Preliminary study on coffee type identification and coffee mixture analysis by light emitting diode induced fluorescence spectroscopy

Luo, Si; Yan, Chunsheng; Chen, Daru

doi:10.1016/j.foodcont.2022.109044

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…It is noted that the chlorophyll contents are not completely removed during the cherry processing method such as during the drying and fermentation process. Even after roasting, the fluorescence of the chlorophyll contents of ground-roasted coffee was also identified at a wavelength of 660 nm, as reported by Luo et al [30]. A similar result was also observed in other green beans.…”

Section: Typical Fluorescence Spectral Datasupporting

confidence: 85%

“…On the other hand, Suhandy et al [29] reported an authentication method based on portable and single LED-based fluorescence spectroscopy to assess adulteration in stingless bee honey (SBH). Preliminary laboratory work on the application of LEDbased fluorescence spectroscopy on instant coffee geographic discrimination has been reported [30]. However, according to the literature and to the best of our knowledge, the application of portable and single LED-based fluorescence spectroscopy for intact green coffee bean authentication has not yet been studied.…”

Section: Introductionmentioning

confidence: 99%

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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: Typical Fluorescence Spectral Datasupporting

confidence: 85%

Section: Introductionmentioning

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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“…Other studies that addressed coffee classification issues, regardless of the approach, include but are not limited to [38][39][40][41][42][43][44][45].…”

Section: Electronic Nosementioning

confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

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“…Tim Konsultan menyampaikan kepada Ketua Adat Kutei Lubuk Kembang kegiatan need assessment terkait pengelolaan lahan kebun kopi berkelanjutan. Budidaya kopi secara anorganik adalah petani yang menggunakan pupuk, pestisida dan herbisida dari bahan kimia (Luo et al, 2022;Manson et al, 2022). Budidaya kopi secara organik adalah petani yang menggunakan pupuk kandang/organik dan secara tradisional adalah petani yang mengandalkan kesuburan alami lahan.…”

Section: Pelaksanaanunclassified

Pertanian Berkelanjutan: Penyuluhan Model Kopi Organik Bengkulu

Sumartono¹,

Siswahyono²,

Nugroho³

et al. 2023

JPMWP

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“Petani kebun kopi anorganik tanggung”, mungkin sangat tepat disematkan pada praktik berkebun kopi yang dilakukan oleh petani komunitas Kutei Cawang Ann di wilayah Kecamatan Selupu Rejang dan Kutei Lubuk Kembang di wilayah Kecamatan Curup Utara, Kabupaten Rejang Lebong Provinsi Bengkulu. Jumlah dosis pupuk yang sangat rendah, jauh kurang dari yang seharusnya diberikan kepada setiap batang tanaman kopi setiap tahunnya, menyebabkan produksi biji kopi yang rendah. Metode dilakukan dengan need assessment dan Agriculture Training. Praktik berkebun campur (dapat disebut sebagai agroforestry tanaman utama kopi organik, pada akhirnya telah menjadi salah satu alternatif salah satu anggota komunitas Kutei Cawang Ann. Input produksi yang rendah memiliki produksi yang lebih tinggi dibandingkan dengan praktik kebun kopi anorganik tanggung. kualitas biji yang dihasilkan dari praktik kebun campur organik juga dihargai lebih tinggi oleh pedagang pengumpul biji kopi setempat. Menemukenali secara partisipatif proses-proses degradasi lahan kebun kopi yang dikelola komunitas, di samping succes story penerapan tani organik kopi oleh anggota komunitas yang dapat dijadikan rujukan belajar bagi petani lain. Pentingnya pengayaan (enrichment) kebun campur berbasis tanaman utama kopi, pentingnya pembuatan rorak dan lubang angin/biopori sebagai strategi konservasi lahan kebun kopi, pentingnya pembuatan pupuk organik, herbisida dan pestisida organik serta pengendalian hayati agar produktivitas lahan kebun kopi meningkat secara organik.

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Preliminary study on coffee type identification and coffee mixture analysis by light emitting diode induced fluorescence spectroscopy

Cited by 10 publications

References 21 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

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Pertanian Berkelanjutan: Penyuluhan Model Kopi Organik Bengkulu

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