This article enfolds a rapid and sensitive high-performance thin-layer chromatographic (HPTLC) method for the estimation of four triterpenoids, namely, betulin (BU), betulinic acid (BA), lupeol (LU), and oleanolic acid (OA), from the bark, roots, and leaves of Betula utilis D. Don, an endangered Himalayan tree. All the four phytoconstituents have high therapeutic value. Separation was performed on thin-layer chromatography (TLC) aluminum plates precoated with silica 60 F254 (20 × 20 cm) followed by detection of betulin, lupeol, and oleanolic acid carried out by derivatizing the plate with ceric ammonium sulfate followed by heating at 110°C for 5 min. For betulinic acid, the plate was dried and visualized after spraying with Liebermann‒Burchard reagent. CAMAG TLC Scanner 4 equipped with winCATS software was used for densitometric scanning at 500–550 nm. The proposed technique was further validated in terms of linearity, precision, accuracy, and sensitivity as per the International Conference on Harmonisation (ICH) guidelines. A good linear relationship was obtained for the calibration plots with r2 = 0.9994, 0.9995, 0.9969, and 0.9998 for betulin, lupeol, oleanolic acid, and betulinic acid, respectively. Accuracy of the method was checked by recovery study conducted at three different levels with the average recovery between 98.9% and 99.3% for all the four markers.
Amongst different approaches, dynamic time warping has shown promising results during the online signature verification competitions of previous years. To improve the results of dynamic time warping, different preprocessing steps may be applied and different dimensions of the samples may be compared. The choice of preprocessing steps and comparing dimensions may significantly influence the results. Thus, to aid researchers with these decisions, a comparison made between the results of promising preprocessing algorithms as horizontal scaling, vertical scaling and alignment using dynamic time warping in different dimensions and their combinations on two datasets (SVC2004 and MCYT-100). The results showed that preprocessing methods made a very promising improvement in the verification accuracy.