Discrimination of citrus fruits using FT-IR fingerprinting by quantitative prediction of bioactive compounds
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High throughput screening of citrus samples containing elevated concentrations of total carotenoids, flavonoids, and phenolic compounds was accomplished using ultraviolet–visible spectroscopy and Fourier transform infrared (FT-IR) spectroscopy, combined with multivariate analysis. Principal component analysis and partial least squares discriminant analysis using FT-IR spectra were able to differentiate seven citrus fruit groups into three distinct clusters corresponding to their taxonomic relationship. Quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds in citrus fruit was established using a partial least squares regression algorithm from the FT-IR spectra. The regression coefficients (R 2) of predicted and estimated values of total carotenoids, flavonoids, and phenolic compounds were all 0.99. The results indicated that accurate quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from citrus fruit FT-IR spectra, and that the resulting quantitative prediction model might be useful as a rapid selection tool for citrus fruits containing elevated carotenoids, flavonoids, and phenolic compounds.
KeywordsCitrus Fourier transform infrared spectroscopy Partial least square-discriminant analysis Partial least squares regression Principal component analysis
This research was supported by the 2016 scientific promotion program funded by Jeju National University. We are grateful to Sustainable Agriculture Research Institute in Jeju National University for providing the experimental facilities and to Research Institute for Subtropical Agriculture and Biotechnology for providing citrus materials.
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
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