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. 2017:2017:6274178.
doi: 10.1155/2017/6274178. Epub 2017 Aug 20.

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Diding Suhandy  1 , Meinilwita Yulia  2
Affiliations

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Diding Suhandy et al. Int J Food Sci. 2017.

Abstract

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPDcal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD p and RER values.

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Figures

Figure 1
Figure 1
Original spectra of unadulterated and adulterated coffee samples in the UV-Vis region.
Figure 2
Figure 2
Number of LVs versus RMSECV for PLS calibration model for determination of luwak content.
Figure 3
Figure 3
PLS calibration and validation model for luwak content determination using SG smoothing spectra in the range 190–700 nm.
Figure 4
Figure 4
Scatter plot of actual versus predicted luwak content calculated using the best PLS calibration model.
Figure 5
Figure 5
Regression coefficients versus wavelength of coffee samples.
Figure 6
Figure 6
X-loading weights versus wavelength of the top two latent variables (LV1 and LV2) of coffee samples.
See this image and copyright information in PMC

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