Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review
Abstract
:1. Introduction
2. The Outline of Chemometric Tools
3. Selection
4. Classification
5. Properties (Prediction and Correlation)
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Classification Object | Chemometric Tool | Evaluated Parameters | Results—Groups of Solvents | Ref. |
---|---|---|---|---|
83 organic solvents | PCA |
| 9 groups of solvents:
| Chastrette et al. (1985) [43] |
101 organic solvents | Parker–Reichardt classification | correlation between dielectric β parameter and empirical solvent polarity parameter | 4 groups (and 2 subgroups) of solvents:
| Dutkiewicz (1990) [44] |
51 solvents | KNN | Empirical scale parameters:
| 8 groups of solvents:
| Pytela (1989) [45] |
152 organic solvents | KNN, CP-ANN, QSPR | 4 molecular descriptors (theoretical descriptions of the molecular structure) | 5 groups of solvents:
| Gramatica et al. (1999) [46] |
76 solvents | ANN | 9 characteristics (application in a field of C60 fullerene solubility) | 9 groups of solvents:
| Pushkarova and Kholin (2014) [47] |
236 industrial solvents | PCA, CA | quantum and experimental parameters | 10 groups of solvents:
| Levet et al. (2016) [48] |
72 solvents | FCM, FLDA | Chemical parameters connected with polarity and selectivity developed by Snyder (related to different polar interactions):
| FCM—8 groups (selected examples):
| Guidea and Sârbu(2020) [49] |
Predicted Property | Chemometric Tools | Evaluated Objects | Way of Estimation | Ref. |
---|---|---|---|---|
Carbon dioxide solubility | RB, MLP, MQR, MPE |
| experimental thermodynamic data and molecular structure information | Torrecilla et al. (2008) [67] |
Melting point | ANN | 97 imidazolium salts with varied anions | 14 molecular descriptors | Torrecilla et al. (2008) [68] |
Viscosity | ANN | 58 ionic liquids at several temperatures | molecular mass of the anion and cation, the mass connectivity index, and the density at 298 K | Valderrama et al. (2011) [69] |
Electric conductivity | MLR, BP-ANN | 35 ILs at different temperatures | structural descriptors | Cao et al. (2013) [70] |
Density | ER, ANN | mixtures of ionic liquids and molecular solvents (water, alcohols, ketones, ethers, hydrocarbons, esters, and acetonitrile) | molar mass, critical volume, temperature, acentric factor of each component of the IL mixtures | Huang et al. (2014) [71] |
Design of ionic liquids | PCA, CA | 172 ILs | structural similarity and identification of structure aspects responsible for a given IL physicochemical properties (viscosity, n-octanol–water partition coefficient, solubility and enthalpy of fusion via ILPC predictor) | Barycki et al. (2016) [72] |
Lipophilicity | QSPR, PCA | selected ionic liquid (only imidazolium-based cations) | comparison of hydrophobic or hydrophilic character according to some methods: chromatographic analysis, statistical, and chemometric approach | Studzińska et al. (2007) [73] |
Toxicity | PCR, PLS, decision tree(s) model | various combinations of cations (imidazole, pyridinium, quinolinium, ammonium, phosphonium) and anions (BF4, Cl, PF6, Br, CFNOS, NCN2, C6F18PBF4, C6F18P) | molecular descriptors and EC50 concentrations for inhibition of acetylcholinesterase | Ž. Kurtanjek (2014) [74] |
Toxicity | PCA | 375 ILs with six different types of cations namely, imidazolium, ammonium, phosphinium, pyridinium, pyrolidinium, and sulfonium | multiple endpoints for various organisms based on WHIM descriptors | Sosnowska et al. (2014) [75] |
Toxicity | QSAR, MLR, ELM | 160 ILs with 57 cations and 21 anions | toxicity towards AChE based on theSEP area and the screening charge density distribution area (S ) descriptors | Zhu et al. (2019) [76] |
Toxicity | QSPR, MLR | 304 ILs of different combinations of 8 cations (ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium) and 12 anions (chloride, bis(trifluoromethylsulfonyl) amide, bromide, iodide ion, sulfonate, borate, phosphate, fatty acid, dicyanamide, formate, thiocyanate, acetate, etc.) | toxicity against leukaemia rat cell line IPC-81 (logEC50) based on 33 descriptors describing the structural features of ionic liquids related to toxicity (i.e., chain length of the cationic head group) | Wu et al. (2020) [77] |
Abbreviations: AChE—Acetylcholinesterase; BP-ANN—Back Propagation Artificial Neural Network; ELM—Extreme Learning Machine; ILPC—Ionic Liquid PhysicoChemical; MPE—Mean Prediction Error; SEP—Surface Electrostatic Potential; WHIM descriptors—Weighted Holistic Invariant Molecular descriptors |
Chemical Compound | Chemometric Tool | Organism | Toxicity Results | Ref. |
---|---|---|---|---|
Metals as: TI, Cd, and Ag | RSM | growth of cabbage seedlings | Ag is observed to be the most toxic, while Tl and Cd, although toxic, exhibited fairly similar effects. | Allus et al. (1988) [78] |
Nitrobenzenes | LS-SVM, QSPR, PLS, PCA, GA-PLS, MLR | Tetrahymena pyriformis [79] | n/a | Niazi et al. (2007) [80] |
Organic compounds (including some pharmaceuticals) | QSTR, PLS | human (human lethal concentration) | The ETA models suggest that the toxicity increases with bulk, chloro (hydrophobic) functionality, presence of heteroatoms within a chain or a ring and unsaturation, and decreases with hydroxyl (polar) functionality and branching. | Roy and Ghosh (2008) [81] |
Chemical compounds | SVM, ANN | Pimephales promelas | n/a | Tan et al. (2010) [82] |
Organic chemicals | QSAR, MLR, PLS, GFA, G/PLS | Daphnia magna | Higher lipophilicity and electrophilicity, less negative charge surface area and presence of ether linkage, hydrogen bond donor groups and acetylenic carbons are responsible for greater toxicity of chemicals. Diversity in chemically different compounds in mechanisms of toxic actions is observed. | Kar and Roy (2010) [83] |
Per- and polyfluorinated (PFCs) chemicals | PCA, QSAR, MLR, GA, | rodents (oral) | The importance of negative hydrophobicity and positive electronegativity for the overall toxicity of PFCs for rodents. | Bhhatarai and Gramatica (2011) [84] |
Herbicides | ANN, QSAR | rat (oral) | n/a | Hamadache et al. (2016) [85] |
Agrochemicals (fungicides, herbicides, insecticides, and microbiocides) | QSAR | Daphnia magna | The toxicity increases with lipophilicity and decreases with polarity. | Khan et al. (2019) [86] |
Silver nanoparticles | CA, PCA | links between ecotoxicity and physicochemical features (Daphnia magna, Thamnocephalus platyurus, and Daphnia galeata) | n/a | Nedyalkova et al. (2017) [87] |
Silver nanoparticles | PCA, CA, k-means clustering, MLR | Daphnia magna, Thamnocephalus platyurus, Escherichia coli, Pseudomonas fluorescens, Pseudokirchneriella subcapitata, Pseudomonas putida, Pseudomonas aeruginosa, Staphylococcus aureus, mammalian cells, algae, yeast, and fungi | The relation AT/ZP (acute toxicity measure, EC50/LC50/zeta potential of nanomaterial in the test) is not very indicative for the toxic impact of the AgNPs studied. | Nedyalkova et al. (2019) [88] |
Abbreviations: ETA—Extended Topochemical Atom; GA-PLS—Genetic Algorithm-Partial Least Square; GFA—Genetic Function Approximation; G/PLS—Genetic Partial Least Squares; QSTR—Quantitative Structure Toxicity Relationship; RSM—Response Surface Methodology |
Partition Coefficient | Chemometrics Tool | Evaluated Objects | Way of Estimation | Ref. |
---|---|---|---|---|
n-octanol-ir partition coefficient | QSAR/QSPR, PCA, PCR | chloronaphthalene congeners | 190 different quantum-chemical, thermodynamical, and topological characteristics of chloronaphthalenes as descriptors | Puzyn and Falandysz (2005) [91] |
Water-polydimethylsiloxane partition coefficient | QSPR, GA, MLR, ANN | organic compounds | molecular descriptors: minimum atomic orbital electronic population, Kier shape index, polarity parameter/square distance, and complementary information content | Golmohammadi and Dashtbozorgi (2010) [92] |
n-octanol-water partition coefficient | LS-SVM, QSPR, MLR, SVR, ANN | organic compounds (derivative phenolic compounds) | n/a | Goudarzi and Goodarzi (2008) [21] |
n-octanol-water partition coefficient | QSPR, mRMR-GA-SVR | aromatic compounds | 68 molecular descriptors derived solely from the structures of the aromatic compounds | Yang et al. (2008) [93] |
n-octanol-water partition coefficient | QSPR, MLR/PLS/RBF-PLS | organic compounds | Goudarzi and Goodarzi (2010) [94] | |
n-octanol-water partition coefficient | QSAR, CoMFA, CoMSIA | 21 polychlorinated naphthalenes (PCNs) congener | 3D descriptors according to the experimental values of logKOW for 21 PCNs | Gu et al. (2017) [95] |
polyurethane foam-air partition coefficients | QSPR, MLR, ANN, SVM | 170 organic compounds comprising 9 distinct classes (PAHs, benzenes, esters, aliphatic and cyclic hydrocarbons, polychlorinated biphenyls, musk, nitrogen and sulphur compounds, pesticides, other compounds) | 368 molecular descriptors | Zhu et al. (2020) [96] |
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Bystrzanowska, M.; Tobiszewski, M. Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review. Symmetry 2020, 12, 2055. https://doi.org/10.3390/sym12122055
Bystrzanowska M, Tobiszewski M. Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review. Symmetry. 2020; 12(12):2055. https://doi.org/10.3390/sym12122055
Chicago/Turabian StyleBystrzanowska, Marta, and Marek Tobiszewski. 2020. "Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review" Symmetry 12, no. 12: 2055. https://doi.org/10.3390/sym12122055