RESUMEN
The establishment of near infrared (NIR) spectroscopy model mostly relies on chemometrics, and spectral analysis combined with artificial intelligence (AI) provides a new way of thinking for pharmaceutical quality inspection, new algorithms such as back propagation artificial neural networks (BP-ANN) and swarm intelligence optimization algorithms such as sparrow search algorithm (SSA) provide core technical support. In order to explore the application of AI in the pharmaceutical field, in this study, Angelica dahurica formula granules with a relatively complex system were selected as the research object. Quantitative analysis models were established by using partial least squares regression (PLSR) with a micro-NIR spectrometer, and BP-ANN modeling results were compared. For the best PLSR models of six characteristic components in the continuous counter-current extract of Angelica dahurica, R2v of imperatorin was lower than 0.90, and the RPD values of imperatorin, phellopterin, and isoimperatorin were even lower than 1. When the prediction model established by SSA-BP-ANN was used for quantitative analysis, R2v of six components were all higher than 0.92, and the RPD values all higher than 1.5, which proved that the BP-ANN method was better than PLSR. This study confirmed that in the continuous counter-current extraction progress of Angelica dahurica formula granules, the use of micro-NIR spectrometer combined with AI could realize the rapid prediction of the contents of six characteristic components. The comparison results provided a scientific reference for the process analysis and on-line monitoring in the production process of traditional Chinese medicine by micro-NIR spectrometer combined with AI.
Asunto(s)
Angelica , Espectroscopía Infrarroja Corta , Espectroscopía Infrarroja Corta/métodos , Angelica/química , Análisis de los Mínimos Cuadrados , Medicamentos Herbarios Chinos/química , Medicamentos Herbarios Chinos/aislamiento & purificación , Medicamentos Herbarios Chinos/análisis , Redes Neurales de la Computación , Inteligencia Artificial , Algoritmos , Furocumarinas/aislamiento & purificación , Furocumarinas/química , Furocumarinas/análisisRESUMEN
The fruit of Crataegus sp. is known as "Shanzha (SZ)" in China and is widely used in the food, beverage, and traditional Chinese medicine (TCM) industries. SZ usually requires thermal processing to reduce the irritation of its acidity to the gastric mucosa. Different processed products of SZ resulting from thermal processing have different or even opposite functions in clinical applications. In addition, 5-hydroxymethylfurfural (5-HMF) intermediates produced during thermal processing are carcinogenic to humans. Therefore, the aim of this study was to explore a rapid and accurate method by Fourier transform infrared spectroscopy (FT-IR) for the identification of different processed products and the determination of 5-HMF in extracts. In qualitative identification, a three-stage infrared spectroscopy identification method (raw spectra, the second derivative spectra, and two-dimensional correlation (2DCOS) spectra) was developed to distinguish different processed products of SZ step by step. In quantitative determination, partial least squares regression combined with different variable selection methods, especially the 2DCOS method, was applied to determine the 5-HMF content. The results show that temperature-induced 2DCOS synchronous spectra can effectively identify different processed products of SZ by shape, intensity, and position of auto-peaks or cross-peaks, and the variables selected by power spectra from concentration-induced 2DCOS synchronous spectra have better prediction ability for 5-HMF compared to full variables. The above results demonstrate that 2D-COS analysis is a potential tool in qualitative and quantitative analysis, which can improve sample identification accuracy and determination capabilities. This study not only establishes a rapid and accurate method for the identification of different processed products but also provides a practical reference for food safety and the efficient use of TCM.