目的 建立楮實(shí)子Broussonetiae Fructus中槲皮素、木犀草素、芹菜素、大黃素、大黃素甲醚、鵝掌楸堿、白屈菜紅堿、氯化兩面針堿、巖藻甾醇、胡蘿卜苷、β-谷甾醇11種成分定量檢測(cè)方法，同時(shí)檢測(cè)其醇溶性浸出物、總灰分和酸不溶性灰分含量，并結(jié)合化學(xué)模式識(shí)別和Logistic回歸分析對(duì)不同產(chǎn)地楮實(shí)子進(jìn)行等級(jí)預(yù)測(cè)。方法 采用Synergi Max-RP色譜柱；以乙腈-0.3%磷酸溶液為流動(dòng)相，梯度洗脫；檢測(cè)波長(zhǎng)360 nm（槲皮素、木犀草素和芹菜素）、271 nm（大黃素、大黃素甲醚、鵝掌楸堿、白屈菜紅堿和氯化兩面針堿）和210 nm（巖藻甾醇、胡蘿卜苷和β-谷甾醇）；采用外標(biāo)法檢測(cè)楮實(shí)子中11種成分含量。按《中國(guó)藥典》2020年版四部檢測(cè)楮實(shí)子中醇溶性浸出物、總灰分和酸不溶性灰分含量。利用SPSS 26.0和SIMCA 14.1軟件對(duì)14個(gè)定量檢測(cè)指標(biāo)結(jié)果進(jìn)行化學(xué)模式識(shí)別分析，采用Logistic回歸分析建立不同產(chǎn)地楮實(shí)子的等級(jí)預(yù)測(cè)模型，并進(jìn)行驗(yàn)證。結(jié)果 11種成分在各自質(zhì)量濃度范圍內(nèi)線性關(guān)系良好（r＞0.999）；平均加樣回收率為96.98%～100.08%，RSD為0.71%～1.84%；精密度、重復(fù)性和穩(wěn)定性良好（RSD均＜2.0%）。45批楮實(shí)子中槲皮素、木犀草素、芹菜素、大黃素、大黃素甲醚、鵝掌楸堿、白屈菜紅堿、氯化兩面針堿、巖藻甾醇、胡蘿卜苷和β-谷甾醇質(zhì)量分?jǐn)?shù)分別為（2.319±0.377）、（1.957±0.342）、（4.818±0.779）、（0.301±0.054）、（0.701±0.158）、（0.936±0.158）、（1.771±0.295）、（0.431±0.085）、（0.123±0.037）、（0.088±0.023）、（0.409±0.084）mg/g；醇溶性浸出物、總灰分和酸不溶性灰分含量分別為（18.1±2.6）%、（6.8±0.6）%和（0.9±0.4）%。主成分分析結(jié)果顯示有2個(gè)主成分的特征值大于1，45批楮實(shí)子聚為3類；因子分析顯示S17～S31排序靠前、S1～S16排序靠中、S32～S45排序靠后；正交偏最小二乘判別分析顯示芹菜素、大黃素甲醚、槲皮素、木犀草素、白屈菜紅堿和β-谷甾醇是不同產(chǎn)地楮實(shí)子質(zhì)量差異因子。Logistic回歸分析顯示45批楮實(shí)子所對(duì)應(yīng)的預(yù)測(cè)歸屬等級(jí)明確，擬合概率P值均大于98.0%。結(jié)論 HPLC多成分定量檢測(cè)、化學(xué)模式識(shí)別及Logistic回歸分析模型操作便捷、結(jié)果準(zhǔn)確，可用于不同產(chǎn)地楮實(shí)子的等級(jí)預(yù)測(cè)，為中藥楮實(shí)子的質(zhì)量評(píng)價(jià)標(biāo)準(zhǔn)制定提供數(shù)據(jù)參考。

Objective To establish a quantitative detection method for 11 components of quercetin, luteolin, apigenin, emodin, physcion, liriodenine, chelerythrine, nitidine chloride, fucosterol, daucosterol and β-sitosterol in Chushizi (Broussonetiae Fructus), at the same time, the contents of alcohol-soluble extract, total ash and acid-insoluble ash were detected, and the grades of Broussonetiae Fructus from different producing areas were predicted by chemical pattern recognition and Logistic regression analysis. Methods All samples were analyzed by Synergi Max-RP column and eluted with acetonitrile-0.3%phosphoric acid performing gradient elution, and the detection wavelength were 360 nm (quercetin, luteolin and apigenin), 271 nm (emodin, physcion, liriodenine, chelerythrine and nitidine chloride ) and 210 nm ( fucosterol, daucosterol and β-sitosterol). The contents of 11 components were detected by external standard method. The contents of alcohol-soluble extract, total ash and acid-insoluble ash in Broussonetiae Fructus were determined according to Chinese Pharmacopoeia (Volume IV). Using SPSS 26.0 and SIMCA 14.1 software, chemical pattern recognition analysis was performed on the results of 14 quantitative detection indicators. Logistic regression analysis was used to establish and verify the grade prediction model of Broussonetiae Fructus from different producing areas.Results The linear relationship was good within the respective quality concentration ranges for all 11 components (r > 0.999), the average recoveries were 96.98%—100.08% with RSDs of 0.71%—1.84%. The RSDs of precision, repeatability and stability were all less than 2.0%. The mass fractions of quercetin, luteolin, apigenin, emodin, physcion, liriodendrine, chelerythrine, nitidine chloride, fucosterol, daucosterol and β-sitosterol in 45 batches of Broussonetiae Fructus were (2.319 ±0.377), (1.957 ±0.342), (4.818 ±0.779), (0.301 ±0.054), (0.701 ±0.158), (0.936 ±0.158), (1.771 ±0.295), (0.431 ±0.085), (0.123 ±0.037), (0.088 ±0.023) and (0.409 ±0.084) mg/g, respectively. The contents of alcohol-soluble extract, total ash and acid-insoluble ash were (18.1 ±2.6)%, (6.8 ±0.6)% and (0.9 ±0.4)%, respectively. Principal component analysis showed that the eigenvalues of two principal components were greater than 1, and 45 batches of BroussonetiaeFructus were clustered into three categories. Factor analysis showed that S17—S31 was in the front, S1—S16 was in the middle, and S32—S45 was in the back. Orthogonal partial least squares discriminant analysis showed that apigenin, physcion, quercetin, luteolin, chelerythrine and β-sitosterol were quality difference factors of Broussonetiae Fructus from different producing areas. Logistic regression analysis showed that the predicted attribution levels corresponding to 45 batches of Broussonetiae Fructus were clear, and the fitting probability P values were all greater than 98.0%. Conclusion HPLC multi-component quantitative detection, chemical pattern recognition and Logistic regression analysis model are convenient and accurate, which can be used to predict the grade of Broussonetiae Fructus from different producing areas, and provide data reference for the establishment of quality evaluation standard of Broussonetiae Fructus.

R286.2

河南省醫(yī)學(xué)科技攻關(guān)計(jì)劃聯(lián)合共建項(xiàng)目（LHGJ20210992）