Chapter 7 - Chemoinformatics—multivariate mathematical–statistical methods for data evaluation
Publisher Summary
This chapter provides an introduction to chemoinformatics covering multivariate mathematical–statistical methods for data evaluation. It is expedient to distinguish the variables on the basis of three scales: nominal, ordinal, and numeric. The nominal scales are qualitative only and can be measured solely in terms of whether the individual items belong to some distinctively different categories. The ordinal scales are also qualitative, but they can rank (order) the items measured in terms of which has less and which has more of the quality represented by the variable, and the numerical scale is quantitative in nature. There are numerous applications evaluating results of instrumental–analytical methods such as mass spectrometry, NMR spectroscopy, and chromatography using chemoinformatics. Combinations involving NMR spectroscopy and chromatography have many applications in the biomedical field. On the other hand, although chemometric techniques are frequently applied to analyze mass spectral data, applications in the biomedical field are rare. Multivariate data analysis is applied to mass spectrometry, especially revealing relationships of mass spectral data and chemical structure. Pyrolysis mass spectrometry and chemometrics are coupled to analyze the adulteration of orange juice quantitatively, to test the authenticity of honey, and to discriminate the unfractionated plant extracts.
References (0)
Cited by (38)
Integrated approach for sustainable degradation of tolperisone hydrochloride from water by photodegradation: Chemometrics, chemical kinetics, intermediates, and environmental toxicity assessment
2024, Journal of Photochemistry and Photobiology A: ChemistryEven though we have only one planet to live on, environmental pollution is still an urgent global issue. Among all-natural threatened resources, the drinkable water has one of the most burdening influences on the life quality and economy. The pharmaceutically active ingredients are the most concerning pollutants. In this study, heterogeneous photocatalysis was used as a sustainable alternative for tolperisone hydrochloride (TLP) removal from water. The degradation pathway of TLP was also analyzed using various scavengers and it was found that the contribution of reactive species changed in following order: ˃ ≫ > . Furthermore, 12 photodegradation intermediates of TLP were identified using LC–ESI–MS/MS. In addition, the toxicity of TLP and its degradation intermediates were examined on barley, using chemometrics. The most significant variations were observed on barley plant after 4 days of germination and on the root, during the barley biomass production. The results also showed that TLP and its intermediates are harmful to the plants, due to deteriorating water uptake with toxic TLP-compounds that reach the vital parts of plants. Also, slightly variations in total nitrogen amount were observed. The high genetic variability efficiently guards barley from various environmental stresses.
Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review
2024, Spectrochimica Acta - Part A: Molecular and Biomolecular SpectroscopyFood safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
Chemical profiling and computational identification of potential antibacterials from Adenostemma species
2023, South African Journal of BotanyAdenostemma has a long history of use in folk medicine to treat conditions such as fever, inflammation, and lung damage. Despite their importance, no scientific studies have reported their antibacterial properties, and most previous studies have used bioassay-guided methods for chemical investigations. Therefore, the aims of this study were to analyze the chemical profiles of different Adenostemma species using metabolomics method and to investigate their antibacterial properties using an in silico method. Leaf samples were extracted with methanol using sonication and analyzed using liquid chromatography coupled with mass spectrometry (LC-MS/MS). Based on these results, 35 putative compounds were identified, many of which have not been previously reported in Adenostemma species. Multivariate statistical analysis revealed separate clusters for the three species, confirming the substantial chemical differences between their extracts. Molecular docking analysis showed that dicaffeoylquinic acid had the strongest binding for Staphylococcus aureus UDP-GlcNAc 2-epimerase (PDB ID:5ENZ). Eriodictyol 7-O-sophoroside and pectolinarigenin exhibited the best docking scores for Pseudomonas aeruginosa pyochelin synthase PchD (PDB ID:7TYB) and Escherichia coli LpxD acyltransferase (PDB ID:6P86) respectively. Molecular dynamics simulations revealed that all ligand-protein complexes were stable. These results indicated that metabolomics and in silico methods have greatly benefited the rapid screening of metabolites and in preliminary studies to determine the antibacterial potential of Adenostemma as a medicinal plant.
<sup>19</sup>F Solid-state NMR characterization of pharmaceutical solids
2022, Solid State Nuclear Magnetic ResonanceCitation Excerpt :This allows straightforward detection of the component of interest in the mixture. Moreover, multivariate chemometric analysis has also been demonstrated [92,93]. NMR spectra provide a wealth of information of the sample, and chemometric models can analyze the differences in the spectral feature to achieve quantification purposes [94].
Solid-state NMR has been increasingly recognized as a high-resolution and versatile spectroscopic tool to characterize drug substances and products. However, the analysis of pharmaceutical materials is often carried out at natural isotopic abundance and a relatively low drug loading in multi-component systems and therefore suffers from challenges of low sensitivity. The fact that fluorinated therapeutics are well represented in pipeline drugs and commercial products offers an excellent opportunity to utilize fluorine as a molecular probe for pharmaceutical analysis. We aim to review recent advancements of 19F magic angle spinning NMR methods in modern drug research and development. Applications to polymorph screening at the micromolar level, structural elucidation, and investigation of molecular interactions at the Ångström to submicron resolution in drug delivery, stability, and quality will be discussed.
Chemometric approach in environmental pollution analysis: A critical review
2022, Journal of Environmental ManagementWith the ever-increasing global population and industrialization, it has become a call of the hour to start taking care of the environment to balance the ecosystem. For this, effective monitoring and assessment are required, which involves collecting and measuring environmental details, temporal and spatial readings of environmental data, and parameters. However, assessment of the environment is very tedious as it includes monitoring target analytes, identifying their sources, and reporting, which invariably implies that detailed environmental monitoring would be an intricate and expensive process. The traditional protocols in environmental measures are often manual and time demanding, which makes it further difficult. Moreover, several changes also occur within the environment, which could be chemical, physical, or biological, and since these environmental impacts are often cumulative, it becomes difficult to measure an isolated system. Furthermore, the chances of skipping significant results and trends become high. Also, experimental data obtained from the environmental analysis are usually non-linear and multi-variant due to different associations among various contributing variables. Therefore, it is implied that accurate measurements and environment monitoring are not using traditional analytical protocols. Thus, the need for a chemometric approach in environmental pollution analysis becomes paramount due to the inherent limitations associated with the conventional approach of analyzing environmental datasets. Chemometrics has appeared as a potential technique, which enhances the particulars of the chemical datasets by using statistical and mathematical analysis methods to analyze chemical data beyond univariate analysis. Utilizing chemometrics to study the environmental data is a revolutionary idea as it helps identify the relationship between sources of contaminations, environmental drivers, and their impact on the environment. Hence, this review critically explores the concept of chemometrics and its application in environmental pollution analysis by briefly highlighting the idea of chemometrics, its types, applications, advantages, and limitations in the environmental domain. An attempt is also made to present future trends in applications of chemometrics in environmental pollution analysis.
A review on chemometric techniques with infrared, Raman and laser-induced breakdown spectroscopy for sorting plastic waste in the recycling industry
2022, Resources, Conservation and RecyclingMismanagement of plastic waste globally has resulted in a multitude of environmental issues, which could be tackled by boosting plastic recycling rates. Chemometrics has emerged as a useful tool for boosting plastic recycling rates by automating the plastic sorting and recycling process. This paper will comprehensively review the recent works applying chemometric methods to plastic waste sorting. The review begins by introducing spectroscopic methods and chemometric tools that are commonly used in the plastic chemometrics literature. The spectroscopic methods include near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS). The chemometric tools include principal component analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS), k-nearest neighbors (k-NN), support vector machines (SVM), random forests (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs) and K-means clustering. This review revealed four main findings. (1) The scope of plastic waste should be expanded in terms of types, contamination and degradation level to mirror the heterogeneous plastic waste received at recycling plants towards understanding potential application in the recycling industry. (2) The use of hybrid spectroscopic method could potentially overcome the limitations of each spectroscopic methods. (3) Develop an open-sourced standardized database of plastic waste spectra would help to further expand the field. (4) There is limited use of more novel machine learning tools such as deep learning for plastic sorting.