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Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)
Journal of Near Infrared Spectroscopy ( IF 1.8 ) Pub Date : 2021-04-28 , DOI: 10.1177/0967033520979425
Muhammad Bilal 1 , Zou Xiaobo 1 , Muhmmad Arslan 1 , Haroon Elrasheid Tahir 1 , Yue Sun 1 , Rana Muhammad Aadil 2
Affiliation  

In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples. The compositional parameters, such as DPPH, ABTS, FRAP, TPC, FCA, TFC, and TAC, were quantified using NIR spectroscopy. The developed models were assessed using correlation coefficients of the calibration (R2) and prediction (r2); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The results of the R2 and r2 set varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.



中文翻译:

近红外光谱耦合化学计量学算法预测花生种子(Arachis hypogaea)的抗氧化活性。

在目前的研究工作中,近红外(NIR)光谱结合化学计量学算法,例如偏最小二乘(PLS)回归和一些有效的变量选择算法(协同区间-PLS(Si-PLS),向后区间-PLS(Bi- (PLS)和遗传算法-PLS(GA-PLS))用于量化花生种子样品的抗氧化性能。使用NIR光谱定量分析了DPPH,ABTS,FRAP,TPC,FCA,TFC和TAC等成分参数。使用校准(R 2)和预测(r 2); 交叉验证的均方根标准误,RMSECV;预测的均方根误差RMSEP和残余预测偏差RPD。与经典的PLS模型相比,使用Si-PLS,Bi-PLS和GA-PLS可以显着提高开发模型的效率。R 2和r 2集的结果分别从0.76到0.95和0.72到0.94不等。获得的结果表明,近红外光谱结合不同的化学计量学算法,具有用于快速评估花生种子抗氧化性能的潜力。

更新日期:2021-04-29
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