Prediction of key aroma development in coffees roasted to different degrees by colorimetric sensor array

Su Yeon Kim; Jung A. Ko; Bo Sik Kang; Hyun Jin Park

doi:10.1016/j.foodchem.2017.07.139

Prediction of key aroma development in coffees roasted to different degrees by colorimetric sensor array

Su Yeon Kim, Jung A. Ko, Bo Sik Kang, Hyun Jin Park

Department of Biotechnology

Research output: Contribution to journal › Article › peer-review

32 Citations (Scopus)

Abstract

We developed a colorimetric sensor array (CSA) that is sensitive to highly contributory volatile compounds of coffee aroma for discrimination of coffee samples roasted to different roast degrees. Strecker aldehydes and α-diketones were significantly higher for the medium roast than the other roast degrees. The development of several sulfur compounds was pronounced in the medium-dark and dark roasts, except for dimethyl sulfide, which was only detected in the light roast. The CSA method coupled with principal component analysis or hierarchical cluster analysis successfully distinguished the roasted coffee samples according to roast degree. Partial least squares regression results showed that the CSA responses were well-correlated with the concentrations of volatile compounds in the coefficient of determination (r_p²) range of 0.686–0.955. These results demonstrate that the CSA rapidly responded to coffee aroma compounds and was capable of predicting coffee aroma development.

Original language	English
Pages (from-to)	808-816
Number of pages	9
Journal	Food Chemistry
Volume	240
DOIs	https://doi.org/10.1016/j.foodchem.2017.07.139
Publication status	Published - 2018 Feb 1

Keywords

Coffee roasting
Colorimetric artificial nose
Cross-responsive sensor
Multivariate analysis
Roast degree

ASJC Scopus subject areas

Analytical Chemistry
Food Science

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10.1016/j.foodchem.2017.07.139

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title = "Prediction of key aroma development in coffees roasted to different degrees by colorimetric sensor array",

abstract = "We developed a colorimetric sensor array (CSA) that is sensitive to highly contributory volatile compounds of coffee aroma for discrimination of coffee samples roasted to different roast degrees. Strecker aldehydes and α-diketones were significantly higher for the medium roast than the other roast degrees. The development of several sulfur compounds was pronounced in the medium-dark and dark roasts, except for dimethyl sulfide, which was only detected in the light roast. The CSA method coupled with principal component analysis or hierarchical cluster analysis successfully distinguished the roasted coffee samples according to roast degree. Partial least squares regression results showed that the CSA responses were well-correlated with the concentrations of volatile compounds in the coefficient of determination (rp2) range of 0.686–0.955. These results demonstrate that the CSA rapidly responded to coffee aroma compounds and was capable of predicting coffee aroma development.",

keywords = "Coffee roasting, Colorimetric artificial nose, Cross-responsive sensor, Multivariate analysis, Roast degree",

author = "Kim, {Su Yeon} and Ko, {Jung A.} and Kang, {Bo Sik} and Park, {Hyun Jin}",

note = "Funding Information: This research was supported by the Individual Basic Science & Engineering Research Program funded by National Research Foundation (NRF), Republic of Korea (grant of NRF-2016R1D1A1B03933896) and by both School of Life Sciences and Biotechnology for BK21PLUS and Institute of Biomedical Science & Food Safety, Korea University, Republic of Korea. Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd",

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doi = "10.1016/j.foodchem.2017.07.139",

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AU - Kim, Su Yeon

AU - Ko, Jung A.

AU - Kang, Bo Sik

AU - Park, Hyun Jin

N1 - Funding Information: This research was supported by the Individual Basic Science & Engineering Research Program funded by National Research Foundation (NRF), Republic of Korea (grant of NRF-2016R1D1A1B03933896) and by both School of Life Sciences and Biotechnology for BK21PLUS and Institute of Biomedical Science & Food Safety, Korea University, Republic of Korea. Publisher Copyright: © 2017 Elsevier Ltd

PY - 2018/2/1

Y1 - 2018/2/1

N2 - We developed a colorimetric sensor array (CSA) that is sensitive to highly contributory volatile compounds of coffee aroma for discrimination of coffee samples roasted to different roast degrees. Strecker aldehydes and α-diketones were significantly higher for the medium roast than the other roast degrees. The development of several sulfur compounds was pronounced in the medium-dark and dark roasts, except for dimethyl sulfide, which was only detected in the light roast. The CSA method coupled with principal component analysis or hierarchical cluster analysis successfully distinguished the roasted coffee samples according to roast degree. Partial least squares regression results showed that the CSA responses were well-correlated with the concentrations of volatile compounds in the coefficient of determination (rp2) range of 0.686–0.955. These results demonstrate that the CSA rapidly responded to coffee aroma compounds and was capable of predicting coffee aroma development.

AB - We developed a colorimetric sensor array (CSA) that is sensitive to highly contributory volatile compounds of coffee aroma for discrimination of coffee samples roasted to different roast degrees. Strecker aldehydes and α-diketones were significantly higher for the medium roast than the other roast degrees. The development of several sulfur compounds was pronounced in the medium-dark and dark roasts, except for dimethyl sulfide, which was only detected in the light roast. The CSA method coupled with principal component analysis or hierarchical cluster analysis successfully distinguished the roasted coffee samples according to roast degree. Partial least squares regression results showed that the CSA responses were well-correlated with the concentrations of volatile compounds in the coefficient of determination (rp2) range of 0.686–0.955. These results demonstrate that the CSA rapidly responded to coffee aroma compounds and was capable of predicting coffee aroma development.

KW - Coffee roasting

KW - Colorimetric artificial nose

KW - Cross-responsive sensor

KW - Multivariate analysis

KW - Roast degree

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Prediction of key aroma development in coffees roasted to different degrees by colorimetric sensor array

Abstract

Keywords

ASJC Scopus subject areas

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