The classification of arabica gayo wine coffee using UV-visible spectroscopy and PCA-DA method

Abstract

The unique processing of Arabica Gayo Wine coffee produces special attributes to the beverage and could increase its value. However, it is important to prove the authenticity of Arabica Gayo Wine coffee using reliable methods. The objective of this study was to evaluate the potential of UV-visible spectroscopy and principal component analysis-discriminant analysis (PCA-DA) method for classification of ground roasted Arabica Gayo Wine coffee. A number of 200 samples of Arabica Gayo Wine coffee and 200 samples of Arabica Gayo normal (not Wine) coffee was used. The spectral data obtained in the UV-visible region were analyzed using PCA-DA with standard normal variate (SNV) and followed by Savitzky-Golay smoothing with different number of smoothing point (NSP). The results showed that the best PCA-DA model was obtained with NSP = 23 with coefficient of determination for calibration (R2) = 0.99, root mean square error of calibration (RMSEC) = 0.005692 and root mean square error of validation (RMSEV) = 0.006112. Using this model, a good classification between Gayo Wine and Gayo normal in prediction step was achieved with 100% accuracy, sensitivity and specificity. Thus, the proposed method can be used for the evaluation of authenticity of ground roasted Arabica Gayo Wine coffee.