基于智能感官技术结合化学计量学的蜜化橘红炮制程度快速辨识研究

Research on rapid identification of processing degree of honey-processed Citri Grandis Exocarpium based on intelligent sensory technology combined with chemometrics

关键词:  蜜化橘红  智能感官信息  化学计量学  HPLC指纹图谱  关联性分析

Key words:  honey-processed Citri Grandis Exocarpium  Intelligent sensory information  stoichiometry  HPLC fingerprint spectrum  Correlation analysis

摘要:

【】目的:基于色差计技术,建立蜜化橘红炮制程度判别函数,并探究蜜化橘红外观颜色与内在成分的关联性。方法:使用色差计获取80份蜜化橘红样品的智能感官信息,利用专家评分、偏最小二乘法判别、Fisher线性判别对80份样品进行分析并建立蜜化橘红炮制程度的判别函数；使用色差计对蜜化橘红炒制过程中的样品进行色度值测定,结合HPLC指纹图谱分析蜜化橘红炒制过程中样品的共有成分相对含量的变化,同时利用多元统计学分析方法对蜜化橘红炒制过程中的样品进行颜色与成分的关联性分析。结果:建立的判别函数为:Y1=2.197ΔL*-0.150Δa*-0.484Δb*+1.444ΔE*ab+3.064；Y2=-0.479ΔL*-0.319Δa*-0.795Δb*-0.433ΔE*ab+0.37,判别正确率为95%~100%。外观颜色与内在成分斯皮尔曼分析结果显示,与颜色参数值显著相关的内在成分主要为峰1~峰4、峰9~峰11、峰15~峰16；共有成分PLS-DA分析,得到VIP ＞1 的分别为峰11、峰5、峰10、峰15、峰7、峰13,其中峰3、峰11分别5-HMF、佛手酚。结论:基于智能感官技术结合化学计量学建立的蜜化橘红炮制程度判别函数可用于大部分蜜化橘红样品判别,预测准确度较高,为蜜化橘红炮制程度快速辨识提供参考；内在成分与外观颜色的动态关联研究中,筛选出既与颜色变化有显著相关性,又在蜜化橘红炒制过程中含量变化较大的峰11、峰10、峰15。其中峰11为佛手酚,推荐可将其作为蜜化橘红饮片质量控制候选标志物。

Abstract:

ABSTRACT：OBJECTIVE Based on colorimeter technology, to establish a discrimination function for the processing degree of honey-processed Exocarpium Citri Grandis（HPECG） and explore the correlation between the appearance color and internal components of HPECG. METHODS The intelligent sensory information of 80 samples of HPECG was obtained using a colorimeter. The 80 samples were analyzed using expert scoring, partial least squares discriminant analysis (PLS-DA), and Fisher linear discriminant analysis to establish a discrimination function for the processing degree of HPECG. The chromatic values of the samples during the frying process of HPECG were measured using a colorimeter. The changes in the relative content of common components in the samples during the frying process were analyzed in combination with HPLC fingerprint analysis. Meanwhile, the correlation between color and components in the samples during the frying process of HPECG was analyzed using multivariate statistical analysis methods. RESULTS The established discrimination function was:Y1=2.197ΔL*-0.150Δa*-0.484Δb*+1.444ΔE*ab+3.064；Y2=0.479ΔL*-0.319Δa*-0.795Δb*+0.433ΔE*ab+0.371, with a discrimination accuracy rate of 95% to 100%. The Spearman analysis results of the appearance color and internal components showed that the internal components significantly correlated with the color parameter values were mainly peaks 1 to 4, 9 to 11, and 15 to 16. The PLS-DA analysis of common components revealed tht those with VIP > 1 were peaks 11, 5, 10, 15, 7, and 13, among which peaks 3 and 11 were 5-HMF and bergaptol, respectively. CONCIUSION The discrimination function for the processing degree of HPECG established based on intelligent sensory technology combined with chemometrics can be used for the discrimination of most HPECG samples, with a relatively high prediction accuracy, providing a reference for the rapid identification of the processing degree of HPECG. In the dynamic correlation study of internal components and appearance color, peaks 11, 10, and 15 were screened out, which were not only significantly correlated with color changes but also had significant changes in content during the frying process of HPECG. Among them, peak 11 is bergaptol, and it is recommended to be used as a candidate marker for quality control of HPECG decoction pieces.