Simple analytical method using ultraviolet spectral dataset and chemometrics for the authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications

Abstract

The possible application of a simple analytical method based on a UV (ultraviolet) spectral dataset coupled with SIMCA (soft independent modeling of class analogy) for authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications (GIs) was demonstrated. Three types of Indonesian specialty ground roasted coffee were used: GIs arabica coffee from Gayo Aceh (96 samples), GIs liberica coffee from Meranti-Riau (119 samples), and GIs robusta coffee from Lampung (150 samples) with 1 g weight of each sample. All samples were extracted using hot distilled water and 3 mL aqueous filtered samples were pipetted into a 10 mm quartz cell. Original UV spectral datasets were recorded in the range of 190-399 nm. The pre-processed spectral dataset was generated using three simultaneous different preprocessing techniques: moving average smoothing with 11 segments, standard normal variate (SNV), and Savitzky-Golay (SG) first derivative with window size and polynomial order value of 11 and 2. The supervised classification based on the SIMCA method was applied for preprocessed selected spectral data (250-399 nm). The PCA data showed that GIs coffee with different botanical and geographical indications can be well separated. The SIMCA classification was accepted with 100 % of correct classification (100 % CC). This dataset demonstrated the potential use of UV spectroscopy with chemometrics to perform simple and affordable authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications (GIs).

Keywords: Food authentication; Geographical indications; PCA; SIMCA; Specialty coffee; Spectral data; UV spectroscopy supervised classification.

Fig. 1

The average of the original UV spectral dataset of Indonesian specialty ground roasted coffee samples with different botanical and geographical indications (GIs) in the range of 190–399 nm.

Fig. 2

The average preprocessed UV spectral dataset of Indonesian specialty ground roasted coffee samples with different botanical and geographical indications (GIs) in the range of 190–399 nm.

Fig. 3

The origin of coffee samples used in the datasets from three different botanical and geographical indications (GIs).

Fig. 4

Q-residual vs. Hotelling's T² statistic plot of Indonesian specialty ground roasted coffee samples with different botanical and geographical indications (GIs) calculated with a 5 % confidence level based on preprocessed spectra in the range of 250–399 nm.

Fig. 5

The PCA score plot of Indonesian specialty ground roasted coffee samples with different botanical and geographical indications (GIs) calculated based on preprocessed spectra in the range of 250–399 nm.

Fig. 6

The loading plot for the first and second principal components was calculated based on preprocessed spectra in the range of 250–399 nm.

Fig. 7

The Cooman's plot of SIMCA analysis (95 % confidence limit) using model SIMCA of Arabica Gayo and Liberica Meranti-Riau.

Fig. 8

The Cooman's plot of SIMCA analysis (95 % confidence limit) using model SIMCA of Arabica Gayo and Robusta Lampung.

Fig. 9

The Cooman's plot of SIMCA analysis (95 % confidence limit) using model SIMCA of Liberica Meranti Riau and Robusta Lampung.