Yu Dai-Xin, Qu Cheng, Xu Jia-Yi, Lu Jia-Yu, Wu Di-di, Wu Qi-Nan
Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China.
School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
Food Chem X. 2024 Oct 5;24:101881. doi: 10.1016/j.fochx.2024.101881. eCollection 2024 Dec 30.
Perillae Folium (PF) is a well-known food and herb containing different chemotypes, which affect its quality. Herein, a method was proposed to classify and quantify PF chemotypes using gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near infrared spectroscopy (FT-NIR). GC-MS results revealed that PF contains several chemotypes, including perilla ketone (PK) type, -asarone (PP-as) type, and dillapiole (PP-dm) type, with the PK type being the predominant chemotype. Based on FT-NIR data, different chemotypes were accurately classified. The random forest algorithm achieved >90 % accuracy in chemotype classification. Furthermore, the main components of perilla ketone and isoegomaketone in PF were successfully quantified using partial least squares regression models, with prediction to deviation values of 3.76 and 2.59, respectively. This method provides valuable insights and references for the quality supervision of PF and other foods.
紫苏叶是一种含有不同化学型的知名食品和草药，这会影响其质量。在此，提出了一种使用气相色谱-质谱联用仪（GC-MS）和傅里叶变换近红外光谱仪（FT-NIR）对紫苏叶化学型进行分类和定量的方法。GC-MS结果表明，紫苏叶含有几种化学型，包括紫苏酮（PK）型、细辛脑（PP-as）型和莳萝脑（PP-dm）型，其中PK型是主要化学型。基于FT-NIR数据，不同的化学型被准确分类。随机森林算法在化学型分类中准确率超过90%。此外，使用偏最小二乘回归模型成功地对紫苏叶中紫苏酮和异欧前胡素的主要成分进行了定量，预测偏差值分别为3.76和2.59。该方法为紫苏叶及其他食品的质量监管提供了有价值的见解和参考。
