China
Africa
Explore chapters and articles related to this topic
Qualitative and Quantitative Determination of Bioactive Phytochemicals in Selected Cassia Species Using HPLC-ESI-QTOF-MS and UPLC-ESI-QqQLIT-MS/MS
Published in Brijesh Kumar, Vikas Bajpai, Vikaskumar Gond, Subhashis Pal, Naibedya Chattopadhyay, Phytochemistry of Plants of Genus Cassia, 2021
Brijesh Kumar, Vikas Bajpai, Vikaskumar Gond, Subhashis Pal, Naibedya Chattopadhyay
PCA is the most widely used chemometrics method for multivariate data analysis (Senousy et al., 2014). PCA was carried out based on the quantitative data to compare and evaluate the quality of species based on the characteristics of the contents of 18 investigated compounds in leaves, stem and root of five Cassia species.
Herbal Drug Development: Challenges and Opportunities
Published in Megh R. Goyal, Durgesh Nandini Chauhan, Plant- and Marine-Based Phytochemicals for Human Health, 2018
Bhagyashree Kamble, Neelam Athawale, Anand Gugale, Ashika Advankar, Ashwini Ghagare, Shankar Katekhaye, Abhishek Kulkarni, Priyanka Kanukuntla
Chemometrics is a statistical approach to analyze instrumental data. It gives more faster and precise assessment of components present in product or physical or sensory properties. These components under assessment can be carbohydrate, fat, fiber, or dairy product. Applications include: classification of sample into several categories and prediction of property of interested compound.
Characterizing Outdoor Air Using Microbial Volatile Organic Compounds (MVOCs)
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Sonia Garcia-Alcega, Frédéric Coulon
Chemometrics is the mathematical and statistical analysis of the chemical data obtained from the chromatogram of a sample. By chemometrics, the maximum information about the compounds of study is extracted by optimizing signal and data analysis processes and performing multivariate analysis in order to study chemical trends (Vivó-Truyols et al., 2005). The use of this approach to identify microorganisms that are in the air is receiving increased attention as chemometrics is a cost effective and fast analysis in comparison with the more traditional molecular or cell culturing techniques (Lemfack et al., 2014).
A new class of efficient and debiased two-step shrinkage estimators: method and application
Published in Journal of Applied Statistics, 2022
Muhammad Qasim, Kristofer Månsson, Pär Sjölander, B. M. Golam Kibria
The primary aim of this article is to introduce a new two-step shrinkage estimator (TSSE) that provides an alternative method to mitigate the problem of multicollinearity in the multiple linear regression model. This new method encompasses OLSE and RRE as exceptional cases. In addition, we proposed an almost unbiased version of the TSSE, and this estimator will be called debiased TSSE (DTSSE). We compare the matrix mean square error (MMSE) properties analytically and prove the superiority of our new methods under certain conditions. Then, we show the superiority of the proposed estimator in finite samples using a Monte Carlo simulation study. Finally, we apply the methods on two different chemometric datasets. Regression models are widely used in chemistry to build efficient and robust prediction models. In the first example, we use the classical Portland cement data analyzed by Lukman et al. [21]. This example models the heat evolved after 180 days of curing cement, measured in calories per gram of cement by four highly correlated variables. In the second illustration, we use a dataset from Qasim et al. [32] where the dependent variable corresponds to the boll weight during the cropping season and experimental material consisted of thirty-two upland cotton accessions. Five highly correlated explanatory variables explain the biochemical traits. In both examples, the benefit of the new estimator is compared to the OLSE and other different shrinkage estimators.
Untargeted metabolomics-assisted comparative cytochrome P450-dependent metabolism of fenbendazole in human and dog liver microsomes
Published in Xenobiotica, 2022
Young-Heun Jung, Dong-Cheol Lee, Jong Oh Kim, Ju-Hyun Kim
Drug metabolite identification studies have been highlighted from the perspective of drug safety evaluations (FDA 2020; Schadt et al. 2018). HRMS combined with a chemometrics approach has enabled remarkable advancements in the methodology of drug metabolite identification (Meyer and Maurer 2012; Wen and Zhu 2015). Owing to such background, the current study conducted the global profiling of FBZ metabolites and metabolic comparisons between humans and dogs using a metabolomics approach. Untargeted metabolomics combined with multivariate analysis is an unbiased method for exploring and characterising differences between experimental groups in a metabolic environment (Chen et al. 2007; Meyer and Maurer 2012). Several considerations were applied throughout the process, from sample preparation to data analysis, to ensure the quality and reliability of the results. The efficiency of data acquisition was improved by applying an exclusion list to decrease the unwanted MS/MS acquisition throughout the data-dependent MS2 acquisition (Defossez et al. 2023). After mass spectral data acquisition and data processing, the variables corresponding to the parent molecular ions of FBZ were excluded before multivariate analysis to improve drug metabolite detection and eliminate potential bias, as parent ion signals from in vitro incubation systems are exceptionally higher than others.
Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer
Published in Expert Review of Molecular Diagnostics, 2019
Abdullah C.S. Talari, Shazza Rehman, Ihtesham U Rehman
Spectral range was analyzed between 600 and 3400 cm−1. Breast biopsies were examined using DXR Raman confocal microscope. A total of 400 spectra were collected for this study and mean spectrum of each subtype was collected for comparison studies. AI approach involved in noise filtering, fluorescence removal, and spectrum normalization. Substrate subtraction and peak measurements were accomplished using OMNIC AtlµsTM software and TQ Analyst (Thermo Scientific, Madison, WI, USA). Data analysis was performed using Unscrambler X 10.2 software (Camo software, Oslo, Norway). Chemometric methods such as PCA, LDA, and cluster analysis (CA) were applied in data analysis. The data were first base line corrected and unit vector normalized (UVN). Every PCA was setup with a minimum of 10 orthogonal variables depending on the spectral region used, where the number of PCs chosen for each setup described >99% of the variation. PCA was performed on high-wavenumber (3200–2600 cm−1), amide I (1750–1500 cm−1), amide III (1400–1200 cm−1), and nucleic acid (980–610 cm−1) regions. CA was performed on full spectral range (3200–400 cm−1) using Wards’ method squared Euclidean distance. Linear discrimination analysis (LDA) was applied on classification and prediction accuracy. LDA model was setup over full spectral range. Spectral processing for all LDA models were baseline correction and unit vector normalization. Five samples from each group were left out at each pass until total number of 30 spectra of each subtype predicted.