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Haruna SA, Li H, Wei W, Geng W, Luo X, Zareef M, Yao-Say Solomon Adade S, Ivane NMA, Isa A, Chen Q. Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms. Spectrochim Acta A Mol Biomol Spectrosc 2023; 285:121854. [PMID: 36162210 DOI: 10.1016/j.saa.2022.121854] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/14/2022] [Accepted: 09/03/2022] [Indexed: 06/16/2023]
Abstract
Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaofeng Luo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Adamu Isa
- Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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