MATEC Web Conf.
Volume 197, 2018The 3rd Annual Applied Science and Engineering Conference (AASEC 2018)
|Number of page(s)||5|
|Published online||12 September 2018|
Identification of fresh and expired ground roasted robusta coffee using UV-visible spectroscopy and chemometrics
Lampung State Polytechnic, Department of Agricultural Technology, Jl. Soekarno Hatta No. 10, Rajabasa Bandar Lampung, 35141, Indonesia
2 The University of Lampung, Faculty of Agriculture, Department of Agricultural Engineering, Spectroscopy Research Group (SRG), Laboratory of Bioprocess and Postharvest Engineering, Jl. Prof. Dr. Soemantri Brojonegoro No.1, Bandar Lampung, 35145, Indonesia
* Corresponding author: firstname.lastname@example.org
The freshness of ground roasted coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted coffee must be taken into account for acceptability of coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted robusta coffee. A number of 200 samples of robusta fresh coffee and 200 samples of robusta expired coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted robusta coffee with a correct classification rate of 100%.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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