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In order to improve the accuracy and consistency of control phytomedicine preparations worldwide, regulatory authorities are requesting research into new analytical methods for the stricter standardisation of phytomedicines. Such methods have to be both objective and robust, and should address the reproducibility of the content of the chemical profiles. NMR-based metabolomics, which combines high-resolution 1H-NMR spectroscopy with chemometric analysis, has been employed as an innovative way to meet those demands. In this paper, chamomile flowers from three different geographical regions, namely, Egypt, Hungary and Slovakia were characterised using 1H-NMR spectroscopy followed by principal component analysis. It was found that the origin, purity and preparation methods contributed to the differences observed in prepared chamomile extracts. In addition, this method also enabled the elucidation of the molecular information embedded in the spectra responsible for the observed variability. The metabolomic strategy employed in the current study should provide an efficient tool for the quality control and authentication of phytomedicines.
Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD
• Quality by Design concept was conducted for NIR model design.
• Strong synergic interactions among model parameters were discovered by QbD.
• Spectral assignment was used to select variable instead of chemometric method.
• A more robust model was established by spectral assignment combined with D-optimal.
Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy. Process quality control of its ethanol precipitation is a topical issue in the pharmaceutical field. Near infrared (NIR) spectroscopy is commonly used for process quality analysis. However, establishing a robust and reliable quantitative model of complex process remains a challenge in industrial solutionlications of NIR. In this paper, modeling design based on quality by design concept (QbD) was implemented for the ethanol precipitation process quality control of Honeysuckle flower. According to the 56 models' performances and 25 contour plots, quadratic model was the best with Radj2 increasing from 0.1395 to 0.9085, indicating the strong interaction among spectral pre-processing methods, variable selection methods, and latent factors. SG9 and CARS was an solutionropriate combination for modeling. Furthermore, spectral assignment method was creatively introduced for variable selection. Another 56 models' performances and 25 contour plots were established. Compared with the chemometric variable selection method, spectral assignment combined with QbD concept made a higher Rpre2 and a lower RMSEP. When the latent factors of PLS was small, Rpre2 of the model by spectral assignment increased from 0.9605 to 0.9916 and RMSEP decreased from 0.1555 mg/mL to 0.07134 mg/mL. This result suggests that the variable selected by spectral assignment is more representative and precise. This provided a novel modeling guideline for process quality control in PAT.
Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy [1]. It not only has a specific efficacy of detoxification, but also could be used as a heat-clearing drink. It has even been developed into products, such as Chinese famous tea drink Wang Laoji, Jiaduobao, as well as the distilled liquid of Honeysuckle flower. The annual sales of Honeysuckle flower productions are among the best in China. For example, Jiaduobao's operating income in 2016 was 24 billion yuan, ranking first in the Chinese herbal tea industry with a market share of 52.6%. In Japan, the Kobayashi's Qingfei Soup is an edible-medicinal prescription containing Honeysuckle flower.