Journal
JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
Volume 12, Issue 4, Pages 2366-2376Publisher
SPRINGER
DOI: 10.1007/s11694-018-9853-x
Keywords
NIR spectroscopy; Black wolfberry; Antioxidant assays; Chemometric algorithms; Variable selection
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Funding
- International Science and Technology Cooperation Project of Jiangsu Province [BZ2016013]
- Natural Science Foundation of Jiangsu Province [BK20160506, BE2016306]
- China Postdoctoral Science Foundation [2016M590422, 2017M611736]
- National Natural Science Foundation of China [31671844, 31601543, 31750110458]
- National Key Research and Development Program of China [2016YFD0401104]
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In this study, near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression and various efficient variable selection algorithms, synergy interval-PLS (Si-PLS), backward interval PLS (Bi-PLS) and genetic algorithm-PLS (GA-PLS) were applied comparatively for the prediction of antioxidant activity in black wolfberry (BW). The eight assays were used for quantification of antioxidant content. The developed models were assessed using correlation coefficients (R-2) of the calibration (Cal.) and prediction (Pre.); root mean square error of prediction, RMSEP; standard Error of Cross-Validation, RMSECV and residual predictive deviation, RPD. The performance of the built model greatly improved by the application of Si-PLS, Bi-PLS and GA-PLS compared with full spectrum PLS. The R-2 values determined for calibration and prediction set ranged from 0.8479 to 0.9696 and 0.8401 to 0.9638, respectively. These findings revealed that NIR spectroscopy combined with chemometric algorithms can be used for quantification of antioxidant activity in BW samples.
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