Bioactive Compounds Concentrations and Stability in Leaves of Ilex paraguariensis Genotypes

: Yerba mate consumption has been stimulated by scientiﬁc discoveries that have identiﬁed high concentrations of bioactive compounds and their health beneﬁts. We were interested in quantifying caffeine, theobromine, total phenolic compounds and protein concentrations in yerba mate genotypes and their stability over four years on the same plants. Mature leaves from adult yerba mate genotypes selected on a provenance and progenies trial were collected in August of 2015, 2016, 2017 and 2018. Methylxanthines (caffeine and theobromine) were quantiﬁed by High-Performance Liquid Chromatography (HPLC), total phenolic compounds by Folin–Ciocalteau spectrophotometric method and total protein analysis by the micro-Kjeldahl method. Genotype stability was analyzed through the AMMI (Additive Main effects and Multiplicative Interaction) procedure. Our results indicate large variations between genotypes regarding caffeine (0.035 to 2.385 g 100 g − 1 ), theobromine (0.0004 to 1.772 g 100 g − 1 ), total phenolic compounds (7.028 to 9.424 g 100 g − 1 ), proteins (10.39 to 16.58 g 100 g − 1 ) contents and the high stability of those compounds over the four evaluated years, on the same plants. This information, combined with the stability of bioactive compounds, establishes a signiﬁcant potential for innovation within the Ilex paraguariensis species.


Introduction
Ilex paraguariensis A.St.-Hil.or yerba mate is native to South America and traditionally consumed as a tonic and non-alcoholic stimulant beverage.From the first reports of its consumption by Guarani Indians to current consumption models, yerba mate has played a central role in the socio-economic and cultural evolution of South American people.Mainly consumed in Argentina, Brazil, Paraguay and Uruguay, its consumption has become a cultural expression [1], reaching 10 kg per person per year in some regions [2].It is a competitive product in terms of price, quality, regulatory compliance and innovation [3], although regional consumption forms do not favor its commercial expansion to expression markets, with significantly lower consumption than its competitors Coffea spp. in Europe and North America and Camelia sinensis in Asia and Europe [4].
The genetic resources of yerba mate are fundamentally important for selecting specific genotypes that can subsidize the industry and the emergence of new products, thus directing science and innovation in this market [5,6].Focusing on regional consumption, mate breeding programs have been selecting genotypes with characteristics adapted to Forests 2023, 14, 2411 2 of 18 biotic and abiotic factors and the high production of commercial biomass for decades [5].Recently, emphasis has been placed on selecting genotypes that contain higher concentrations of certain chemical compounds [4,[7][8][9][10].Such findings have aroused interest and increased consumption worldwide, mainly because of the health benefits provided by these compounds [3,4,11].
Consumed among indigenous people of different ethnic groups, yerba mate has been empirically used to treat different pathologies.Recently, scientific proof of its antioxidant, anti-inflammatory, anti-obesity and cardioprotective functions has been reported [2,4,12].Yerba mate's dry leaf biomass composition is quite rich and diverse, including nutrients, minerals and water-soluble vitamins [4].Its chemical composition contains polyphenols (phenolic acids, flavonoids), alkaloids (methylxanthines, including caffeine, theobromine, theophylline) and terpenes (carotenoids, saponins) [1].Despite its variety of compounds, most studies have been focused on evaluations of commercial products or brands of yerba mate [3,4,11,13] and not on the level of individuals, which can help breeding programs achieve effective genetic material selection, with superior interests for the industry.
Chemical compounds present in yerba mate are responsible for its stimulant and medicinal properties [2,4,12].However, the absence of genetic materials selected for this purpose makes the variation in this content one of the limitations of its use, depending strongly on the preparation mode and quantity ingested but mainly on the raw material used for production [14][15][16].
There is a gap in studies on the stability of concentrations of these bioactive compounds during different years in the same plants, and our hypothesis is that this variation is not influenced by the harvest year but by genetic factors.Thus, we evaluated caffeine, theobromine, total phenolic compounds, and protein on leaves of yerba mate genotypes for four consecutive years on the same plants to determine their composition and stability over time.

Plant Material and Sample Preparation
Fifty-five yerba mate half-sibling genotypes (seedlings originating from the same mother tree), obtained from natural pollination, were selected previously based on their productivity, leaf morphotype and sex from a provenance and progenies trial in Ivaí-PR, Brazil (25 • 01 S and 50 • 48 W, 600 m asl, 1500-1600 mm).This trial began in November 1997 with 156 progenies in 3 × 2 m spacing and full sun conditions [5], and voucher specimens were preserved at the Fernando Cardoso da Silva Herbarium (HFC) with voucher (HFC 10592) in Colombo-PR, Brazil.The Ivaí climate type, according to Köppen-Geiger's classification, was Cfa, corresponding to a humid subtropical climate with hot summers, infrequent frosts and the tendency of rains concentrated in summer months without a defined dry season [17].The means of the minimum annual temperatures were 16 • C (2015), 14  2018), and the mean annual relative humidity was 79% (2015), 78% (2016), 76% (2017) and 77% (2018).The soil was rhodic hapludox, comprising 72% clay, and was acidic with a low base saturation and a high aluminum saturation [18], and the relief was smoothly wavy with slopes around 4% [5].
In August of 2015, 2016, 2017 and 2018, mature leaves free of lesions were collected from adult mother trees (selected genotypes) at a height of ~1.6 m.Collections were consistently made from the same plants over the four years to standardize successive evaluations across multiple production cycles.Leaves were stored in "kraft" packages, identified and sent to the Non-Timber Products Technology Laboratory of Embrapa Florestas.Samples were dried in a microwave oven (power 1500 W, frequency 2450 MHz) for approximately Forests 2023, 14, 2411 3 of 18 4 min, alternating leaves position at 60 s intervals for homogenous drying [19].Subsequently, leaves were crushed, sieved at 0.5 mm, packed and stored in a freezer (−20 • C).
To determine the content of caffeine, theobromine and total phenolic compounds, aqueous extracts containing 0.1 g of plant material were prepared in 50 mL of ultrapure water type I heated to its boiling temperature (100 • C under pressure of 1 atm).Then, they were homogenized in ultrasound (Ultracleaner 1400 A) for 30 min, cooled to room temperature and filtered, with volume completed to 100 mL in a volumetric flask, and frozen (−20 • C) [20].

Determination of Methylxanthines
Sample preparation was carried out using the duplicate extraction process, generating two independent sample preparations.For each of these preparations, aliquots were collected to fill 2 vials (4 samples) [21].Extracts were thawed and manually homogenized for 30 s.About 2 mL of extracts were filtered in a 0.22 µm membrane with a syringe and holder.After that, an aliquot was transferred to a 1.5 mL amber vial with a teflon cap.These samples were injected directly into the chromatograph.Methylxanthines (caffeine and theobromine) were quantified through High-Performance Liquid Chromatography (HPLC) using Agilent 1260 Infinity model controlled by ChemStation software (Rev B.04.03-SP1-87) and equipped with a G1311B quaternary pump, G1329B automatic injector, DAD G4212B detector and FLD G1321B detector available at the Multi-User Laboratory of Environmental Equipment and Analysis of the Federal Technological University of Paraná (LAMEAA-UTFPR).The Acclaim 120 C18 column (Dionex ® 2.1 × 150 mm, 3 m, Lane Cove West, Australia) with an Acclaim C18 2.1 mm, 5 µm guard cartridge was used to separate compounds.The conditions used to separate compounds from the aqueous extracts (10 µL injection) were 30 • C with flow of 0.3 mL min −1 of eluent with the mobile phase A (H 2 O: acetic acid J.T. Baker-99.5:0.5 v/v) and B (Merck ® acetonitrile-100%, Rahway, NJ, USA).
A chromatogram of a standard sample and an extract sample was made to determine the retention time determined for caffeine and theobromine.Furthermore, using the equipment's own operating system software (ChemStation software, Rev B.04.03-SP1-87), an analysis of the purity content of these compounds was carried out.
The wavelength used to detect the compound was 280 nm (fixed).The gradient elution program was 0-8 min (4% B), 8-812 min (4%-85% B), 12-830 min (5% B).The compounds caffeine (1,3,7-trimethylxanthene) and theobromine (3.7-dimethylxane) were quantified using analytical curves obtained with Sigma ® standards for caffeine (R 2 = 0.99989) and theobromine (R 2 = 0.99964).The analytical curves were made in a range of 1 to 30 mg L −1 for caffeine and 0.1 to 10 mg L −1 for theobromine.The last square treatment of calibration data was applied to generate the linear regression.Based on calibration parameters described, the limits of quantification (LOQ) and limit of detection (LOD) were calculated.For quantification, the stock standard solutions were prepared in methanol, stored at −80 • C in dark and diluted in water for working solutions.The calibration curves in concentration range from 1 to 30 mg L −1 for caffeine and 0.1 to 10 mg L −1 for theobromine were prepared in water.The parameters used were described in the Anvisa RDC 166/2017 resolution and in the Eurachem Guide method validation guide [22,23].
The results were expressed in g of compound per 100 g of dry sample (g 100 g −1 ).We used 54 genotypes for caffeine and 55 genotypes for theobromine evaluation.For caffeine, one sample was lost during the analysis.

Determination of Total Phenolic Compounds
Quantification of total phenolic compounds was made according to Folin-Ciocalteau spectrophotometric method [24], with modifications.Briefly, 0.1 mL of extract, 6.0 mL of distilled water and 0.5 mL of Folin-Ciocalteau reagent were added to a volumetric flask and stirred for 1 min.Afterward, 2 mL of 15% Na 2 CO 3 solution was added and stirred for another 30 s. Final volume was adjusted with distilled water to 10 mL.The reaction was kept in dark at room temperature for 2 h, and subsequently, absorbances were recorded in a spectrophotometer at 760 nm.Analytical curve was obtained with total phenolic compounds (3,4,5-trihydroxybenzoic acid) between concentrations of 0.25 and 13 mg L −1 (R 2 = 0.9988), and the results expressed in mg of gallic acid were equivalent to the total phenolic compounds per gram of dry sample (mg GAE g −1 ).We used 55 genotypes for total phenolic compounds evaluation.

Determination of Moisture and Proteins
The analyses of moisture and total proteins followed the official methods from Association of Official Analytical Chemists International [25].To determine moisture content, we used a thermogravimetric method at 105 degrees, up to constant mass and total protein content by the micro-Kjeldahl method, using total nitrogen content multiplied by conversion factor of 6.25 to obtain the total protein content.We studied 54 genotypes for protein and moisture content.

Statistical Analysis
Genotype stability was observed throughout four years based on the existence of interaction with years analyzed by the AMMI (Additive Main effects and Multiplicative Interaction) procedure.This procedure complemented the deviance analyses obtained from a generalized linear model with Gamma probability function.The AMMI method was used to decompose sources of variation of the additive effects for genotypes and years and the multiplicative effects for interaction via main components [26].This method provides more accurate estimates of genotypic responses and a graphical interpretation of the results through the biplot procedure with scores of the interaction effects for each genotype and year plotted simultaneously [27].Interpretation is based on the magnitude and signal of coefficients for each main component axis.Low values indicate genotypes and/or years that contribute little or almost nothing to the interaction, demonstrating statistical stability.
Preliminarily, the model of casualized blocks, processed for each year, was adjusted according to the equation: where the concentration of caffeine, theobromine, total phenolic compounds and proteins of the 'i' genotype in the 'j'-th year and the 'k'-th block (y ijk ) was estimated by the overall mean (µ) of the concentration, comprising all the genotypes per block and per year; by the effect of the 'k'-th block in the 'j'-th year ( b a jk ); by the effect of the 'i'-th genotype ( g i ); by the effect of the 'j'-th year ( a j ; by the effect of the interaction of the 'i'-th genotype with the 'j'-th environment (ga ij ); added to the random error (ε ijk ).Following this adjustment, the model AMMI was applied in accordance with the analysis of joint variance proposed by Duarte and Vencovsky [28]: where the average response of the concentration of caffeine, theobromine, total phenolic compounds and proteins of the 'i' genotype in the year 'j' for the purpose of stability was obtained through the overall mean (µ ) of the concentration comprising all the genotypes by year; by the effect of the 'i' genotype (g i ); by the effect of the year 'j'(a j ); by the summation of the product between the singular value (λ k ), the singular vector (γ ik ) corresponding to the 'i'-th genotype in the 'k'-th column vector and the singular vector (α jk ) corresponding to the 'j'-th year in the 'k'-th row vector in the matrix of per-year genotype interaction; by the non-controlled trial variance, named noise (ρ ij ) of the classical interaction of the 'i' genotype with the year 'j', and the mean trial error (e ij ).The decomposition of the interaction matrix into row and column vectors was performed through the analysis of principal components.The recovery of variation, for the purpose of measuring genotypic stability, was only performed by the deterministic portion of the components most strongly Forests 2023, 14, 2411 5 of 18 associated with the interaction (rows and columns of the matrix (ga ij ), discarding the additional residue ρ ij .The interpretation of the biplot regarding the GxA interaction was carried out by observing the magnitude and sign of genotype and production year scores on the interaction axes.Therefore, low scores (close to zero) are indicative of genotypes that have contributed little or almost nothing to the interaction, characterizing them as stable.The "ward.D2" method minimizes the sum of the squares of the differences in axis scores within the groups formed during hierarchical clustering [29].

Results
As shown in Figure 1, the retention time in the standard sample was determined to be 8 min and 26.2 min for caffeine and theobromine, respectively.
interaction; by the non-controlled trial variance, named noise ( ) of the classical interaction of the 'i' genotype with the year 'j', and the mean trial error ( ).The decomposition of the interaction matrix into row and column vectors was performed through the analysis of principal components.The recovery of variation, for the purpose of measuring genotypic stability, was only performed by the deterministic portion of the components most strongly associated with the interaction (rows and columns of the matrix ( ), discarding the additional residue .The interpretation of the biplot regarding the GxA interaction was carried out by observing the magnitude and sign of genotype and production year scores on the interaction axes.Therefore, low scores (close to zero) are indicative of genotypes that have contributed li le or almost nothing to the interaction, characterizing them as stable.The "ward.D2" method minimizes the sum of the squares of the differences in axis scores within the groups formed during hierarchical clustering [29].

Results
As shown in Figure 1, the retention time in the standard sample was determined to be 8 min and 26.2 min for caffeine and theobromine, respectively.The purity content of these compounds was carried out, obtaining responses of >99%.Linear regression showed a correlation coefficient above 0.9999 for both compounds.The limits of quantification (LOQ) presented values of 0.6115 and 0.1849 mg L −1 for caffeine and theobromine, respectively.The limit of detection (LOD) presented values of 0.2018 and 0.06102 mg L −1 for caffeine and theobromine, respectively.Calibration curves for caffeine and theobromine concentrations are shown in Figure 2.
The purity content of these compounds was carried out, obtaining responses of >99%.Linear regression showed a correlation coefficient above 0.9999 for both compounds.The limits of quantification (LOQ) presented values of 0.6115 and 0.1849 mg L −1 for caffeine and theobromine, respectively.The limit of detection (LOD) presented values of 0.2018 and 0.06102 mg L −1 for caffeine and theobromine, respectively.Calibration curves for caffeine and theobromine concentrations are shown in Figure 2. The joint analysis of variance for caffeine, theobromine, total phenolic compounds and proteins revealed significant differences (p < 0.01) for genotypes (G), evaluation years (A) and their interaction (GxA).We observed that, even with only four evaluation years, it was possible to detect differences among them, indicating rather varied conditions for genotype evaluation.In terms of absolute F-statistic values, we noted that genotype variation source magnitude was much higher for caffeine and theobromine, being accountable for the majority of the observed variation.For total phenolic compounds and proteins, the magnitude of the year-variation source was higher.It can thus be inferred that the year's effects contributed more significantly to the variation of these traits.The interaction results for all studied traits indicated either the need to identify and select genotypes capable of capturing genetic gains across different yield years or that those are less susceptible to environment variations due to the year.Based on these results, a more detailed study of the significant interaction was conducted through the analysis of genotypes' phenotypic stability via AMMI.The multiplicative effect of the interaction GxA was diagnosed through the analysis of the main compounds by decomposing the sum of the squares of the interaction (SQ) in three axes (IPCA), adopting the positive criterion (use of hypothesis tests) through the F-test presented by Cornelius et al. [30].

Caffeine
Descriptive statistics results suggest wide genetic variability for caffeine content in yerba mate genotypes, with genotype strongly influencing its concentration.The first component describes 58.35% of the variability observed in the genotype-environment interaction (Table 1).The individually examined years tended to contribute more to the GxE interaction than genotypes.The joint analysis of variance for caffeine, theobromine, total phenolic compounds and proteins revealed significant differences (p < 0.01) for genotypes (G), evaluation years (A) and their interaction (GxA).We observed that, even with only four evaluation years, it was possible to detect differences among them, indicating rather varied conditions for genotype evaluation.In terms of absolute F-statistic values, we noted that genotype variation source magnitude was much higher for caffeine and theobromine, being accountable for the majority of the observed variation.For total phenolic compounds and proteins, the magnitude of the year-variation source was higher.It can thus be inferred that the year's effects contributed more significantly to the variation of these traits.The interaction results for all studied traits indicated either the need to identify and select genotypes capable of capturing genetic gains across different yield years or that those are less susceptible to environment variations due to the year.Based on these results, a more detailed study of the significant interaction was conducted through the analysis of genotypes' phenotypic stability via AMMI.The multiplicative effect of the interaction GxA was diagnosed through the analysis of the main compounds by decomposing the sum of the squares of the interaction (SQ) in three axes (IPCA), adopting the positive criterion (use of hypothesis tests) through the F-test presented by Cornelius et al. [30].

Caffeine
Descriptive statistics results suggest wide genetic variability for caffeine content in yerba mate genotypes, with genotype strongly influencing its concentration.The first component describes 58.35% of the variability observed in the genotype-environment interaction (Table 1).The individually examined years tended to contribute more to the GxE interaction than genotypes.
Caffeine content varied from 0.0348 to 2.3846 g 100 g −1 on different genotypes (Table 2).Similarity observed in the distribution of years and genotypes makes it possible to group samples according to caffeine concentration; however, the large number of genotypes generates overlap, especially in areas of interest.Figure 3 shows genotypes' stability, with 44.5% of them stable at a 1% probability of error (represented in blue).Additionally, 24.1% of genotypes are stable at 5% (represented in green) and 31.5% of genotypes are not stable throughout the four years (represented in red).Figure 3 shows genotypes' stability, with 44.5% of them stable at a 1% probability o error (represented in blue).Additionally, 24.1% of genotypes are stable at 5% (represente in green) and 31.5% of genotypes are not stable throughout the four years (represented i red).

Theobromine
The statistics results suggest wide genetic variability for theobromine content i leaves of yerba mate genotypes (Table 3).Homogeneity of genotype distribution from

Theobromine
The statistics results suggest wide genetic variability for theobromine content in leaves of yerba mate genotypes (Table 3).Homogeneity of genotype distribution from 0 and 0.5 g 100 g −1 shows lower theobromine concentrations when compared to caffeine.Principal component analysis shows that the first two components describe 81.12% of the total variance, implying the great dependence of genotypes on theobromine concentrations.The theobromine concentrations ranged from 0.0004 to 1.7719 g 100 g −1 in the 55 analyzed genotypes (Table 4), with the first component describing 59.78% of the variance observed in genotype-environment interaction.Figure 4 shows the stability of genotypes, with 69.1% of genotypes probability of error (represented in blue).Additionally, 10.9% of the genot at 5% (represented in green), while 20% of genotypes are non-stable (repre

Total Phenolic Compounds
Similar to methylxanthines, a large variability in total phenolic com gested (Table 5).Principal component analysis for total phenolic compound shows that the first two components describe 88.13% of the total variance.

Total Phenolic Compounds
Similar to methylxanthines, a large variability in total phenolic compounds is suggested (Table 5).Principal component analysis for total phenolic compound concentration shows that the first two components describe 88.13% of the total variance.The total phenolic compounds ranged from 7.028 to 9.424 g 100 g −1 in the 55 genotypes (Table 6).The first component describes 45.60% of the variability observed in the genotypeenvironment interaction.Table 6.Mean genotypic values and analysis of parameter estimates (µ + g) the mean interaction GxE (µ + g + ge) for total phenolic compounds in yerba mate genotypes (EC) tested across four consecutive years.Figure 5 shows the stability of genotypes, with 32.7% of them stable at a 1% probability of error (represented in blue), while 36.4% are stable at 5% (represented in green) and 30.9% are not stable (represented in red).Figure 5 shows the stability genotypes, with 32.7% of them stable at a 1% proba bility of error (represented in blue), while 36.4% are stable at 5% (represented in green and 30.9% are not stable (represented in red).

Proteins
The results suggest wide genetic variability in the protein composition of yerba mat leaves, with strong genetic influence and an increase of up to 59.49% among genotype

Proteins
The results suggest wide genetic variability in the protein composition of yerba mate leaves, with strong genetic influence and an increase of up to 59.49% among genotypes Forests 2023, 14, 2411 13 of 18 (Table 7).The first component describes 49.31% of the variability observed in the genotypeenvironment interaction.Principal component analysis for the distribution of total protein concentrations showed a variation from 10.39 to 16.58 g 100 g −1 in the 54 analyzed genotypes (Table 8).Results show that 31.5% of genotypes were stable at a 1% probability of error (represented in blue) and 42.6% stable at 5% (represented in green), while 25.9% of them were not stable (represented in red) (Figure 6).

EC41
13 Results show that 31.5% of genotypes were stable at a 1% probability of error (represented in blue) and 42.6% stable at 5% (represented in green), while 25.9% of them were not stable (represented in red) (Figure 6).
Caffeine is naturally present in several products, and its content in teas varies greatly depending on the species, genotype, cultivation and phenological stage of the leaves [33][34][35], which can influence the product quality, making it bitter and altering the final product flavor [3,36].
Although caffeine presence in yerba mate is well known [1,4,7,20,36], the high variability among genetic materials (0.01 to 2.96 g 100 g −1 ) is still dubious [20,37], and the lack of knowledge about its stability is clear.When these characteristics are standardized and evaluated successively over several production cycles, more reliable analyses can be established to support the emergence of new yerba mate cultivars with different caffeine concentrations for different uses.
Over time, a high complexity between samples and processing protocols has led yerba mate to be characterized as a biomass with lower levels of caffeine when compared to Camellia sinensis, even though it presents the highest transfer percentages during infusion (80.9% to 85.2%) [38].Results of PCA from caffeine levels identified in this study confirm the broad potential for developing different products with low (or decaffeinated), medium and high caffeine concentrations from yerba mate biomass.The great dependence of genotype on caffeine concentrations verified in our study is a result of high genetic heritability (h 2 ) in the caffeine content, also verified in studies conducted on yerba mate (h 2 from 0.60 to 0.83) [35,39,40].
Lower theobromine concentrations in yerba mate plants have constantly been reported [2], ranging from 0.01 to 0.95 g 100 g −1 [20] and 0.002 and 0.503 g 100 g −1 [34].In Camelia sinensis plants, inversely proportional relationships between caffeine and theobromine have frequently been identified [36,41].According to the results observed herein, there was a negative correlation between both compounds; however, it was too low to draw any conclusions.Such information is based on caffeine biosynthesis, which involves a series of reactions and can culminate in the conversion of theobromine into caffeine [8,42].
The presence of polyphenols in yerba mate is well known and has aroused interest in its antioxidant concentrations [2,13].There is a positive correlation between antioxidant capacity and the quantity of total phenolic compounds among yerba mate genotypes [19], highlighting the antioxidant power of the species.The concentration of polyphenols in extracts and commercial products is determined by genetic [35,37] and environmental [43] variability, processing forms [19], in addition to the time between harvesting and processing plant material [34] and is sometimes higher in yerba mate than in products such as green tea and red wine [44].The presence of these compounds could be preponderant for nutritional and sensory improvement, as well as significantly increase the shelf life of industrialized products [45].However, this characteristic fails to represent the genetic potential of yerba mate in supplying antioxidants.In our study, the amplitude of phenolic compounds distribution in yerba mate was clarified, generating new perspectives for standardized leaf biomass production with wide industrial applications.
The principal component analysis for total phenolic compound concentration agrees with the conclusion of a study conducted by Sturion and collaborators [46] that shows that total polyphenol content has low genetic control.On the other hand, the stability found for total phenolic compound concentrations in 63.64% of the evaluated genotypes over four consecutive years represents significant evolution and technical and scientific foundation in the search for clonal materials.The availability of genetic materials with high concentrations of total phenolic compounds could favor the consumption and absorption of antioxidants without increasing the consumption volume of yerba mate products by population [47].The known variability between products made from commercial biomass (leaves and thin branches) of yerba mate is attributed to biomass characteristics and its processing, inversely proportional to the increased proportion of branches/leaves, reduction of the surface area of leaf biomass and highly sensitive to variation in the raw material production and processing [16].
Our results for total protein concentration are higher than those found for industrially processed yerba mate leaves.For example, 7.97 ± 0.17 g 100 g −1 from 15-year-old [48] and 10.06 ± 0.12 g 100 g −1 for 80-year-old [49] plants, both native from the shaded environment, and 9.52 ± 0.42 g 100 g −1 from leaves of 12-year-old planted trees [49].The high genetic variability in protein concentration and good stability over the years (around 75%) in our studied genotypes also help yerba mate producers develop new cultivars with different protein concentrations for different uses.Great variation among yerba mate genotypes was also found by Vieira and collaborators [50] in a recent study.This variation can be used in industrial innovation according to the chemical characteristics of each genotype.
In another study, Rakocevic et al. [51] evaluated the stability of metabolic concentrations in yerba mate leaves and its relationship with plants' secondary dimorphism.They concluded that methylxanthines and proteins were stable over time and had no association with the expression of secondary sexual dimorphism.Considering that the metabolic composition of yerba mate leaves depends on genetics, sex, plant and leaf age, environmental variations and cultural treatments, these results suggest that variation in chemical composition is strongly influenced by genotype and presents high stability over the years, corroborating the results of our study.

Conclusions
Our results indicate large variations between different selected genotypes of Ilex paraguariensis in relation to caffeine, theobromine, total phenolic compounds, protein concentrations and the high stability of those compounds over the four evaluated years on the same plants.

Figure 2 .
Figure 2. Calibration curves for caffeine and theobromine concentration in yerba mate leaves.

Figure 2 .
Figure 2. Calibration curves for caffeine and theobromine concentration in yerba mate leaves.

Figure 3 .
Figure 3. Genotypic stability clustering using Ward's Minimum Variance Method of genotype-e vironment ratio of yerba mate genotypes (EC) for caffeine concentration according to PC1 and PC

Figure 3 .
Figure 3. Genotypic stability clustering using Ward's Minimum Variance Method of genotypeenvironment ratio of yerba mate genotypes (EC) for caffeine concentration according to PC1 and PC2.

Figure 4 .
Figure 4. PCA of genotype-environment relationships of the yerba mate genotyp bromine concentration according to PC1 and PC2.

Figure 4 .
Figure 4. PCA of genotype-environment relationships of the yerba mate genotypes (EC) for theobromine concentration according to PC1 and PC2.
estimated by the variance model; Chi-Square: Chi-square significance test; Pr > ChiSq error probability according to the CHI-Square test.

Figure 5 .
Figure 5. PCA of genotype-environment relationship of the yerba mate genotypes (EC) for tota phenolic compounds concentration according to PC1 and PC2.

Figure 5 .
Figure 5. PCA of genotype-environment relationship of the yerba mate genotypes (EC) for total phenolic compounds concentration according to PC1 and PC2.

Figure 6 .
Figure 6.PCA of the genotype-environment ratio of the yerba mate genotypes (EC) for total protein concentration according to PC1 and PC2.

Figure 6 .
Figure 6.PCA of the genotype-environment ratio of the yerba mate genotypes (EC) for total protein concentration according to PC1 and PC2.

Table 1 .
Variance analysis associated with the breakdown of interaction effect (GxA) for caffeine concentration of the yerba mate genotypes tested across four consecutive years of yield.
GL: liberty levels; SQ: sum of the square; Pr > F: probability estimate by F-test (Fischer-Snedecor).

Table 2 .
Mean genotypic values and analysis of parameter estimates (µ + g) the mean interaction GxE (µ + g + ge) for caffeine concentration of yerba mate genotypes (EC) tested across four consecutive years.
Means: means estimated by the variance model; Chi-Square: Chi-square significance test; Pr > ChiSq: error probability according to the CHI-Square test.

Table 3 .
Variance analysis associated with the breakdown of interaction effect (GxA) for theobromine concentration of yerba mate genotypes (EC) tested across four consecutive years.
GL: liberty levels; SQ: sum of the square; Pr > F: probability estimate by F-test (Fischer-Snedecor).

Table 4 .
Mean genotypic values and analysis of parameter estimates (µ + g) the mean interaction GxE (µ + g + ge) for theobromine concentration in yerba mate genotypes (EC) tested across four consecutive years.Figure4shows the stability of genotypes, with 69.1% of genotypes stable at a 1% probability of error (represented in blue).Additionally, 10.9% of the genotypes are stable at 5% (represented in green), while 20% of genotypes are non-stable (represented in red).
14,ns: means estimated by the variance model; Chi-Square: Chi-square significance test; Pr > ChiSq: error probability according to the Chi-Square test.Forests 2023,14, 2411Means: means estimated by the variance model; Chi-Square: Chi-square significance error probability according to the Chi-Square test.

Table 5 .
Variance analysis associated with the breakdown of interaction effect (GxA) for total phenolic compounds concentration of yerba mate genotypes (EC) tested across four consecutive years.
GL: liberty levels; SQ: sum of the square; Pr > F: probability estimate by F-test (Fischer-Snedecor).

Table 7 .
Variance analysis associated with the breakdown of interaction effect (GxA) for protein concentration on yerba mate genotypes (EC) tested across four consecutive years.

Table 8 .
Mean genotypic values and analysis of parameter estimates (µ + g) the mean interaction GxE (µ + g + ge) for total protein concentration in yerba mate genotypes (EC) tested across four consecutive years.
Means: means estimated by the variance model; Chi-Square: Chi-square significance test; Pr > ChiSq: error probability according to the Chi-Square test.