Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine - Sing, Dilip; Banerjee, Subhadip; Jana, Shibu Narayan; Mallik, Ranajoy; Dastidar, Sudarshana Ghosh; Majumdar, Kalyan; Bandyopadhyay, Amitabha; Bandyopadhyay, Rajib; Mukherjee, Pulok K. (Frontiers in Pharmacology, 2021)
Andrographis paniculata (Burm. F) Nees, has been widely used for upper respiratory tract and several other diseases and general immunity for a historically long time in countries like India, China, Thailand, Japan, and Malaysia. The vegetative productivity and quality with respect to pharmaceutical properties of Andrographis paniculata vary considerably across production, ecologies, and genotypes. Thus, a field deployable instrument, which can quickly assess the quality of the plant material with minimal processing, would be of great use to the medicinal plant industry by reducing waste, and quality grading and assurance. In this paper, the potential of near infrared reflectance spectroscopy (NIR) was utilized to estimate the major group active molecules, the andrograholides in Andrographis paniculata, from dried leaf samples and leaf methanol extracts and grade the plant samples from different sources. The calibration model was developed first on the NIR spectra obtained from the methanol extracts of the samples as a proof of concept and then the raw ground samples were estimated for gradation. To grade the samples into three classes: good, medium and poor, a model based on a machine learning algorithm - support vector machine (SVM) on NIR spectra was built. The tenfold classification results of the model had an accuracy of 84.21% using combination of standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing.