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Article

Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration

by
Jianhua Ran
1,2,
Victoria G. Pryazhnikova
2 and
Felix Y. Telegin
3,*
1
Department of Chemical Technology of Fibrous Materials, Ivanovo State University of Chemistry and Technology, 153000 Ivanovo, Russia
2
State Key Laboratory of New Textile Materials and Advanced Processing Technologies, Wuhan Textile University, Wuhan 430073, China
3
Department of Inorganic Chemistry, Ivanovo State University of Chemistry and Technology, 153000 Ivanovo, Russia
*
Author to whom correspondence should be addressed.
Colorants 2022, 1(3), 280-297; https://doi.org/10.3390/colorants1030017
Submission received: 17 March 2022 / Revised: 27 May 2022 / Accepted: 24 June 2022 / Published: 5 July 2022
(This article belongs to the Special Issue Colorants: Ancient and Modern)

Abstract

:
The efficiency of chemoinformatics methods based on a fragment approach for the analysis of relationships between the chemical structure of textile dyes and colour fastness of the dyeings have been shown by examining a large set of properties, including the light fastness of acid dyes on wool and polyamide fibres, the sensitivity of acid dyes on wool to oxygen bleaching, the wash fastness of acid dyes on wool, the adsorption of direct dyes on cotton, and the photodegradation of azo dyes in solution. An analysis of the developed regression models depicted the contribution of ten substructural molecular fragments for each indicator of the colour fastness properties of acid and direct azo dyes on textile materials. The similarity of several individual multi-atomic fragments for acid and direct azo dyes was found for wool, polyamide, and cotton fibres, which indicates the coinciding mechanisms of the physicochemical processes that accompany the destruction of dyes while testing the light fastness and sensitivity of the dyeings to oxygen bleaching, as well as their adsorption/desorption with the wash fastness and dyeability of wool and cotton.

1. Introduction

The study of structure–property relationships was essential for textile chemistry research in previous years. It was based on a detailed study of the physicochemical aspects of dye coloration and the properties of dyes that were adsorbed by the fibres [1,2,3,4,5,6,7,8,9]. Currently, QSPR/QSAR research (QSPR/QSAR-quantitative structure–property/activity relationships) and chemoinformatics analysis cover almost every fundamental and applied field in chemical studies.
Presently, 27,000 individual products under 13,000 generic names are incorporated in the Colour Index [10]. A lot of information regarding the properties of commercial textile dyes for all the technical groups of dyes is provided on the website, World Dye Variety [11]. Water-soluble dyes contribute to about 50% of the total amount of dyes. The Max Weaver Dye Library [12] at Eastman Kodak Company represents a collection of 98,000 vials of custom-made water-soluble dyes. As a part of this collection, temporary, water-soluble hair dyes were collected and analysed for this research [13,14]. Some results from recent research on the bio-elimination of large groups of commercial acids indicates that direct and reactive dyes [15,16,17] are suitable for the discussion about dye affinity for cellulose. A chemometric analysis of different classes of dyes was performed through a series of research studies. The different dye classes studied included: acid dyes for silk [18,19,20], acid dyes for wool and nylon [21], and disperse dyes for synthetic fibres [22,23,24]. One of the early examples of software for QSPR analysis was the software, SPARC, which was widely used up to now for predicting the ionization constants, pKa, and hydrophobicity, LogP, of organic compounds—such as azo dyes and their related compounds [25].
The problem with QSPR analysis of the dye affinity for textile fibres and the physicochemical properties of dyeings became a key point in the development of new ideas during the eve of 2000. A fundamental contribution to the problem was provided through the research of S. Timofei and co-authors [26]. A review cited above makes reference to 14 papers from their research published since 1994 that were based on the application of the comparative molecular field analysis method. The beginning of their research started directly with vat, disperse, acid, and direct dyes. Later on, the authors continued the modification of the method for acid dyes, which is represented in their recent research [27]. Their results and a database for anionic dyes are used in this research [28,29,30,31]. Recent studies [32,33] have continued the development of the chemoinformatics approach based on the experimental data that showed the affinity of anionic dyes for cellulose fibres, which were collected in the abovementioned research [26,27].
A wide variety of empirical properties of water-soluble dyes are covered by chemoinformatics research, for example, the tinctorial properties of acid dyes on cellulose fibres in the domestic washing of mixed cellulose/polyamide/wool materials [34], the photodegradation [35] and catalytic elimination [36] of dyes in wastewater, the behavior of dyes during the advanced oxidation process [37,38], the ecotoxicity of dyes [39,40,41], etc.
Acid and direct dyes as anionic water-soluble dyes play an important role in fundamental research on colour fading properties [42,43,44,45] and adsorption on fibrous materials [2,3,34,46,47,48,49,50]. Reactive dyes are a special class of anionic dyes due to their irreversible chemical fixation on the fibres, therefore their wash fastness properties could not be analysed within a single concept for anionic dyes. Studies on the substantivity of the hydrolysed forms of reactive dyes [51] do not provide information about the chemical structure of these dyes. On the other hand, fundamental research on the colour fading properties of reactive dyes by peroxide [52] and the light fastness of the dyeings [53] does not contain sufficient information for the application of the statistical tools of chemoinformatics.
Recent advances in QSPR methods are analysed in several reviews [54,55,56,57]. The particular problems with the chemoinformatics of dyes are reviewed in Refs. [58,59]. Analysis of the chemical structure and property relationships of dyes usually applies multiple linear regression models or neuro-models based on chemoinformatics software, the most typical of which are listed below in Table 1.
Different kinds of descriptors are represented by the physicochemical parameters, functional groups, topology and geometry of the molecules, fragments of a different kind, etc. Those descriptors are collected, and the databases are arranged through the use of chemoinformatics software—for example, ChemAxon [69].
The fragment approach mentioned above [30,63,64,65,66] explores the descriptors based on the chemical structure of the molecule through the defragmentation of the molecule on the substructures of chains, branches, and cycles of atoms. The idea of the fragment approach was used in the research done by Refs. [70,71,72], which focused on the problems in textile chemistry and the photophysics of fluorescent BODIPY dyes.
Current research aims to develop a database of acid and direct dyes for textile coloration and QSPR analysis using the fragment approach and the software, NASAWIN. This is a universal method of calculating atomic fragments without any reference to the physicochemical properties of the molecule, meeting the clear understanding of the chemists and providing an easy interpretation of the results, which are useful for developing the different chemical and physicochemical properties of dyed fabrics. Each database of dyes is characterised by multiple linear regression models with substructural fragments that describe dye sorption in terms of the fibres, wash fastness, and light fastness of anionic dyes adsorbed on cotton, wool, and polyamide fibres. The results of the analysis provide new insight into dye structure–property relationships and the role of the nature of fibres in textile coloration, which is useful for developing new dyes and dyeings on high-performance textile materials. Several results for the prediction of a series of dyes demonstrate the robustness of the developed models.

2. Materials and Methods

2.1. Database for Colour Fastness of Wool, Polyamide, and Cotton Fibres Dyed with Acid and Direct Dyes

The experimental data for the chemoinformatics analysis of colour fastness properties of textiles are provided in various sources of information.
The data for the light fastness, oxygen bleaching sensitivity, and wash fastness of commercial azo dyes on wool are taken from the World Dye Variety [11]. The information reported by different companies corresponds to the ISO standards. Validation of data presented on the web is performed by comparing the selected entries for the properties of the dyeings with those published in the literature. Data for the oxygen bleaching of azo dye on polyamide and cotton are not available on the web or in the literature. In general, this topic is discussed in systematic research for both cotton [24,25,26,27,28,29] and nylon fibres [32,33], however, detailed information for various dyes is not reported.
The data for the research on light fastness in acid azo dyes on polyamide fibres (Nylon 6.6) were collected from the following publications: Grecu and Pieroni 1981 [73]; Carpignano et al. 1983, 1985 [74,75]; Barni et al. 1984 [76]; De Giorgi et al. 1994, 1997 [19,21]; Kraska 1984 [77]; Blus 1992, 1993, 2005 [78,79,80,81]; Kraska and Blus 1996 [82]. In total, the database includes acid dyes with azobenzene, azonaphthalene, and azopyrazolone structures.
The data representing the adsorption of direct dyes on cotton were collected from Refs. [83,84] for the different initial concentrations of dye in the dyebath: 0.1%, 0.5%, 1.0%, and 2.5% of the weight of the fabric. Such information is suitable for the discussion of the mechanism of adsorption of newly synthesised disazo and trisazo direct dyes, which were studied by one group of researchers. The abovementioned experimental data for the affinity of direct dyes are less reliable due to the various approaches used to evaluate the physicochemical quantity in the collected research papers. The data for the wash fastness of direct dyes that were reported in scientific research have been omitted due to the low statistical reliability of the chemoinformatics results.
As a whole, the database explored in the research serves as an example of the collection of miscellaneous properties that reflect the colour fastness of anionic azo dyes on fibres of various natures.

2.2. Chemoinformatics Tools

The database was prepared using JChem for Office software [69], implementing tools for drawing dye molecules, checking their chemical structure, and generating an sdf-file. Further analysis of the database by NASAWIN [66] makes it possible to decompose the whole database of dyes into sub-molecular descriptors without the calculation of any special physicochemical parameters of the molecules. Originally, the total amount of fragment descriptors for the database of about 130 dye molecules, as an example, counts around 6000 descriptors, which yields a so-called underdetermined system of linear equations. Further calculation procedures, including the partial square regression (PLS) method, and removing the correlated parameters yield an overestimated matrix for the determination of the regression coefficients of the multiple linear regression model. Finally, the single-step procedure of eliminating the regression coefficients based on Student-criteria, Fischer-criteria, and the regression coefficient yields a multiple linear regression model with a limited number of descriptors (usually about ten), thereby characterising the properties of the database with appropriate precision.
Statistical parameters attributed to the model are represented by the following quantities: N–total amount of compounds included in the database; R–regression coefficient, which shows how close the data points are to fitting a curve or line; R—adj-adjusted regression coefficient, which indicates how well the terms fit a curve or line but adjusts for the number of terms in a model; RMSE–root-mean-square error, i.e., square root of mean square error, a measure of the differences between the values predicted by a model and the values observed; MAE–mean absolute error, a measure of the average vertical distance between each point and the Y = X line; s–standard deviation, a measure of how much the data is spread out; F–Fischer number, which characterises how small the dispersion of the predicted data is related to the average dispersion of the data; T-stat–Student number for each regression coefficient of the model.

3. Results and Discussion

3.1. Light Fastness of Commercial Acid Azo Dyes on Wool

The results of the chemoinformatics analysis of the database for the light fastness of commercial acid azonaphthalene dyes are shown below in Figure 1. Table 2 shows the molecular fragments for the exemplified dye congeners along with the values of the regression coefficients for the multiple linear regression model and corresponding Student numbers T-stat. The sign of the regression coefficient characterises the positive or negative impact of the correspondent fragment on the light fastness of dyed fabrics, and the absolute value of the coefficient shows the impact of the descriptor on the light fastness. Student number T-stat characterises the reliability of the coefficient; the standard deviation of the coefficient could be evaluated as the relation coeff/T-stat. The results represented below display the high statistical quality of the regression coefficients.
Analysis shows that the chemical fragments of the dye molecules increase the light fastness of acid azo dyes on wool, including the nitrogen atoms for all the compounds (fragment 1); the azo-bond connecting aromatic chain, an aromatic chain with m-methoxy- or o-ethoxy-groups (fragment 5 and 6); and the azo group connecting the aromatic chain and the aromatic chain with a sulphonic group (fragment 7). The fragments that decrease the light fastness are exemplified by the aromatic chain with primary amino- or substituted amino groups (fragments 3 and 4). Another two examples are the branch or chain fragments of the aromatic carbon atoms with nitrogen in an sp3-hybridisation form (nitrogen of double azo-bond or nitrogen of pyrazolone cycle) (fragments 2 and 10).

3.2. Colour Fastness to Oxygen Bleaching of Commercial Acid Azo Dyes on Wool

The chemoinformatics analysis of the database for the oxygen bleaching sensitivity of acid azonaphthalene dyes is represented in Figure 2 and Table 3.
Oxygen bleaching sensitivity is explained by dye destruction in the domestic washing treatment of textiles, therefore it is quite reasonable that some fragments, which decrease light fastness, increase oxygen beaching sensitivity—for example, the aromatic chain with a primary amino-group or an azo-bond connecting two aromatic chains of carbon atoms from both sides (fragments 3, 4, 7, 9). There is a high negative impact on sensitivity when there is a branch fragment connecting the aliphatic carbon atom and the two aromatic ones (fragment 10). There is a stabilizing effect on OB-sensitivity when there are two azo-bonds in disazo acid dyes with an aliphatic ethyl-group (fragments 5 and 10). Some of the results correlate with common knowledge, for example, polyazo dyes, which were found to be more stable during biodegradation than monoazo dyes in early QSPR analysis [85].

3.3. Wash Fastness of Commercial Acid Azo Dyes on Wool

The results of the analysis of the database for the wash fastness of acid azonaphthalene dyes on wool fibres are shown in Figure 3 and Table 4.
Wash fastness is a property that reflects the affinity of the dyes to fibres. A simple correlation analysis demonstrated the properties of disperse dyes on acetate fibres [86,87], which proves this statement directly.
In our case, wash fastness reflects the intermolecular bonding of acid dyes with wool keratin. Several fragments exhibit positive effects, such as any atom of the molecule (i.e., all atoms of any nature) (fragment 1), the 12- and 15-atom fragment containing two azo-bonds (fragments 5 and 9), as well as the 12- and 13-atomic fragments containing an azo-bond and hydrophobic methyl group (fragment 6). On the other hand, a negative effect is demonstrated when there is a chain of conjugated double bonds containing an azo-bond and hydrophilic hydroxy-group (fragment 7), as well as the four fragments containing sulphonic groups (fragments 3, 4, 8, 10).

3.4. Light Fastness of Acid Dyes on Polyamide (Nylon) Fibres

The database includes acid dyes with azobenzene, azonaphthalene, and azopyrazolone structures. The results of the computational analysis of the database are shown in Figure 4 and Table 5.
The light fastness of polyamide fibres dyed with acid dyes demonstrates the similarity of several descriptors that are responsible for the light fastness of wool. For example, fragments containing primary amino- (fragment 2) or nitrogen atoms with azo-bonds (fragment 6) decrease the light fastness of dyed polyamide fibres. The same role is demonstrated by the substituted amino group in a chain with a nitro-group (fragment 7) and a tri-substituted amino-group in a fragment with an azo group and o-methoxy-group (fragment 8). On the other hand, fragments containing aromatic carbons (fragment 4) as well as azo-bonds with aromatic carbons without other substituents (fragment 5) demonstrate a positive impact.
This positive role is demonstrated by a short C(ar)-S(vi) fragment (fragment 1); a long fragment including a tri-substituted nitrogen atom, azo-bond, and o-sulphonic group (fragment 9); as well as a longer fragment including, in addition, a substituted acetamide group and aromatic carbon atoms (fragment 10).

3.5. Adsorption Properties of Direct Dyes on Cotton Fibres

A dataset of direct dyes on cotton was selected from Refs. [83,84] for four different initial concentrations of dye in a dyebath from 0.1–2.5% of the weight of the fabric. The results for the joint model reflecting the different concentrations in solution are shown in Figure 5. The coefficients of the multiple linear regression model are provided in Table 6 along with the coefficients of the T-statistics.
It is quite natural that fragments including sulphonic groups exhibit a negative impact that demonstrates a repulsion from the negatively-charged carboxylic end-groups of cellulose fibres, which appeared in the fibres due to the application of different kinds of oxidizing agents during the pre-treatment processes. However, a negative impact from non-ionic polar groups, such as –OH, –NH2, or –NH- groups, disproves the traditional point of view about their significant role in hydrogen bonding with macromolecules of cellulose. Those bonds with polar groups of dyes could be easily destroyed by polar solvents like water.
It was found that only one 13-atom fragment that included a conjugated system of nitrogen with a carbonyl group, azo-bond, and sequence of aromatic bonds was characterised by a positive regression coefficient, Coeff8, which is responsible for the increasing amount of dye adsorption. The other six fragments exhibit a negative effect on direct dye adsorption in cellulose fibres. High coefficient values for T-statistics characterise the high level of reliability of each regression coefficient. As for the total robustness of the regression model, the regression coefficient and Fischer number are extremely high: R = 0.9979, F = 5095. Finally, a standard deviation of the Log (adsorption, g/kg) of direct dyes by cotton is rather low: s = 0.03, which is expressed in the units of Log (g/kg).

3.6. Photodegradation of Azo Dyes in Solution

Studies of dye photolysis in solution are useful for understanding the fundamental basis for dye destruction as well as the applied aspects of photochemistry of dye removal from wastewater. A small dataset of 22 azo dyes [35], including acid, direct, and disperse dyes, which are characterised by the first-order kinetics constant of photodestruction at different pH levels of the solution, is used as a short example. The results of the chemoinformatics analysis of the process are shown in Table 7 for pH 6.
It is noteworthy that one regression coefficient, Coeff6, has a large positive impact on the rate of photolysis, which indicates the significant role of the unsubstituted benzene ring of the naphthol residue in photofading; other regression coefficients exhibit a negative impact. The highest level of the photostabilisation effect is demonstrated by fragment 4, which is characterised by a long aromatic chain and a sulphonic group. Another fragment that decreases the rate of photodestruction, fragment 5, includes two conjugated aromatic chains connected by an azo-bond.

3.7. Comparative Analysis of Fragment Descriptors of Regression Models for Different Kinds of Fibres and Colour Fastness Tests

A combination of the models proposed above for various fibres and colour fastness tests is of interest for the comparative analysis of the physicochemical mechanism of dye destruction and their interaction with the fibres. Table 8, Table 9, Table 10 and Table 11 demonstrate several molecular fragments of similar chemical nature that indicate the coinciding physicochemical routes of dye destruction and adsorption regardless of the nature of the fibre.
A negative (destructive) effect on dye chromophore was observed in the tests for the light fastness of dyeings on wool and polyamide, as well as the sensitivity of the dyeings on wool for oxygen bleaching, which are controlled by the fragments shown in Table 8, including primary or substituted amino groups, an azo-bond as a part of a chain of conjugated double bonds with primary or substituted amino groups, a chain of aromatic carbon atoms, and the nitrogen atom of an azo-bond.
A positive (stabilizing) effect on the dye chromophore was observed in the tests for the light fastness of dyes on wool and polyamide fibres as well as the sensitivity of the dyes on wool to oxygen bleaching, which is explained by the fragments shown in Table 9, including an azo group as a part of a chain of conjugated double bonds, an azo group as a part of a chain of conjugated double bonds and a sulphonic group, and an azo-bond as a part of a chain of conjugated double bonds and a carbamide group.
A comparison of the molecular fragments controlling the light fastness of dyed textiles demonstrates the similarity of several fragments during the photolysis of azo dyes in solution. For instance, in the case of polyamide fibres, molecular fragment 4’s (LF-PA-4, Table 5) decrease of the light fastness of azo acid dye is comparable to fragment 6’s (Table 7) increase of the rate of dye photodestruction in water. On the other hand, the high stabilising effect of the sulphonic group on light fastness is demonstrated by molecular fragment 1 (LF-PA-1, Table 5), which corresponds with fragment 4’s (Table 7) decrease in the rate of dye photodestruction. Similarly, the positive role of the sulphonic group for dyed wool fibres is displayed by fragment 7 (LF-W-7, Table 2)
The fragment shown in Table 10 has a positive effect on the wash fastness of the dyeings on wool and the adsorption on cotton fibres, including two azo groups as a part of a chain of conjugated double bonds and a chain of conjugated double bonds containing an azo group and hydrophobic substituent.
Both chain fragments play the role of a bulky hydrophobic fragment.
In contrast to the above, the fragments presented in Table 11 decrease the wash fastness of acid dyes on wool and the adsorption of direct dyes on cotton. These fragments include an aromatic chain with a terminal sulfonic group or an azo group as a part of a chain of conjugated double bonds with hydrophilic substituents such as –NH2 or –OH.

4. Conclusions

The application of chemoinformatics tools for the analysis of large databases of dyes demonstrated the efficiency of the method for analyzing the dye chemical structure–property relationships in several case studies that explored: the light fastness of acid dyes on wool and acid dyes on polyamide, the sensitivity of acid dyes on wool to oxygen bleaching, the wash fastness of acid dyes on wool, the adsorption of direct dyes on cotton, and the photodestruction of azo dyes in solution. The fragment approach of QSPR depicts several substructural descriptors that reflect the mechanism of destruction and the dye–fibre interaction.
The similarity of the fragments for acid and direct azo dyes on fibres of different natures is shown; furthermore, this indicates the coinciding mechanisms of the physicochemical destruction of dyes in light fastness tests and the adsorption/desorption in wash fastness and dyeability tests. It is found that the light fastness of dyeings on wool and polyamide, as well as the sensitivity of dyes on wool fiber to oxygen bleaching, is decreased in the presence of molecular fragments, such as fragments that contain primary or substituted amino groups, an azo-bond and primary or substituted amino group, and a chain of conjugated double bonds with a nitrogen atom on the azo group. The positive (stabilising) effect on dye chromophore is demonstrated by the fragments with an azo group as a part of a chain of conjugated double bonds and the sulfonic group as a substituent or an azo group as a part of conjugated double bonds with a carbamide group. The fragment represented by two azo groups as a part of a chain of conjugated double bonds has a positive effect on the wash fastness on wool and the sorption on cotton fibres. On the contrary, a negative effect is demonstrated by the fragments with a chain of conjugated double bonds with a terminal sulfonic group or azo groups as a part of a chain of conjugated double bonds with hydrophilic substituents such as –NH2 or –OH.

Author Contributions

Conceptualization, F.Y.T.; methodology, F.Y.T.; software, F.Y.T.; validation, J.R. and V.G.P.; formal analysis, J.R. and V.G.P.; investigation, J.R. and V.G.P.; data curation, J.R. and V.G.P.; writing—original draft preparation, J.R. and V.G.P.; writing—review and editing, J.R., V.G.P. and F.Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work received financial support from the Ministry of Science and Higher Education of the Russian Federation (No. 075-15-2021-579). The study was carried out using the resources of the Center for Shared Use of Scientific Equipment of the ISUCT (with the support of the Ministry of Science and Higher Education of Russia, grant No. 075-15-2021-671).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Authors are grateful to I.I. Baskin for providing the software NASAWIN, to ChemAxon (www.chemaxon.com) for providing an academic license for JChem for Office, and Citavi (www.citavi.com) for providing a license for the reference manager.

Conflicts of Interest

The authors declare no conflict of interests.

References

  1. Burkinshaw, S.M. Physico-Chemical Aspects of Textile Coloration; SDC, John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2016; ISBN 978-1-118-72569-6. [Google Scholar]
  2. Burkinshaw, S.M. The role of inorganic electrolyte (salt) in cellulosic fibre dyeing: Part 1 fundamental aspects. Coloration Technol. 2021, 137, 421–444. [Google Scholar] [CrossRef]
  3. Burkinshaw, S.M. The role of inorganic electrolyte (salt) in cellulosic fibre dyeing: Part 2 theories of how inorganic electrolyte promotes dye uptake. Coloration Technol. 2021, 137, 547–586. [Google Scholar] [CrossRef]
  4. Vickerstaff, T. The Physical Chemistry of Dyeing, 2nd ed.; Oliver & Boyd: Edinburgh, UK, 1954. [Google Scholar]
  5. Peters, R.H. Textile Chemistry: The Physical Chemistry of Dyeing; Elsevier Sci. Publ. Co.: Amsterdam, The Netherlands; Oxford, UK; New York, NY, USA, 1975. [Google Scholar]
  6. Giles, C.H.; Duff, D.G.; Sinclair, R.S. Relation between the molecular structure of dyes and their technical properties: Chapter VII. In The Chemistry of Synthetic Dyes; Venkataraman, K., Ed.; Academic Press: Cambridge, MA, USA, 1978; pp. 279–329. [Google Scholar]
  7. The Theory of Coloration of Textiles, 2nd ed.; Johnson, A. (Ed.) Society of Dyers and Colourists: Bradford, UK, 1989. [Google Scholar]
  8. Colorants and Auxiliaries. Organic Chemistry and Application Properties; Colorants; Shore, J., Ed.; Society of Dyers & Colourists: Bradford, UK, 2002; Volume 1, ISBN 9780901956774. [Google Scholar]
  9. Telegin, F.; Shushina, I.; Ran, J.; Biba, Y.; Mikhaylov, A.; Priazhnikova, V. Structure—Property relationships for dyes of different nature. Adv. Mater. Res. 2013, 821–822, 488–492. [Google Scholar] [CrossRef]
  10. SDC; AATCC. Colour Index™ Online. Available online: https://colour-index.com/about (accessed on 29 April 2022).
  11. World Dye Variety. Available online: http://www.worlddyevariety.com/ (accessed on 29 April 2022).
  12. Kuenemann, M.A.; Szymczyk, M.; Chen, Y.; Sultana, N.; Hinks, D.; Freeman, H.S.; Williams, A.J.; Fourches, D.; Vinueza, N.R. Weaver’s historic accessible collection of synthetic dyes: A cheminformatics analysis. Chem. Sci. 2017, 8, 4334–4339. [Google Scholar] [CrossRef] [Green Version]
  13. Williams, T.N.; Kuenemann, M.A.; Van Den Driessche, G.A.; Williams, A.J.; Fourches, D.; Freeman, H.S. Toward the rational design of sustainable hair dyes Using cheminformatics approaches: Step 1. Database development and analysis. ACS Sustain. Chem. Eng. 2018, 6, 2344–2352. [Google Scholar] [CrossRef]
  14. Williams, T.N.; Van Den Driessche, G.A.; Valery, A.R.B.; Fourches, D.; Freeman, H.S. Toward the rational design of sustainable hair dyes using cheminformatics approaches: Step 2. Identification of hair dye substance database analogs in the Max Weaver dye library. ACS Sustain. Chem. Eng. 2018, 6, 14248–14256. [Google Scholar] [CrossRef]
  15. Churchley, J.; Greaves, A.; Hutchings, M.; Phillips, D.A.S.; Taylor, J.A. A chemometric approach to understanding the bioelimination of anionic, water-soluble dyes by a biomass—Part 2: Acid dyes. Coloration Technol. 2000, 116, 222–228. [Google Scholar] [CrossRef]
  16. Churchley, J.H.; Greaves, A.J.; Hutchings, M.G.; Phillips, D.A.S.; Taylor, J.A. A chemometric approach to understanding the bioelimination of anionic, water-soluble dyes by a biomass—Part 3: Direct dyes. Coloration Technol. 2000, 116, 279–284. [Google Scholar] [CrossRef]
  17. Churchley, J.H.; Greaves, A.J.; Hutchings, M.G.; Phillips, D.A.S.; Taylor, J.A. A chemometric approach to understanding the bioelimination of anionic, water-soluble dyes by a biomass—Part 4: Reactive dyes. Coloration Technol. 2000, 116, 323–329. [Google Scholar] [CrossRef]
  18. De Giorgi, M.R.; Cerniani, A.; Carpignano, R.; Savarino, P. Design of high fastness acid dyes for silk: A chemometric approach. J. Soc. Dye. Colour. 1993, 109, 405–410. [Google Scholar] [CrossRef]
  19. De Giorgi, M.R.; Carpignano, R.; Scano, P. Structure optimization in a series of dyes for wool and cotton. A chemometric approach. Dyes Pigments 1994, 26, 175–189. [Google Scholar] [CrossRef]
  20. De Giorgi, M.; Carpignano, R. Design of dyes of high technical properties for silk by a chemometric approach. Dyes Pigments 1996, 30, 79–88. [Google Scholar] [CrossRef]
  21. De Giorgi, M.R.; Carpignano, R.; Crisponi, G. Structure optimization in a series of acid dyes for wool and nylon. Dyes Pigments 1997, 34, 1–12. [Google Scholar] [CrossRef]
  22. De Giorgi, M.; Carpignano, R.; Cerniani, A. Structure optimization in a series of thiadiazole disperse dyes using a chemometric approach. Dyes Pigments 1998, 37, 187–196. [Google Scholar] [CrossRef]
  23. Kats, M.D.; Krichevskii, G.E. Mathematical model for the relationship between lightfastness and chemical structure of monoazo disperse dyes. Izv. Vuzov. Techn. Text. Prom 1979, 60–63. [Google Scholar]
  24. Kats, M.D.; Lysun, N.V.; Mostoslavskaya, E.I.; Krichevskii, G.E. Studies of the relationships between chemical structure of diperse monoazo dyes and their light protecting properties on polyamide fibres. Zh. Prikl. Khim. 1988, 1196–1199. [Google Scholar]
  25. Hilal, S.H.; Carreira, L.A.; Baughman, G.L.; Karickhoff, S.W.; Melton, C.M. Estimation of ionization constants of azo dyes and related aromatic amines: Environmental implication. J. Phys. Org. Chem. 1994, 7, 122–141. [Google Scholar] [CrossRef]
  26. Timofei, S.; Schmidt, W.; Kurunczi, L.; Simon, Z. A review of QSAR for dye affinity for cellulose fibres. Dyes Pigments 2000, 47, 5–16. [Google Scholar] [CrossRef]
  27. Funar-Timofei, S.; Fabian, W.M.; Kurunczi, L.; Goodarzi, M.; Ali, S.T.; Heyden, Y.V. Modelling heterocyclic azo dye affinities for cellulose fibres by computational approaches. Dyes Pigments 2012, 94, 278–289. [Google Scholar] [CrossRef]
  28. Polanski, J.; Gieleciak, R.; Wyszomirski, M. Comparative molecular surface analysis (CoMSA) for modeling dye-fiber affinities of the azo and anthraquinone dyes. J. Chem. Inf. Comput. Sci. 2003, 43, 1754–1762. [Google Scholar] [CrossRef]
  29. Polanski, J.; Gieleciak, R.; Wyszomirski, M. Mapping dye pharmacophores by the Comparative Molecular Surface Analysis (CoMSA): Application to heterocyclic monoazo dyes. Dyes Pigments 2004, 62, 61–76. [Google Scholar] [CrossRef]
  30. Zhokhova, N.I.; Baskin, I.I.; Palyulin, V.A.; Zefirov, A.N.; Zefirov, N.S. A study of the affinity of dyes for cellulose fiber within the framework of a fragment approach in QSPR. Russ. J. Appl. Chem. 2005, 78, 1013–1017. [Google Scholar] [CrossRef]
  31. Wang, X.; Sun, Y.; Wu, L.; Gu, S.; Liu, R.; Liu, L.; Liu, X.; Xu, J. Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network. Chemom. Intell. Lab. Syst. 2014, 134, 1–9. [Google Scholar] [CrossRef]
  32. Yu, S.; Zhou, Q.; Zhang, X.; Jia, S.; Gan, Y.; Zhang, Y.; Shi, J.; Yuan, J. Hologram quantitative structure–activity relationship and topomer comparative molecular-field analysis to predict the affinities of azo dyes for cellulose fibers. Dyes Pigments 2018, 153, 35–43. [Google Scholar] [CrossRef]
  33. Kumar, P.; Kumar, A. In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation. SAR QSAR Environ. Res. 2020, 31, 697–715. [Google Scholar] [CrossRef]
  34. Oakes, J.; Dixon, S. Adsorption of dyes to cotton and inhibition by polymers. Coloration Technol. 2003, 119, 140–149. [Google Scholar]
  35. Zhang, G.; Zhang, S. Quantitative structure-activity relationship in the photodegradation of azo dyes. J. Environ. Sci. 2020, 90, 41–50. [Google Scholar] [CrossRef]
  36. Xu, Y.; Chen, X.; Li, Y.; Ge, F.; Zhu, R. Quantitative structure–property relationship (QSPR) study for the degradation of dye wastewater by Mo–Zn–Al–O catalyst. J. Mol. Liq. 2016, 215, 461–466. [Google Scholar] [CrossRef]
  37. Li, B.; Dong, Y.; Ding, Z. Heterogeneous Fenton degradation of azo dyes catalyzed by modified polyacrylonitrile fiber Fe complexes: QSPR (quantitative structure property relationship) study. J. Environ. Sci. 2013, 25, 1469–1476. [Google Scholar] [CrossRef]
  38. Awfa, D.; Ateia, M.; Mendoza, D.; Yoshimura, C. Application of Quantitative Structure–Property Relationship Predictive Models to Water Treatment: A Critical Review. ACS EST Water 2021, 1, 498–517. [Google Scholar] [CrossRef]
  39. Umbuzeiro, G.d.A.; Albuquerque, A.F.; Vacchi, F.I.; Szymczyk, M.; Sui, X.; Aalizadeh, R.; von der Ohe, P.C.; Thomaidis, N.S.; Vinueza, N.R.; Freeman, H.S. Towards a reliable prediction of the aquatic toxicity of dyes. Environ. Sci. Eur. 2019, 31, 76. [Google Scholar] [CrossRef]
  40. Funar-Timofei, S.; Ilia Gheorghe. QSAR Modeling of Dye Ecotoxicity. In Ecotoxicological QSARs; Roy, K., Ed.; Springer: New York, NY, USA, 2020; pp. 405–436. ISBN 978-1-0716-0149-5. [Google Scholar]
  41. Ecotoxicological QSARs; Roy, K. (Ed.) Springer: New York, NY, USA, 2020; ISBN 978-1-0716-0149-5. [Google Scholar]
  42. Oakes, J.; Gratton, P.; Clark, R.; Wilkes, I. Kinetic investigation of the oxidation of substituted arylazonaphthol dyes by hydrogen peroxide in alkaline solution. J. Chem. Soc. Perkin Trans. 1998, 2, 2569–2576. [Google Scholar] [CrossRef]
  43. Oakes, J. Principles of colour loss. Part 1: Mechanisms of oxidation of model azo dyes by detergent bleaches. Rev. Prog. Color. Relat. Top. 2002, 32, 63–79. [Google Scholar] [CrossRef]
  44. Oakes, J. Principles of colour loss. Part 2: Degradation of azo dyes by electron transfer, catalysis and radical routes. Rev. Prog. Color. Relat. Top. 2003, 33, 72–84. [Google Scholar] [CrossRef]
  45. Chudgar, R.J.; Oakes, J. Dyes, Azo. Kirk-Othmer Encyclopedia of Chemical Technology; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2003; ISBN 9780471238966. [Google Scholar]
  46. Oakes, J.; Dixon, S. Adsorption of dyes to cotton and inhibition by surfactants, polymers and surfactant–polymer mixtures. Coloration Technol. 2003, 119, 315–323. [Google Scholar] [CrossRef]
  47. Burkinshaw, S.M.; Salihu, G. The role of auxiliaries in the immersion dyeing of textile fibres part 2: Analysis of conventional models that describe the manner by which inorganic electrolytes promote direct dye uptake on cellulosic fibres. Dyes Pigments 2019, 161, 531–545. [Google Scholar] [CrossRef] [Green Version]
  48. Burkinshaw, S.M.; Salihu, G. The role of auxiliaries in the immersion dyeing of textile fibres: Part 3 theoretical model to describe the role of inorganic electrolytes used in dyeing cellulosic fibres with direct dyes. Dyes Pigments 2019, 161, 546–564. [Google Scholar] [CrossRef]
  49. Burkinshaw, S.M.; Salihu, G. The role of auxiliaries in the immersion dyeing of textile fibres: Part 4 theoretical model to describe the role of liquor ratio in dyeing cellulosic fibres with direct dyes in the absence and presence of inorganic electrolyte. Dyes Pigments 2019, 161, 565–580. [Google Scholar] [CrossRef] [Green Version]
  50. Burkinshaw, S.M.; Salihu, G. The role of auxiliaries in the immersion dyeing of textile fibres: Part 5 practical aspects of the role of inorganic electrolytes in dyeing cellulosic fibres with direct dyes. Dyes Pigments 2019, 161, 581–594. [Google Scholar] [CrossRef] [Green Version]
  51. Ferus-Comelo, M. An analysis of the substantivity of hydrolysed reactive dyes and its implication for rinsing processes. Coloration Technol. 2013, 129, 24–31. [Google Scholar] [CrossRef]
  52. Bredereck, K.; Schumacher, C. Structure reactivity correlations of azo reactive dyes based on H-acid. III. Dye degradation by peroxide. Dyes Pigments 1993, 23, 121–133. [Google Scholar] [CrossRef]
  53. Bredereck, K.; Schumacher, C. Structure reactivity correlations of azo reactive dyes based on H-acid. IV. Investigations into the light fastness in the dry state, in the wet state, and in presence of perspiration. Dyes Pigments 1993, 23, 135–147. [Google Scholar] [CrossRef]
  54. Katritzky, A.R.; Karelson, M.; Lobanov, V.S. QSPR as a means of predicting and understanding chemical and physical properties in terms of structure. Pure Appl. Chem. 1997, 69, 245–248. [Google Scholar] [CrossRef]
  55. Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; et al. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem. 2014, 57, 4977–5010. [Google Scholar] [CrossRef] [Green Version]
  56. Tetko, I.V.; Engkvist, O.; Koch, U.; Reymond, J.-L.; Chen, H. BIGCHEM: Challenges and opportunities for big data analysis in chemistry. Mol. Inform. 2016, 35, 615–621. [Google Scholar] [CrossRef] [Green Version]
  57. Muratov, E.N.; Bajorath, J.; Sheridan, R.P.; Tetko, I.V.; Filimonov, D.; Poroikov, V.; Oprea, T.I.; Baskin, I.I.; Varnek, A.; Roitberg, A.; et al. QSAR without borders. Chem. Soc. Rev. 2020, 49, 3525–3564. [Google Scholar] [CrossRef] [PubMed]
  58. Luan, F.; Xu, X.; Liu, H.; Cordeiro, M.N.D.S. Review of quantitative structure-activity/property relationship studies of dyes: Recent advances and perspectives. Coloration Technol. 2013, 129, 173–186. [Google Scholar] [CrossRef]
  59. Yu, X.; Wang, H. Support vector machine classification model for color fastness to ironing of vat dyes. Text. Res. J. 2021, 91, 1889–1899. [Google Scholar] [CrossRef]
  60. Hilal, S.H.; Karichhoff, S.W.; Careira, L.A. Prediction of Chemical Reactivity Parameters and Physical Properties of Organic Compounds from Molecular Structure Using; SPARC: Research Triangle Park, NC, USA, 2003. [Google Scholar]
  61. CODESSA PRO: COmprehensive DEscriptors for Structural and Statistical Analysis. Available online: www.codessa-pro.com (accessed on 29 April 2022).
  62. DRAGON. Available online: https://chm.kode-solutions.net/products_dragon.php (accessed on 29 April 2022).
  63. Baskin, I.I.; Palyulin, V.A.; Zefirov, N.S. A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds. J. Chem. Inf. Comput. Sci. 1997, 37, 715–721. [Google Scholar] [CrossRef]
  64. Baskin, I.I.; Ait, A.O.; Halberstam, N.M.; Palyulin, V.A.; Alfimov, M.V.; Zefirov, N.S. Application of methodology of artificial neural networks for predicting the properties of sophisticated molecular systems: Prediction of the long-wave absorption band position for symmetric cyanine dyes. In Doklady Physical Chemistry; Consultants Bureau: New York, NY, USA, 1997; Volume 357, pp. 353–355. [Google Scholar]
  65. Baskin, I.I.; Keschtova, S.V.; Palyulin, V.A.; Zefirov, N.S. Combining Molecular Modelling with the Use of Artificial Neural Networks as an Approach to Predicting Substituent Constants and Bioactivity. In Molecular Modeling and Prediction of Bioactivity; Gundertofte, K., Jørgensen, F.S., Eds.; Springer: Boston, MA, USA, 1999. [Google Scholar]
  66. Baskin, I.; Varnek, A. Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening. In Chemoinformatics Approaches to Virtual Screening; Varnek, A., Tropsha, A., Eds.; RSC Publishing: Cambridge, UK, 2008; pp. 1–43. ISBN 9780854041442. [Google Scholar]
  67. CORAL Software/Databases. Available online: www.insilico.eu/coral/ (accessed on 29 April 2022).
  68. Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A.K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V.V.; Tanchuk, V.Y.; et al. Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information. J. Comput. Aided Mol. Des. 2011, 25, 533–554. [Google Scholar] [CrossRef] [Green Version]
  69. ChemAxon: Free Academic License for JChem. Available online: www.chemaxon.com (accessed on 12 January 2017).
  70. Telegin, F.Y.; Ran, J.H.; Morshed, M.; Pervez, M.N.; Sun, L.; Zhang, C.; Priazhinikova, V.G. Structure and Properties of Dyes in Coloration of Textiles. Application of Fragment Approach. KEM 2016, 703, 261–266. [Google Scholar] [CrossRef]
  71. Telegin, F.Y.; Ran, J.; Pryazhnikova, V.G. Colour, colour fastness and chemical constitution of anionic azo dyes: Chemoinformatics analysis. In The Scientific Notes of Color Society of Russia. First Russian Congress on Color; Schindler, V., Griber, M., Yulia, A., Eds.; Smolensk State University: Smolensk, Russia, 2019; Volume 1, pp. 97–102. [Google Scholar]
  72. Telegin, F.Y.; Marfin, Y.S. Polarity of borondipyrrins and their structure: Semiempirical and chemoinformatics analysis. Zh. Neorg. Khimii/J. Inorg. Chem. Russ. 2022, 67, 384–396. [Google Scholar] [CrossRef]
  73. Grecu, R.; Pieroni, M. Quantitative relationships between chemical structure and technical properties of arylazoindole sulphonic acid dyes. Dyes Pigments 1981, 2, 305–318. [Google Scholar] [CrossRef]
  74. Carpignano, R.; Barni, E.; Di Modica, G.; Grecu, R.; Bottaccio, G. Quantitative relationships between chemical structure and technical properties of 4-aminoazobenzene sulphonic acid dyes. Dyes Pigments 1983, 4, 195–211. [Google Scholar] [CrossRef]
  75. Carpignano, R.; Savarino, P.; Barni, E.; Clementi, S.; Giulietti, G. Quantitative Structure–Property relationships study of azo dyes using partial least squares analysis in latent variables (PLS). Dyes Pigments 1985, 6, 189–212. [Google Scholar] [CrossRef]
  76. Barni, E.; Savarino, P.; di Modica, G.; Carpignano, R. Monoazo Dyes for Polyamide Derived from 4-Alkylamido-Shydroxybenzoic Acids. Dyes Pigments 1984, 5, 15–36. [Google Scholar] [CrossRef]
  77. Kraska, J.; Blus, K. Synthesis and properties of acid dye derivatives of arylsulphonanilides. Dyes Pigments 1984, 5, 415–430. [Google Scholar] [CrossRef]
  78. Blus, K. Synthesis and Properties of Acid Dyes Derived from 3-Hydroxy-2-naphthanilides. Dyes Pigments 1992, 18, 163–177. [Google Scholar] [CrossRef]
  79. Blus, K.; Kraska, J. The influence of arylamide groups on the properties of acid dyes. Dyes Pigments 1993, 22, 163–172. [Google Scholar] [CrossRef]
  80. Blus, K. Synthesis and properties of acid dyes derived from 1-phenyl-3-methyl-5-pyrazolone. Dyes Pigments 1992, 20, 53–65. [Google Scholar] [CrossRef]
  81. Blus, K. Photo-stability of Acid Dyes, Derivatives of 1-phenyl-3-methylpyrazol-5-one in Polyamide Fibers. Fibres Text. East. Eur. 2005, 13, 70–74. [Google Scholar]
  82. Kraska, J.; Blus, K. Synthesis and properties of disazo dyes derived from 4-amino-2′-nitrodiphenylamine. Dyes Pigments 1996, 31, 97–109. [Google Scholar] [CrossRef]
  83. Chao, Y.C.; Yang, S.S. Disazo direct dyes derived from 4,4′-diamino derivatives of benzanilide, diphenylamine-2-sulfonic acid and stilbene-2,2′-disulfonic acid. Dyes Pigments 1995, 29, 131–138. [Google Scholar] [CrossRef]
  84. Chao, Y.C.; Pan, Y.I. Trisazo dyes derived from 4,4′-diaminodiphenylsulphide: Substitutes for C.I. Direct black 38 and C.I. Direct Green 1. Dyes Pigments 1996, 31, 253–262. [Google Scholar] [CrossRef]
  85. Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology; Karcher, W.; Devillers, J. (Eds.) Springer: Dordrecht, The Netherlands, 1990; ISBN 0792308271. [Google Scholar]
  86. Telegin, F.Y.; Khaylenko, E.S.; Telegin, P.F. Quantitative relationships for design of disperse dyes of high technical properties. In Proceedings of the 21th IFATCC Congress, New Horizons in Textile Finishing, Barcelona, Spain, 6–9 May 2008. [Google Scholar]
  87. Telegin, F.Y. Structure and properties of dyes in theory and practice of coloration. Des. Mater. Technol. 2009, 11, 163–167. [Google Scholar]
Figure 1. Correlation between calculated and experimental values of light fastness of commercial azo acid dyes on wool.
Figure 1. Correlation between calculated and experimental values of light fastness of commercial azo acid dyes on wool.
Colorants 01 00017 g001
Figure 2. Correlation between calculated and experimental values of oxygen bleaching sensitivity of commercial azo acid dyes on wool fibres.
Figure 2. Correlation between calculated and experimental values of oxygen bleaching sensitivity of commercial azo acid dyes on wool fibres.
Colorants 01 00017 g002
Figure 3. Correlation between calculated and experimental values of wash fastness of commercial azo acid dyes on wool fibres.
Figure 3. Correlation between calculated and experimental values of wash fastness of commercial azo acid dyes on wool fibres.
Colorants 01 00017 g003
Figure 4. Correlation between calculated and experimental values of light fastness of dyeings on polyamide fibres.
Figure 4. Correlation between calculated and experimental values of light fastness of dyeings on polyamide fibres.
Colorants 01 00017 g004
Figure 5. Correlation between calculated and experimental values of dye adsorption on cotton.
Figure 5. Correlation between calculated and experimental values of dye adsorption on cotton.
Colorants 01 00017 g005
Table 1. Examples of modern software for structure–property analysis of organic compounds.
Table 1. Examples of modern software for structure–property analysis of organic compounds.
SoftwareNumber of Descriptors
SPARC, by L.A. Carreira et al., ARChem, USA, 1994 [60]not specified
CODESSA, by A.R. Katritzky, M. Karelson, R. Petrukhin, University of Florida USA, 2001-2005 [61]about 1500
DRAGON, by Kode Chemoinformatics, R. Todecini et al., Pisa, Italy, 1994 [62]5270
NASAWIN, by I.I. Baskin et al., Moscow State University, Russia, 1995 [63,64,65,66]unlimited
CORAL, Mario Negri Institute, E. Benfenati, A.A. Toropov, A.P. Toropova, Italy, 2010 [67]unlimited
OCHEM, I.I. Tetko, et al., International project, 2011 [68]unlimited
Table 2. Coefficients of multiple linear regression model for light fastness of wool dyed with commercial azo acid dyes and fragments of exemplified dye congeners.
Table 2. Coefficients of multiple linear regression model for light fastness of wool dyed with commercial azo acid dyes and fragments of exemplified dye congeners.
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Coeff0 = 3.161944, T-stat = 18.3618Coeff1 = 0.414375, T-stat = 8.8966
LF-W-1
Colorants 01 00017 i001
Coeff2 = −0.604511, T-stat = −4.3458
LF-W-2
Colorants 01 00017 i002
Coeff3 = −1.037494, T-stat = −6.3766
LF-W-3
Colorants 01 00017 i003
Coeff4 = −0.287592, T-stat = −3.3646
LF-W-4
Colorants 01 00017 i004
Coeff5 = 0.685249, T-stat = 4.1154
LF-W-5
Colorants 01 00017 i005
Coeff6 = 0.668728, T-stat = 7.1565
LF-W-6
Colorants 01 00017 i006
Coeff7 = 0.288798, T-stat = 4.1107
LF-W-7
Colorants 01 00017 i007
Coeff8 = −0.492495, T-stat = −3.5045
LF-W-8
Colorants 01 00017 i008
Coeff9 = −0.376856, T-stat = −3.9398
LF-W-9
Colorants 01 00017 i009
Coeff10 = −0.55531, T-stat = −6.5707
LF-W-10
Colorants 01 00017 i010
Table 3. Coefficients of multiple linear regression model for oxygen bleaching sensitivity of wool dyed with commercial azo acid dyes and fragments of exemplified dye congeners.
Table 3. Coefficients of multiple linear regression model for oxygen bleaching sensitivity of wool dyed with commercial azo acid dyes and fragments of exemplified dye congeners.
Regression Coefficient, Molecular FragmentRegression Coefficient, Molecular FragmentRegression Coefficient, Molecular Fragment
Coeff0 = −0.714316, T-stat = −2.9669Coeff1 = 0.065831, T-stat = 10.1430
OB-W-1
Colorants 01 00017 i011
Coeff2 = −0.938693, T-stat = −4.1047
OB-W-2
Colorants 01 00017 i012
Coeff3 = 1.477873, T-stat = 4.8356
OB-W-3
Colorants 01 00017 i013
Coeff4 = 2.706476, T-stat = 7.3958
OB-W-4
Colorants 01 00017 i014
Coeff5 = −1.376763, T-stat = −5.2326
OB-W-5
Colorants 01 00017 i015
Coeff6 = 0.101659, T-stat = 4.6905
OB-W-6
Colorants 01 00017 i016
Coeff7 = 1.20228, T-stat = 6.8330
OB-W-7
Colorants 01 00017 i017
Coeff8 = 0.492824, T-stat = 5.1165
OB-W-8
Colorants 01 00017 i018
Coeff9 =−1.134224, T-stat = −5.6996
OB-W-9
Colorants 01 00017 i019
Coeff10 = 2.800875, T-stat = 7.3944
OB-W-10
Colorants 01 00017 i020
Table 4. Coefficients of multiple linear regression model for wash fastness of wool dyed with commercial azo acid dyes and fragments of exemplified dye congeners.
Table 4. Coefficients of multiple linear regression model for wash fastness of wool dyed with commercial azo acid dyes and fragments of exemplified dye congeners.
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Coeff0 = 0.963902, T-stat = 4.9380
Coeff1 = 0.058948, T-stat = 12.5825
WF-W-1
Colorants 01 00017 i021
Coeff2 = −0.309573, T-stat = −3.9703
WF-W-2
Colorants 01 00017 i022
Coeff3 = −0.335361, T-stat = −3.8736
WF-W-3
Colorants 01 00017 i023
Coeff4 = −0.256796, T-stat = −3.8610
WF-W-4
Colorants 01 00017 i024
Coeff5 = 0.199498, T-stat3 = 3.3687
WF-W-5
Colorants 01 00017 i025
Coeff6 = 0.511187, T-stat = 4.1279
WF-W-6
Colorants 01 00017 i026
Coeff7 = −0.532824, T-stat = −3.5236
WF-W-7
Colorants 01 00017 i027
Coeff8 = −0.938293, T-stat = −3.4897
WF-W-8
Colorants 01 00017 i028
Coeff9 = 0.407356, T-stat = 6.1331
WF-W-9
Colorants 01 00017 i029
Coeff10 = −0.0876, T-stat = −4.8110
WF-W-10
Colorants 01 00017 i030
Table 5. Coefficients of multiple linear regression model for the light fastness of dyeing on polyamide fibres and fragments of exemplified dye congeners with a 1:1 concentration of dye.
Table 5. Coefficients of multiple linear regression model for the light fastness of dyeing on polyamide fibres and fragments of exemplified dye congeners with a 1:1 concentration of dye.
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Coeff0 = 4.930911, T-stat = 30.6418Coeff1 = 0.43608, T-stat = 7.3894
LF-PA-1
Colorants 01 00017 i031
Coeff2 = −2.4247, T-stat =−17.7498
LF-PA-2
Colorants 01 00017 i032
Coeff3 = 0.85488, T-stat = 10.6573
LF-PA-3
Colorants 01 00017 i033
Coeff4 = −0.31445, T-stat = −7.2016
LF-PA-4
Colorants 01 00017 i034
Coeff5 = 0.758169, T-stat = 8.1588
LF-PA-5
Colorants 01 00017 i035
Coeff6 = −1.66501, T-stat = −19.3869
LF-PA-6
Colorants 01 00017 i036
Coeff7 = −0.77779, T-stat = −9.5075
LF-PA-7
Colorants 01 00017 i037
Coeff8 = −0.42405, T-stat = −5.5203
LF-PA-8
Colorants 01 00017 i038
Coeff9 = 0.416039, T-stat = 7.6620
LF-PA-9
Colorants 01 00017 i039
Coeff10 = 0.552282, T-stat = 4.6392
LF-PA-10
Colorants 01 00017 i040
Table 6. Coefficients of multiple linear regression model for adsorption of direct dyes on cotton and fragments of exemplified dye congeners.
Table 6. Coefficients of multiple linear regression model for adsorption of direct dyes on cotton and fragments of exemplified dye congeners.
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
N = 225, R = 0.9979, R_adj = 0.9978, F = 5095, s = 0.0338, RMSE_t = 0.00330, MAE_t = 0.0259
Coeff0 = 0.824114, T-stat = 70.9156
Coeff1(C=1%) = 0.421999, T-stat = 53.6620
Coeff2 (C=0.5%) = −0.21474, T-stat = −27.3063
Coeff3 (C=0.1%) = −0.88427, T-stat = −112.4455
Coeff4 = −0.13195, T-stat = −11.8450
A-C-4
Colorants 01 00017 i041
Coeff5 = −0.01797, T-stat = −5.4787
A-C-5
Colorants 01 00017 i042
Coeff6 = −0.00622, T-stat = −6.4462
A-C-6
Colorants 01 00017 i043
Coeff7 = −0.01134, T-stat = −3.4668
A-C-7
Colorants 01 00017 i044
Coeff8 = 0.002434, T-stat = 5.8381
A-C-8
Colorants 01 00017 i045
Coeff9 = −0.06541, T-stat = −4.0627
A-C-9
Colorants 01 00017 i046
Coeff10 = −0.01152, T-stat = −6.5432
A-C-10
Colorants 01 00017 i047
Table 7. Coefficients of multiple linear regression model for photodegradation of azo dyes in solution at pH 6.
Table 7. Coefficients of multiple linear regression model for photodegradation of azo dyes in solution at pH 6.
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
Regression Coefficient,
Molecular Fragment
N = 22, R = 0.9814, R_adj = 0.9726, F = 65.43, s = 0.111, RMSE_t = 0.0917, MAE_t = 0.0774
Coeff0 = −2.2095; T-stat = −30.6200
Coeff1 = −0.0509; T-stat = −3.4380
Colorants 01 00017 i048
Coeff2 = −0.0672; T-stat = −5.7746
Colorants 01 00017 i049
Coeff3 = −0.1204; T-stat = −4.6107
Colorants 01 00017 i050
Coeff4 = −0.2012; T-stat = −5.9598
Colorants 01 00017 i051
Coeff5 = −0.1825; T-stat = −3.3489
Colorants 01 00017 i052
Coeff6 = 0.5413; T-stat = 137513
Colorants 01 00017 i053
Table 8. Comparison of fragments responsible for the destruction of dyes in light fastness and sensitivity of dyeings to oxygen bleaching tests.
Table 8. Comparison of fragments responsible for the destruction of dyes in light fastness and sensitivity of dyeings to oxygen bleaching tests.
The Primary or Substituted Amino GroupAzo-Bond and the Primary or
Substituted Amino Group
Aromatic Chain and Nitrogen of Azo Group
Coeff3 = −1.037494, T-stat = −6.3766
LF-W-3, Table 2
Coeff10 = −0.55531, T-stat = −6.5707
LF-W-10, Table 2
Coeff3 = 1.477873, T-stat = 4.8356
OB-W-3, Table 3
Coeff4 = 2.706476, T-stat = 7.3958
OB-W-4, Table 3
Coeff2 = −2.4247, T-stat = −17.7498
LF-PA-2, Table 5
Coeff8 = −0.42405, T-stat = −5.5203
LF-PA-8, Table 5
Coeff6 = −1.66501, T-stat = −19.3869
LF-PA-6, Table 5
Table 9. Comparison of fragments responsible for stabilization of dyes in light fastness and sensitivity of dyeings to oxygen bleaching tests.
Table 9. Comparison of fragments responsible for stabilization of dyes in light fastness and sensitivity of dyeings to oxygen bleaching tests.
Azo Group in a Chain of Conjugated Double BondsAzo Group in a Chain of Conjugated Double Bonds and Sulphonic GroupAzo-Bond in a Chain of Conjugated Double Bonds and Carbamide Group
Coeff7 = 0.288798, T-stat = 4.1107
LF-W-7, Table 2
Coeff9 = −1.134224, T-stat = −5.6996
OB-W-9, Table 3
Coeff5 = 0.758169, T-stat = 8.1588
LF-PA-5, Table 5
Coeff9 = 0.416039, T-stat = 7.6620
LF-PA-9, Table 5
Coeff10 = 0.552282, T-stat = 4.6392
LF-PA-10, Table 5
Table 10. Comparison of fragments responsible for positive impact on wash fastness and sorption of dyes on wool and cotton.
Table 10. Comparison of fragments responsible for positive impact on wash fastness and sorption of dyes on wool and cotton.
Two Azo-Bonds in a Chain of Conjugated Double BondsAzo-Bond in a Chain of Conjugated
Double Bonds and Terminal Hydrophobic Terminal Group
Coeff5 = 0.199498, T-stat = 3.3687
WF-W-5, Table 4
Coeff6 = 0.511187, T-stat = 4.1279
WF-W-6, Table 4
Coeff8 = 0.002434, T-stat = 5.8381
A-C-8, Table 6
Table 11. Comparison of fragments responsible for negative impact on wash fastness and adsorption of dyes on wool and cotton.
Table 11. Comparison of fragments responsible for negative impact on wash fastness and adsorption of dyes on wool and cotton.
Sulphonic GroupAzo-Bond and Hydrophilic Terminal Group
Coeff3 = −0.335361, T-stat = −3.8736
WF-W-3, Table 4
Coeff7 = −0.532824, T-stat = −3.5236
WF-W-7, Table 4
Coeff10 = −0.01152, T-stat = −6.5432
A-C-10, Table 6
Coeff9 = −0.06541, T-stat = −4.0627
A-C-9, Table 6
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Ran, J.; Pryazhnikova, V.G.; Telegin, F.Y. Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration. Colorants 2022, 1, 280-297. https://doi.org/10.3390/colorants1030017

AMA Style

Ran J, Pryazhnikova VG, Telegin FY. Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration. Colorants. 2022; 1(3):280-297. https://doi.org/10.3390/colorants1030017

Chicago/Turabian Style

Ran, Jianhua, Victoria G. Pryazhnikova, and Felix Y. Telegin. 2022. "Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration" Colorants 1, no. 3: 280-297. https://doi.org/10.3390/colorants1030017

APA Style

Ran, J., Pryazhnikova, V. G., & Telegin, F. Y. (2022). Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration. Colorants, 1(3), 280-297. https://doi.org/10.3390/colorants1030017

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