傅里叶变换红外光谱技术对金银花中有效成分定量模型建立及含量测定

doi:10.3964/j.issn.1000-0593(2024)02-0467-07

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Abstract Chinese medicinal materials are “safe, stable, effective and reliable”, which is the real demand for the modernization and development of Chinese medicine. To explore new rapid, accurate and non-destructive content determination methods for Chinese medicinal materials with varying quality is one of the urgent problems in the field of quality control of Chinese medicinal materials. The active ingredients of Lonicerae Japonicae Flos are complex and fluctuate in quality. The efficient, non-destructive and rapid determination of the active ingredients of Lonicerae Japonicae Flos using Fourier transform infrared spectroscopy may be an effective measure to promote its quality control. In this study, the HPLC method was combined with Fourier transform infrared spectroscopy to establish a model for the determination of the active ingredients of Lonicerae Japonicae Flos using chemometric methods, in order to provide a new method for the rapid and accurate content determination of Lonicerae Japonicae Flos. In this study, the infrared spectra of 64 Lonicera Japonica Flos samples from Henan, Hebei and Shandong provinces were collected using a Fourier transform spectrometer. Simultaneous determination of the contents of six active ingredients in Lonicera Japonica Flos including chlorogenic acid, sweroside, secoxyloganin, cynaroside, isochlorogenic acid A and isochlorogenic acid C by HPLC. Multiple scattering correction (MSC), stand ard normal transformation (SNV), partial least squares (PLS), stepwise multiple linear regression (SMLR), first derivative (1st), second derivative (2nd), SG curl smoothing and Norris derivative filtering (ND) in TQ analysis software are used for spectral processing to establish the model after the chemometric analysis of 64 Lonicera Japonica Flos samples from Henan, Hebei and Shand ong. The result shows that the model of “PLS+MSC+2nd Der+NS” has the best prediction effect on chlorogenic acid content, with a correlation coefficient of 0.754 9 and an average absolute deviation of 0.24%. The “PLS+MSC+1st Der+ND” model has the best prediction effect on sweroside content, with a correlation coefficient of 0.936 9 and an average absolute deviation of 0.00%. The “PLS+MSC+1st Der+SG” model has the best prediction effect on secoxyloganin content, with a correlation coefficient of 0.967 8 and an average absolute deviation of -0.06%, the model of “PLS+MSC+1st Der+NS, PLS+MSC+1st Der+SG and PLS+SNV+1st Der+SG” has the best prediction effect on cynaroside content, with a correlation coefficient of 0.859 0 and an average absolute deviation of 0.01%. The model of “PLS+MSC+2nd Der+ND” has the best prediction effect on isochlorogenic acid A content, with a correlation coefficient of 0.933 9 and an average absolute deviation of 0.11%. The model of “PLS+MSC+2nd Der+SG” has the best prediction effect on isochlorogenic acid C content, with a correlation coefficient of 0.866 1 and an average absolute deviation of 0. 01%. It can be seen that the quantitative model of the active ingredients in Lonicerae Japonicae Flos established by Fourier transform spectral data can realize the content prediction of the active ingredients in unknown Lonicerae Japonicae Flos samples. This study provides a new method for the rapid and non-destructive testing of the active ingredients of Lonicerae Japonicae Flos, and also helps to achieve stable and controllable quality of traditional Chinese medicinal materials.

Key words：Lonicera Japonica Flos; Quantitative model; FT-infrared spectrum; Content prediction

Received: 2022-06-15 Accepted: 2022-10-28

ZTFLH:

R284.1

Corresponding Authors: YANG Lin-lin, DONG Cheng-ming E-mail: yangll-hatcm@hactcm.edu.cn；dcm371@sohu.com

Cite this article:

GU Xu-peng,YANG Lin-lin,QI Da-ming, et al. Quantitative Modeling and Content Determination of Active Ingredients in Lonicera Japonica Flos by Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 467-473.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)02-0467-07 OR https://www.gpxygpfx.com/EN/Y2024/V44/I02/467

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