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Data from: Differentiating defective roasted coffee beans grown in Hawaii: volatile analysis with e-nose and HS-GC-MS

Published by Agricultural Research Service | Department of Agriculture | Catalog Last Checked: May 05, 2026 at 11:40 PM | Dataset Last Updated: April 10, 2026
Damage to coffee during growing, processing, roasting, or brewing can cause changes to the volatile chemicals or flavors produced in the beverage. Dynamic headspace sampling (HS) coupled to gas chromatography-mass spectrometry (GC-MS) was compared with an electronic nose (e-nose) to investigate changes in the volatile profiles of multiple defects in roasted coffee. Coffee from four farms in Hawai’i were sorted for phenotypic defects such as pinhole, black, chipped, moldy, and tan beans. Principle component analysis of volatile profiles showed clustering by defect type with both HS-GC-MS and e-nose. However, while results were generally consistent between the two volatile analysis techniques, HS-GC-MS was able to resolve differences (non-overlapping 95% confidence intervals) between more defects than the e-nose. HS-GC-MS was able to differentiate undamaged coffee from all defect coffee samples in 4 out of the 5 experiments, while e-nose was able to do the same in only 2 out of 5. While the e-nose was more labor intensive and less accurate in differentiating between types of coffee damage, the e-nose is much less expensive and more portable than HS-GC-MS, potentially offering advantages for deployment in quality control throughout the coffee supply chain.

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Complete Metadata
@type dcat:Dataset
accessLevel public
bureauCode 
[
  "005:18"
]
contactPoint 
{
  "fn": "Siderhurst, Matthew, S.",
  "hasEmail": "mailto:matthew.siderhurst@usda.gov"
}
description <p dir="ltr">Damage to coffee during growing, processing, roasting, or brewing can cause changes to the volatile chemicals or flavors produced in the beverage. Dynamic headspace sampling (HS) coupled to gas chromatography-mass spectrometry (GC-MS) was compared with an electronic nose (e-nose) to investigate changes in the volatile profiles of multiple defects in roasted coffee. Coffee from four farms in Hawai’i were sorted for phenotypic defects such as pinhole, black, chipped, moldy, and tan beans. Principle component analysis of volatile profiles showed clustering by defect type with both HS-GC-MS and e-nose. However, while results were generally consistent between the two volatile analysis techniques, HS-GC-MS was able to resolve differences (non-overlapping 95% confidence intervals) between more defects than the e-nose. HS-GC-MS was able to differentiate undamaged coffee from all defect coffee samples in 4 out of the 5 experiments, while e-nose was able to do the same in only 2 out of 5. While the e-nose was more labor intensive and less accurate in differentiating between types of coffee damage, the e-nose is much less expensive and more portable than HS-GC-MS, potentially offering advantages for deployment in quality control throughout the coffee supply chain.</p>
distribution 
[
  {
    "@type": "dcat:Distribution",
    "title": "Exp 1 HS-GC-MS.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "downloadURL": "https://ndownloader.figshare.com/files/63350311"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 2 HS-GC-MS.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "downloadURL": "https://ndownloader.figshare.com/files/63350314"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 3 HS-GC-MS.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "downloadURL": "https://ndownloader.figshare.com/files/63350317"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 4 HS-GC-MS.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "downloadURL": "https://ndownloader.figshare.com/files/63350320"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 5 HS-GC-MS.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "downloadURL": "https://ndownloader.figshare.com/files/63350323"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 1 e-nose.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "downloadURL": "https://ndownloader.figshare.com/files/63350326"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 2 e-nose.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "downloadURL": "https://ndownloader.figshare.com/files/63350329"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 3 e-nose.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "downloadURL": "https://ndownloader.figshare.com/files/63350332"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 4 e-nose.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "downloadURL": "https://ndownloader.figshare.com/files/63350335"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Exp 5 e-nose.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "downloadURL": "https://ndownloader.figshare.com/files/63350338"
  }
]
identifier 10.15482/USDA.ADC/31904569.v1
keyword 
[
  "Coffea arabica",
  "coffee beans",
  "damaged beans",
  "defects",
  "electronic nose",
  "gas chromatography"
]
license https://www.usa.gov/publicdomain/label/1.0/
modified 2026-04-10
programCode 
[
  "005:040"
]
publisher 
{
  "name": "Agricultural Research Service",
  "@type": "org:Organization"
}
temporal 2018-06-25/2018-07-28
title Data from: Differentiating defective roasted coffee beans grown in Hawaii: volatile analysis with e-nose and HS-GC-MS

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