International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2018, Vol. 3, Issue 1, Part F

Artificial neural network based FTIR spectroscopy for the detection of adulteration in virgin sesame oil


Author(s): MK Pandurangan, S Murugesan, P Gajivaradhan and N Shettu

Abstract: There are large varieties and trademarks of vegetable oils in India. Vegetable oils have characteristics quite similar to each other and often cannot be distinguished by only observing the color, odor or taste. The methods used traditionally for the classification of these oils are often costly and time consuming and they usually take advantage of techniques from analytical chemistry and chemometrics to increase their efficiency. Due to the wide variety of products, more efficient methods are badly needed to qualify, characterize and classify these substances, because the final price should reflect the excellence of the product that reaches the consumer. In the present study, an attempt has been made to investigate the virgin sesame oil adulterants by using FTIR spectral data. At the end of the analysis, the adulteration of 10% palm oil and 5% palm oil with the virgin sesame oil did not reveal any significant form of adulteration, while an adulterations of 5%, 10% and 15% groundnut oil have respectively revealed 30.5%, 31.6% and 25.9% virgin sesame oil, which is somewhat definitely significant.

Pages: 456-459 | Views: 430 | Downloads: 7

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How to cite this article:
MK Pandurangan, S Murugesan, P Gajivaradhan, N Shettu. Artificial neural network based FTIR spectroscopy for the detection of adulteration in virgin sesame oil. Int J Stat Appl Math 2018;3(1):456-459.
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