College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
Food Chem. 2012 Dec 15;135(4):2147-56. doi: 10.1016/j.foodchem.2012.07.011. Epub 2012 Jul 10.
This study was carried out to evaluate the feasibility of using near infrared (NIR) spectroscopy for determining three antioxidant activity indices of the extract of bamboo leaves (EBL), specifically 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing/antioxidant power (FRAP), and 2,2'-azinobis-(3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS). Four different linear and nonlinear regressions tools (i.e. partial least squares (PLS), multiple linear regression (MLR), back-propagation artificial neural network (BP-ANN), and least squares support vector machine (LS-SVM)) were systemically studied and compared in developing the model. Variable selection was first time considered in applying the NIR spectroscopic technique for the determination of antioxidant activity of food or agricultural products. On the basis of these selected optimum wavelengths, the established MLR calibration models provided the coefficients of correlation with a prediction (r(pre)) of 0.863, 0.910, and 0.966 for DPPH, FARP, and ABTS determinations, respectively. The overall results of this study revealed the potential for use of NIR spectroscopy as an objective and non-destructive method to inspect the antioxidant activity of EBL.
本研究旨在评估近红外(NIR)光谱法用于测定竹叶提取物(EBL)三种抗氧化活性指数的可行性,具体为 2,2-二苯基-1-苦基肼基(DPPH)、铁还原/抗氧化能力(FRAP)和 2,2'-联氮双-(3-乙基苯并噻唑啉-6-磺酸)(ABTS)。本研究系统地研究和比较了四种不同的线性和非线性回归工具(即偏最小二乘法(PLS)、多元线性回归(MLR)、反向传播人工神经网络(BP-ANN)和最小二乘支持向量机(LS-SVM)),以建立模型。本研究首次在应用近红外光谱技术测定食品或农产品的抗氧化活性时考虑了变量选择。基于这些选择的最佳波长,建立的 MLR 校准模型对 DPPH、FRAP 和 ABTS 的测定分别提供了预测(r(pre))为 0.863、0.910 和 0.966 的相关系数。本研究的总体结果表明,近红外光谱法作为一种客观、非破坏性的方法,有望用于检测 EBL 的抗氧化活性。