Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: Near-infrared spectroscopic discrimination of adulterated olive oils
- Authors
- Sohng, Woosuk; Park, Yeonju; Jang, Daeil; Cha, Kyungjoon; Jung, Young Mee; Chung, Hoeil
- Issue Date
- May-2020
- Publisher
- ELSEVIER
- Keywords
- Temperature-induced spectral variation; Two-dimensional correlation analysis; Near-infrared spectroscopy; Olive oil authentication; Power spectrum; Discriminant analysis
- Citation
- TALANTA, v.212, pp.1 - 9
- Indexed
- SCIE
SCOPUS
- Journal Title
- TALANTA
- Volume
- 212
- Start Page
- 1
- End Page
- 9
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145776
- DOI
- 10.1016/j.talanta.2020.120748
- ISSN
- 0039-9140
- Abstract
- A strategy of combining temperature-induced spectral variation and two-dimensional correlation (2D-COS) analysis as a potential tool to improve accuracy of sample discrimination is suggested. The potential application of this method was evaluated using near-infrared (NIR) spectroscopic discrimination of adulterated olive oils. Rather than utilizing static spectral information at a certain temperature, dynamic spectral features induced by an external perturbation such as temperature change would be more informative for sample discrimination, and 2D-COS analysis was a reliable choice to characterize temperature-induced spectral variation. For evaluation, NIR spectra of 9 pure olive oils and 90 olive oils adulterated with canola, soybean, and corn oils (adulteration rate: 5%) were collected at four different temperatures (20, 27, 34, 41 degrees C). In constant-temperature measurements, the scores of pure and adulterated samples obtained by principal component analysis (PCA) were considerably overlapped. When 2D-COS analysis was performed using temperature-varied (20-41 degrees C) spectra and the resulting power spectra from 2D synchronous correlation spectra were used for PCA, identification of the two groups was noticeably enhanced and subsequent k-nearest neighbor (k-NN)-based discrimination accuracy substantially improved to 86.4%. While, the accuracies resulted in the constant-temperature measurements ranged only from 50.9 to 55.8%. The dynamic temperature-induced spectral variation of the samples effectively featured by 2D-COS analysis was ultimately more informative and allowed improvement in accuracy.
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