Zhou Rong-Rong, Yu Yi, Zeng Wen, Hu Ming-Hua, Fan Luo-di, Chen Lin, Qiu Zi-Dong, Song Chuan, Zhang Shui-Han, Guo Lan-Ping, Huang Lu-Qi
State Key Laboratory of Dao-di Herbs， National Resource Center for Chinese Materia Medica， China Academy of Chinese Medical Sciences， Beijing 100700， China.
Institute of Chinese Materia Medica, Hunan Academy of Chinese Medicine, Changsha 410013, China.
Zhongguo Zhong Yao Za Zhi. 2018 Aug;43(16):3243-3248. doi: 10.19540/j.cnki.cjcmm.20180514.002.
Near infrared spectroscopy combined with chemometrics methods was used to distinguish Ganoderma lucidum samples collected from different origins, and a prediction model was established for rapid determine polysaccharides contents in these samples. The classification accuracy for training dataset was 96.87%, while for independent dataset was 93.33%; as for the prediction model, 5-fold cross-validation was used to optimize the parameters, and different signal processing methods were also optimized to improve the prediction ability of the model. The best square of correlation coefficients for training dataset was 0.965 4, and 0.851 6 for validation dataset; while the root-mean-square deviation values for training dataset and validation dataset were 0.018 5 and 0.023 6, respectively. These results showed that combining near infrared spectroscopy with suitable chemometrics approaches could accuracy distinguish different origins of G. lucidum samples; the established prediction model could precious predict polysaccharides contents, the proposed method can help determine the activity compounds and quality evaluation of G. lucidum.
采用近红外光谱结合化学计量学方法对不同产地的灵芝样品进行鉴别，并建立预测模型以快速测定这些样品中的多糖含量。训练数据集的分类准确率为96.87%，独立数据集的分类准确率为93.33%；对于预测模型，采用5折交叉验证来优化参数，同时也对不同的信号处理方法进行了优化以提高模型的预测能力。训练数据集的最佳相关系数平方为0.965 4，验证数据集的最佳相关系数平方为0.851 6；训练数据集和验证数据集的均方根偏差值分别为0.018 5和0.023 6。这些结果表明，将近红外光谱与合适的化学计量学方法相结合能够准确鉴别不同产地的灵芝样品；所建立的预测模型能够准确预测多糖含量，所提出的方法有助于灵芝活性成分的测定和质量评价。
