目的 基于智能感官技術(shù)對不同發(fā)酵程度的制枳殼飲片外觀性狀進行分析，并構(gòu)建制枳殼發(fā)酵程度的快速辨識模型以及外觀性狀與內(nèi)在質(zhì)量的逐步回歸模型，以期為制枳殼飲片質(zhì)量評價提供參考。方法 通過測色儀、電子鼻測定不同發(fā)酵程度制枳殼的外觀性狀指標(biāo)（顏色、氣味），結(jié)合主成分分析（principal component analysis，PCA）、正交偏最小二乘-判別分析（orthogonal partial least squares-discriminant analysis，OPLS-DA）、Fisher線性判別分析、反向傳播（back propagation，BP）神經(jīng)網(wǎng)絡(luò)算法等多種化學(xué)計量學(xué)方法，建立不同發(fā)酵程度制枳殼飲片快速辨識模型。采用HPLC法對枳殼中8種黃酮類成分（柚皮苷、新橙皮苷、蕓香柚皮苷、橙皮苷、枸橘苷、橙皮素-7-O-葡萄糖苷、柚皮素、橙皮素）進行定量測定，并將制枳殼飲片外觀性狀與黃酮類成分進行相關(guān)性分析和逐步回歸分析，建立顏色、氣味與內(nèi)在成分之間的定量模型。結(jié)果 單源的色度值、氣味特征值不能將生枳殼與不同發(fā)酵程度制枳殼完全區(qū)分，基于“色度-氣味”數(shù)據(jù)融合建立的BP神經(jīng)網(wǎng)絡(luò)判別模型較Fisher線性判別模型的分類預(yù)測效果更好，能夠快速、準(zhǔn)確地辨識不同發(fā)酵程度制枳殼飲片。相關(guān)性分析結(jié)果顯示，制枳殼飲片色度值與所含8種黃酮類成分含量具有顯著的相關(guān)性，電子鼻氣味響應(yīng)值與成分之間呈現(xiàn)不同程度的相關(guān)性，進一步構(gòu)建的逐步回歸模型可通過顏色、氣味的外觀性狀參數(shù)快速預(yù)測制枳殼飲片主要黃酮類成分的含量變化。結(jié)論 基于“色度-氣味”構(gòu)建的BP神經(jīng)網(wǎng)絡(luò)辨識模型可以快速準(zhǔn)確判別制枳殼發(fā)酵程度，“外觀性狀-成分”回歸模型的建立為制枳殼質(zhì)量的快速檢測提供科學(xué)依據(jù)。

Objective The appearance characteristics of processed Aurantii Fructus (PAF) with different fermentation degrees were analyzed using intelligent sensory technology. A rapid identification model of fermentation degrees of PAF and a stepwise regression model of appearance and intrinsic quality were established to provide a reference for the quality evaluation of PAF pieces. Methods The appearance characteristics (color and odor) of PAF with different fermentation degrees were determined by spectrophotometric colorimeter and electronic nose. Multivariate statistical analysis techniques including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), Fisher linear discriminant analysis, and back propagation (BP) neural network algorithm were applied to establish a rapid identification model of PAF with different fermentation degrees. The contents of eight flavonoids in PAF (naringin, neohesperidin, narirutin, hesperidin, poncirin, hesperetin-7-O-glucoside, naringenin, and hesperetin) were determined by HPLC to establish correlation between their chemical components and appearance characteristics. The quantitative model between color, odor, and intrinsic components was established by stepwise regression analysis. Results The colorimetric values and odor characteristic value of a single source could not completely differentiate between Aurantii Fructus (AF) and PAF with different fermentation degrees. The BP neural network discrimination model based on source fusion of “chroma-odor” had better classification and prediction effect than the Fisher linear discrimination model, and could quickly and accurately identify the fermentation degree of PAF. Correlation analysis found that chromaticity values of PAF were significantly correlated with the contents of eight flavonoids, and electronic nose odor response values showed different degrees of correlation with the components. The changes in the content of main flavonoids in PAF could be quickly predicted through the parameters of color and odor using the stepwise regression model. Conclusion The BP neural network discrimination model based on “chroma-odor” can identify the fermentation degree of PAF quickly and accurately, and the establishment of the “appearance characteristics-composition” regression model can provide a scientific basis for rapid quality detection of PAF.

R283.6

國家自然科學(xué)基金資助項目（81873003）;國家中醫(yī)藥管理局科技司項目“特色炮制技術(shù)規(guī)律發(fā)掘—蒸制”（GZY-KJS-2022-054）