Abstract
Numerous samples of two essential oils, patchouli oil and amyris oil, were analyzed by gas chromatography over a period of several years; samples were also subjected to sensory evaluation by experienced assessors. Thus, a large database of sensory and compositional information was built up for each oil. These data sets were analyzed using a variety of chemometric techniques, including partial least squares (PLS) discriminant analysis and artificial neural network simulation (ANNS); the results obtained demonstrate clearly how such techniques may successfully be employed to correlate sensory judgments with data obtained by routine gas chromatography. This approach has potential application in the QC/QA area, where total quality management (TQM) is becoming ever more important in the customer/supplier relationship.
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