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Authenticity green coffee bean species and geographical origin using near‐infrared spectroscopy combined with chemometrics.

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Title: Authenticity green coffee bean species and geographical origin using near‐infrared spectroscopy combined with chemometrics.
Authors: Nguyen Minh, Quan1,2 (AUTHOR), Lai, Quoc Dat1,2 (AUTHOR) lqdat@hcmut.edu.vn, Nguy Minh, Hoang1,2 (AUTHOR), Tran Kieu, Minh Tu1,2 (AUTHOR), Lam Gia, Ngoc1,2 (AUTHOR), Le, Uyen1,2 (AUTHOR), Hang, My Phung1,2 (AUTHOR), Nguyen, Hoang Dung1,2 (AUTHOR), Chau, Tran Diem Ai1,2 (AUTHOR), Doan, Ngoc Thuc Trinh1,2 (AUTHOR)
Source: International Journal of Food Science & Technology. Jul2022, Vol. 57 Issue 7, p4507-4517. 11p.
Subjects: Near infrared spectroscopy, Coffee beans, Chemometrics, Agricultural resources, Biological evolution, Infrared spectroscopy, Least squares, Wireless geolocation systems
Abstract: Summary: To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near‐infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS‐DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross‐validation of 0.0261. PLS‐DA model of mean centre and first‐order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post‐harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Food Science & Technology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 157755760
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Authenticity green coffee bean species and geographical origin using near‐infrared spectroscopy combined with chemometrics.
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  Data: <searchLink fieldCode="AR" term="%22Nguyen+Minh%2C+Quan%22">Nguyen Minh, Quan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lai%2C+Quoc+Dat%22">Lai, Quoc Dat</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lqdat@hcmut.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Nguy+Minh%2C+Hoang%22">Nguy Minh, Hoang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tran+Kieu%2C+Minh+Tu%22">Tran Kieu, Minh Tu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lam+Gia%2C+Ngoc%22">Lam Gia, Ngoc</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Le%2C+Uyen%22">Le, Uyen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hang%2C+My+Phung%22">Hang, My Phung</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Hoang+Dung%22">Nguyen, Hoang Dung</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chau%2C+Tran+Diem+Ai%22">Chau, Tran Diem Ai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Doan%2C+Ngoc+Thuc+Trinh%22">Doan, Ngoc Thuc Trinh</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Food+Science+%26+Technology%22">International Journal of Food Science & Technology</searchLink>. Jul2022, Vol. 57 Issue 7, p4507-4517. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Near+infrared+spectroscopy%22">Near infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Coffee+beans%22">Coffee beans</searchLink><br /><searchLink fieldCode="DE" term="%22Chemometrics%22">Chemometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+resources%22">Agricultural resources</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+evolution%22">Biological evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Infrared+spectroscopy%22">Infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+geolocation+systems%22">Wireless geolocation systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Summary: To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near‐infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS‐DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross‐validation of 0.0261. PLS‐DA model of mean centre and first‐order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post‐harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Food Science & Technology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1111/ijfs.15786
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 4507
    Subjects:
      – SubjectFull: Near infrared spectroscopy
        Type: general
      – SubjectFull: Coffee beans
        Type: general
      – SubjectFull: Chemometrics
        Type: general
      – SubjectFull: Agricultural resources
        Type: general
      – SubjectFull: Biological evolution
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      – SubjectFull: Infrared spectroscopy
        Type: general
      – SubjectFull: Least squares
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      – SubjectFull: Wireless geolocation systems
        Type: general
    Titles:
      – TitleFull: Authenticity green coffee bean species and geographical origin using near‐infrared spectroscopy combined with chemometrics.
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            – D: 01
              M: 07
              Text: Jul2022
              Type: published
              Y: 2022
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