Classification of Arabica Java Coffee Beans Based on Their Origin using NIR Spectroscopy

Kurniawan, Fajar; Budiastra, I Wayan; Sutrisno, Sutrisno; Widyotomo, Sukrisno

doi:10.1088/1755-1315/309/1/012006

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…A discriminant analysis (DA) with an accuracy rate of 95-100% was used to classify three arabica green coffee beans from Java Island, Indonesia (Java Preanger, Bondowoso, and Malang). Long wavelength NIR spectra were measured for intact green coffee bean samples in the range of 1000-2500 nm [62]. Bona et al [63] reported the application of Fourier transform near-infrared (FT-NIR) spectroscopy combined with the support vector machine (SVM) classification method to differentiate several arabica green coffee beans from different regions in Brazil.…”

Section: Classification Model Development Results Using Pls-da Lda An...mentioning

confidence: 99%

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Yulia,

Analianasari,

Widodo

et al. 2023

Foods

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Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares–discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis–linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.

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Section: Classification Model Development Results Using Pls-da Lda An...mentioning

confidence: 99%

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Yulia,

Analianasari,

Widodo

et al. 2023

Foods

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“…It provides several advantages ; fast and easy to do, it does not require chemicals, and able to perform internal quality tests on agricultural products non-destructively. This method has also widely studied in agriculture field to determine physicochemical content of agricultural products such as catechin in gambir [3], fat and free fatty acid in jatropha [4], trigonelline, chlorogenic acid, caffeine content of coffees [5] [6], and classification of coffee origin [7]. Research on the estimation of vanillin and moisture content in Thailand' vanilla using NIRS has been carried out [8].…”

Section: Introductionmentioning

confidence: 99%

Determination of Chemical Content of Vanilla Pods (Vanilla planifolia) Non-destructively Using NIR Spectroscopy

Budiastra,

Ramadhana,

Nurfadila

2023

IOP Conf. Ser.: Earth Environ. Sci.

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This study aims to assess Near infrared (NIR) spectroscopy to predict vanillin and moisture content non-destructively. Twenty-four samples of vanilla pods (@55 grams/sample) from Sumatra and Sulawesi Island were tested. The reflectance spectra of vanilla pod were measured using a NIRFlex N-500 spectrometer at the wavelength of 1000-2500 nm. After that, the vanillin and moisture content were measured using the reference method. The reflectance spectra were transformed to absorbance spectra and several pre-treatment methods of NIR spectra were applied, and the results were calibrated with chemical data using principal component regression (PCR) and partial least square (PLS) methods. The best estimation for vanillin content using absorbance spectra with OSC pre-treatment at 3 PLS factors with the accuracy parameters of r = 0.91, SEC = 1.31%, SEP = 1.34%, CV = 45.60%, RPD = 2.30 with a consistency of 97.96%. The best prediction of vanillin content using PCR method was obtained with SNV pre-treated spectra at 6 PCR factors with the accuracy parameters of r = 0.89, SEC = 1.47%, SEP = 1.49%, CV = 43.21%, RPD = 2.09 and 101.40% consistency. The best estimation for water content using the PLS method is using the SNV pre-treatment spectra with a PLS factor of 5 with the accuracy parameters of r = 0.99, SEC = 2.11%, SEP = 2.13%, CV = 6.12%, RPD = 7.98 with consistency = 98.86%. While using the PCR method, the best estimation for moisture content was also obtained by SNV pre-treated spectra at 4 PCR factors with the accuracy values of r = 0.99, SEC = 2.33%, SEP = 2.25%, CV = 6.25%, RPD = 7.58 and consistency = 103.90%. The PLS calibration provide a better accuracy than the PCR. The NIR spectroscopy associated with the selected pre-treatments and factor numbers of PLS and PCR can be used for determination of chemical content of vanilla pods.

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“…The application of NIR method in coffee products has been conducted and the result exhibited a high correlation between NIR data and the chemical data while building NIR model determination for several chemical concentration such as caffeine[2,4-5], trigonelline [6], lipid [7], chlorogenic acid [6], protein, carbohydrate [7] and water [8] content using various data processing method. Beside that, study about coffee characterization based on NIR data according to its growing location also had done [9][10].…”

Section: Introductionmentioning

confidence: 99%

Environmentally friendly method in determining the water content of coffee by NIR Spectroscopy

Ayu,

Sebayang,

Sinamo

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Coffee quality could be determined from several aspects, one of those is the water content inside. Along the storage, it must be low and for coffee export it must be below 12.5%. Nowadays, the determination of water content is conducted by the chemical method like the gravimetry method which spend time, destructive also can not represent all of the products. Therefore, it is not in line with the industries which need an actual measurement that could measure all of the products nondestructively and also keep considering the environment. This study aimed to develop a PLS model to determine the water content of green bean coffee, nondestructively. This study used green bean coffee from several region in Indonesia with a high range data concentration (7.18% - 12.16%), a NIR wavelength of 1000-2500 nm, followed by determination of water content using gravimetry method. After that, the NIR data was processed using data pretreatment such as normalization, first derivative, second derivative, multiple scatter correction and standard normal variate and followed by the calibration using PLS and validation. Result showed that NIR spectroscopy could be used to calculate the percentage of water inside coffee beans, indicated by high R value above 0.8% and high consistency above 90%.

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Classification of Arabica Java Coffee Beans Based on Their Origin using NIR Spectroscopy

Cited by 8 publications

References 14 publications

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Determination of Chemical Content of Vanilla Pods (Vanilla planifolia) Non-destructively Using NIR Spectroscopy

Environmentally friendly method in determining the water content of coffee by NIR Spectroscopy

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