Abstract

   Sweet peppers are consumed worldwide, and traditional uses have sparked
   interest in their applications as dietary antioxidants, which can be
   enhanced in plants using elicitors. These are endowed with
   phytochemicals with potential health benefits such as antioxidants,
   bioavailability, and bioaccessibility. The trend in metabolomics shows
   us chemical fingerprints linking metabolomics, innovative analytical
   form, and bioinformatics tools. The objective was to evaluate the
   impact of multiple stress interactions, elicitor concentrations, and
   electrical conductivity on the concentration of secondary metabolites
   to relate their response to metabolic pathways through the foliar
   application of a cocktail of said elicitors in pepper crops under
   greenhouse conditions. The extracts were analyzed by spectrophotometry
   and gas chromatography, and it was shown that the PCA analysis
   identified phenolic compounds and low molecular weight metabolites,
   confirming this as a metabolomic fingerprint in the hierarchical
   analysis. These compounds were also integrated by simultaneous gene and
   metabolite simulants to obtain effect information on different
   metabolic pathways. Showing changes in metabolite levels at T6 (36 mM
   H[2]O[2] and 3.6 dS/m) and T7 (0.1 mM SA and 3.6 dS/m) but showing
   statistically significant changes at T5 (3.6 dS/m) and T8 (0.1 mM SA,
   36 mM H[2]O[2], and 3.6 dS/m) compared to T1 (32 dS/m) or control. Six
   pathways changed significantly (p < 0.05) in stress-induced treatments:
   aminoacyl t-RNA and valine-leucine-isoleucine biosynthesis, and
   alanine-aspartate-glutamate metabolism, glycoxylate-dicarboxylate
   cycle, arginine-proline, and citrate. This research provided a complete
   profile for the characterization of metabolomic fingerprint of bell
   pepper under multiple stress conditions.

   Keywords: sweet pepper, elicitor, salicylic acid, hydrogen peroxide,
   electrical conductivity, bioactive compounds, functional food,
   metabolomics’

1. Introduction

   One of the most important crops worldwide is Pepper (Capsicum spp.).
   Pepper fruit crops reached around 28 million tonnes of at least 2
   million ha [[42]1]. The fruits have been used as a coloring in the food
   industry and gastronomy and their traditional uses are fresh or cooked
   vegetables, condiments, or spice. The source of health-related
   metabolites include pepper, with increased metabolites, such as
   ascorbic acid, and phytochemical compounds (carotenoids, tocopherols,
   flavonoids and capsaicinoids) [[43]2,[44]3], these compounds are
   recognized as secondary metabolites. Recently, Wahyuni, et al. [[45]4]
   presented that different Capsicum with the application of elicitors
   shows ability of enhance the morphological characters, and
   concentrations and types of metabolites with effects on health. These
   secondary metabolites are involved in various metabolic pathways of the
   plant, as well as in processes such as growth and reproduction, and
   also as a defense mechanism against biotic or abiotic stress
   [[46]5,[47]6,[48]7]; Therefore, treatment with specific chemical,
   natural or enzymatic compounds can improve the production of secondary
   metabolites, called elicitors, characterized by behave as a substance
   that, Initiates or enhances the biosynthesis of specific compounds,
   when inserted in small amounts of concentrations. To a living system,
   [[49]8,[50]9,[51]10,[52]11]. Salicylic acid (SA) has presented a
   remarkable capacity as an elicitor in plants related to the potencies
   of phenolic compound. It occurs naturally in plants in small amounts.
   It participates in the stomata regulation system, responding rapidly to
   environment changes, nutrient uptake, biological and enzymatic
   activity, and exogenously sprayed SA interact with stress signaling
   mechanisms [[53]12,[54]13]. Catalase and ascorbate peroxidase are the
   main enzymes involved in the removal of H[2]O[2] in plants, and SA can;
   inhibit their activities; this inhibition leads to a local increase in
   H[2]O[2] levels, which enhances adaptive responses in plants, such as
   the growth response to various stimuli such phenylalanine ammonia-lyase
   (PAL), is the first enzyme and committed in the phenolic metabolic
   pathway [[55]9,[56]13,[57]14]. Based on the critical role of H[2]O[2]
   in stress signaling mechanisms and because this molecule is
   comparatively stable, it can be considered an elicitor [[58]15,[59]16].
   Salinity level used to nutrient solutions for tomato cultivar in
   soilless systems, it is so in the range of 25 mM (closed irrigation
   system) to 40–75 mM (open irrigation system) overall ion concentration
   (equal to an EC of 1.6–5.0 mS.cm^−1). Greenhouse growers maintain a
   balance between the vegetative and generative part of a crop plant to
   control water range, if they modify the electrical conductivity (EC).
   Cell walls in plants are strengthened applying high EC during the early
   phase of establishment of the plants. However, optimum yield and
   quality occasionally differ in the requirements for different ECs of
   the nutrient solution and depend on the relations between intercrops
   relationships, environmental condition, and cultural practices.
   However, commercial yield and fruit size are affected, by high
   electrical conductivity, improving their quality and sensory profile.
   On the other hand, the nutraceutical properties of crops have been the
   subject ofsome types of research related to the influence of salinity
   and change in the profile of secondary metabolites of crops
   [[60]13,[61]17]. Metabolites are essential elements at the biological
   levels and the mechanism in cells at the metabolite level. Metabolomics
   profiles show that metabolites and low molecules weight compounds
   interact with different types of genes and proteins that leave behind
   specific cellular processes, evidencing the phenotypic compared to
   genomic analyses [[62]18,[63]19]. Metabolomics has helped to find
   biomarkers and metabolomics fingerprints in humans and crops, which
   analyzes each meabolote thet makes up a biological sample
   [[64]20,[65]21,[66]22,[67]23]. Recently, high-pressure liquid
   chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS)
   has been used to recognize a set of metabolites well-known as
   metabolomics. The identity of the metabolites can be simultaneously
   detected in a biological sample. This assay may help identify changes
   in secondary metabolites profile to identify compounds correlated with
   different traits of interest such as pungency, color, or flavor in
   fruits. Metabolomics-assisted has focused on applying metabolomics
   strategies and their integration with other high-throughput
   technologies of metabolite-based quality traits [[68]24,[69]25,[70]26].
   In this regard, this work aimed to assess the effect of
   multiples-stress interactions of different concentrations of elicitors
   (SA and H[2]O[2]) and three different levels of electrical conductivity
   on the concentration of secondary metabolites and correlate their
   changes with pathways topology in each on a pepper crop under
   greenhouse conditions to predict plant responses to future climate
   changes. Since several distinct environmental stimuli can affect plant
   development in combination, many stress-responsive signal pathways must
   be integrated to adapt resource allocation between defense, growth, and
   reproduction most efficiently.

2. Results

2.1. Metabolomic Profiling of Sweet Pepper Red

   The data collected for phenolic compounds, carotenoids, vitamins,
   aminoacids, organic acids, fatty acids and phytosterols in the
   different treatments of sweet pepper were analyzed. Metabolic profiling
   nowadays appears as central tools in chemical plants for screening the
   pool of bioactive compounds and elucidate regulative principles and
   pathways. Phenolic compounds are an efficient way to describe
   interpretation of plant protection metabolites might wholly require
   monitoring metabolites in the plant’s tissue or on its shallow. It
   needs to be mentioned that the relationship between sweet pepper and
   metabolic profile is that flavor mostly comes from the ratio of
   reducing sugars to organic acids. In this regard, carotenoids
   contribute to the particular Flavor, aroma and color of sweet pepper.
   Fatty acids and phytosterols are also related to biosynthesis of
   metabolic pathways.

2.2. The Phenolic Profile of Sweet Pepper

   In the [71]Table 1 showed that treatments only affected bioactive
   compounds related to phenolic compounds, carotenoids, vitamins,
   aminoacids, organic acids, fatty acids and phytosterols. [72]Table 1
   shows the F-ratios and p-values acquired in this statistical
   assessment. Whenever, significant differences among means were sensed,
   multiple range tests (MRT) were approved to evaluate them. The phenolic
   profile of sweet pepper treated with salicylic acid (SA) (0, 0.01, and
   0.1 mM), hydrogen peroxide (H[2]O[2]) (0, 18 and 36 mM) and Electrical
   conductivity (EC) (3.2, 3.4 and 3.6 dS/m) are shown in ([73]Table 1).
   For phenolic compounds the different elicitor treatments only showed
   significant differences for the compounds identified showed an increase
   in the chlorogenic and caffeic acid, and resveratrol (F = 214.88; p =
   0.000), (F = 10.439; p = 0.001), and (p = 8.474; F = 0.002)
   respectively. Treatment 6 increased the concentration chlorogenic and
   caffeic acid, treatments 2, 3, 7 showed significant differences in the
   concentration in resveratrol ([74]Table 1) found in treatments treated
   with stress-induced for CE compared to the control.

Table 1.

   Chromatographic profile of low molecular weight, and secondary
   metabolites identified in sweet pepper (Capsicum annuum L.).
   Treatments
   Compound (Groups) 1 2 3 4 5 6 7 8 9 F
   (F-Ratio) p
   (p-Values)
   Phenolic compounds
   Chlorogenic acid - - - - 2746.79 a 2414.41 a 4229.29 b 2376.57 a
   2538.03 a 214.88 0.000
   Epicatechin - - - - 577.30 a 3989.41 a 8896.08 b - 1052.83 a 17.69
   0.000
   Caffeic acid - 717.21ab - - - 1353.34 b 1239.91 b - - 10.439 0.001
   Sinapic acid - - - - 98.59 a - - - - 2.326 0.115
   Quercetin 175.02 a 71.09 a 73.53 a 77.32 a 159.95 a 61.04 a 82.79 a
   85.45 a 229.73 a 2.772 0.075
   Resveratrol - 180.42 b 197.42 b 161.74 ab - 158.13 ab 177.85 b - -
   8.474 0.002
   Carotenoids
   Luthein 0.0017 ab 0.0015 ab 0.0015 ab 0.0013 ab 0.0016 ab 0.0009 a
   0.0019 b 0.0029 c 0.0017 ab 7.965 0.000
   Tocopherol 0.0075 b 0.0072 b 0.0074 b 0.0074 b 0.0082 b 0.0081 b 0.0097
   b 0.0173 a 0.0114 b 7.214 0.000
   β-cryptoxanthin 0.0012 a 0.0012 a 0.0012 a 0.0012 a 0.0012 a 0.0012 a
   0.0012 a 0.0012 a 0.0015 a 1.074 0.398
   Lycopene 0.0060 a 0.0057a 0.0018 a 0.0027 a 0.0048 a 0.0057 a 0.0115 ab
   0.0227 b 0.0128 ab 5.178 0.000
   Carotene 0.0149 ab 0.0102a 0.0133 ab 0.0129 a 0.0127 a 0.0160 ab 0.0164
   ab 0.0230 b 0.0190 ab 3.256 0.005
   Vitamins
   Ascorbic acid 0.84 a 1.12 b 1.09 b 0.85 a 1.13 b 0.96 ab 1.12 b 1.31 c
   0.98 ab 0.731 0.664
   Aminoacids
   L-Alanine - - - - 1.087 a - - - 0.000 0.000
   L-Valine - - - - 2.167 a - - - 0.000 0.000
   L-Leucine - - - - 1.07 a - - 0.771 a - 0.021 0.281
   L-Isoleucine - - - - 0.441 a - - 0.553 a - 0.008 0.000
   L-Proline - - - - 0.796 a - - 0.731 a - 0.724 0.001
   L-Serine 0.84 b 0.317 a 1.094 c - 0.908 b - 0.963 0.000
   L-Treonine - - - - 1.845 c 0.834 b 0.454 a - 0.384 0.000
   5-oxo-L-Proline 10.996 b 16.181 bc 10.517 b 1.887 a 18.465 c 15.526 bc
   2.399 a - 0.551 0.391
   L- Asparagine 1.63 c 0.982 bc 3.181 a 6.612 b 2.757 a 1.325 a - 1.522
   0.000
   Organic acids
   Lactic acid - 1.709 a 3.423 ab 3.083 ab 1.207 a 7.663 b 3.843 a - 1.083
   0.000
   Glycolic acid - - - - 1.103 a - - 0.977 a - 0.774 0.000
   Succinic acid 1.751 b 0.893 ab 2.827 a 0.988 ab 3.366 a 3.284 a 0.691 a
   2.753 a 1.691 0.000
   Glyceric acid - - - - 0.696 a - - 0.487 a 0.838 a 0.478 0.000
   Fumaric acid - - - - 0.184 a - - 0.136 a - 0.096 0.291
   Maleic acid - - 0.529 a - - - 0.671 a - - 0.4523 0.000
   Malic acid 4.103 a 4.6 a 4.59 a 13.577 b 5.627 a 9.011 ab 10.592 ab
   2.722 a 8.48 ab 1.527 0.002
   Aspartic acid - - - - 3.528 a - - 1.588 a - 0.988 0.000
   4-aminobutyric acid - - - - 0.393 a 1.122 a - - - 0.193 0.000
   Threonic acid - - - 3.512 a - 2.879 a 4.364 a - - 01364 0.145
   Lauric acid - - - - 0.488 a - - 0.548 a - 0.001 0.000
   Citric acid 46.515 ab 56.955 ab - 42.347 ab - - - 27.936 a - 10.955
   0.005
   Ascorbic acid 2.745 a 10.005 ab 4.495 a 43.774 bc - 8.855 ab 24.466 b
   82.472 c 4.954 0.000
   Fatty acids
   Palmitic acid 7.131 ab 4.657 a 9.961 b 9.068 b 6.166 ab 8.678 ab 8.458
   ab 8.165 ab 1.811 a 1.911 0.000
   Linoleic acid 4.56 a 5.195 a 6.556 a 6.473 a 5.993 a 5.629 a 9.204 ab
   9.397 ab 1.203 b 1.032 0.230
   a-linolenic acid - - - - 2.492 a - - 4.541 a 0.675 a 0.025 0.050
   Estearic acid 5.652 a 3.405 a 5.312 a 3.846 a 2.985 a 5.415 a 5.226 a
   3.834 a 0.811 a 0.862 0.000
   Phytosterols 0.000
   γ-Tocopherol 0.531 a 3.274 b 2.132 ab - 0.316 a 2.241 ab 1.238 ab 0.172
   a 0.082 a 0.351 0.002
   (+)-a-Tocopherol - 0.609 ab 12.41 c 0.799 ab 2.298 b 13.129 c 8.75 bc
   1.576 ab 0.335 a 0.309 0.000
   Campesterol 3.037 ab 5.165 ab 5.971 ab 0.624 a 1.264 a 5.771 ab 4.196
   ab 0.753 a 0.195 a 0.357 0.000
   Stigmasterol 1.031 ab 2.845 b 1.438 ab 0.157 a 0.306 a 1.68 ab 1.209 ab
   0.235 a 0.049 0.249 0.000
   β-Sitosterol 9.478 ab 8.739 ab 17.216 b 1.758 a 2.412 a 16.887 b 10.763
   ab 1.7 a 0.296 a 2.587 0.810
   [75]Open in a new tab

   Results are expressed as μg of compounds per g of sweet pepper and are
   the average of three independent determinations ± SE. Different letters
   indicate significant statistical differences for each compound (p <
   0.05, tukey’s test), P: probability levels.

   On the other hand, the carotenoids showed an increase in the majority
   lutein (F = 7.965; p = 0.000), lycopene (F = 5.178; p = 0.000), and
   β-carotene (F = 3.256; p = 0.005). Treatment 8 increased the
   concentration lutein, lycopene and β-carotene. This treatment was
   treated with cocktail of stress-induced showed an increase in most of
   these compounds compared to control treatment [76]Table 1. Also,
   ascorbic acid showed in the treatments affected for stress-induced
   significant differences (F = 0.731; p = 0.664). [77]Table 1 identifies
   low molecular weight polar compounds obtained by GC–MS analysis, such
   as organic acids, amino acids and phytosterols. stress-induced
   treatment led to the presence of metabolites not detected in the
   control; for instance, six amino acids, nine organic acid and
   α-tocopherol were detected in sweet pepper obtained from plants treated
   with stress-induced, but not in the control sample. Although the point
   of fact that elicitation may significantly lofty the production of
   secondary metabolites. Elicitation with H[2]O[2] caused a significant
   decline in amino acid content. The uppermost levels of amino acids were
   determined in treatment 5 elicited with EC 3.6 dS/m, increasing by 8
   amino acid compared to control. However, in the treatment 2 of 36 mM
   H[2]O[2] and EC 3.2 the reduction was much higher 2 amino acid in
   comparison to control. Our previous studies showed that H[2]O[2]
   applied as cocktail of SA and EC in surface of a sweet pepper increased
   stress tolerance effectively. This caused a similar response in organic
   acids.

2.3. Comparative Profiling of Metabolites in the Different Treatments under
Elicitor Application and Electric Conductivity Levels by PCA and HCA

   Metabolic profiling of the pepper samples allowed identifying 43
   compounds belonging to 6 different chemical groups by HPLC and GC-MS.
   Metabolites of the pepper fruits were first analyzed by PCA separating
   compounds by chemical groups. The analysis for phenolic compounds is
   shown in ([78]Figure 1a), the first two principal components (PC)
   described 78.87% of the variation among treatments. PC1 described
   47.26% of variation, whereas PC2 described 31.61%. Epichatechin,
   caffeic acid, quercetin and resveratrol mainly described the variation
   in PC1. PC2 was described by chlorogenic acid. HCA grouped treatments
   according to similarities in metabolite profile in 4 clusters,
   treatments 1, 8 y 9 were grouped by their similitudes in quercetin and
   sinapic acid, while treatments 6 and 7 were characterized by
   epicatechin, caffeic acid, and chlorogenic acid. Resveratrol was
   characteristic for treatments 2, 3, and 4.

Figure 1.

   [79]Figure 1
   [80]Open in a new tab

   Principal component analysis (PCA) of (a) phenolic compounds and (b)
   carotenoids in pepper samples. Hierarchical clustering analysis (HCA)
   allowed identification of clusters.

   In the carotenoid analysis, the PCA revealed that the first two PCs
   described 93.53% of the variation ([81]Figure 1b). According to the
   carotenoid profile, PC1 (73.44%) separated the lutein, tocopherol,
   lycopene and carotene for the treatments. These compounds were
   characteristic for treatment 7 and 8, while PC2 (20.08%) described
   treatment 9 in terms of β-cryptoxanthin.

   PCA for the aminoacids profile showed an 86.14% of the variation
   described by the first two PCs. L-alanine, L-valine, L-leucine,
   L-isoleucine, L-proline, L-treonine, and L-asparagine described PC1
   (66.03%). PC2 (20.11%) was described for L-serine and 5-oxo-L-proline.
   According to HCA, 5 clusters were identified among treatments.
   Treatments 1, 3 and 6 are well described by L-serine and
   5-oxo-L-proline, for treatment 5 the more characteristic compounds were
   L-asparagine, L-treonine, L-alanine, and L-valine. For treatment 8 are
   well described by L-leucine, L-proline, and L-isoleucine ([82]Figure
   2c).

Figure 2.

   [83]Figure 2
   [84]Open in a new tab

   Principal component analysis (PCA) for (a) aminoacids, (b) organic
   acids for pepper samples. Clusters identification performed by HCA.

   The PCA for the organic acids identified by GC-MS revealed a 61.72% of
   the total variation explained by PC1 and PC2. PC1 (42.31%) was
   characterized by glycolic, succinic, glyceric, fumaric, malic,
   aspartic, threonic, and lauric acids. PC2 (19.41%) was described by
   citric acid. After HCA 3 clusters were generated, treatments 1, 2 and 3
   can be well described by citric acid in PC2. Treatments 8 and 5 are
   described by lauric, glyceric, fumaric, glycolic, aspartic and ascorbic
   acids. Treatments 4, 6 and 7 were well described in terms of succinic,
   malic, threonic, maleic and 4-aminobutyric acids ([85]Figure 2d).

   The PCA for the fatty acids profile on sweet pepper samples explained a
   92.46% of the total variation with the first two PCs. Palmitic,
   linoleic, and stearic acids described PC1 (60.48%). In comparison, PC2
   (31.98%) was described by a-linoleic acid ([86]Figure 3e). According to
   HCA three different clusters were generated, treatments 1, 3, 4, 6, and
   7 were grouped and described by stearic and palmitic acids. Treatment 8
   was well correlated with a-linoleic ad linoleic acids. The last group
   of compounds was the phytosterols, PCs 1 and 2 explained a 97.56% of
   the total variation, contributing PC1 with 80.90% and PC2 with 16.66%.
   All the phytosterols were characteristic for the PC1. (+)-α-tocopherol
   was well correlated with treatments 3, 6 and 7 ([87]Figure 3f).

Figure 3.

   [88]Figure 3
   [89]Open in a new tab

   Principal component analysis (PCA) for (a) fatty acids and (b)
   phytosterols for pepper samples. Clusters identification performed by
   HCA.

2.4. Metabolic Profiles of Sweet Pepper (C. annum L.) Stress-Induced

   The commercial NIST library showed to a comparison the spectra of the
   treatments recognized thirty-one metabolites. A plotting map of all
   recognized metabolites is presented in [90]Figure 4 to present the
   differences between the stress-induced and control treatments. The
   metabolite levels enhanced moderately in the stress-induced treatments
   compared with the control treatment. Eighteen metabolites were
   disturbed by stress-induced treatments, respectively. The
   stress-induced treatments significantly increased six metabolites,
   namely campesterol, stigmasterol, (-tocopherol, succinic acid,
   asparagine, and ascorbic acid. In addition to glyceric, fumaric, maleic
   acid and isoleucine any and every of another one metabolites showed
   higher changes in the stress-induced treatments. β-sitosterol was
   decreased in treatment 4, 5, 8 and linoleic acid was decreased more by
   treatment 9 than other treatments. The affinity of three more
   metabolites showed a trend: the levels of 4-amonobutyric acid, alanine,
   and valine were not present in most treatments but increased in
   treatment 5. The metabolites in the bottom group were present at
   diverse comparative abundances in everything treatments verified. Their
   exclusion pattern in the treatment 5, 8 were not specific, since
   genotypes with upper and lower affluence of these metabolites were
   present lower borderline detection. Metabolites in top cluster
   collected at relatively upper abundance in the sweet pepper fruits.
   treatment 2, 3, 6, 7, were specific with higher abundance of these
   metabolites, some of this group were putatively identified as steroidal
   glycosides detected anteriorly in C. annuum var. acuminatum seeds with
   a antimicrobial activity opposite yeast and fungi [[91]27,[92]28].

Figure 4.

   [93]Figure 4
   [94]Open in a new tab

   Heat map of 32 metabolites accumulated in sweet pepper (C. annuum L.).
   The lower bars indicate the sample classes (control, T2, T3, T4, T5,
   T6, T7, T8, T9). The columns represent stress-induced (SA and
   H[2]O[2]/EC), and the rows refer to distinct metabolites. The values of
   the metabolite’s concentration have depicted with an encoded-color
   matrix from the dark/light in every genotype, which has been log[2-]
   changed and mean-centered.

   It was used MetaboAnalyst’s to recognize and analyze an improved
   planning of metabolomics studies and biological interpretation in
   pathway. The eighteen metabolites predominated to identify the
   expression of various pathways during multiple stress. Some of these
   compounds were responsible for activating multiple pathways. On the
   other hand, intermediates were detected, such as fatty acids (linoleic
   acid and palmitic acid), organic acids (malic acid and succinic acid),
   phytosterols (Campesterol and sitosterol), phenolic compounds
   (epicatechin and caffeic acid). The detailed diagram indicates the
   metabolites that have the greatest effect on the metabolic pathways
   shown in [95]Figure 5. Thus, six pathways were significantly (p < 0.05)
   confounded in the stress-induced treatments: aminoacyl biosynthesis
   t-RNA, alanine, aspartate and glutamate, valine, leucine, and
   isoleucine biosynthesis, glyoxylate and dicarboxylate metabolism,
   citrate cycle (TCA cycle) and arginine and proline metabolism.
   High-quality KEGG metabolic pathways were established and used by
   researchers to find the most significant pathways. Compared to control,
   flavonoids and phenolic compounds show activation and upregulation in
   all stressed groups.

Figure 5.

   [96]Figure 5
   [97]Open in a new tab

   Graphic of the metabolome subsequent the metabolite pathway plotting of
   the impacted metabolites recognized after result stress-induced in
   sweet pepper (C. annuum L.). A color coded matrix indicates
   concentration values of the metabolic pathway and result of each
   metabolite, which has been log[2-] changed and mean-centered. (Color
   figure online). The investigation was achieved using the MetaboAnalyst
   software. 5.0.

3. Discussion

   The main compounds identified in the sweet pepper were phenolic acids,
   flavan-3-ols, flavonols and flavonones. Vanillic increased in ginger
   treated with 10-3 and 10-5 M SA, but not in controls. Sinapic acid have
   coumaric acid as a precursor, which could be related with the
   stimulation of phenylalanine ammonia-lyase. Another enzyme regulated in
   flavonoid synthesis is chalcone synthase [[98]29,[99]30,[100]31].
   Studies with tomatoes and carrots using different EC with salts
   solutions in the irrigation system not showed difference between
   treatments in the concentrations of catechin, epicatechin,
   trans-resveratrol [[101]32,[102]33,[103]34]. Therefore, this study
   confirms the results, in this investigation as there are no differences
   between treatments applied in sweet pepper by the EC. Furthermore,
   Medina–Juárez, et al. [[104]35] observed in the extract, caribe and
   sweet pepper corresponding to the highest levels of resveratrol,
   epicatechin, luteolin, rutin, gallic and chlorogenic acid, also acid
   ascorbic (r ≥ 0.85). To compare our results we found that the
   treatments treated with the cocktail of elicitors have higher
   concentration of these compounds to those reported in sweet pepper
   without stress-induced. These results propose that applying cocktails
   elicitor, SA and H[2]O[2], may cause oxidative stress in the sweet
   pepper, resulting in increased production and synthesis of different
   phenolic compounds. Also, the main compounds identified in the sweet
   pepper were Lutein, tocopherol, β-criptoxantin, licopene and
   β-carotene. Treatments 8 showed an increase in the majority in lutein,
   and β-carotene compared to treatment control. Peppers mostly contained
   violaxanthin, capsanthin, lutein, and β-carotene. Therefore, in the
   chloroplasts tended to decrease the content of the major on these
   compounds in all maturation time and at the same time were formed new
   xanthopylls to become a mono- and/or diesters with the union of a fatty
   acid [[105]36]. The level of carotenoid compounds varied with different
   colored peppers. Yellow pepper had the uppermost level of carotenoids,
   although green pepper had the lowermost. Deli, et al. [[106]37]
   investigated yield of carotenoids (β-cryptoxanthin, α- and β-carotene)
   peaked and then decreased in yellow pepper (C. annuum lycopersiciforme
   flavum) during the maturation. On the other hand, they also identified
   lycopene, zeaxanthin, lutein, and capsanthin as secondary metabolites
   used in chronic-degenerative diseases [[107]38,[108]39,[109]40]. The
   results of these research carotenoid concentrations are higher than
   those previously reported in peppers of different colors optimal
   ripeness. Our results were found that the treatment 8 treated with the
   cocktail of elicitors have higher concentration of these compounds to
   those reported in sweet pepper. These results suggest that applying
   cocktails elicitor, SA and H[2]O[2] and EC, may cause oxidative stress
   in the sweet pepper, resulting in increased production synergism and
   higher synthesis of carotenoids. The ascorbic acid contents of sweet
   pepper cultivars resemble other pepper crops previously studied by
   Howard, et al. [[110]41] and Simonne, et al. [[111]42]. Nevertheless,
   other research’s described higher ascorbic acid concentrations in upper
   levels with the current results without stress-induced. Ascorbic acid
   advanced bleaching of the β-carotene emulsion [[112]43]. Our result
   also presented that ascorbic acid had pro-oxidant effect. Then, we
   propose that the antioxidant activity in the methanol extracts ought to
   be the likeness of the result of phenolic compounds and the pro-oxidant
   result of ascorbic acid [[113]44]. The ascorbic acid contents of the
   treatments were higher of 1.3 mg/g than those reported. These changes
   can be consistent by EC. The nutrition value can assess the content of
   ascorbic acid used standards US DRI which indicates the consumption of
   ascorbic acid are 75 mg/day for males and 90 mg/day for females (IOM,
   2000). Taking into account these values, to consume 17–72% for males
   (ages 19–50 years), and 20–86% for female (ages 19–50 years) of
   ascorbic acid per day, they require consumption of 100 g of mature
   fruit and this depends on the type of crop. On the other hand, sweet
   peppers have improved stress tolerance to sprinkled as cocktail of
   SA/H[2]O[2] and EC about to superficies of sweet pepper. These
   cocktails also have increased the amount of organic acids. On the other
   hand, HancockandSahl [[114]45] showed that minimum concentrations of
   the H[2]O[2] molecule have not shown the receptor proteins that
   intervene in the detection of the same. They evidenced that amino acid
   perception was controlled by other characteristics of the proteins
   having active thiol groups as redox targets. Oxidation of the thiol
   groups by H[2]O[2] and other bioactive compounds, was generated by
   oxidants for the thiol groups [[115]46]. Furthermore, SA is an
   important regulator of photosynthesis, since it increases the activity
   of two key enzymes in carbohydrate synthesis, RuBisCO and carbonic
   anhydrase [[116]47]. Furthermore, some studies showed that phenolic
   acid synthesis in crops not heighten the carbohydrate concentration,
   since the phenolic acid pathway uses products of carbohydrate
   metabolism as precursors [[117]48,[118]49,[119]50]. Furthermore, the
   amount of organic acids as palmitic, stearic, oleic, linoleic acid were
   increased by changes in electrical conductivity, similar results are
   shown by Kaewnaree, et al. [[120]51] with seed sweet pepper.

   This study discovered that the metabolite levels were significantly
   altered in the treatment 5, 6, 7, 8 with the control. The
   stress-induced radiation increased the secondary metabolites such as
   phytosterols, fatty and organic acids, amino acids. Rahimi, et al.
   [[121]52] investigated the increase to the essential oil and quality of
   cumin (Cuminum cyminum L.) were caused for to induced-stress of
   salicylic acid and methyl jasmonate, which main effect was an increase
   by the application of SA. Moreover, the influence of increasing doses
   of salicylic acid (SA) showed growth-promoting effect with decrease in
   tissue water content, chlorophylls and soluble proteins in Matricaria
   chamomilla plants. As well as decrease in chlorogenic acid
   [[122]53,[123]54]. On the other hand, Hao, et al. [[124]55]
   investigated the effect of SA and H[2]O[2] in Salvia miltiorrhiza and
   yield rosmarinic acid. These researches showed that yield in H[2]O[2]
   production, induced by stress of SA increased rosmarinic acid and
   phenylalanine ammonia-lyase (PAL) activity. Other investigation showed
   that H[2]O[2] is a signaling molecule that allow in develop signals on
   different plant metabolic pathways [[125]56] and increasing to activity
   other molecules of key signaling (Ca^2+, SA, ABA, JA, ethylene, NO) of
   plants [[126]57]. Also, the catabolism of amino acids and fatty acid
   elongation in the plant produce the acyl residues of capsaicinoids
   [[127]58,[128]59]. P-coumaric acid is the key signal in the capsaicin
   pathway and likewise is important in synthesis of lignins, flavonoids,
   hydroxycinnamic polyamides and pigments [[129]60]. Coumaroyl, feruoyl
   and cinnamaldehydes were reduce by cinnamoyl CoA and sinapoyl-CoA
   respectively; therefore, the phenylpropanoid pathway is to point of
   signaling by lignin biosynthesis, being of great importance in
   detection of capsaicinoid levels [[130]61,[131]62]. In this study,
   fatty acids symbolized the main group of differential metabolites. The
   plant responds to pathogen attack high salinity, drought and low
   temperature, producing higher fatty acid [[132]63,[133]64,[134]65]. On
   the other hand, interact synergistically lipids with tannins in barley
   were try in bacterial and fungal pathogens inhibiting the growth
   [[135]64]. Likewise in radish, maize, and grape [[136]64,[137]66].
   Moreover, the lipids could inhibit sensitive pathogens, because were
   scattered above the plant and in the vascular tissues
   [[138]64,[139]67,[140]68]. Forefather phyto-oxylipin biosynthesis in
   linolenic acid al also produces indiced-stress in plants [[141]69]. The
   chloroplast oleic acid levels affect systemic acquired resistance in
   Arabidopsis [[142]70]. Likewise, the decrease of fungi and the
   mycotoxin produced by Aspergillus spp could to eliminated with oleic
   and linoleic acid [[143]71]. Hence, Capsaicinoid pathway could increase
   the activity with methyl-branched fatty acid esters, this is produced
   in the placenta, and transported into the apoplast and later, to
   tissues or organs [[144]72]. In addition stress-induced can
   increase/decrease to production of hydrocarbons, phytosterols, fatty
   and amino acids, TCA cycle intermediates. The pathway enrichment
   analysis showed stress-induced that increase of six pathways as such as
   citrate cycle, minoacyl-tRNa biosynthesis, nitrogen metabolism. The
   plant activity was affected by nitrogen metabolism. Also, the
   generation of energy is produced with nitrogen metabolism that is the
   point more important and the union of the metabolic pathway. Amino
   acids play important roles and the conditions of salt stress on sugar
   beets (Beta vulgaris L.) resulted in a quick increase in proline
   content. The free amino acids were moderately improved by means of
   stress-induced of the plant, the basic amino acids and amides were lot
   improved y the aspartic and glutamic acids were slightly affected of
   stress-induced [[145]73]. The overall metabolite profiles (Vitamins,
   carotenoids, flavonoids and capsaicinoids) have greater effect by the
   synergistic interaction against the individual effects of each type of
   metabolite [[146]4,[147]74]. Under stress conditions less energy and
   nutrients are allocated to growth processes and more is invested in
   defense responses. This requires a fine tuning of nutrient and energy
   allocation between growth/reproduction and defense associated processes
   by complex signaling networks integrating incoming developmental and
   environmental signals. Since phytohormons often magnify the initial
   signal and trigger a new signaling event either following the same
   pathway or using further components of other signaling pathways
   [[148]75]. Combined occurrence of abiotic and biotic stress may result
   in synergistic, and antagonistic or dominance interactions. The outcome
   will depend on the timing, nature, and severity of each stress
   [[149]76]. Potentiation of health-related secondary metabolites can be
   caused by the stress-induced in the plants caused by the use of
   elicitor and biotic or abiotic stress. These metabolites are always
   requested by the consumer as these produce texture taste or color or
   raise the pungency produced by carotenoids, flavonoids or
   capsaicinoids.

4. Materials and Methods

4.1. Greenhouse Climatic Conditions

   This study was conducted under greenhouse conditions from August to
   January, using red sweet peppers (Capsicum annuum L) fasinato variety
   in a community close to Ezequiel Montes, Queretaro, and México. Plants
   were grown hydroponically using rock wool a substrate and fertilized by
   drip irrigation to provide all the nutrients to the plant. Pepper
   plants were distributed with planting distances of 1.5 m among rows and
   0.3 cm amid plants in each row as such a final density of 2.3 plants
   m^−2. Climate conditions were monitored daily; the mean values were 25
   °C and 85% HR. Pepper fruit when fully ripen (surface coloration was
   completely red).

4.2. Experimental Design

   Research design was applied a factorial complete [[150]77]. Treatment
   factors in this experiment were different concentrations of salicylic
   acid (SA), hydrogen peroxide (H[2]O[2]) and three different electrical
   conductivities. 9 treatments resulted from the different of mixture of
   factors and their levels ([151]Table 2)

Table 2.

   Treatments Applied to Sweet Pepper.
   Treatments SA
   (mM) H[2]O[2]
   (mM) EC
   (dS/m)
   1 0 0 3.2
   2 0 36 3.2
   3 0.1 0 3.2
   4 0.1 36 3.2
   5 0 0 3.6
   6 0 36 3.6
   7 0.1 0 3.6
   8 0.1 36 3.6
   9 0.01 18 3.4
   [152]Open in a new tab

4.3. Fruit Material and Preparation

   Samples of 1.5 kg of fully mature sweet pepper were collected seven
   weeks after fruit set. The stems were removed and sweet pepper was cut
   into cubes (2 cm^2), and they were frozen in liquid nitrogen to be
   subsequently lyophilized. Subsequently, a lyophilizer (Labconco
   FreeZone 4.5 L model 230 V) was used at a temperature of −50 °C with a
   0.01 atm vacuum system until the sample dried [[153]78]. The sample was
   stored in 50 cm falcon tubes where the O[2] in the headspace was
   eliminated to be stored in cool and dry spaces for analysis. The
   preparation of the sample for the analysis of phytochemical compounds
   will be carried out according to the methodology described by
   Cardador–Martínez, et al. [[154]79], 25 mg of dry sample will be placed
   and 2.5 mL of methanol will be added to each sample, performing samples
   in triplicate. They were kept out of light and shaken for 24 h.
   Centrifuging at 5000 rpm/10 min/4 °C, the sediment formed at the bottom
   was removed, leaving the supernatant.

4.4. Identification of Phenolic Compounds

   Phenolic compounds in the sweet pepper were determined and measured two
   varieties of compounds flavonoids and phenolic acids using an Agilent
   1200, HPLC system, coupled with an auto-sampler and a UV/Vis detector.
   The separation was performed on a Sorbaz eclipse plus column (C18 and
   (250 × 4.6 mm, 5 μm)) reversed-phase. Samples (10 μL) were inserted
   with a gradient elution involved two solvents: A (formic acid 1%/water,
   v/v) and B (acetonitrile), at a current rate of 1 mL/min. The solvent
   gradient was programmed in a ratio A:B (98:2 v/v, 0 min; 68:32 v/v, 30
   min; 45:55 v/v, 48 min; 5:95 v/v, 53 min; 98:2 v/v, 57 min), with 67
   min of run time. The standards of the suspected phenolic acids and
   flavonoids used (chlorogenic, ellagic, caffeic, coumaric, sinapic and
   rosmarinic acids as well as gallocatechin-gallate, epicatechin,
   vainillin, rutin, quercetin, hesperidin and resveratrol) (Sigma-Aldrich
   with 99.99% purity) were measured in a range 260–320 nm.

4.5. Identification of Carotenoids

   Carotenoids in the sweet pepper were recognized and quantified using an
   Agilent 1200, HPLC-DAD system, coupled with an auto-sampler and a
   UV/Vis detector. The separation was performed on a Sorbaz Eclipse Plus
   C18 column (250 × 4.6 mm, 5 μm) in reversed –phase. Samples (20 μL)
   were inserted with a gradient elution involved two solvents: (A)
   methanol and (B) acetonitrile, in a ratio (A:B) of 55:45 at a current
   of 1 mL/min up to 13.59 min and subsequently the 14 min to 30 min a
   current of 2 mL/min, a total run time of 30 min. Absorbance magnitudes
   were from 290–450 nm. Carotenoids were identified using standards such
   as (lutein, zeaxanthin, tocopherol, β-croptoxantina, lycopene and pure
   β-carotene) (sigma-Aldrich with 99.99% purity).

4.6. Identification of Ascorbic Acid

   Ascorbic acid (AA) was definite in respected of the method of
   Ruiz–Cruz, et al. [[155]80]. First, samples of a homogeneous peppers
   suspension (2 g) of 4% metaphosphoric acid (15 mL) were added. Then,
   the sample was sonicated/5 min (Sonicator VWR model 150 D; VWR
   International., West Chester, PA, USA) and centrifuged at 1610× g/10
   min at room temperature (Centrifuge Thermo Scientific Sorvall ST16,
   Porton Down, UK). The sample supernatant was filtered through a
   polyethylene cartridge of 0.45 μm of pore size (Millipore Corp.,
   Bedford, MA) and identified and quantified using a Agilent 1200,
   HPLC-DAD system, coupled with an auto-sampler and a UV/Vis detector.
   Ascorbic acid was scanned in reversed-phase at λ = 254 nm on a Sorbaz
   Eclipse Plus C18 column (250 × 4.6 mm, 5 μm). Samples (20 μL) were
   inserted with a gradient elution isocratic and the flow rate 1 mL/min,
   which was kept at 25 °C. The mobile phase was programmed of 0.2 M
   KH[2]PO[4], pH 2.3 with 85% o-phosphoric acid. Ascorbic acid was
   prepared, identified in pepper extracts as a standard compound, and
   used for quantification calibration curves of pure Ascorbic acid
   (Sigma-Aldrich, DEU). Each extract was analyzed three times.

4.7. Gas-Chromatography-Mass Spectrometry (GC-MS) Analysis

   Methanol extract was prepared (1 mg/mL), the methanol vaporized and
   subsequently was added 50 μL of derivatizing agent, BSTFA (N,O-bis
   [trimethylsilyl] trifluoroacetamide) + 1% TMCS (trimethylchlorosilane),
   and agitated for 2 min at room temperature. Finally, sample (1 μL) was
   inserted at a GC–MS system (Agilent GC Series 7890A and an Agilent
   single quadrupole MS detector Agilent model 5975C)), with electron
   impact model (70 eV) and the mass range at 50–700 m/z. The separation
   was performed on an HP-5MS capillary column (30 m × 0.25 mm i.d. × 0.25
   μm) and splitless injector (at 250 °C) was used to 2.5 min/splitless
   time. The initial oven temperature was kept at 100 °C for 1 min and
   raised to 220 °C at 6 °C/min, and kept constant for 1.23 min, afterward
   raised to 290 °C at 10 °C/min, and raised to 310 °C at 40 °C/min, and
   kept constant for 7.5 min. The carrier gas (helium) flow rate was
   maintained at 1 mL/min. The GC–MS control and data processing was
   identified by comparing their mass spectra using Chem-Station (Agilent
   Technologies, DEU) software. Metabolite identifications was analyzed
   the spectral (MS) matching with commercialized libraries (NIST) based
   upon retention time, m/z, and percentage of spectral similarity greater
   than 60% and the internal standard.

4.8. Statistical Analysis

   All results obtained by high pressure chromatography liquid were the
   mean ± standard error. Furthermore, one-way ANNOVA and changes amongst
   processing evaluated data were evaluated by comparison of means using
   Tukey’s test. The level of statistical significance was considered at p
   < 0.05. In addition, principal component analysis (PCA) with an auto
   scaling using mean centering method and hierarchical clustering
   analysis (HCA) with centroid linkage clustering for the responses of
   the identified compounds were performed to characterize effect of
   treatments on metabolic profiling using XLSTAT 2014.05.03.

4.9. Hierarchical Clustering and Pathway Enrichment Analysis

   Hierarchical clustering was achieved on the log-transformed
   standardized data with each sample as a separate cluster and then
   combining them until all samples belong to one cluster. Each
   standardized value to enable log change was added a small value (unity)
   with a level of statistical significance p < 0.05. Hierarchical
   clustering was integrated by the log-transformed data acquired of the
   median-centered per metabolite for better visualization. The similarity
   metric was evaluated by Pearson’s correlation and a clustering method
   ward’s linkage. The metabolites’ heat maps were carried out using color
   scheme light/dark. These color scheme showed which metabolic pathways
   are influenced per stress-induced (SA and H[2]O[2]/EC), these results
   were analyzed and mapped using the MetaboAnalyst 5.0 software
   [[156]81]. The identified metabolites were compared to Arabidopsis
   thaliana (Thale cress) pathway catalogue, since it is one of the most
   important systems for the study of many aspects of the biology of
   plants and to prevent the great economic losses that cause diseases,
   they have been applied to the study of the mechanisms that regulate
   resistance in wheat, potato or tomato and pepper; were employed in over
   representation analysis the hypergeometric test, and the out-degree
   centrality algorithms. The P-value was adjusted to improve or adapt
   pathway and an impact factor from the pathway physical map for each
   analysis.

5. Conclusions

   In conclusion, some plant responses to unfavorable growth conditions
   are specific. In contrast, others are general responses providing
   tolerance to several stress conditions as stress response mechanisms
   include changes in some metabolic pathways and induction of antioxidant
   defense pathways against reactive oxygen species. Generally, it is
   assumed that stress sensing acts at multidimensional levels integrating
   several sensing mechanisms in specific stress conditions. Whence, to
   improve the understanding of plant responses to combined stress
   situations. We applied the system analyzing (PCA) of three stress
   factors and the overall metabolite profiles in plants subjected to the
   triple stress of SA, H[2]O[2] and EC. One of the key findings of this
   work was that increase of secondary metabolites. For example, the
   interaction of SA and H[2]O[2] in treatments 2, 3, 6 and 7 shows a
   significant effect on producing phenolic compounds. Related to
   carotenoids, treatment 8 promoted a gain in the concentration of these
   compounds. Otherwise, treatments 5 and 8 also showed a significant
   effect on the concentration of aminoacids, fatty acids, and
   phytosterols compared to the control treatment. The latter compounds
   are derived mainly from aminoacyl-tRNA biosynthesis; valine, leucine
   and isoleucine biosynthesis; alanine; aspartate and glutamate;
   glyoxylate; and dicarboxylate metabolism.

   In conclusion elicitors can promote important changes in secondary
   metabolites profile, aminoacids, and fatty acids which can impact
   sensory traits affecting the quality of pepper due to their
   interactions with environment conditions. Marked disturbances in the
   metabolites in sweet pepper a factor of defense against insects or
   microorganisms, also this can change the resistance to various stresses
   during the grown period. In abstract, the results of our investigation
   propose that the wide biochemical difference in sweet pepper is under
   metabolic control. Combining metabolomics and the biochemical data sets
   will help growers to increase new pepper varieties that unite a
   desirable taste and nutritional profile with resistance to important
   chronic degenerative illnesses. Likewise, the responses of biotic and
   abiotic interactions could identify gene expression patterns, which are
   unique to combined stress situations and might give possible
   explanations for a changed pathogen behavior, and the importance to
   study biotic and abiotic stress combinations to predict plant responses
   to future climate changes.

6. Patents

   This research did not generate any patent during its development.

Acknowledgments