Rapid estimation of piperine in black pepper: Exploration of Raman spectroscopy.

INTRODUCTION: The major chemical marker of black pepper (Piper nigrum L) is piperine and its estimation is extremely important for quality assessment of black pepper. The methods for piperine quantification, to date, are laboratory based and use high end instruments like chromatographs, which require tedious sample processing and cause sample destruction. OBJECTIVES: In this article, we present a simple, rapid and green analytical method based on Raman spectroscopy for the quantitative assessment of piperine. MATERIAL AND METHODS: To assess the potential of the technique, we report the complete vibrational characterisation of the piperine with density functional theory (DFT) calculations. RESULTS: The theoretical peaks were obtained at 1097 cm-1 , 1388 cm-1 , 1528 cm-1 , 1578 cm-1 , and at 1627 cm-1 , and this result was verified in a Raman spectrometer followed by a preliminary experiment. Twenty black pepper samples were analysed using high-performance liquid chromatography (HPLC) and used as reference data for Raman analysis. The Raman shift spectra were analysed using partial least squares (PLS) and good prediction accuracy with correlation coefficient of prediction (Rp2 ) = 0.93, root mean square error of prediction (RMSEP) = 0.13 and residual prediction deviation (RPD) = 3.9 obtained. CONCLUSIONS: The results demonstrate the efficacy of the Raman technique for the estimation of piperine in the dry fruit of Piper nigrum.

Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine.

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 varies 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 to estimate the major group active molecules, the andrographolides 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 83% using standard normal variate (SNV) preprocessing.