Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Sep 5;277:121249. doi: 10.1016/j.saa.2022.121249. Epub 2022 Apr 18.
Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (R) in the independent test sets were 12.7768 g kg and 0.9586, 1.1346 g kg and 0.9670, 0.3938 g kg and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.
铁皮石斛常被用作日常饮品，其有益功效日益受到关注。铁皮石斛的质量评价对于确保其保健价值和保护消费者利益具有重要意义。鉴于传统的铁皮石斛质量分析方法通常耗时耗力，本研究开发了一种综合策略，具有快速高效的优点，可利用近红外（NIR）光谱和化学计量学对来自不同产地的铁皮石斛进行质量评价。以多糖、多酚、总黄酮和总生物碱为质量指标进行定量分析。本研究采用协同区间（SI）、遗传算法（GA）和竞争自适应重加权采样（CARS）三种波长选择方法进行模型优化。从定性角度出发，利用近红外光谱结合偏最小二乘判别分析（PLS-DA）和支持向量分类（SVC）对铁皮石斛的产地进行区分。基于 CARS 选择的波长构建的 PLS 模型在预测铁皮石斛中质量指标含量方面表现出最佳性能。在独立测试集中，RMSEP 和 R 的值分别为 12.7768 g/kg 和 0.9586、1.1346 g/kg 和 0.9670、0.3938 g/kg 和 0.8803、0.0825 和 0.7031，分别对应多糖、多酚、总黄酮和总生物碱。对于产地识别，非线性 SVC 优于线性 PLS-DA，在校准集和预测集中的正确识别率分别高达 100%和 100%。总的来说，本研究结果表明 NIR 光谱和化学计量学在快速测定质量参数和产地方面具有潜力。本研究为更快速、更全面地评价铁皮石斛的质量提供了有价值的参考。
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