Rapid quality evaluation of moutan cortex (Paeonia suffruticosa Andrews) by near-infrared spectroscopy and bionic swarm intelligent optimization algorithm.

Moutan Cortex (MC), recognized as a traditional Chinese medicinal herb, possesses significant therapeutic properties. The existing quality assessment method only measures the content of one component in MC, which is obviously not comprehensive enough. Besides, the determination process is time-consuming and laborious.Thus, this article presents a novel approach for the rapid, precise, and efficient quality assessment of MC based on near-infrared spectroscopy (NIR) technology in combination with the bionic swarm intelligent optimization algorithms. First, MC samples were collected and acquired with the NIR spectra in diffuse reflectance mode. Second, the content of paeonol, paeoniflorin, and gallic acid in MC was determined by high-performance liquid chromatography, and the content of total flavonoids and phenols was determined by UV-visible spectrophotometry. Afterward, all the measured content was analyzed in correlation with the NIR spectra of MC, and the partial least squares regression method was utilized to build the models. Especially, to improve the models' performance, five famous bionic swarm intelligent optimization algorithms were investigated to perform the wavelength selection. As a result, the models' performance was significantly enhanced. The coefficient of determination (R) > 0.9 and residual prediction deviation (RPD) > 3 were observed on the calibration set and the prediction set. Thus, we believe that bionic swarm intelligent optimization algorithms have the potential to enhance the performance of quantitative models considerably, which offers substantial support for the quality assessment of MC and shows promising applications in the domain of NIR analysis.