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

Suhandy, Diding; Yulia, Meinilwita

doi:10.1155/2017/6274178

Cited by 50 publications

(29 citation statements)

References 25 publications

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“…There are two types of samples: Luwak coffee samples (robusta pure Luwak coffee, 50 samples) and non-Luwak coffee samples (robusta pure non-Luwak coffee, 50 samples).Luwak and non-Luwak coffee samples were directly collected from coffee farmer at Liwa, Lampung, Indonesia. An aqueous extraction procedure of the coffee samples was performed based on Suhandy and Yulia (2017a;2017b). For multivariate analysis, the samples were divided into two groups: calibration sample set (70 samples) and prediction sample set (30 samples).…”

Section: Materials and Methods Samplesmentioning

confidence: 99%

Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods

Suhandy¹,

Yulia²

2018

Aceh Int. J. Sci. Technol

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UV-Vis spectroscopy has been used as a promising method for coffee quality evaluation including in authentication of several high-economic coffee types. In this paper we have compared the abilities of linear discriminant analysis (LDA) and support vector machines classification (SVMC) methods for Luwak coffee classification. UV-Vis spectral data of 50 samples of pure Luwak coffee and 50 samples of pure non-Luwak coffee were acquired using a UV-Vis spectrometer in transmittance mode. The results show that UV-Vis spectroscopy combined with LDA and SVMC was effective method to classify Luwak and non-Luwak coffee samples. The classification result was acceptable and yielded 100% classification accuracy for both LDA and SVMC methods. However, due to the simplicity and volume of required calculation, in this present study LDA method is superior to SVMC method.

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Section: Materials and Methods Samplesmentioning

confidence: 99%

Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods

Suhandy¹,

Yulia²

2018

Aceh Int. J. Sci. Technol

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“…Indonesian specialty Arabica coffees (Gayo, Kintamani and Wamena) were purchased from a local market in Bandar Lampung, Lampung, Indonesia (roasted coffee bean). Sample preparation including grinding and sieving was performed based on the procedure described in previous published works [9][10]. Altogether, 296 samples (each sample consist of 1 g weight) of either Gayo, Kintamani or Wamena were prepared.…”

Section: Coffee Samplesmentioning

confidence: 99%

“…Altogether, 296 samples (each sample consist of 1 g weight) of either Gayo, Kintamani or Wamena were prepared. A standard aqueous extraction of the coffee samples was then performed in accordance to previous works [9][10][11]. After cooling to room temperature (for 20 min), all extracts were then diluted in a 1:20 (mL: mL) proportion with distilled water.…”

Section: Coffee Samplesmentioning

confidence: 99%

“…In addition, although UV-visible spectroscopy has been used for quality evaluation of ground roasted coffee, no record has been found in the literature on its application for the discrimination of Indonesian ground roasted coffees. [9][10][11] Therefore, the main objective of the current research was to develop a supervised classification method using partial least squares discriminant analysis (PLS-DA) capable of discriminating coffees produced in Indonesia according to their origin using UV-visible spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

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The potential of UV-visible spectroscopy and chemometrics for determination of geographic origin of three specialty coffees in Indonesia

Suhandy

Yulia²

2018

AIP Conference Proceedings

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The growing global trading market for specialty coffee increases the need for better coffee quality evaluation methods. Several Arabica coffees in Indonesia have high commercial value. For this reason, the development of analytical methods with high sensitivity and accuracy for detection of its adulteration was important. This research evaluated the potential of UV-visible spectroscopy and partial least squares discriminant analysis (PLS-DA) for determining the geographic origin of three specialty coffees (Gayo, Kintamani and Wamena) in Indonesia. In this research, 296 coffee samples from three different origins (Gayo, Kintamani and Wamena) were used. All coffee samples were ground using a home-coffee-grinder. We sieved all coffee samples through a nest of US standard sieves (mesh number of 40) on a Meinzer II sieve shaker for 10 minutes to obtain a particle size of 420 μm. All samples were extracted with distilled water and then filtered. For each sample, 3 mL of extracted sample then was pipetted into 10 mm cuvettes for spectral data acquisition. The spectral data were acquired using a Genesys 10s UV-visible spectrometer in the range of 190-1100 nm. A PLS-DA classification model was estimated to classify the origin of specialty coffees by their UVvisible spectra. The best PLS-DA model accurately classified the specialty coffee samples of the prediction sample set with prediction ability of 100% of correct classification for Gayo, Kintamani and Wamena, respectively. The results demonstrate that UV-visible spectroscopy coupled with PLS-DA provides a sensitive and accurate analytical method to distinguish ground roasted coffee samples geographically.

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“…Before spectral acquisition, an extraction procedure using hot water was performed for each sample as described in the previously reported studies. 16,[24][25][26]…”

Section: Samplesmentioning

confidence: 99%

Partial least squares with discriminant analysis and UV– visible spectroscopy for qualitative evaluation of Arabica and Robusta coffee in Lampung

Yulia¹,

Asnaning²,

Waluyo

et al. 2018

AIP Conference Proceedings

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Arabica coffee is considered to be of better quality than Robusta coffee. It has superior taste and aroma, better than Robusta coffee. To develop an authentication system for Arabica coffee, it is highly necessary to discriminate between pure Arabica coffee and Arabica adulterated with Robusta coffee. Ground roasted coffee samples are most difficult to discriminate from each other: visual inspection by the naked eye or even machine vision methods becomes very problematic. For this reason, we here propose a relatively new analytical method based on UV-visible spectroscopy for discrimination the pure and adulterated Arabica ground roasted coffee. In this study, 100 samples were used as samples with different degrees of adulteration (0%-60% of Robusta concentration in an Arabica-Robusta coffee blend). Spectral data of samples were acquired using a UV-visible spectrometer in the range of 190-1100 nm (Genesys 10s, Thermo Scientific, USA). Partial least square discriminant analysis (PLS-DA) was applied to discriminate between the pure and adulterated Arabica coffee based on UV-visible spectra data. Several pre-processing spectra were also tested to determine which one provides an appropriate discrimination model. The PLS-DA model has coefficient of correlation 0.89 (R 2 = 0.79) with low Root mean square error of calibration (RMSEC) 0.226. The full-cross validation resulted in Q 2 = 0.74 and low Root mean squared error of cross-validation (RMSECV) 0.254. Using this PLS-DA model, a total rate of correct classification of 97.5% was obtained in the prediction set. In conclusion, UV-visible spectroscopy in tandem with PLS-DA is a promising analytical method for differentiating between pure and adulterated Arabica ground roasted coffee.

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

Cited by 50 publications

References 25 publications

Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods

Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods

The potential of UV-visible spectroscopy and chemometrics for determination of geographic origin of three specialty coffees in Indonesia

Partial least squares with discriminant analysis and UV– visible spectroscopy for qualitative evaluation of Arabica and Robusta coffee in Lampung

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