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Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches

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Abstract

Aroma is a fundamental property of coffee and is modulated by volatile compounds (VCs). The VC fingerprint based on untargeted SPME-GC/MS can be used for coffee origin prediction. Coffee of Indonesian origin has been attracting considerable research interest in recent years. However, data analysis of the untargeted VC analysis remains one of the greatest challenges for the prediction of origins. Therefore, this study aimed to investigate the ability of untargeted SPME-GC/MS-based volatile fingerprinting to predict the origins of Indonesian coffee using machine learning (ML) approaches. Indonesian coffee samples from economically precious origins were studied. An SPME arrow was used to extract the VCs from the coffee headspace with optimised temperatures coupled to a GC/MS system. Several machine learning models were compared to obtain the most accurate origin prediction. This study found that adsorption at 70 °C for 10 min extracted the most reliable VCs for coffee origin prediction. The untargeted headspace-SPME volatile fingerprint of coffee employing 200 samples identified 224 features out of 656 detected signals. In the exploratory dataset, RF and PLS-DA models are comparable in predicting Indonesian coffee origins with accuracies of 97% and 95.2%, respectively. They also reached an AUC of 100% and 95.8% in the validation dataset, respectively. Furthermore, both models indicated promising results in selecting the important features. These features illustrate a clear classification in the visualisation using unsupervised models. Overall, the results of the study demonstrate the reliability of the current workflow for the predictive modelling of Indonesian coffees. This study contributes to the advancement of coffee origin prediction and classification for further authentication.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

AUC:

Area under the curve

CAR/DVB/PDMS:

Carboxen/divinylbenzene/polydimethylsiloxane

CM:

Confusion matrix

GC/MS:

Gas chromatography mass spectrometry

HCA:

Hierarchical clustering analysis

HPLC:

High-Performance liquid chromatography

HS-SPME:

Headspace solid phase microextraction

LC/MS:

Liquid chromatography mass spectrometry

MDA:

Mean-decreased accuracy

MDG:

Mean-decreased in Gini

ML:

Machine learning

PAH:

Polycyclic aromatic hydrocarbons

PCA:

Principal component analysis

RF:

Random forest

PLS-DA:

Partial least square discriminant analysis

RI:

Retention index

RT:

Retention time

SVM:

Support vector machine

SS:

Similarity score

TIC:

Total ion chromatogram

kNN:

K-Nearest neighbour

VC(s):

Volatile compound(s)

VIP:

Variable important in projection

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Acknowledgements

The authors acknowledge the support for FSA from UGSAS, Gifu University and the Doctoral Program of Universitas Sebelas Maret for the double degree sponsorships. We also thank Mr. Jun Iwata, Baisen Ko-Bo Sora coffee roaster for providing professional roasting equipment and assisting the roasting method.

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Authors and Affiliations

  1. Doctoral Program of Agricultural Science, Faculty of Agriculture, Universitas Sebelas Maret, Jl. Ir. Sutami 36 A Kentingan, Surakarta, Central Java, 57126, Indonesia

    Fawzan Sigma Aurum, Muhammad Zukhrufuz Zaman, Edi Purwanto & Danar Praseptiangga

  2. Department of Food Science and Technology, Faculty of Agriculture, Universitas Sebelas Maret, Jl. Ir. Sutami 36 A Kentingan, Surakarta, Central Java, 57126, Indonesia

    Muhammad Zukhrufuz Zaman & Danar Praseptiangga

  3. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan

    Fawzan Sigma Aurum, Teppei Imaizumi, Manasikan Thammawong & Kohei Nakano

  4. Research Center for Food Technology and Processing, National Research and Innovation Agency, Jl. Jogja-Wonosari Km 31.5, Yogyakarta, 55861, Indonesia

    Fawzan Sigma Aurum

  5. Department of Agricultural Engineering, The University of Lampung, Bandar Lampung, Lampung, 35145, Indonesia

    Diding Suhandy

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  1. Fawzan Sigma Aurum
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Correspondence to Danar Praseptiangga.

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Aurum, F.S., Imaizumi, T., Thammawong, M. et al. Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches. Eur Food Res Technol 249, 2137–2149 (2023). https://doi.org/10.1007/s00217-023-04281-2

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