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Evaluation of Industrial Roasting Degree of Coffee Beans by Using an Electronic Nose and a Stepwise Backward Selection of Predictors

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Abstract

Online monitoring of coffee roasting in an industrial plant is becoming an important issue as the experience of the roast master still plays an important role. Despite several approaches have been tested, some limitations were not surmountable as difficulties in scalability from bench scale to industrial roaster, the use of expensive analytical instrumentation, and the need to handle a large dataset of variables. In this paper, response of an electronic nose sampling, the headspace of roasted beans, was correlated with brightness and mean density, using the generalized least square regression in combination with a stepwise backward selection of predictors. To avoid scalability issues, roasting took place in an industrial plant using two Arabica (Brazil and Costa Rica) and two Robusta (Vietnam and India) origins. Regression showed R 2 ranging in the interval 0.994–0.999, with statistical significance p < 0.0001. The present approach has the potential to be used effectively instead of roast master, in the online monitoring of coffee roasting in industrial plants.

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Acknowledgments

The authors acknowledge the University of Bari “Aldo Moro” for financial support from the Athenaeum funds year 2012 and SAICAF SpA, Bari, Italy, for providing the coffee samples and roasting. The authors would like to thank Prof. E. Quaranta and A. Tursi for permissions to use spectrophotometers of the Scientific and Technological Pole “Magna Grecia” in Taranto, Prof. G. de Gennaro for the electronic nose usage, and Prof. M. Forina for having furnished us the software PARVUS. This work is the result of the authors’ commitment, starting from the idea and ending with its accomplishment. Particularly, each author contributed as follows: Giungato P. for the study conception, electronic nose and color measurements, statistics, discussion of results, and drafting of manuscript; Nicolardi V. for statistics, discussion of results, and drafting of manuscript; and Laiola E. for sampling, density measurements, and discussion of results.

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Authors and Affiliations

  1. Department of Chemistry, University of Bari “Aldo Moro”, via Alcide de Gasperi, 74100, Taranto, IT, Italy

    P. Giungato

  2. Department of Economics, University of Foggia, Via R. Caggese, 1, 71121, Foggia, IT, Italy

    E. Laiola

  3. Department of Economics and Mathematical Methods, University of Bari “Aldo Moro”, Largo Abbazia Santa Scolastica (già Via Camillo Rosalba, 53), 70124, Bari, IT, Italy

    V. Nicolardi

Authors
  1. P. Giungato
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  2. E. Laiola
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  3. V. Nicolardi
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Corresponding author

Correspondence to P. Giungato.

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Funding

This study was funded by the Athenaeum funds of the University of Bari Aldo Moro (grant number 2012).

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

Conflict of Interest

Giungato Pasquale declares that he has no conflict of interest. Nicolardi Vittorio declares that he has no conflict of interest. Laiola Elisabetta declares that she has no conflict of interest.

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Giungato, P., Laiola, E. & Nicolardi, V. Evaluation of Industrial Roasting Degree of Coffee Beans by Using an Electronic Nose and a Stepwise Backward Selection of Predictors. Food Anal. Methods 10, 3424–3433 (2017). https://doi.org/10.1007/s12161-017-0909-z

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  • DOI: https://doi.org/10.1007/s12161-017-0909-z

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