Identification of Lampung fine Robusta peaberry green coffee beans with different fermentation methods using portable front-face fluorescence spectroscopy

High-quality Robusta peaberry coffees in Lampung underwent various processing techniques, including two distinct fermentation methods: Codot-based fermentation (CF) and natural-based fermentation (NF). The variation in fermentation techniques significantly affects the flavor and cost of Lampung's excellent Robusta peaberry coffees. This study examines the potential application of untargeted portable front-face fluorescence spectroscopy and chemometrics to differentiate Lampung fine Robusta peaberry coffees based on various fermentation procedures. Two varieties of green coffee beans were prepared for sampling: CF (n = 60) and NF (n = 60). Three kernels of green coffee beans from CF and NF were placed in a sample holder for each sample. The fluorescence spectral data were obtained using a portable front-face fluorescence spectrometer, including a 365 nm light-emitting diode (LED) as the excitation source. The creation of supervised classification models for CF and NF utilized three distinct classifiers: partial least squares-discriminant analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA), and linear discriminant analysis (LDA). The findings indicate that employing preprocessed spectral data yielded a classification accuracy of 100% (p < 0.01) across all classifiers. The current results affirm that assessing Lampung fine Robusta peaberry coffees utilizing various fermentation processes through low-cost front-face fluorescence spectroscopy is feasible.

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Publication Details

Journal
Discover Applied Sciences
Published
2025-04-18
DOI
https://doi.org/10.1007/s42452-025-06820-w
Citations
1
Primary Topic
Coffee research and impacts
Type
article
Field-Weighted Citation Impact
1.39
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article

Identification of Lampung fine Robusta peaberry green coffee beans with different fermentation methods using portable front-face fluorescence spectroscopy

Anisur Rahman, Diding Suhandy, Slamet Widodo, Meinilwita Yulia et al.
1 citations
Discover Applied Sciences
Coffee research and impacts
1.39
article

Identification of Lampung fine Robusta peaberry green coffee beans with different fermentation methods using portable front-face fluorescence spectroscopy

Anisur Rahman, Diding Suhandy, Slamet Widodo, Meinilwita Yulia, Dimas Firmanda Al Riza, Hirotaka Naito
article en
1 citations

Abstract

High-quality Robusta peaberry coffees in Lampung underwent various processing techniques, including two distinct fermentation methods: Codot-based fermentation (CF) and natural-based fermentation (NF). The variation in fermentation techniques significantly affects the flavor and cost of Lampung's excellent Robusta peaberry coffees. This study examines the potential application of untargeted portable front-face fluorescence spectroscopy and chemometrics to differentiate Lampung fine Robusta peaberry coffees based on various fermentation procedures. Two varieties of green coffee beans were prepared for sampling: CF (n = 60) and NF (n = 60). Three kernels of green coffee beans from CF and NF were placed in a sample holder for each sample. The fluorescence spectral data were obtained using a portable front-face fluorescence spectrometer, including a 365 nm light-emitting diode (LED) as the excitation source. The creation of supervised classification models for CF and NF utilized three distinct classifiers: partial least squares-discriminant analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA), and linear discriminant analysis (LDA). The findings indicate that employing preprocessed spectral data yielded a classification accuracy of 100% (p < 0.01) across all classifiers. The current results affirm that assessing Lampung fine Robusta peaberry coffees utilizing various fermentation processes through low-cost front-face fluorescence spectroscopy is feasible.

Discover Applied SciencesVol. 7(5)
Lampung University (ID), University of Brawijaya (ID), Mie University (JP), IPB University (ID), Bangladesh Agricultural University (BD), Universitas Bandar Lampung (ID)
Zero hunger
Openalex Percentile: Top 21%
Coffee research and impacts
1.39
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