Quality Control of Ginkgo Biloba Leaves by Real Time Release Testing in Combination with near Infrared Spectroscopy

Abstract

Ginkgo leaves are widely utilised in Chinese herbal medicines and functional food additives. However, the quality of ginkgo leaves fluctuates obviously due to the variety of geographical environments and climate conditions. Real time release testing (RTRT) combined with near infrared (NIR) spectroscopy was used to improve the quality control of ginkgo leaves. The RTRT of ginkgo leaves was achieved by qualitative and quantitative analysis using NIR spectroscopy and acceptable releasing criteria. Partial least squares regression models were developed for quantitative analysis of flavonol glycoside (FG), moisture and extract contents in ginkgo leaves. The coefficients of determination for leave-one-out cross-validation in calibration were 0.93, 0.92 and 0.89 for FG, moisture and extract contents, respectively, and relative standard errors of prediction were 9.01%, 6.67% and 3.22%, respectively. A discriminant analysis model was developed for qualitative analysis of ginkgo leaves. The Mahalanobis distance values were used as the qualitative releasing criteria of RTRT based on the discriminant analysis. In addition, FG content ≥0.7%, moisture content ≤12% and extract content ≥25% were used as the quantitative releasing criteria of RTRT according to the Chinese Pharmacopoeia. The accuracy of RTRT for ginkgo leaves was 86.7% according to qualitative and quantitative analyses based on NIR spectra. The results obtained in this work demonstrated that RTRT combined with NIR spectroscopy is a powerful tool for the quality control of ginkgo leaves.

Detection and analysis of hyaluronic acid raw materials from different sources by NIR and aquaphotomics

“…In the past few years, near-infrared (NIR) spectroscopy has developed rapidly as a non-destructive testing technique that has the characteristics of convenient analytical instruments, 8 no sample preparation, 9,10 and real-time monitoring. 11,12 NIR is widely used in food, [13][14][15] medicine, 16,17 and other elds. With chemometric analysis and machine learning on NIR spectroscopic data, objects can be quickly compared and identied.…”

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

“…Li (2018) used hyperspectral data to estimate nutrient elements in the leaves and canopy of Pyrus sinkiangensis 'Kuerlexiangli'. Liu et al (2022) identified anthracnose and black spot of pear leaves on near-infrared hyper spectroscopy.…”