目的 优化黄芩Astragali Radix不同炮制品（炒黄芩、黄芩炭、酒黄芩）的最佳炮制工艺，探究其外观色泽、气味、滋味等感官性状与主要有效成分之间的相关性。方法 利用热分析技术分析热解温度范围，并结合多指标-响应面法（response surface methodology，RSM），以黄芩苷、黄芩素、汉黄芩苷、汉黄芩素含量及黄芩炮制品外观性状评分为评价指标，利用层次分析法与熵权法计算各指标权重系数及综合评分；进一步利用中心组合设计（central combination design，CCD）设计-RSM考察炮制温度与炮制时间（酒黄芩增加闷润时间、黄酒比例）对炮制工艺的影响；利用电子感官技术（电子眼、电子鼻、电子舌）分析不同炮制程度样品的响应值，并通过相关性分析探究感官性状（色泽L^*、a^*、b^*值、气味传感器响应值、滋味传感器响应值）与化学成分之间的相关性。结果 确定最佳工艺参数：炒黄芩为205.4℃、6.5 min，黄芩炭为227.4℃、16 min，酒黄芩为180℃、5 min（闷润50 min，黄酒用量18.2%）。相关性分析表明，不同炮制程度的黄芩具有可区分的传感器响应模式：炒黄芩中气味传感器S7、S10与滋味传感器P6、P10～P12对黄芩苷与汉黄芩苷呈同步强正相关；黄芩炭和酒黄芩中则普遍转为多传感器与黄芩苷的强负相关，且汉黄芩素在黄芩炭中与多传感器呈强正相关，而在炒黄芩中呈强负相关；电子眼色度值L^*、a^*、b^*与炮制程度显著关联（ΔE^*_ab＞1.5）。结论 优化工艺稳定可行，基于电子感官技术构建的质量评价体系可客观区分炮制程度，揭示的“性状-成分”相关性为炮制终点判定及质量控制提供了理论依据与创新方法。

Objective To optimize the optimal processing technology of different processed products of Huangqin (Scutellariae Radix, SR) [fried Scutellariae Radix (fSR), Scutellariae Radix charcoal (SRc), wine Scutellariae Radix (wSR)], and explore the correlation between sensory traits such as appearance, color, odor, taste, and the main active ingredients. Method The multi-index response surface methodology (RSM) was used, with baicalin, baicalein, wogonoside, wogonin, and appearance trait scores as evaluation indicators. The analytic hierarchy process and entropy weight method were used to calculate the weight coefficients and comprehensive scores of each indicator. Further utilize central combination design (CCD)-RSM to investigate the effects of temperature and processing time (soaking time and the alcohol ratio) on the processing technology. Using electronic sensory technology (electronic eye, electronic nose, electronic tongue) to analyze the response values of samples with different processing levels, and exploring the correlation between sensory traits (color L^*, a^*, b^* value, odor sensor response value, taste sensor response value) and chemical composition through correlation analysis. Results The optimal process parameters were determined as follows: fSR at 205.4 ℃, 6.5 min, SRc at 227.4 ℃, 16 min, and wSR at 180 ℃, 5 min (moistened for 50 min, with a yellow wine dosage of 18.2%). Correlation analysis shows that different degrees of processing of SR have distinguishable sensor response patterns: odor sensors S7 and S10 and taste sensors P6 and P10—P12 in fSR have a strong positive correlation with baicalin and wogonoside; In SRc and wSR, there is a strong negative correlation between multiple sensors and baicalin, while wogonin shows a strong positive correlation with multiple sensors SRc and a strong negative correlation with fSR. The electronic eye color values (L^*, a^*, b^*) are significantly correlated with the degree of processing (ΔE^*_ab > 1.5). Conclusion Optimizing the process is stable and feasible. The quality evaluation system based on electronic sensory technology can objectively distinguish the degree of processing, and the correlation between characteristics and components revealed provides theoretical basis and innovative methods for determining the endpoint of processing and quality control.

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