West Anhui University, College of Biotechnology and Pharmaceutical Engineering, Lu'an City 237012, China.
Anhui Province Key Laboratory for Quality Evaluation and Improvement of Traditional Chinese Medicine, Lu'an City 237012, China.
J AOAC Int. 2024 Oct 1;107(5):801-810. doi: 10.1093/jaoacint/qsae036.
The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions.
This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions.
The models were produced under different conditions to distinguish the origins distinctly. Ten preprocessing methods have been used to preprocess the original spectra (OS) and to select the most optimal spectral preprocessing method. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables.
The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate.
The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua.
The utilization of NIR spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating QA measures.
确定黄精的地理来源尤为重要，因为不同产地的黄精由于生长环境和气候条件的差异，质量和市场价值差异很大。
本研究利用黄精的近红外光谱（NIR）（n=400）建立定性模型，有效区分不同产地的黄精。
采用不同条件下的模型来区分产地。使用了十种预处理方法对原始光谱（OS）进行预处理，并选择了最优化的光谱预处理方法。主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）和正交偏最小二乘判别分析（OPLS-DA）被用于确定合适的模型。为了简化起见，通过不同的波长选择方法计算预处理后的全光谱，并选择四个最显著的变量作为判别指标变量。
结果表明，采用 OPLS-DA 对一系列样品进行分析，可以有效地区分不同产地的黄精。OPLS-DA 模型的准确性也令人满意，具有良好的区分率。
研究结果表明，利用光谱结合多元分析来识别黄精的地理来源是可行的。
近红外光谱结合化学计量学在辨别中药材和食品的产地方面具有很高的功效，从而促进了质量保证措施。
