Pasture Science and other Crops

Estimation of secondary compounds in Tithonia diversifolia from the regrowth age and primary metabolites

0000-0002-6660-8102M. Silva-Déley¹  , 0000-0003-3772-5200Blanca M. Toro-Molina¹  , 0000-0002-4505-4438D. M. Verdecia-Acosta^{4  *}  , 0000-0001-9590-6451E. Chacón-Marcheco¹  , 0000-0001-9065-7126J. A. Roca Cedeño³  , 0000-0001-5195-1496J. L. Ledea-Rodríguez³  , 0000-0002-0956-0245J. L. Ramírez-De la Ribera²  , 0000-0003-1424-6311R. S. Herrera⁵ 

¹Facultad de Medicina Veterinaria. Universidad Técnica de Cotopaxi. Cantón Latacunga, El Ejido, Sector San Felipe, Ecuador

²Facultad de Ciencias Agropecuarias, Universidad de Granma, Apartado Postal 21, Bayamo, CP. 85 100, Granma, Cuba.

³Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López. Carrera de Medicina Veterinaria. Calceta-Manabí-Ecuador

⁴Universidad Autónoma de Baja California Sur (UABCS) km 5.5. CP 23080. La Paz, Baja California Sur. México

⁵Instituto de Ciencia Animal, Apartado Postal 24, San José de las Lajas, Mayabeque, Cuba.

ABSTRACT

With the objective of determine the effect of regrowth age and the content of primary metabolites (nitrogen, fructose, glucose and sucrose) on the estimation of secondary metabolites in Tithonia diversifolia this study was develop fallowing a random block design with three treatments (60, 120 and 180 days) and six repetitions. For the estimation of this compounds the content of nitrogen (N), glucose (Glu), fructose (Frut), sucrose (Suc) and the plant maturity (age) was evaluated. For the validation the difference between the observed and estimated values was established as well as the relation of them and the variability. For the relation between both values (observed and estimated) when this one is near to unit (1) and to the variability (VC). During the rainy where there were relations between estimated and observed values between 0.84 and 1.39, as well as variation coefficients between 0.01-13 %; the highest values for VC at 60 days were found in total condensed tannins, raffinose and flavonoids (6.96, 8.63 and 8.17 %), 120 days free condensed tannins and saponins (10.88 and 10.18 %), during the dry season the relation values of 0.96 to 1.07 for the studied metabolites, except for verbascose, alkaloids and total steroids which were away from the unit (1) with 0.64, 0.76 and 1.44, respectively. So, it is showed that the estimation of secondary metabolites from the multiple regression lineal models in both seasons of the year, they can be applied as result that regardless to the different criteria that exits about the biochemical paths for the synthesis of them, their precursors will be the primary metabolites because of the dependence of the content of nitrogen, glucose, fructose and sucrose, age and plant phenology.

Key words: sugars; mathematical model; nitrogen; estimation; validation

Due to the own characteristics of tropical grasses, which has low digestible protein levels and a high fiber rate, the foliage of the shrubs it has been considered, in many cases, as a nutritional strategy in the ruminants supplementation in the tropic, with the purpose on improvement the productive and alimentary level of animals, mainly during the lack of forage periods (^{Verdecia et al. 2020}). In this sense Tithonia diversifolia is one of the non legumes plants considered as promising for their use in the feeding of different animal’s species (^{Rivera et al. 2018}) and it use has been increasing in the last years (^{Li et al. 2020}).

It is important to taking into account an element that in certain circumstance can constitutes a limitation in the use of these resources and it is the presence of secondary metabolites. During millions of years most of this species of plants has been survived thanks to their ability for producing substances that will protect them of their predatory. Even when some of these compound (condensed tannins, phenols, alkaloids, oligosaccharides and saponins) are able to produce a violent and immediate reaction, in most of the cases has a refine effect which is show with the prolonged ingestion (^{Li et al. 2020}).

Secondary compounds as tannins, phenols, flavonoids and alkaloids constitutes defense mechanisms against the pathogen microorganisms and the predation by insects or herbivorous. Also, they are important in the interaction of the plant with its environment, when attracting organisms that pollinate and disperse the seeds (^{Isah 2019}). These ones, however, affect the metabolic processes of the animal and the growth rate of some microorganisms after their intake (^{Guillén-Andrade et al. 2019}). In accordance with ^{Martín (2017)} and ^{Isah (2019)}, the presence and concentration of these compounds can vary between species due to the effects of the biotic (^{Herrera et al. 2020}) and abiotics factors, aspects which favor decrease of the photosynthetic activity of the plant and as a result the levels of the carbohydrates (glucose, fructose and sucrose) decreased and they are move for the production of secondary metabolites; as well as, the nitrogenous nutrients are also destined to the synthesis of more complex substances coming from the plant secondary metabolism as defense mechanism (^{Verdecia et al. 2021}).

So, it is important to know the effect of the regrowth age and content of primary metabolites (nitrogen, fructose, glucose and sucrose) on the estimation of secondary metabolites in Tithonia diversifolia.

Materials and methods

Research area, climate and soil. The study was developed in areas from the Departamento Docente-Productivo de la Universidad de Granma, which is located in the southeast of Cuba, in Granma province, at 17.5 km from Bayamo city. Studies during two years were performed (2014-2015), and two seasons were considered, the rainy (May-October) and dry (November-April).

The soil was calcic haptustept (^{Soil Survey Staff 2014}), with pH of 6.2. The content of P₂O₅, K₂O and total N was 2.4, 33.42 and 3 mg/100g of soil, respectively, with 3.6 % of organic matter.

During the rainy season, the rainfalls were of 731.4 mm; the average, minimum and maximum temperature registered values of 26.73, 22.31 and 33.92 ºC, respectively and the average, minimum and maximum relative humidity was 80.78, 51.02 and 96.22 %, respectively. In the dry season, the rainfalls reached values of 270 mm; the temperature was 24.05, 18.29 and 31.58 ºC for the average, minimum and maximum, respectively and the minimum, average and maximum relative humidity with averages of 76.21, 44.16 and 97.03 %, values which corresponded with the historical mean for the region.

Treatment and experimental design. A random block design with four replications (plots) was used for taken the samples and the regrowth ages 60, 120 and 180 days were considered as treatments.

Procedures. For the established specie (T. diversifolia) at the beginning of each seasonal period a homogeneity cut was made at 15 cm above the soil. The samplings in each plot (0.0282 ha) were carried out taking 10 plants row eliminating the first one and the last one to avoid border effect in an area of 0.5. The sample was homogenized and later weighed manually separating leaves, petioles and stems, these latter with diameter less than 2cm considered as edible biomass. Later 1kg for each of the treatments was taken for the laboratory analysis. During the experimental stage irrigation and fertilization was not applied.

Chemical analysis. The samples were dried at room temperature in a dark and ventilated room for 12 days. Then they were milled 30 g for each repetition to a 1mm particle size. They were stored in amber bottles at room temperature until their analysis.

It was determined: nitrogen (N) in accordance with the ^{AOAC (2016)}, while the contents of glucose, fructose and sucrose according to the titration method of Lane and Eynon, which is based on the reduction of Cu ⁺² to Cu ⁺¹ by the reducing sugars, using as indicator methylene blue (^{AOAC 2016}).

Models for the estimation of secondary metabolites. For the estimation of secondary metabolites, the multiple linear regression models obtained by ^{Verdecia (2014)} (tables 1 and 2) were considered, as a way to validate the functioning of them, from the obtained results (of primary metabolites) at the ages of 60, 120 and 180 days previously described in chemical analysis. Those multiple regression equations are fallowing showed where the age and nitrogen, glucose, fructose and sucrose are related in both seasons of the year.

Table 1 Multiple linear regression equations for Tithonia diversifolia in the dry season 

Metabolites	Models	R^2	Significance of the model
Total phenols	17.91-0.03(age)-703.28(fructose)	0.99	P<0.001
Total tannins	10.513-0.019(age)-626.99(glucose)	0.99	P<0.001
Total condensed tannins	33.2-0.06(age)-0.35(nitrogen)	0.81	P<0.0001
Total bound tannins	8.18+0.01(age)+197.12(fructose)	0.99	P<0.0001
Free condensed tannins	0.67+0.01(age)+0.06(nitrogen)-84.97(fructose)	0.99	P<0.0001
Stachyose	0.0015+0.000009(age)+0.21(fructose)	0.99	P<0.0001
Raffinose	0.02-0.00005(age)+3.35(fructose)-1.47(sucrose)	0.99	P<0.0001
Flavonoids	12.78+0.18(age)-0.27(nitrogen)	0.99	P<0.0001
Saponins	-0.17+0.01(age)+53.28(fructose)	0.99	P<0.0001
Alkaloids	0.57+0.0026(age)+4.16(fructose)	0.99	P<0.0001
Triterpenes	6.54+0.01(age)-73.89(glucose)	0.99	P<0.0001
Total steroids	4.32+0.05(age)	0.99	P<0.0001

Table 2 Multiple linear regression equations for Tithonia diversifolia in the dry season 

Metabolites	Models	R^2	Sig. model
Total phenols	4.77+0.01(age)	0.98	P<0.001
Total condensed tannins	8.88+0.02(age)	0.90	P<0.001
Total bound condensed tannins	6.76+0.01(age)+162.30(glucose)	0.99	P<0.001
Free condensed tannins	-3.08+0.019(age)+543.39(glucose)-85.07(fructose)	0.99	P<0.001
Verbascose	0.01-0.000032(age)	0.94	P<0.001
Flavonoids	21.83+0.19(age)-0.37(nitrogen)	0.99	P<0.001
Saponins	1.25+0.01(age)	0.85	P<0.001
Alkaloids	1.30+0.0016(age)-0.01(nitrogen)-45.82(glucose)	0.99	P<0.001
Triterpenes	-0.73+0.04(age)+0.06(nitrogen)+279.72(glucose)	0.99	P<0.001
Total steroids	5.01+0.055(age)	0.99	P<0.001

For the validation of these models the ^{Giraldo et al. (1998)} criteria were fallowed. For which the difference between observed and estimated values were established, as well as the relation of these ones and the variability. For the relation between both values (observed and estimated), when these one is near to the unit (1) and the variability (VC) is within the normal ranges (16) the model prediction is correct.

Statistical analysis and calculation. Kolmogorov-Smirnov tests were performed for the normal distribution of data (^{Massey 1951}), homogeneity of variances (^{Bartlett 1937}). The statistical program SPSS version 22 was used.

Results

In table 3 are showed the results of the validation models for the content of secondary metabolites for Tithonia diversifolia during the rainy season where relations between estimated and observed values between 0.84 and 1.39 were found; as well as variation coefficients between 0.01-13 %; the highest values for VC at 60 days were found in total condensed tannins, raffinose and flavonoids (6.96, 8.63 and 8.17 %), 120 days Phenols condensed total and saponins (10.88 and 10.18 %) and 180 days phenols condensed tannins, raffinose and saponins (7.09, 12.37 and 9.03 %), respectively.

Table 3 Estimation of secondary metabolites of Tithonia diversifolia during the rainy season from multiple linear regression models 

Age (days)	60				120				180
Metabolites	Observed	Estimated	Relation Est/Obs	VC, %	Observed	Estimated	Relation Est/Obs	VC, %	Observed	Estimated	Relation Est/Obs	VC, %
TF	6.20	6.16	0.99	0.46	12.38	12.55	1.01	0.96	7.58	7.87	1.04	2.65
TT	0.56	0.78	1.39	3.21	5.36	5.34	0.996	0.26	3.17	3.22	1.02	1.11
TCT	14.29	15.77	1.10	6.96	13.66	15.01	1.10	6.66	14.56	13.17	0.90	7.09
TBCT	11.37	11.57	1.02	1.23	9.54	9.87	1.03	2.40	10.79	11.28	1.05	3.14
FCT	2.92	2.44	0.84	2.66	4.13	3.54	0.86	10.88	3.77	3.49	0.93	5.45
Stac	0.005	0.0051	1.02	1.40	0.0032	0.0031	0.97	2.24	0.0046	0.0045	0.98	1.55
Raf	0.02	0.0226	1.13	8.63	0.0096	0.0119	1.24	5.13	0.012	0.0143	1.19	12.37
Fla	11.50	12.91	1.12	8.17	24.43	25.90	1.06	4.13	38.39	38.06	0.99	0.61
Sap	1.26	1.18	0.94	4.64	1.34	1.16	0.87	10.18	2.25	1.98	0.88	9.03
Alk	0.79	0.78	0.99	0.90	0.90	0.89	0.99	0.79	1.07	1.06	0.99	0.66
Trit	6.24	6.13	0.98	1.26	7.68	7.40	0.96	2.63	8.30	7.78	0.94	4.57
ST	7.46	7.32	0.98	1.34	10.70	10.32	0.96	2.56	13.80	13.32	0.97	2.50

TF: Total phenos; TT: Total tannins; TCT: Total condensed tannins; TBCT: Total bound condensed tannins; FCT: Free condensed tannins; Stac: Stachyose; Raf: Raffinose; Fla: Flavonoids; Sap: Saponines; Alk: Alkaloids; Trit: Triterpenes; ST: Total steroids

During the dry season (table 4) values of relation from 0.96 to 1.07 were obtained for the studied metabolites, except for verbascose, alkaloids and total steroids which were more away from the unit (1) with 0.64; 0.76 and 1.44, respectively. While, the variability was between 0.13 to 4.00 %, although for verbascose, saponine, alkaloid and steroid total were between 6 and 10 %, with the highest differences between the estimated and observed values in verbascose and the steroids of 0.0016 and 3.9 g kg^-1.

Table 4 Estimation of secondary metabolites of Tithonia diversifolia during the dry season from multiple linear regression models 

Age (días)	60				120				180
Metabolites	Observed	Estimated	Relation Est/Obs	VC, %	Observed	Estimated	Relation Est/Obs	VC, %	Observed	Estimated	RelationEst/Obs	VC, %
TF	5.36	5.37	1.00	0.13	5.82	5.97	1.03	1.80	6.47	6.57	1.02	1.08
TCT	10.46	10.08	0.96	2.62	11.05	11.28	1.02	1.46	13.13	12.48	0.95	3.59
TBCT	8.77	8.53	0.97	1.96	9.35	8.87	0.95	3.73	10.13	9.41	0.93	5.21
FCT	1.69	1.65	0.98	1.69	1.70	1.66	0.98	1.68	3.00	2.94	0.98	1.43
Verb	0.0071	0.0081	1.14	9.30	0.0043	0.0062	1.44	5.59	0.0032	0.0042	1.31	9.11
Fla	15.68	16.05	1.02	1.65	28.55	29.14	1.02	1.45	44.71	45.29	1.01	0.91
Sap	1.66	1.85	1.11	7.66	1.87	2.45	1.31	8.99	2.37	3.05	1.29	7.74
Alk	0.79	0.60	0.76	9.33	0.99	0.82	0.83	3.28	1.19	1.06	0.89	8.17
Trit	6.31	6.47	1.03	1.77	7.78	8.16	1.05	3.37	9.05	9.67	1.07	4.68
ST	8.28	5.34	0.64	10.52	11.73	5.67	0.48	9.25	14.90	11.00	0.40	6.22

TF: Total phenols; TT: Total tannins; TCT: Total condensed tannins; TBCT: Total bound condensed tannins; FCT: Free condensed tannins; Stac: Stachyose; Raf: Raffinose; Fla: Flavonoids; Sap: Saponines; Alk: Alkaloids; Trit: Triterpenes; ST: Total steroids

Discussion

sugars require advanced technology euipment and reagents with certain chemical There are evidences that with the use of adequate mathematical methods can save time and resources to calculate or predict certain variables. So, ^{Herrera and Hernández (1986)} predicted the grass digestibility from their chemical constituents and ^{Ramírez (2010)} the digestibility of five tropical grasses from the structural and soluble components. Likewise, ^{Verdecia (2014)} used the age and primary components for his prediction and ^{Estrada-Jiménez et al. (2023)} by artificial intelligence used regression algorithms.

^{Verdecia (2014)} pointed out that the plants used a significant amount of the absorbed carbon, nitrogen and the energy to the synthesis of a wide varieties of organic molecules that didn’t seem to have a direct function in photosynthetic and respiratory process, nutrients assimilation, solute transport or synthesis of proteins, carbohydrates or lipids, and they named secondary metabolites and to the variability of these ones with the age.

Lineal multiple regression equations were established where the dependent variable were the secondary metabolites and the independent variable consisted on the age, nitrogen, glucose, fructose and sucrose. For the selection of the better expression and its goodness of fit were taking into account the criteria ^{Keviste et al. (2002)}, ^{Guerra et al. (2003)} and ^{Torres and Ortiz (2005)} related with high value of the determination coefficient (R²), high signification of the expression and its indicators, low standard errors of the expression and its indicators, residues analysis and test of concordance between the observed and estimated values.

That is why for T. diversifolia (table1) in the rainy season there was not fit for the Verbascose, while in the dry season (table 2) the stachyose, raffinose and total tannins didn’t has fit in their models, the rest of metabolites has values of R² higher than 0.85 and the total phenols, total bound condensed tannins, free condensed tannins, flavonoids, alkaloids, triterpenes and total steroids were highlighted with values of 0.99.

The biosynthetic ways of the secondary metabolites of the plants is a topic that powerfully calls the attention. The synthesis of each compound tend to be restricted to specific states of the development of each organism, specialized cells and stress period caused by the lack of nutrients or by the attack of microorganisms. This phenomenon is due to the formation, dependent of phase, of the specific enzyme and their activity, which mean that the expression of the secondary metabolite is based on a differentiation process (^{Lozano and Pichón 2018} and ^{Cruz 2019}).

From collateral ways to the photosynthesis, the plants synthesized the secondary metabolites (^{Sieiro Miranda et al. 2020}), which has non nutritional functions, but very important for their survival. They are compounds that are used to protect from external factors, in which are the flavonoides, tannins, lignans, coumarisms, alkaloides, terpenes and saponines, among others. They are stored in some organelles of the cell. For example, the flavonoides, which are synthesized in the chloroplast and are transported to the endoplasmic reticulum and to the vacuoles (^{García Parra et al. 2018}).

On the other hand, according to ^{Verdecia et al. (2021)} reports there is a close relation between the carbohydrates composition, age and the N content, on the secondary metabolites content. In accordance with this study, in addition of these ones, the conditions of soil and climate can determine the composition of some compounds, particularly the α-galactosides. The content of sucrose and verbascose could be genetically determined, while those of raffinose and stachyose depend, in less extension, of the environmental conditions, and mainly of the photosynthetic activity of the plants and the production of secondary metabolites.

Taking the relation of the secondary compounds with the age and the content of their precursors metabolites (nitrogen, glucose, fructose and sucrose). It is important to estimate from the models obtained by ^{Verdecia (2014)} for establishing of a low cost and precise system, since, for the determination of secondary metabolites and sugars is require advanced technology equipments and reagents with certain chemical specification which make possible that their analysis will be difficult and limit the number of samples that can be performed. The country and in special the eastern region didn’t have the sufficient analytical basic for the quantification of the secondary metabolites, which is limited by the lack of material resources and financial availability to obtain them.

It could be studied other expressions, but the main objective was to determined by found models of relatively simplicity, easy to interpret and it was not of difficult management and interpretation by the technicians, specialists, researcher up to small and medium farmers and farmers.

It is of state that the models (tables 1 and 2) were specifics for each plant in each seasonal period and there was not uniformity in the amount of primary metabolites that integrate each expression and there was always significant effect of the age.

These models reaffirm the previous principles and showed that is necessary to develop future researchers which take into account guarantee these results in other edaphoclimatic conditions and in other species, as well as to develop researchers that allow to deep and to complement the obtained results. Hence that the results showed in tables 3 and 4 about the estimation from the models showed the close relation between the precursor metabolites (nitrogen, glucose, fructose and sucrose) and the age on the different secondary metabolites, since only the verbascose and steroids in T. diversifolia in the dry season, had not close relations to one which make difficult according to ^{Giraldo et al. (1998)} that the model prediction will be perfect.

The natural substances which are form in the plants are synthesized through many metabolic ways and are divided into primary and secondary metabolites, been these latter the most important regarding their applications in the nutrition and, animal and human health. The primary metabolites are considered essentials or precursors; they are nutritional molecules and can be used in the synthesis of compounds of high structural and chemical complexity. In this group are the soluble carbohydrates, proteins, lipids and nitrogenous compounds, among others (^{Wang et al. 2020}).

In contrast to other organisms, the plants destined great amount of the assimilated carbon and of the energy to the synthesis of wide variety of organic molecules that seems not to have a direct relation with the photosynthetic process, respiratory process, nutrients assimilation and solutes transport, among others (^{Giménez et al. 2020}).

These elements justify the reason for the primary metabolites (nitrogen, glucose, fructose and sucrose) were used in the predictions of secondary metabolites. The nitrogen is one of the elements of great importance in the yield and quality of plants because the influence on the synthesis of phenolic compounds. It has been showed that fertilization decreased these substances (^{Li et al. 2020}) in early stages of the plant development (^{Cartera et al. 2011}). These was reaffirm in Labisia pumila when increasing the fertilization from 0 to 270 g kg^-1 of N ha^-1 decreased the polyphenols (^{Ibrahim et al. 2011}).

However, the clarification of the biosynthesis of phenolic compounds could provide the precise magnitude of the differences in the content of them between species and it tissues, and basically its ecological consequences and will evolve (^{Salminen and Karonen 2011}). Although it cannot discount that the future knowing of genes which codify this process (^{Hichri et al. 2011}) modified or clarified this variety of criteria.

^{Herlina et al. (2018)} stated that the nitrogenous fertilization has effect on the alkaloids content. Optimums concentrations of this element stimulates the formation of vegetative organs and increase the alkaloids and flavonoids, while ^{Herlina et al. (2019)} showed that their high concentrations were found in fruits, roots, leaves and stems, in this order. However, recent studies ^{Ryan et al. (2014)} showed that this response is influence by the concentration of CO₂ in the environment.

For this, the relation C/N is frequently used as a general indicator of the levels of natural chemical protection of the plant against the impact of some biotic factors. The ingredients of the metabolism for the production of rich carbon substances are associated to the increase of the synthesis of defensive compounds as phenols and terpenoids (^{Karolewski et al. 2010}).

For the understanding of these results it was necessary to start with the collection of essential aspects related with the synthesis of secondary metabolites and some factors that influence, on their content due to the diversity of criteria that there are about these topics.

The performance of secondary metabolites in T. diversifolia according to ^{Verdecia et al. (2021)} is due to the characteristics of each species and the effects of the edaphoclimatic conditions in which the culture is developed. ^{Herrera et al. (2017b)} found in this plant between 0.43 and 2.6 g kg^-1 of total phenol and of 3 .1 up to 4.9 g.kg^-1 of condensed tannins. While, ^{Paumier et al. (2020)} and ^{Herrera et al. (2017a} and ²⁰²⁰⁾ reported of 6.4 to 8.7 g kg^-1 total phenols, although the triterpenes and total steroids were higher with 10.2 to13.9 and of 25.4 to 29.2 g kg^-1 of dry matter, respectively.

In most of the cases these compounds have defensive functions and their concentrations depends on the nutritional and physiological status of the plant, and the proportion of phenolic compounds derived of the lignin which take part of the fibrous fraction (^{Cabrera-Carrión et al. 2017} and ^{Hernández-Espinosa et al. 2020}).

The results of the estimation of secondary metabolites from the age and precursor metabolites are similar to those obtained by ^{Verdecia (2014)} that when grouping different species of trees, shrubs and leguminous during the rainy and dry season the evaluated species in this research (T. diversifolia) showed the best results integrally with the lower concentrations of phytochemical compounds.

Conclusions

It was showed that the estimation of secondary metabolites from the multiple linear regression models in both season of the year, can be applied as result that independently of the different criteria about the biochemical pathways for the synthesis of these ones, their precursors will be the primary metabolites by the dependence on the content of nitrogen, glucose, fructose and sucrose, age and plant phenology.

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Received: April 25, 2023; Accepted: July 15, 2023

^*Email: [e-mail]

Conflict of interest: The authors declare that there was not conflict among them.

Authors contribution: Lucia M. Silva-Déley: Conceptualization, Data curation, Funding acquisition, Methodology, Validation, Visualization, Writing - original draft. Blanca M. Toro-Molina: Conceptualization, Data curation, Methodology, Validation, Writing - original draft. D.M. Verdecia-Acosta: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing. E. Chacón-Marcheco: Investigation, Data curation, Formal analysis, Funding acquisition, Validation, Visualization. J. A. Roca-Cedeño: Data curation, Formal analysis, Funding acquisition, Validation, Visualization, Writing - original draft. J. L. Ledea-Rodríguez: Data curation, Formal analysis, Writing - original draft, Funding acquisition, Validation, Visualization. J.L. Ramírez-de la Ribera: Conceptualization, Investigation, Data curation, Project administration, Supervision, Writing - review & editing. R.S. Herrera: Conceptualization, Investigation, Data curation, Methodology, Supervision, Writing - review & editing