Dendrobe (Dendrobium spp.) is a traditional medicinal and edible food, which is rich in nutrients and contains biologically active metabolites. The quality and price of dendrobe are related to its geographical origins, and high quality dendrobe is often imitated by low quality dendrobe in the market. In this work, near-infrared (NIR) spectroscopy sensor combined with porphyrin and chemometrics was used to distinguish 360 dendrobe samples from twelve different geographical origins. Partial least squares discriminant analysis (PLSDA) was used to study the sensing performance of traditional NIR and tera-(4-methoxyphenyl)-porphyrin (TMPP)-NIR on the identification of dendrobe origin. In the PLSDA model, the recognition rate of the training and prediction set of the TMPP-NIR could reach 100%, which was higher than the 91.85% and 91.34% of traditional NIR. And the accuracy, sensitivity, and specificity of the TMPP-NIR sensor are all 1.00. The mechanism of TMPP improving the specificity of NIR spectroscopy should be related to the π-π conjugated system and the methoxy groups of TMPP interact with the chemical components of dendrobe. This study reflected that NIR spectrum with TMPP sensor was an effective approach for identifying the geographic origin of dendrobe.

中文翻译：

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结合卟啉和化学计量学的近红外光谱传感器追踪石dro（石Origin）的地理起源

石dro（石endspp。）是一种传统的药用和食用食品，它富含营养成分，并含有生物活性代谢产物。树枝的质量和价格与其地理来源有关，高质量的树枝通常被市场上低质量的树枝所模仿。在这项工作中，结合了卟啉和化学计量学的近红外（NIR）光谱传感器用于区分来自十二个不同地理来源的360个树突状样品。使用偏最小二乘判别分析（PLSDA）来研究传统NIR和tera-（4-甲氧基苯基）-卟啉（TMPP）-NIR在鉴定树突来源方面的传感性能。在PLSDA模型中，TMPP-NIR训练和预测集的识别率可以达到100％，高于传统NIR的91.85％和91.34％。准确性 TMPP-NIR传感器的灵敏度和特异性均为1.00。TMPP改善近红外光谱特异性的机制应与分子生物学相关。π - π共轭体系和TMPP的甲氧基与树突的化学成分相互作用。这项研究表明，采用TMPP传感器的近红外光谱是识别树突状树的地理起源的有效方法。