Malaysia

Tel: 604-6533888 ext 4222, fax: 604-6563443, e-mail: zhari@usm.my

Received: 22 December 2004 / Accepted: 14 February 2005 / Published: 17 February 2005

Abstract: This paper reports a novel application of biomimetic sensor system for phytomedicine analysis. The in-house fabricated multichannel artificial lipid-polymer membrane sensor consists of eight non-specific lipid-polymer membranes with partially overlapping selectivity and cross-sensitivity towards complex liquid extracts from Eurycoma longifolia. The generated multidimensional electrical potential response pattern due the interaction of the lipid sensing element with the chemical constituent present in the liquid samples were analyzed qualitatively by potentiometric fingerprint profile and further processing using chemometric pattern recognition algorithms namely hierarchical cluster analysis (HCA) and principal component analysis (PCA). The score plot of the PCA of the marker compounds and various sample treatment (mode of extraction and drying method) indicated the sensor responded linearly towards increasing concentration. Further quantification of the E. longifolia isolates and extracts using simple linear regression gives good calibration curve (R2 >0.90) except eurycomanone (R2 = 0.55). This study extends the areas of sensor application, thus demonstrating the capability of the developed sensor system as a promising tool for quality control and standardizing phytomedicinal products.

Keywords: lipid-polymer membrane, Eurycoma longifolia, potentiometric fingerprint, principal component analysis, hierarchical cluster analysis

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