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Special Issue "Chemometrics in Analytical Chemistry"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Cross-Field Chemistry".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 14904

Special Issue Editor

Prof. Dr. Lukasz Komsta
E-Mail Website
Guest Editor
Faculty of Pharmacy, Medical University of Lublin, Jaczewskiego 4, 20-090 Lublin, Poland
Interests: chemometrics; chemoinformatics; chromatography; pharmaceutical analysis; pharmacology

Special Issue Information

Dear Colleagues,

Due to the enormous development of computer technology during the last decades, chemometrics has become the leading and preferred methodology for the experimental data analysis, especially in analytical chemistry. A significant interest in chemometric methods is also connected with the availability of open-source software, removing the financial barriers of expensive software packages. Today, chemometric methods are available for every interested researcher equipped with an average computer. Meanwhile, current supercomputers also have a hard task—they allow us to analyze really huge datasets (a topic reserved for our imagination and science-fiction literature not so long ago).

Therefore, chemometrics can be present everywhere—from simple experimental designs, through multivariate analysis of collected data, up to huge datasets containing millions of samples or variables.

This Special Issue focuses on all aspects of chemometrics in analytical chemistry—experimental design, instrumental data analysis, signal processing, image processing, multivariate data mining, neural networks, genetic algorithms, multi-way methods, and multivariate curve resolution—both in context of new methods and algorithms, as well as novel applications of known approaches. Reviews are also welcome.

Prof. Dr. Lukasz Komsta
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • chemometrics
  • data mining
  • experimental design
  • signal processing
  • image processing

Published Papers (17 papers)

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Research

Article
Volatile Profiling of Magnolia champaca Accessions by Gas Chromatography Mass Spectrometry Coupled with Chemometrics
Molecules 2022, 27(21), 7302; https://doi.org/10.3390/molecules27217302 - 27 Oct 2022
Viewed by 451
Abstract
Magnolia champaca (L.) Baill. ex Pierre of family Magnoliaceae, is a perennial tree with aromatic, ethnobotanical, and medicinal uses. The M. champaca leaf is reported to have a myriad of therapeutic activities, however, there are limited reports available on the chemical composition of [...] Read more.
Magnolia champaca (L.) Baill. ex Pierre of family Magnoliaceae, is a perennial tree with aromatic, ethnobotanical, and medicinal uses. The M. champaca leaf is reported to have a myriad of therapeutic activities, however, there are limited reports available on the chemical composition of the leaf essential oil of M. champaca. The present study explored the variation in the yield and chemical composition of leaf essential oil isolated from 52 accessions of M. champaca. Through hydrodistillation, essential oil yield was obtained, varied in the range of 0.06 ± 0.003% and 0.31 ± 0.015% (v/w) on a fresh weight basis. GC-MS analysis identified a total of 65 phytoconstituents accounting for 90.23 to 98.90% of the total oil. Sesquiterpene hydrocarbons (52.83 to 65.63%) constituted the major fraction followed by sesquiterpene alcohols (14.71 to 22.45%). The essential oils were found to be rich in β-elemene (6.64 to 38.80%), γ-muurolene (4.63 to 22.50%), and β-caryophyllene (1.10 to 20.74%). Chemometrics analyses such as PCA, PLS-DA, sPLS-DA, and cluster analyses such as hierarchical clustering, i.e., dendrogram and partitional clustering, i.e., K-means classified the essential oils of M. champaca populations into three different chemotypes: chemotype I (β-elemene), chemotype II (γ-muurolene) and chemotype III (β-caryophyllene). The chemical polymorphism analyzed in the studied populations would facilitate the selection of chemotypes with specific compounds. The chemotypes identified in the M. champaca populations could be developed as promising bio-resources for conservation and pharmaceutical application and further improvement of the taxa. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Integrated Hydrogeological, Hydrochemical, and Isotopic Assessment of Seawater Intrusion into Coastal Aquifers in Al-Qatif Area, Eastern Saudi Arabia
Molecules 2022, 27(20), 6841; https://doi.org/10.3390/molecules27206841 - 12 Oct 2022
Viewed by 566
Abstract
Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion [...] Read more.
Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses (18O and 2H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl, Br), and seawater fraction (fsw) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl, Br concentration, and an increasing fsw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low fsw and EC, TDS, Cl, and Br, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18O and 2H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Effect-Directed Profiling of Strawberry Varieties and Breeding Materials via Planar Chromatography and Chemometrics
Molecules 2022, 27(18), 6062; https://doi.org/10.3390/molecules27186062 - 16 Sep 2022
Viewed by 546
Abstract
Strawberries are an important fruit in the European diet because of their unique taste and high content of essential nutrients and bioactive compounds. The anthocyanins are known to be colorful phenolics in strawberries. In 17 samples of six strawberry cultivars produced in Serbia, [...] Read more.
Strawberries are an important fruit in the European diet because of their unique taste and high content of essential nutrients and bioactive compounds. The anthocyanins are known to be colorful phenolics in strawberries. In 17 samples of six strawberry cultivars produced in Serbia, i.e., the common varieties Alba, Asia, and Clery as well as promising breeding materials (11.29.11, 11.34.6, and 11.39.3), the anthocyanin profile as well as antimicrobial and antioxidative activity profiles were determined. All investigated extracts showed antioxidative and antibacterial activities against Gram-negative Aliivibrio fischeri. The responses were quite similar in number and intensity. The HPTLC-DPPH scavenging assay and HPTLC-Aliivibrio fischeri bioassay coupled with high-resolution mass spectrometry identified pelargonidin-3-O-glucoside (Pg-3-glc) as the main anthocyanin and prominent antioxidative and antimicrobial compound in strawberries. The density functional theory calculations at the M06-2X/6-31+G(d,p) level showed that Pg-3-glc quenches free radicals via sequential proton loss electron transfer mechanism in water and in pentyl ethanoate, where the 5-OH group is the most reactive site for proton and hydrogen atom transfer. The results were confirmed via spectrophotometry. The highest total phenolic content was found in Clery and 11.39.3, while statistically significant differences between the genotypes regarding the antioxidant activity were not confirmed. Although very similar in the anthocyanin, antioxidative, and antimicrobial profile patterns, the strawberry genotypes were successfully classified using principal component analysis. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data
Molecules 2022, 27(18), 5827; https://doi.org/10.3390/molecules27185827 - 08 Sep 2022
Viewed by 713
Abstract
Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification [...] Read more.
Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Quality Evaluation of Tetrastigmae Radix from Two Different Habitats Based on Simultaneous Determination of Multiple Bioactive Constituents Combined with Multivariate Statistical Analysis
Molecules 2022, 27(15), 4813; https://doi.org/10.3390/molecules27154813 - 27 Jul 2022
Viewed by 608
Abstract
Tetrastigmae Radix, also known as Sanyeqing (SYQ) in Chinese, is an important traditional Chinese medicine with a long history. Tetrastigma hemsleyanum Diels et Gilg mainly grows in the south of the Yangtze River and is widely distributed. The content of bioactive constituents in [...] Read more.
Tetrastigmae Radix, also known as Sanyeqing (SYQ) in Chinese, is an important traditional Chinese medicine with a long history. Tetrastigma hemsleyanum Diels et Gilg mainly grows in the south of the Yangtze River and is widely distributed. The content of bioactive constituents in SYQ varies greatly in different habitats, and there are obvious differences in the content of bioactive constituents between southwestern SYQ (WS) and southeastern SYQ (ES). To distinguish and evaluate the quality of ES and WS, an analytical method based on ultrafast performance liquid chromatography coupled with triple quadrupole-linear ion trap mass spectrometry (UFLC-QTRAP-MS/MS) was established for the simultaneous determination of 60 constituents including 25 flavonoids, 9 phenolic acids, 15 amino acids, and 11 nucleosides in 47 samples from ES and WS. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA), t-test, and gray correlation analysis (GRA) were used to discriminate and evaluate the ES and WS samples based on the contents of 60 constituents. The results showed that there were significant differences in the bioactive constituents between ES and WS, and ES was superior to WS in terms of quality evaluation. This study not only provides basic information for differentiating ES and WS but also provides a new perspective for the comprehensive evaluation and quality control of SYQ from two different habitats. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Discrimination and Prediction of Lonicerae japonicae Flos and Lonicerae Flos and Their Related Prescriptions by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Combined with Multivariate Statistical Analysis
Molecules 2022, 27(14), 4640; https://doi.org/10.3390/molecules27144640 - 20 Jul 2022
Viewed by 610
Abstract
LJF and LF are commonly used in Chinese patent drugs. In the Chinese Pharmacopoeia, LJF and LF once belonged to the same source. However, since 2005, the two species have been listed separately. Therefore, they are often misused, and medicinal materials are [...] Read more.
LJF and LF are commonly used in Chinese patent drugs. In the Chinese Pharmacopoeia, LJF and LF once belonged to the same source. However, since 2005, the two species have been listed separately. Therefore, they are often misused, and medicinal materials are indiscriminately put in their related prescriptions in China. In this work, firstly, we established a model for discriminating LJF and LF using ATR-FTIR combined with multivariate statistical analysis. The spectra data were further preprocessed and combined with spectral filter transformations and normalization methods. These pretreated data were used to establish pattern recognition models with PLS-DA, RF, and SVM. Results demonstrated that the RF model was the optimal model, and the overall classification accuracy for LJF and LF samples reached 98.86%. Then, the established model was applied in the discrimination of their related prescriptions. Interestingly, the results show good accuracy and applicability. The RF model for discriminating the related prescriptions containing LJF or LF had an accuracy of 100%. Our results suggest that this method is a rapid and effective tool for the successful discrimination of LJF and LF and their related prescriptions. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Qualitative Analysis and Componential Differences of Chemical Constituents in Lysimachiae Herba from Different Habitats (Sichuan Basin) by UFLC-Triple TOF-MS/MS
Molecules 2022, 27(14), 4600; https://doi.org/10.3390/molecules27144600 - 20 Jul 2022
Cited by 1 | Viewed by 648
Abstract
Lysimachiae Herba (LH), called Jinqiancao in Chinese, is an authentic medical herb in Sichuan Province often used in the prescription of traditional Chinese medicine (TCM). However, in recent years, there has been a lack of comprehensive research on its chemical components. In addition, [...] Read more.
Lysimachiae Herba (LH), called Jinqiancao in Chinese, is an authentic medical herb in Sichuan Province often used in the prescription of traditional Chinese medicine (TCM). However, in recent years, there has been a lack of comprehensive research on its chemical components. In addition, the landform of Sichuan Province varies greatly from east to west and the terrain is complex and diverse, which has an important influence on the chemical constituents in LH. In this study, ultrafast liquid chromatography coupled with triple-quadrupole time-of-flight tandem mass spectrometry (UFLC-triple TOF-MS/MS) was used to analyze the samples of LH from eight different habitats in Sichuan Basin. The constituents were identified according to the precise molecular weight, the fragment ions of each chromatographic peak and the retention time of the compound obtained by high-resolution mass spectrometry, combined with software database searches, standard comparisons and the related literature. Differential chemical constituents were screened using partial least squares discriminant analysis (PLS-DA) and t-tests. The results showed that a total of 46 constituents were identified and inferred, including flavonoids, phenolic acids, amino acids, tannins, fatty acids and coumarins; the fragmentation pathways of the main constituents were preliminarily deduced. According to the variable importance in projection (VIP) and p-values, four common differential constituents were screened out, 2-O-galloylgalactaric acid, quercetin 3-O-xylosyl-rutinoside, nicotiflorin and kaempferol 3-rutinosyl 7-O-alpha-l-rhamnoside. This study provides basic information for the establishment of a comprehensive quality evaluation system for LH. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
DSC, FT-IR and NIR with Chemometric Assessment Using PCA and HCA for Estimation of the Chemical Stability of Oral Antidiabetic Drug Linagliptin in the Presence of Pharmaceutical Excipients
Molecules 2022, 27(13), 4283; https://doi.org/10.3390/molecules27134283 - 03 Jul 2022
Viewed by 778
Abstract
Pharmaceutical excipients should not interact with active substances, however, in practice, they sometimes do it, affecting the efficacy, stability and safety of drugs. Thus, interactions between active substances and excipients are not desirable. For this reason, two component mixtures of oral antidiabetic drug [...] Read more.
Pharmaceutical excipients should not interact with active substances, however, in practice, they sometimes do it, affecting the efficacy, stability and safety of drugs. Thus, interactions between active substances and excipients are not desirable. For this reason, two component mixtures of oral antidiabetic drug linagliptin (LINA) with four excipients of different reactivity, i.e., lactose (LAC), mannitol (MAN), magnesium stearate (MGS) and polyvinylpyrrolidone (PVP), were prepared in a solid state. A high temperature and a high humidity of 60 °C and 70% RH, respectively, were applied as stressors in order to accelerate the potential interactions between LINA and excipients. Differential scanning calorimetry (DSC) as well as Fourier transform infrared (FT-IR) and near infrared (NIR) spectroscopy were used to estimate the changes due to potential interactions. In addition, chemometric computation of the data with principal component analysis (PCA) and hierarchical cluster analysis (HCA) was applied to adequately interpret the findings. Of the excipients used in the present experiment, all of them were not inert in relation to LINA. Some of the interactions were shown without any stressing, whereas others were observed under high-temperature/high-humidity conditions. Thus, it could be concluded that selection of appropriate excipients for LINA is very important question to minimize its degradation, especially when new types of formulations with LINA are being developed and manufactured. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia
Molecules 2022, 27(13), 4220; https://doi.org/10.3390/molecules27134220 - 30 Jun 2022
Viewed by 832
Abstract
Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment [...] Read more.
Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra
Molecules 2022, 27(12), 3954; https://doi.org/10.3390/molecules27123954 - 20 Jun 2022
Cited by 1 | Viewed by 821
Abstract
Poplar bark and leaves can be an attractive source of salicylates and other biologically active compounds used in medicine. However, the biochemical variability of poplar material requires a standardization prior to processing. The official analytical protocols used in the pharmaceutical industry rely on [...] Read more.
Poplar bark and leaves can be an attractive source of salicylates and other biologically active compounds used in medicine. However, the biochemical variability of poplar material requires a standardization prior to processing. The official analytical protocols used in the pharmaceutical industry rely on the extraction of active compounds, which makes their determination long and costly. An analysis of plant materials in their native state can be performed using vibrational spectroscopy. This paper presents for the first time a comparison of diffuse reflectance in the near- and mid-infrared regions, attenuated total reflection, and Raman spectroscopy used for the simultaneous determination of salicylates and flavonoids in poplar bark and leaves. Based on 185 spectra of various poplar species and hybrid powdered samples, partial least squares regression models, characterized by the relative standard errors of prediction in the 4.5–9.9% range for both calibration and validation sets, were developed. These models allow for fast and precise quantification of the studied active compounds in poplar bark and leaves without any chemical sample treatment. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Non-Destructive Analysis of Chlorpheniramine Maleate Tablets and Granules by Chemometrics-Assisted Attenuated Total Reflectance Infrared Spectroscopy
Molecules 2022, 27(12), 3760; https://doi.org/10.3390/molecules27123760 - 10 Jun 2022
Viewed by 596
Abstract
Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data [...] Read more.
Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data with correlation coefficients (R2) of calibration and cross-validation of 0.9716 and 0.9602, respectively. The model capability for the 42 test set samples was proven with R2 between the reference and model prediction values of 0.9632, and a root-mean-square error of prediction (RMSEP) of 1.7786. The successive PLSR model for granules was constructed from SNV and first derivative pretreated ATR-IR spectral data with two latent factors and correlation coefficients (R2) of calibration and cross-validation of 0.9577 and 0.9450, respectively. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Analytical Performance and Greenness Evaluation of Five Multi-Level Design Models Utilized for Impurity Profiling of Favipiravir, a Promising COVID-19 Antiviral Drug
Molecules 2022, 27(12), 3658; https://doi.org/10.3390/molecules27123658 - 07 Jun 2022
Cited by 2 | Viewed by 910
Abstract
In 2018, the discovery of carcinogenic nitrosamine process related impurities (PRIs) in a group of widely used drugs led to the recall and complete withdrawal of several medications that were consumed for a long time, unaware of the presence of these genotoxic PRIs. [...] Read more.
In 2018, the discovery of carcinogenic nitrosamine process related impurities (PRIs) in a group of widely used drugs led to the recall and complete withdrawal of several medications that were consumed for a long time, unaware of the presence of these genotoxic PRIs. Since then, PRIs that arise during the manufacturing process of the active pharmaceutical ingredients (APIs), together with their degradation impurities, have gained the attention of analytical chemistry researchers. In 2020, favipiravir (FVR) was found to have an effective antiviral activity against the SARS-COVID-19 virus. Therefore, it was included in the COVID-19 treatment protocols and was consequently globally manufactured at large-scales during the pandemic. There is information indigence about FVR impurity profiling, and until now, no method has been reported for the simultaneous determination of FVR together with its PRIs. In this study, five advanced multi-level design models were developed and validated for the simultaneous determination of FVR and two PRIs, namely; (6-chloro-3-hydroxypyrazine-2-carboxamide) and (3,6-dichloro-pyrazine-2-carbonitrile). The five developed models were classical least square (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm-partial least squares (GA-PLS), and artificial neural networks (ANN). Five concentration levels of each compound, chosen according to the linearity range of the target analytes, were used to construct a five-level, three-factor chemometric design, giving rise to twenty-five mixtures. The models resolved the strong spectral overlap in the UV-spectra of the FVR and its PRIs. The PCR and PLS models exhibited the best performances, while PLS proved the highest sensitivity relative to the other models. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures
Molecules 2022, 27(12), 3653; https://doi.org/10.3390/molecules27123653 - 07 Jun 2022
Cited by 1 | Viewed by 1388
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same [...] Read more.
Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Statistical FT-IR Spectroscopy for the Characterization of 17 Vegetable Oils
Molecules 2022, 27(10), 3190; https://doi.org/10.3390/molecules27103190 - 17 May 2022
Viewed by 1033
Abstract
Vegetable oils have been utilized for centuries in the food, cosmetic, and pharmaceutical industries, and they contribute beneficially to overall human health, to active skincare, and to effective treatments. Monitoring of the vegetable oils is carried out by the methods described in the [...] Read more.
Vegetable oils have been utilized for centuries in the food, cosmetic, and pharmaceutical industries, and they contribute beneficially to overall human health, to active skincare, and to effective treatments. Monitoring of the vegetable oils is carried out by the methods described in the European Pharmacopeia, which is time-consuming, has poor repeatability, and involves the use of toxic organic chemicals and expensive laboratory equipment. Many successful studies using IR spectroscopy have been carried out for the detection of geographical origin and adulteration as well as quantification of oxidation parameters. The aim of our research was to explore FT-IR spectroscopy for assessing the quality parameters and fatty acid composition of cranberry, elderberry, borage, blackcurrant, raspberry, black mustard, walnut, sea buckthorn, evening primrose, rosehip, chia, perilla, black cumin, sacha inchi, kiwi, hemp, and linseed oil. Very good models were obtained for the α-linolenic acid and linoleic acid contents, with R2 = 1.00; Rv2 values of 0.98, 0.92, 0.89, and 0.84 were obtained for iodine value prediction, stearic acid content, palmitic acid content, and unsaponifiable matter content, respectively. However, we were not able to obtain good models for all parameters, and the use of the same process for variable selection was found to be not suitable for all cases. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Non-Invasive Detection of Anti-Inflammatory Bioactivity and Key Chemical Indicators of the Commercial Lanqin Oral Solution by Near Infrared Spectroscopy
Molecules 2022, 27(9), 2955; https://doi.org/10.3390/molecules27092955 - 05 May 2022
Cited by 1 | Viewed by 777
Abstract
Quality control methods of current traditional Chinese medicine (TCM) preparation is time-consuming and difficult to assess in terms of overall efficiency of the drug. A non-destructive rapid near-infrared spectroscopy detection system for key chemical components and biological activity of Lanqin oral solution (LOS), [...] Read more.
Quality control methods of current traditional Chinese medicine (TCM) preparation is time-consuming and difficult to assess in terms of overall efficiency of the drug. A non-destructive rapid near-infrared spectroscopy detection system for key chemical components and biological activity of Lanqin oral solution (LOS), one of the best-selling TCM formulations, was established for comprehensive quality evaluation. Near infrared spectral scanning was carried out on 101 batches of commercial LOS under the penetrated vial state and traditional state. RAW 264.7 cells were cultured to detect the anti-inflammatory ability of LOS, and the reference concentrations of epigoitrin, geniposide, and baicalin were obtained by HPLC. The quantitative models were optimized by three kinds of variable selection methods. The correlation coefficients of prediction value of the models were greater than 0.94. The system also passed the external validation. The performance of the non-invasive models was similar to the traditional models. The established non-destructive system can be applied to the rapid quality inspection of LOS to avoid unqualified drugs from entering the market and ensure drug effectiveness. The biological activity index of LOS was introduced and predicted by NIRs for the first time, which provides a new idea about the quality control of TCM formulations. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Comparative Metabolite Fingerprinting of Four Different Cinnamon Species Analyzed via UPLC–MS and GC–MS and Chemometric Tools
Molecules 2022, 27(9), 2935; https://doi.org/10.3390/molecules27092935 - 04 May 2022
Cited by 5 | Viewed by 1073
Abstract
The present study aimed to assess metabolites heterogeneity among four major Cinnamomum species, including true cinnamon (Cinnamomum verum) and less explored species (C. cassia, C. iners, and C. tamala). UPLC-MS led to the annotation of 74 secondary [...] Read more.
The present study aimed to assess metabolites heterogeneity among four major Cinnamomum species, including true cinnamon (Cinnamomum verum) and less explored species (C. cassia, C. iners, and C. tamala). UPLC-MS led to the annotation of 74 secondary metabolites belonging to different classes, including phenolic acids, tannins, flavonoids, and lignans. A new proanthocyanidin was identified for the first time in C. tamala, along with several glycosylated flavonoid and dicarboxylic fatty acids reported for the first time in cinnamon. Multivariate data analyses revealed, for cinnamates, an abundance in C. verum versus procyandins, dihydro-coumaroylglycosides, and coumarin in C. cassia. A total of 51 primary metabolites were detected using GC-MS analysis encompassing different classes, viz. sugars, fatty acids, and sugar alcohols, with true cinnamon from Malaysia suggested as a good sugar source for diabetic patients. Glycerol in C. tamala, erythritol in C. iners, and glucose and fructose in C. verum from Malaysia were major metabolites contributing to the discrimination among species. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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Article
Application of a Multilayer Perceptron Artificial Neural Network for the Prediction and Optimization of the Andrographolide Content in Andrographis paniculata
Molecules 2022, 27(9), 2765; https://doi.org/10.3390/molecules27092765 - 26 Apr 2022
Cited by 1 | Viewed by 1142
Abstract
Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and [...] Read more.
Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and selecting suitable sites for the cultivation of A. paniculata with high andrographolide content using a multilayer perceptron artificial neural network (MLP-ANN) approach. A total of 150 accessions of A. paniculata collected from different regions of Odisha and West Bengal in eastern India showed a variation in andrographolide content in the range of 0.28–5.45% on a dry weight basis. The MLP-ANN was trained using climatic factors and soil nutrients as the input layer and the andrographolide content as the output layer. The best topological ANN architecture, consisting of 14 input neurons, 12 hidden neurons, and 1 output neuron, could predict the andrographolide content with 90% accuracy. The developed ANN model showed good predictive performance with a correlation coefficient (R2) of 0.9716 and a root-mean-square error (RMSE) of 0.18. The global sensitivity analysis revealed nitrogen followed by phosphorus and potassium as the predominant input variables influencing the andrographolide content. The andrographolide content could be increased from 3.38% to 4.90% by optimizing these sensitive factors. The result showed that the ANN approach is reliable for the prediction of suitable sites for the optimum andrographolide yield in A. paniculata. Full article
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
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