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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]
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Database: Engineering Source
Description
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]
ISSN:09505423
DOI:10.1111/ijfs.15786