Song, Seung Yeob, Lee, Young Koung, Kim, In-Jung (January 2016) Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis. Food Chemistry, 190. pp. 1027-1032. ISSN 0308-8146
Abstract
A high-throughput screening system for citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1,100 cm-1, 1,300-1,500 cm-1, and 1,500-1,700 cm-1. Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R2) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to citrus sugar and acid contents, excellent citrus lines can be early detected with greater accuracy.
| Item Type: | Paper |
|---|---|
| Uncontrolled Keywords: | Acid content Fourier transform infrared (FT-IR) Partial least square-discriminant analysis Partial least squares regression Principal component analysis Sugar content |
| Subjects: | organism description > plant Investigative techniques and equipment > spectroscopy |
| CSHL Authors: | |
| Communities: | CSHL labs > Ware lab |
| Depositing User: | Matt Covey |
| Date: | 1 January 2016 |
| Date Deposited: | 25 Jun 2015 16:21 |
| Last Modified: | 29 Jul 2015 19:19 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/31600 |
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