Department of Analytical Chemistry, Aragon Institute of Engineering Research I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018, Zaragoza, Spain.
Department of Analytical Chemistry, Aragon Institute of Engineering Research I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018, Zaragoza, Spain.
Talanta. 2020 Jun 1;213:120831. doi: 10.1016/j.talanta.2020.120831. Epub 2020 Feb 15.
The total phenolic content (TPC) and antioxidant capacity have been considered as important quality parameters for plant extracts. In this study, bearberry leaves were regarded as studied subject and a reliable method was established to predict the TPC and antioxidant capacity of bearberry leaves. Ultraviolet-visible spectrometry (UV-Vis) and ultra high pressure liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) were used to provide spectral fingerprinting and metabolomic profiling. The data obtained (separately and merged) were used to build partial least squares (PLS) regression model. The PLS model built by using ultraviolet-visible spectra provided a satisfactory prediction result. Mid-level data fusion using the scores significantly improved the performance of PLS regression model, the residual predictive deviations (RPDs) for TPC and α, α-diphenyl-β-picrylhydrazyl (DPPH) were 6.258 and 6.699, respectively, showing an excellent predictive ability. This study proved the potential of combination of UV-Vis spectrometry and UHPLC/Q-TOF-MS in the prediction of TPC and antioxidant capacity of plant extracts.
总酚含量（TPC）和抗氧化能力已被认为是植物提取物的重要质量参数。在这项研究中，熊果叶被视为研究对象，并建立了一种可靠的方法来预测熊果叶的 TPC 和抗氧化能力。采用紫外-可见分光光度法（UV-Vis）和超高效液相色谱-飞行时间质谱联用（UHPLC/Q-TOF-MS）进行光谱指纹图谱和代谢组学分析。分别和合并获得的数据用于建立偏最小二乘（PLS）回归模型。使用紫外可见光谱建立的 PLS 模型提供了令人满意的预测结果。使用得分的中级数据融合显著提高了 PLS 回归模型的性能，TPC 和 α、α-二苯基-β-苦基肼（DPPH）的剩余预测偏差（RPD）分别为 6.258 和 6.699，显示出优异的预测能力。本研究证明了紫外可见光谱与 UHPLC/Q-TOF-MS 相结合在植物提取物 TPC 和抗氧化能力预测中的潜力。
