Received: 24 June 2022 Accepted: 30 March 2023 Published online: 23 May 2023 DOI: 10.1002/csc2.21004 Crop Science O R I G I N A L A R T I C L E C r o p B r e e d i n g & G e n e t i c s Genetic variability of kernel phenolics in maize (Zea mays L.) inbreds with differing levels of resistance to gibberella ear rot Mehri Hadinezhad Linda J. Harris Susan Shea Miller Danielle Schneiderman Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada Correspondence Mehri Hadinezhad, Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON, K1A 0C6, Canada. Email: Mehri.hadinezhad@agr.gc.ca Assigned to Associate Editor Leah Mchale. Funding information Agriculture and Agri-Food Canada, Grant/Award Numbers: J-000008, J-001580 Abstract A comprehensive study was performed to investigate the kernel phenolics content and profile of a broad range of maize inbreds varying in disease resistance to gibberella ear rot. Three phenolic fractions (soluble free, soluble conjugated, and insoluble bound) at different kernel developmental stages in two growing seasons were ana- lyzed. The phenolics content and profile revealed a genotype dependency. The highest amount and diversity of phenolics were at day 11 after self-pollination in both grow- ing seasons. This was confirmed by a distinctive PCA biplot clustering of phenolics at day 11 compared to other harvesting days for all fractions. A strong negative correla- tion was found between the conjugated and bound phenolics in both growing seasons (in 2016, r = 0.93, p < 0.0001; and in 2018, r = 0.94, p < 0.0001). In both growing seasons, the main phenolic compounds in the free, conjugated, and bound fractions were chlorogenic acid, caffeic acid, and t-ferulic acid, respectively. Comprehensive results were presented for how the phenolics evolved over the period of kernel devel- opment and how their content changed. The PCA-biplot analysis revealed different patterns of clustering for phenolic associations in different fractions as well as in different growing seasons, reflecting the evolution process of individual phenolics during kernel filling period. Overall, genotypes with higher disease resistance pos- sessed higher phenolics in the bound and conjugated fractions at blister stage. This study provides a useful resource of maize inbred phenolics profiles that could be applied in future breeding efforts. Abbreviations: 55-DiFA, 5,5ť diferulic acid; 85-DiFA-BF, 8,5ť diferulic acid benzofuran form; 88THF-DiFA, 8,8ť (tetrahydrofuran) diferulic acid; 8O4-DiFA, 8-O-4ť diferulic acid; CFA, caffeic acid; CGA, chlorogenic acid; D11, D15, D20, D30, D63, Harvesting day (days after self-pollination) and at maturity (9 weeks); DiFAs, diferulic acids; GAE, gallic acid equivalent; GER, gibberella ear rot; LOQ, limit of quantification; NQ, not quantified; PCA, principal component analysis; p-CA, p-coumaric acid; SPA, sinapic acid; SYG, syringic acid; t-FA, t-ferulic acid; VAN, vanillin; VNA, vanillic acid. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 His Majesty the King in Right of Canada. Crop Science published by Wiley Periodicals LLC on behalf of Crop Science Society of America. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada. 1 INTRODUCTION Maize (Zea mays L.) is Canada’s third most valued crop with 13.56 million tons produced in 2020 (https://www150.statcan. gc.ca/t1/tbl1/en/tv.action?pid = 3210001401; https://doi.org/ 10.25318/3210001401-eng). To maintain market competi- tiveness, it is critical for growers to adapt to constantly changing climatic conditions and consumer demands. Mit- igating disease risk in maize production is among the top 2162 wileyonlinelibrary.com/journal/csc2 Crop Science. 2023;63:2162–2183. https://orcid.org/0000-0002-5704-1570 https://orcid.org/0000-0002-9098-305X mailto:Mehri.hadinezhad@agr.gc.ca http://creativecommons.org/licenses/by/4.0/ https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid https://doi.org/10.25318/3210001401-eng https://doi.org/10.25318/3210001401-eng https://wileyonlinelibrary.com/journal/csc2 http://crossmark.crossref.org/dialog/?doi=10.1002%2Fcsc2.21004&domain=pdf&date_stamp=2023-05-23 HADINEZHAD ET AL. 2163Crop Science strategic priorities. Gibberella ear rot (GER) is one of the most detrimental maize ear diseases that is caused by the fungal pathogen Fusarium graminearum Schwabe. Fusar- ium graminearum gains entry to maize kernels either by germinating on exposed silks and travelling down the silk channel to the developing kernels or directly into kernels due to weather or insect damage (Munkvold, 2003). Moderate temperatures and wet weather conditions favor infection and higher levels of mycotoxin accumulation (Martins & Mar- tins, 2002), especially during the silking stage (Munkvold, 2003). The economic losses are significant as a result of yield reduction as well as mycotoxin contamination (zear- alenone and trichothecenes such as deoxynivalenol), which impose health risks for humans and livestock (Ferrigo et al., 2016; Oldenburg et al., 2017). A key management practice to reduce the fungal and mycotoxin load in maize kernels is selection/breeding of resistant genotypes. There is continued pressure toward adapting and develop- ing new sources of resistance to maize diseases such as GER. Various factors including genotype, environmental condi- tions, and flowering time impact the susceptibility of hybrids. However, knowledge of resistance mechanisms is essential for the success of breeding programs. The plant responses to infection, especially in early plant developmental stages, are critical for determining the disease resistance potential of developed genotypes (Edwards, 2004). Phenolic compounds are plant secondary metabolites that enable plants to ameliorate biotic and abiotic stresses includ- ing pathogen attack (Atanasova-Penichon et al., 2016; Bakan et al., 2003). Cereal grain phenolic compounds can be found in three main forms/fractions: soluble free, soluble ester con- jugated, and insoluble bound. The majority of phenolics are concentrated in the outer layer of grains (bran) and mainly in a bound form (Adom & Liu, 2002; Butts-Wilmsmeyer & Bohn, 2016; Ndolo & Beta, 2014; Sosulski et al., 1982). The first two fractions are easily separated from the bound phenolic fraction using an alcohol extraction process (usually 70%– 80% methanol or ethanol). To separate the free fraction from conjugated, an ethyl acetate partitioning step is needed. To release the phenolic compounds in the conjugated fraction, an acid/base hydrolysis step is employed. Ferulic acid is the most common phenolic acid in the bound fraction, and it can be found in di- or tri-merized forms cross-linking arabinoxylans (mainly in the pericarp and aleurone layers) that fortify the cell wall (Bily et al., 2003; Dobberstein & Bunzel, 2010; Fry, 1986; Ishii, 1997). Four main forms of DiFA that have been reported for maize kernels are 8,8ť (tetrahydrofuran) difer- ulic acid, 5,5ť diferulic acid, 8-O-4ť diferulic acid, and 8,5ť diferulic acid benzofuran. Several studies have shown the relationship between maize kernel phenolic compounds and disease resistance includ- ing ferulic acid content correlation with GER (Assabgui Core Ideas ∙ The phenolics distribution in free, conjugated, and bound fractions during maize kernel development was documented. ∙ The evolution of phenolic compounds during maize kernel development was revealed. ∙ Most changes in the phenolics profile occurred dur- ing the early stages of kernel development with high heritability. ∙ Genotypes with higher disease resistance pos- sessed higher bound and conjugated phenolics at blister stage. et al., 1993), diferulic acid content correlation with GER disease severity (Bily et al., 2003), and correlation of phenyl- propanoid levels with disease severity and fumonisin accu- mulation caused by Fusarium verticillioides (Sampietro et al., 2013). However, all these studies analyzed phenolics after harvest time, not at the time that fungi usually invade (early stage of grain filling). Chtioui et al. (2022) recently reviewed the research related to the Fusarium antifungal and antimy- cotoxigenic activity of natural phenolic compounds found in cereal grains. Based on their findings, among phenolic acids, cinnamic acid derivatives with high antioxidant activities are the main contributors to Fusarium head blight resistance by scavenging the reactive oxygen species produced by the fungi during metabolic activities. Past studies have focused on bound phenolics and mainly in mature maize kernels. The role of different phenolic frac- tions in defending against or responding to diseases is not clear, especially conjugated phenolics that have not been eval- uated in most studies. In addition, changes in phenolics during kernel developmental stages have not been comprehensively documented. The objective of this research was to investigate the phe- nolics profile (soluble free, soluble conjugated, and insoluble bound) over several kernel developmental stages of a range of maize inbreds with differing levels of resistance to GER. 2 MATERIALS AND METHODS This study is composed of two main phases: (1) a set of maize genotypes ranging in GER disease resistance was chosen to investigate phenolic variability in immature kernels (11 days and 15 days after self-pollination) and (2) a smaller genotype population was selected to represent the diversity observed in phase one; however, the study was broadened to include a wider period of maize kernel development. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2164 HADINEZHAD ET AL.Crop Science T A B L E 1 Pedigree and gibberella ear rot/kernel disease rating (ERK) of maize inbreds screened in this study. Disease ratings are based on a visual assessment of the extent of fungal infection over the ear; 1 indicates no visible infection and 7 indicates >75% infected (Reid & Hamilton, 1996) Inbred Pedigree ERK mean disease rating B73 BSSS C5 6.1 CO430 Fusarium-resistant synthetic 3.4 CO433 PrideK127 3.7 CO441 Jacques 7700×CO298 3.8 CO449 CO432×CO433 3.5 CO452 (CO388×CO328) CO388 5.8 RIL4 B73×CO441 3.7 RIL24 B73×CO441 4.1 RIL53 B73×CO441 3.5 RIL226 B73×CO441 3.7 RIL251 B73×CO441 6.7 RIL278 B73×CO441 6.7 Source: Blackwell et al. (2022) and Kebede et al. (2016). 2.1 Maize genotypes The maize genotypes used in phase one, 2016 growing season, include five inbred lines (CO430, CO433, CO441, CO449, and CO452) from AAFC’s maize breeding program (Jindal et al., 2019), the public inbred B73, and six recombinant inbred lines (RIL4, RIL24, RIL53, RIL226, RIL251, and RIL278) with differing levels of disease resistance derived from a bi-parental cross between B73 (highly susceptible to GER) and CO441 (moderately resistant to GER; Kebede et al. (2016)). Maize ears were harvested at 11 (D11) and 15 (D15) days after self-pollination. Table 1 depicts the pedigree of chosen inbreds, obtained from Dr. L.M. Reid, Ottawa Research and Development Cen- tre, Agriculture and Agri-Food Canada (AAFC) and their kernel inoculation disease ratings (gibberella ear rot). Based on the phenolics profile of the 2016 population, a subset was chosen for phase two, 2018 growing season, including B73, CO430, CO433, CO441, CO449, CO452, RIL4, and RIL278. The harvesting period was extended up to maturity and included the harvesting dates D11, D20, and D30 after self-pollination as well as at maturity (all inbreds were harvested at 9 weeks after self-pollination; D63). How- ever, due to very dry conditions and poor seed set in 2018, only Day 11 samples were harvested for genotypes CO430, CO433, and RIL4. 2.2 Maize cultivation and harvest Maize genotypes were sown in 2016 and 2018 at the Central Experimental Farm of the Ottawa Research and Develop- ment Centre (45.3875˚ N, 75.7092˚ W) in a randomized block design with four replicated plots. Each plot consisted of two 3.8 m rows (20 seeds/row) for each inbred. In 2016, the self- pollination was conducted between July 22 and August 9, and one ear from each replicate plot was collected at 11 and 15 days after self-pollination (D11 and D15). In 2018, self- pollination was done between July 16 and August 2, and one ear from each replicate plot was collected at four harvest- ing dates after self-pollination (D11, D20, D30, and D63). Each ear was stored separately at −80˚C. The ears were then manually shelled, and kernels were quickly frozen in liquid nitrogen. Kernels were freeze dried and ground using a pinball mill. Ground samples were stored at −20˚C until extraction and analysis. Daily temperatures and precipitation were measured at a meteorological station located very close to the experi- mental field (data accessed at http://climate.weather.gc.ca/ index_e.html), and a summary of temperature and rainfall for the months of July and August for both growing seasons is presented in Table 2. 2.3 Phenolic extraction Before phenolic extraction, the freeze dried and ground maize kernels were defatted by hexane extraction (twice) with a ratio of 1:10 (g/mL) for 1 h at room temperature. The defatted meal was dried under N2 flow. Soluble free, soluble conjugated, and bound phenolics in maize kernels were extracted and fractionated as previously described (Hadinezhad & Miller, 2019) with a minor modi- fication: alkaline hydrolysis of the bound fraction was done using 2 N NaOH. 2.4 HPLC analysis and total phenolic content The HPLC phenolic composition of three fractions was ana- lyzed as per our previous method (Hadinezhad & Miller, 2019); all phenolic standards were purchased analytical grade from Sigma–Aldrich. Diferulic acids (DiFAs) were identified by comparing their relative retention times with published data (Dobberstein & Bunzel, 2010) as well as matching the UV spectra. A ferulic acid standard curve was used to quan- tify DiFAs, and results are reported as t-ferulic acid (t-FA) equivalent. The lowest area under the curve for the phenolic standard chromatograms was 47 mAU.min, which was more than 10 times the signal to noise ratio. Therefore, a constant value of 50 mAU.min was considered the limit of quantification (LOQ) for all measurements (Shrivastava & Gupta, 2011), and peaks with lower values were considered not quantified 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense http://climate.weather.gc.ca/index_e.html http://climate.weather.gc.ca/index_e.html HADINEZHAD ET AL. 2165Crop Science T A B L E 2 C lim at ic co nd iti on s du ri ng ke rn el de ve lo pm en ti n ea ch of th e te st in g ye ar s Pa ra m et er Ju ly 20 16 A ug us t2 01 6 Ju ly 20 18 A ug us t2 01 8 Te m pe ra tu re (˚ C )a M ax . 27 .4 28 .1 29 .8 26 .9 M in . 15 .8 16 .4 17 .1 16 .6 M ea n 21 .6 22 .3 23 .4 21 .8 R ai nf al l( m m ) 10 0. 8 12 2. 4 15 4. 8 64 .4 a T em pe ra tu re M ax ., M in ., an d M ea n w er e ob ta in ed fr om th e av er ag e m ax im um ,m in im um ,a nd m ea n te m pe ra tu re re ad in g of th e da y, re sp ec tiv el y. W ea th er da ta fr om w ea th er st at io n lo ca te d at 45 ˚2 3′ N ,7 5˚ 43 ′W w er e re tr ie ve d fr om ht tp :// cl im at e. w ea th er .g c. ca /in de x_ e. ht m l. (NQ). Then, the lowest quantified concentration for each phe- nolic compound was calculated from their respective standard curves (all in μg/mL solution): chlorogenic acid, CGA (2.87); vanillic acid, VNA (2.42); caffeic acid, CFA (0.86); syringic acid, SYG (1.05); vanillin, VAN (0.62); p-coumaric acid, p- CA (0.48); t-ferulic acid, t-FA (1.41); and sinapic acid, SPA (2.84). Total phenolic content (TPC) of different phenolic fractions was also measured using the Folin–Ciocalteu assay as pre- viously described (Hadinezhad & Miller, 2019). Results are reported as gallic acid equivalent (GAE). 2.5 Statistical analysis All measurements were performed in four replicates, with data reported as mean value ± SD. The correlations of TPC results between phenolic frac- tions were statistically calculated using Regression func- tion in 2016 Microsoft Excel package at 95% confidence level. To investigate the interaction effect of harvest date and genotype on TPC results, a two-way analysis of variance (ANOVA) at 95% confidence level was performed each year separately (for 2018, only those genotypes that had results for all harvest dates were included), using the GraphPad Prism software package (version 9, 2020). The data were first tested for normality using the Shapiro Wilk test. The significant dif- ferences between harvesting dates and genotypes were tested using the Tukey multiple comparison test. The TPC results presented in Tables 3 and 4 and Figure S1 (including absolute values in individual fractions, TPC contri- bution percentages of each fraction, and sum of TPC values) were statistically analyzed using one-way ANOVA (for each harvest period, in 2016 and 2018, separately) with the RStudio software package (version 4.0.2, 2020) with 95% confidence level (RStudio: Integrated Development for R, 2020). The means values with significant differences were ranked using Duncan’s multiple range test (RStudio), annotated by differ- ent letters in Tables 3 and 4. TPC heritability for each fraction at each harvest period and in each year was calculated based on Hallauer et al. (2010), and the analysis of variance com- ponents was performed using the Meta-R software (version 3.5.1, 2020) and based on Alvarado et al. (2020). The formula used to calculate heritability was as follows: H2 = Genotypic variance/(Genotypic variance + Error variance). To analyze and visualize the phenolic profile (indi- vidual phenolic compounds, HPLC results presented in Tables 5–10), the principal component analysis (PCA)-biplot was performed using the RStudio software package (version 4.0.2; 2020) and the “prcomp” function. The biplot visual- ization of individual principal component scores and loading 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense http://climate.weather.gc.ca/index_e.html 2166 HADINEZHAD ET AL.Crop Science T A B L E 3 Ph en ol ic s di st ri bu tio ns (% of ea ch fr ac tio n) an d su m of T PC va lu es (s um of th re e fr ac tio ns ) m ea su re d by Fo lin –C io ca lte u as sa y fo r m ai ze ge no ty pe s ha rv es te d in 20 16 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic di st ri bu tio n (% ) Su m of TP C va lu es ,m g ga lli c ac id eq ui va le nt /g dr ie d an d de fa tte d sa m pl e Fr ee C on ju ga te d Bo un d D 11 B 73 14 ± 2b 19 ± 3b c 67 ± 2e f 5. 12 ± 0. 21 e C O 43 0 9 ± 1e f 28 ± 3a 63 ± 4f 6. 53 ± 0. 53 c C O 43 3 12 ± 2d 11 ± 1e 77 ± 2b c 7. 42 ± 0. 44 b C O 44 1 9 ± 0e f 12 ± 2d e 79 ± 2b 8. 34 ± 0. 67 a C O 44 9 17 ± 1a 10 ± 1e f 73 ± 1d 6. 36 ± 0. 29 c C O 45 2 9 ± 2e f 11 ± 1e 80 ± 2b 5. 32 ± 0. 36 de R IL 4 8 ± 1f 7 ± 1f 85 ± 3a 5. 98 ± 0. 56 cd R IL 24 14 ± 1b 22 ± 4b 64 ± 4f 5. 47 ± 0. 67 de R IL 53 10 ± 0d ef 10 ± 1e f 80 ± 1b 5. 85 ± 0. 55 cd e R IL 22 6 8 ± 0f 18 ± 3c 74 ± 3 cd 4. 27 ± 0. 39 f R IL 25 1 11 ± 1d e 21 ± 2b c 68 ± 2e 5. 33 ± 0. 48 de R IL 27 8 12 ± 3d 15 ± 2d 73 ± 4d 4. 07 ± 0. 37 f D 15 B 73 11 ± 1a 16 ± 1b 73 ± 2f 4. 94 ± 0. 37 ab cd C O 43 0 9 ± 1b c 25 ± 2a 66 ± 2 g 4. 28 ± 0. 03 de C O 43 3 6 ± 1e 10 ± 1e f 84 ± 1a b 5. 11 ± 0. 53 ab c C O 44 1 7 ± 1c de 10 ± 1e f 83 ± 2b c 5. 64 ± 0. 47 a C O 44 9 9 ± 0b c 11 ± 2e f 80 ± 2 cd 4. 68 ± 0. 61 cd e C O 45 2 8 ± 0 cd 14 ± 2b c 77 ± 2d e 3. 95 ± 0. 39 ef R IL 4 7 ± 1c de 6 ± 0 g 87 ± 1a 5. 55 ± 0. 52 ab R IL 24 8 ± 1 cd 14 ± 2b c 78 ± 3d e 5. 26 ± 0. 94 ab c R IL 53 9 ± 0b c 9 ± 2f 82 ± 2b c 4. 99 ± 0. 38 ab cd R IL 22 6 7 ± 1c de 9 ± 1f 84 ± 2a b 4. 85 ± 0. 32 bc d R IL 25 1 11 ± 3a 13 ± 2 cd 76 ± 4e f 4. 56 ± 0. 34 cd e R IL 27 8 9 ± 2b c 12 ± 1d e 79 ± 2 cd 3. 50 ± 0. 33 f No te :W ith in a co lu m n, m ea ns no ts ha ri ng a co m m on le tte r ar e si gn if ic an tly di ff er en ta m on g ha rv es td at es at p < 0. 05 . a D 11 an d D 15 ;h ar ve st in g da te ,1 1 an d 15 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2167Crop Science T A B L E 4 Ph en ol ic di st ri bu tio n (% of ea ch fr ac tio n) an d su m of T PC va lu es (s um of th re e fr ac tio ns ) m ea su re d by Fo lin –C io ca lte u as sa y fo r m ai ze ge no ty pe s ha rv es te d in 20 18 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic di st ri bu tio n (% ) Su m of TP C va lu es ,m g ga lli c ac id eq ui va le nt /g dr ie d an d de fa tte d sa m pl e Fr ee C on ju ga te d Bo un d D 11 B 73 11 ± 1b 21 ± 1b c 68 ± 1a 4. 22 ± 0. 20 d C O 43 0 17 ± 2a 36 ± 3a 47 ± 3c 5. 92 ± 0. 52 bc C O 43 3 13 ± 1b 18 ± 2d e 69 ± 1a 6. 49 ± 0. 47 b C O 44 1 13 ± 2b 15 ± 1e 72 ± 2a 7. 46 ± 0. 58 a C O 44 9 18 ± 3a 24 ± 3b 58 ± 6b 5. 31 ± 0. 32 c C O 45 2 11 ± 2b 18 ± 2d e 71 ± 2a 4. 21 ± 0. 33 d R IL 4 12 ± 2b 17 ± 2d e 71 ± 3a 5. 55 ± 0. 55 c R IL 27 8 13 ± 1b 19 ± 1 cd 68 ± 2a 4. 31 ± 0. 20 d D 20 B 73 12 ± 1a 9 ± 2c 79 ± 1b 2. 28 ± 0. 24 c C O 44 1 6 ± 1c 10 ± 2c 84 ± 3a 3. 91 ± 0. 32 a C O 44 9 9 ± 2b 12 ± 1c 79 ± 3b 3. 26 ± 0. 45 b C O 45 2 6 ± 0c 16 ± 4b 77 ± 4b 1. 86 ± 0. 16 c R IL 27 8 10 ± 1b 21 ± 4a 69 ± 4c 1. 15 ± 0. 14 d D 30 B 73 10 ± 1a 8 ± 2b 82 ± 2a b 3. 22 ± 0. 28 a C O 44 1 4 ± 1c 12 ± 2a 84 ± 2a 3. 12 ± 0. 34 a C O 44 9 8 ± 1b 13 ± 3a 79 ± 4b 2. 52 ± 0. 34 b C O 45 2 7 ± 1b 13 ± 2a 80 ± 3a b 1. 97 ± 0. 31 c R IL 27 8 11 ± 0a 11 ± 1a b 79 ± 1b 2. 34 ± 0. 42 bc D 63 B 73 11 ± 1b 7 ± 0c 82 ± 0a 2. 90 ± 0. 26 a C O 44 1 7 ± 1c 10 ± 2b 83 ± 3a 2. 36 ± 0. 18 b C O 44 9 9 ± 1b 11 ± 2b 80 ± 2a 2. 80 ± 0. 27 a R IL 27 8 18 ± 3a 15 ± 2a 67 ± 4b 2. 59 ± 0. 27 ab No te :W ith in a co lu m n, m ea ns no ts ha ri ng a co m m on le tte r ar e si gn if ic an tly di ff er en ta m on g ha rv es td at es at p < 0. 05 . a D 11 ,D 20 ,D 30 ,a nd D 63 ;h ar ve st in g pe ri od ,1 1, 20 ,3 0, an d 63 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2168 HADINEZHAD ET AL.Crop Science T A B L E 5 H ig h- pe rf or m an ce liq ui d ch ro m at og ra ph y an al ys is of ph en ol ic s co m po si tio n in th e fr ee fr ac tio n of m ai ze ge no ty pe s ha rv es te d in 20 16 M ai ze ge no ty pe / ha rv es tin g da ya Ph en ol ic co m po un d, μg /g dr ie d an d de fa tte d sa m pl e C G A V N A C FA SY G p- C A t-F A SP A 88 TH F- D iF A D 11 B 73 15 2. 8 ± 19 .1 17 .2 ± 3. 0 21 .5 ± 2. 4 N Q 5. 5 ± 1. 2 25 .3 ± 5. 6 31 .5 ± 4. 4 9. 8 ± 1. 3 C O 43 0 18 8. 9 ± 15 .3 N Q N Q 63 .6 ± 5. 1 4. 6 ± 0. 4 11 .4 ± 1. 3 N Q 15 .2 ± 2. 0 C O 43 3 91 .0 ± 3. 2 9. 5 ± 0. 2 N Q 20 .2 ± 3. 6 N Q 11 .3 ± 0. 8 18 .8 ± 0. 8 8. 7 ± 1. 1 C O 44 1 66 .8 ± 8. 2 N Q N Q 39 .4 ± 5. 5 6. 8 ± 0. 6 11 .3 ± 1. 6 20 .1 ± 1. 6 9. 8 ± 1. 1 C O 44 9 33 9. 1 ± 31 .1 10 .9 ± 0. 9 N Q 81 .1 ± 9. 2 5. 8 ± 0. 9 13 .9 ± 1. 6 19 .4 ± 1. 3 N Q C O 45 2 23 .1 ± 2. 0 N Q N Q N Q N Q N Q N Q 9. 4 ± 1. 1 R IL 4 57 .0 ± 9. 2 9. 8 ± 0. 6 N Q 18 .9 ± 2. 5 N Q 9. 4 ± 1. 4 N Q 9. 3 ± 1. 8 R IL 24 19 3. 0 ± 20 .5 19 .7 ± 1. 6 N Q 19 5. 6 ± 34 .3 N Q 42 .9 ± 5. 1 N Q N Q R IL 53 60 .6 ± 6. 7 N Q N Q 22 .1 ± 4. 8 N Q 8. 4 ± 1. 0 N Q 13 .7 ± 1. 9 R IL 22 6 52 .3 ± 5. 2 N Q N Q 37 .9 ± 3. 0 N Q 11 .6 ± 1. 4 N Q N Q R IL 25 1 18 6. 4 ± 21 .4 N Q N Q N Q N Q N Q N Q N Q R IL 27 8 12 2. 5 ± 11 .6 18 .5 ± 2. 4 N Q 9. 9 ± 1. 1 N Q 11 .5 ± 1. 2 N Q 8. 6 ± 0. 4 D 15 B 73 85 .8 ± 11 .8 N Q 7. 4 ± 0. 3 N Q N Q 7. 6 ± 0. 7 25 .3 ± 4. 5 20 .4 ± 3. 0 C O 43 0 97 .7 ± 8. 2 N Q N Q 26 .2 ± 0. 9 N Q 12 .1 ± 1. 7 N Q 15 .9 ± 2. 1 C O 43 3 16 .6 ± 0. 8 N Q N Q 6. 9 ± 0. 3 N Q N Q N Q 12 .9 ± 1. 3 C O 44 1 N Q N Q N Q N Q N Q N Q N Q 13 .0 ± 1. 7 C O 44 9 34 .2 ± 4. 4 N Q N Q 36 .0 ± 3. 6 N Q 7. 8 ± 0. 8 N Q N Q C O 45 2 N Q N Q N Q N Q N Q N Q N Q 15 .3 ± 1. 0 R IL 4 N Q N Q N Q 11 .7 ± 1. 3 N Q 10 .5 ± 2. 6 N Q 24 .0 ± 4. 4 R IL 24 16 .5 ± 1. 2 N Q N Q 54 .1 ± 7. 0 N Q 20 .4 ± 2. 6 N Q 14 .5 ± 1. 7 R IL 53 29 .0 ± 3. 2 N Q N Q 10 .3 ± 1. 9 N Q N Q N Q 15 .6 ± 1. 7 R IL 22 6 N Q N Q N Q 8. 7 ± 1. 5 N Q 9. 1 ± 0. 8 N Q 15 .5 ± 2. 4 R IL 25 1 87 .9 ± 18 .1 N Q N Q N Q N Q N Q N Q N Q R IL 27 8 31 .5 ± 3. 4 N Q N Q N Q N Q N Q N Q N Q A bb re vi at io ns :C FA ,c af fe ic ac id ;C G A ,c hl or og en ic ac id ;8 8T H F- D iF A ,8 ,5 ′ di fe ru lic ac id be nz of ur an fo rm ;N Q ,n ot qu an tif ie d; p- C A ,p -c ou m ar ic ac id ;t -F A ,t -f er ul ic ac id ;S PA ,s in ap ic ac id ;S Y G ,s yr in gi c ac id ;V N A ,v an ill ic ac id . a D 11 an d D 15 ;h ar ve st in g da y, 11 an d 15 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2169Crop Science T A B L E 6 H ig h- pe rf or m an ce liq ui d ch ro m at og ra ph y an al ys is of ph en ol ic s co m po si tio n in th e co nj ug at ed fr ac tio n of m ai ze ge no ty pe s ha rv es te d in 20 16 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic co m po un d, μg /g dr ie d an d de fa tte d sa m pl e V N A C FA SY G p- C A t-F A SP A 88 TH F- D iF A D 11 B 73 11 .6 ± 1. 6 56 3. 6 ± 96 .4 N Q 27 .0 ± 3. 5 19 2. 6 ± 31 .4 41 .5 ± 6. 5 34 .2 ± 4. 7 C O 43 0 18 .5 ± 3. 4 13 16 .7 ± 10 3. 3 28 .0 ± 5. 0 86 .2 ± 8. 9 53 3. 5 ± 70 .7 65 .9 ± 8. 6 10 5. 1 ± 11 .4 C O 43 3 18 .0 ± 2. 4 32 9. 1 ± 38 .8 N Q 39 .3 ± 5. 8 19 6. 3 ± 39 .7 68 .3 ± 5. 4 32 .7 ± 7. 9 C O 44 1 21 .2 ± 2. 0 33 5. 2 ± 38 .1 N Q 49 .9 ± 10 .4 23 3. 8 ± 39 .8 71 .2 ± 11 .9 28 .8 ± 7. 3 C O 44 9 N Q 39 2. 6 ± 53 .7 N Q 31 .3 ± 6. 2 16 5. 1 ± 33 .1 30 .5 ± 7. 0 30 .5 ± 7. 7 C O 45 2 12 .7 ± 1. 1 13 7. 9 ± 22 .2 N Q 35 .1 ± 7. 2 13 3. 1 ± 24 .5 54 .7 ± 4. 9 12 6. 2 ± 22 .7 R IL 4 10 .1 ± 0. 6 14 8. 8 ± 27 .5 N Q 8. 5 ± 0. 7 77 .9 ± 22 .8 33 .6 ± 5. 5 18 .0 ± 2. 1 R IL 24 30 .4 ± 3. 7 37 5. 2 ± 47 .8 10 .7 ± 2. 6 38 .9 ± 3. 7 53 7. 8 ± 41 .5 60 .2 ± 10 .5 N Q R IL 53 11 .6 ± 0. 3 16 5. 5 ± 29 .0 N Q 25 .5 ± 0. 7 20 9. 7 ± 46 .0 60 .6 ± 8. 3 99 .6 ± 20 .7 R IL 22 6 10 .2 ± 1. 3 32 6. 6 ± 54 .8 N Q 17 .7 ± 2. 1 20 7. 4 ± 48 .6 50 .5 ± 5. 3 15 3. 5 ± 19 .5 R IL 25 1 12 .7 ± 1. 3 84 3. 3 ± 48 .9 10 .3 ± 1. 9 31 .5 ± 3. 5 17 8. 4 ± 23 .2 43 .6 ± 4. 0 51 .5 ± 5. 1 R IL 27 8 N Q 28 6. 2 ± 61 .1 N Q 7. 4 ± 0. 6 13 4. 8 ± 22 .7 30 .6 ± 4. 6 51 .6 ± 6. 8 D 15 B 73 11 .0 ± 0. 8 37 8. 3 ± 36 .7 N Q 24 .4 ± 5. 3 10 9. 2 ± 21 .1 66 .3 ± 11 .9 17 0. 6 ± 18 .4 C O 43 0 10 .3 ± 1. 1 70 8. 3 ± 77 .1 13 .2 ± 2. 7 41 .4 ± 5. 1 20 1. 8 ± 12 .0 64 .1 ± 7. 7 90 .4 ± 15 .0 C O 43 3 11 .0 ± 2. 1 67 .8 ± 8. 8 8. 5 ± 1. 2 22 .5 ± 1. 3 92 .8 ± 17 .9 71 .1 ± 11 .0 10 1. 6 ± 16 .6 C O 44 1 11 .3 ± 1. 6 11 1. 4 ± 20 .0 N Q 26 .9 ± 5. 8 90 .1 ± 14 .7 74 .5 ± 7. 4 14 8. 0 ± 21 .1 C O 44 9 N Q 20 2. 4 ± 47 .5 N Q 22 .2 ± 4. 0 12 1. 4 ± 22 .0 38 .1 ± 5. 9 69 .6 ± 10 .4 C O 45 2 N Q 59 .9 ± 2. 0 N Q 19 .5 ± 3. 8 57 .7 ± 6. 3 66 .2 ± 2. 1 21 1. 7 ± 28 .1 R IL 4 12 .1 ± 1. 2 44 .4 ± 10 .0 N Q 10 .8 ± 1. 3 85 .5 ± 7. 3 50 .9 ± 10 .3 93 .4 ± 6. 8 R IL 24 22 .7 ± 3. 5 11 6. 9 ± 27 .1 N Q 34 .1 ± 2. 7 28 1. 5 ± 43 .1 60 .8 ± 7. 6 45 .6 ± 13 .7 R IL 53 9. 7 ± 0. 3 69 .4 ± 3. 6 8. 3 ± 0. 6 17 .8 ± 2. 6 11 2. 6 ± 23 .1 61 .4 ± 5. 7 15 6. 5 ± 39 .0 R IL 22 6 9. 1 ± 0. 3 62 .1 ± 12 .6 19 .2 ± 1. 4 17 .9 ± 1. 9 10 8. 0 ± 8. 5 61 .4 ± 1. 1 19 4. 5 ± 24 .7 R IL 25 1 N Q 34 3. 8 ± 59 .0 8. 1 ± 0. 7 16 .7 ± 3. 7 98 .0 ± 20 .1 43 .1 ± 6. 4 55 .8 ± 11 .4 R IL 27 8 N Q 11 5. 7 ± 22 .1 N Q 6. 0 ± 0. 9 46 .9 ± 13 .7 39 .8 ± 3. 4 17 2. 8 ± 19 .5 A bb re vi at io ns :C FA ,c af fe ic ac id ;C G A ,c hl or og en ic ac id ;8 8T H F- D iF A ,8 ,5 ′ di fe ru lic ac id be nz of ur an fo rm ;N Q ,n ot qu an tif ie d; p- C A ,p -c ou m ar ic ac id ;t -F A ,t -f er ul ic ac id ;S PA ,s in ap ic ac id ;S Y G ,s yr in gi c ac id ;V N A ,v an ill ic ac id . a D 11 an d D 15 ;h ar ve st in g da y, 11 an d 15 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2170 HADINEZHAD ET AL.Crop Science T A B L E 7 H ig h- pe rf or m an ce liq ui d ch ro m at og ra ph y an al ys is of ph en ol ic s co m po si tio n in th e bo un d fr ac tio n of m ai ze ge no ty pe s ha rv es te d in 20 16 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic co m po un d, μg /g dr ie d an d de fa tte d sa m pl e C FA VA N p- C A t-F A SP A 55 - D iF A b 8O 4- D iF A b 85 -D iF A -B Fb D 11 B 73 N Q 38 .4 ± 1. 5 48 4. 5 ± 49 .0 38 31 .6 ± 33 2. 3 11 9. 2 ± 13 .1 N Q 78 .6 ± 3. 6 55 .2 ± 4. 1 C O 43 0 N Q 26 .8 ± 6. 7 58 4. 8 ± 11 4. 3 43 20 .6 ± 27 0. 8 N Q N Q N Q N Q C O 43 3 58 .6 ± 16 .5 63 .9 ± 2. 8 97 2. 2 ± 15 2. 0 66 06 .5 ± 55 9. 2 N Q N Q N Q N Q C O 44 1 95 .0 ± 4. 6 63 .3 ± 9. 0 17 17 .0 ± 21 9. 7 66 84 .7 ± 62 5. 8 16 8. 9 ± 30 .7 N Q 12 0. 5 ± 13 .4 81 .4 ± 10 .8 C O 44 9 39 .1 ± 4. 2 40 .6 ± 6. 6 51 5. 8 ± 58 .7 53 37 .2 ± 38 0. 1 10 0. 2 ± 6. 2 N Q 98 .7 ± 14 .3 54 .1 ± 6. 7 C O 45 2 38 .0 ± 7. 1 38 .5 ± 5. 1 44 8. 3 ± 42 .4 41 72 .5 ± 35 7. 2 97 .5 ± 15 .6 N Q 95 .3 ± 12 .1 61 .7 ± 4. 7 R IL 4 37 .8 ± 3. 3 43 .3 ± 2. 2 64 1. 7 ± 93 .4 57 92 .6 ± 57 6. 3 N Q N Q N Q N Q R IL 24 N Q 37 .4 ± 5. 3 25 4. 8 ± 36 .4 41 91 .7 ± 46 2. 3 N Q N Q N Q N Q R IL 53 48 .5 ± 8. 5 40 .2 ± 2. 5 54 3. 8 ± 42 .4 52 64 .0 ± 52 9. 6 N Q N Q N Q N Q R IL 22 6 N Q 37 .7 ± 5. 9 15 6. 2 ± 30 .1 37 41 .4 ± 28 5. 2 N Q N Q N Q N Q R IL 25 1 N Q 41 .7 ± 5. 7 27 7. 5 ± 38 .3 42 40 .8 ± 58 .0 N Q N Q N Q N Q R IL 27 8 N Q 30 .9 ± 1. 6 13 8. 2 ± 35 .5 31 96 .1 ± 54 0. 4 92 .8 ± 8. 2 N Q 59 .3 ± 5. 7 43 .5 ± 4. 3 D 15 B 73 N Q 34 .8 ± 4. 2 32 9. 6 ± 72 .4 37 82 .1 ± 42 0. 4 12 9. 3 ± 17 .0 N Q 77 .9 ± 5. 7 50 .6 ± 4. 0 C O 43 0 N Q N Q 17 1. 2 ± 35 .1 30 19 .9 ± 13 1. 1 N Q N Q N Q N Q C O 43 3 37 .9 ± 8. 3 28 .1 ± 4. 8 63 1. 1 ± 92 .7 48 42 .6 ± 35 4. 1 N Q N Q N Q N Q C O 44 1 54 .1 ± 5. 6 40 .8 ± 4. 0 10 24 .3 ± 83 .0 49 52 .3 ± 41 7. 5 15 6. 1 ± 17 .5 N Q 89 .4 ± 12 .9 55 .8 ± 5. 1 C O 44 9 37 .7 ± 3. 0 40 .6 ± 7. 2 37 0. 1 ± 53 .5 42 73 .8 ± 41 6. 8 98 .9 ± 13 .3 N Q 91 .5 ± 12 .1 55 .9 ± 7. 1 C O 45 2 N Q N Q 17 8. 9 ± 13 .4 34 19 .5 ± 16 7. 3 11 3. 5 ± 12 .6 N Q 74 .6 ± 5. 9 47 .5 ± 3. 4 R IL 4 41 .8 ± 6. 5 40 .5 ± 7. 1 71 4. 5 ± 15 3. 1 53 25 .6 ± 77 8. 3 N Q N Q N Q N Q R IL 24 N Q 43 .2 ± 1. 5 39 5. 4 ± 35 .7 49 68 .4 ± 10 6. 9 N Q N Q N Q N Q R IL 53 N Q 29 .9 ± 3. 1 34 7. 1 ± 28 .2 45 48 .2 ± 32 7. 8 N Q N Q N Q N Q R IL 22 6 N Q N Q 36 1. 1 ± 30 .4 46 99 .7 ± 56 7. 5 N Q N Q N Q N Q R IL 25 1 N Q N Q 20 0. 3 ± 53 .2 40 94 .1 ± 52 1. 7 N Q N Q N Q N Q R IL 27 8 N Q N Q 43 .0 ± 4. 3 29 12 .5 ± 42 8. 9 87 .0 ± 6. 7 N Q 57 .3 ± 2. 7 41 .0 ± 4. 9 A bb re vi at io ns :C FA ,c af fe ic ac id ;C G A ,c hl or og en ic ac id ;8 8T H F- D iF A ,8 ,5 ′ di fe ru lic ac id be nz of ur an fo rm ;N Q ,n ot qu an tif ie d; p- C A ,p -c ou m ar ic ac id ;t -F A ,t -f er ul ic ac id ;S PA ,s in ap ic ac id ;S Y G ,s yr in gi c ac id ;V N A ,v an ill ic ac id . a D 11 an d D 15 ;h ar ve st in g da y, 11 an d 15 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. b D iF A co m po un ds re po rt ed as μ g t-F A eq ui va le nt /g dr ie d an d de fa tte d sa m pl e. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2171Crop Science T A B L E 8 H ig h- pe rf or m an ce liq ui d ch ro m at og ra ph y an al ys is of ph en ol ic s co m po si tio n in th e fr ee fr ac tio n of m ai ze ge no ty pe s ha rv es te d in 20 18 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic co m po un d, μg /g dr ie d an d de fa tte d sa m pl e C G A V N A C FA SY A VA N p- C A t-F A SP A 88 TH F- D iF A D 11 B 73 12 9. 8 ± 9. 6 29 .0 ± 3. 5 4. 8 ± 0. 5 N Q 5. 4 ± 1. 6 N Q 26 .5 ± 4. 5 32 .5 ± 4. 3 17 .7 ± 2. 7 C O 43 0 42 5. 8 ± 13 .4 42 .9 ± 4. 5 7. 0 ± 0. 8 27 .4 ± 3. 8 12 .2 ± 1. 9 7. 8 ± 1. 6 13 2. 5 ± 16 .6 63 .2 ± 7. 4 25 .1 ± 2. 3 C O 43 3 14 5. 3 ± 5. 7 60 .0 ± 9. 3 N Q 27 .8 ± 4. 8 25 .6 ± 3. 5 14 .8 ± 1. 4 28 9. 4 ± 15 .7 21 0. 9 ± 14 .7 13 .0 ± 1. 2 C O 44 1 14 6. 4 ± 12 .8 18 .0 ± 2. 5 N Q 26 .7 ± 3. 2 9. 9 ± 1. 6 6. 4 ± 1. 1 87 .1 ± 8. 8 38 .1 ± 4. 9 14 .6 ± 4. 5 C O 44 9 15 6. 6 ± 13 .6 15 .4 ± 2. 6 N Q 28 .1 ± 4. 5 26 .2 ± 2. 2 3. 0 ± 0. 9 10 3. 5 ± 10 .7 20 2. 1 ± 4. 6 12 .9 ± 1. 2 C O 45 2 46 .3 ± 4. 6 19 .1 ± 3. 4 8. 1 ± 1. 8 8. 2 ± 1. 2 27 .3 ± 3. 6 3. 8 ± 1. 0 40 .4 ± 3. 0 N Q 34 .2 ± 5. 0 R IL 4 13 8. 1 ± 9. 7 29 .2 ± 1. 5 6. 1 ± 1. 3 9. 9 ± 0. 7 9. 2 ± 1. 3 3. 6 ± 0. 6 93 .1 ± 4. 0 N Q N Q R IL 27 8 13 1. 5 ± 19 .7 23 .3 ± 3. 2 N Q N Q 7. 6 ± 1. 2 N Q 60 .2 ± 7. 0 N Q N Q D 20 B 73 74 .5 ± 12 .9 N Q N Q N Q N Q N Q N Q 19 .4 ± 2. 0 10 .1 ± 0. 7 C O 44 1 N Q N Q N Q N Q 7. 0 ± 1. 3 N Q 13 .2 ± 1. 8 N Q 15 .9 ± 2. 2 C O 44 9 50 .1 ± 6. 6 N Q N Q 7. 5 ± 1. 1 10 .5 ± 3. 0 N Q 15 .2 ± 1. 8 29 .5 ± 2. 5 8. 8 ± 0. 7 C O 45 2 N Q 12 .2 ± 0. 9 N Q N Q 3. 6 ± 0. 6 N Q N Q N Q 12 .7 ± 1. 8 R IL 27 8 18 .3 ± 1. 7 N Q N Q N Q N Q N Q N Q N Q N Q D 30 B 73 16 1. 9 ± 10 .0 N Q N Q N Q N Q N Q N Q N Q 7. 3 ± 0. 7 C O 44 1 N Q N Q N Q N Q N Q N Q N Q N Q N Q C O 44 9 N Q N Q N Q N Q N Q N Q N Q N Q N Q C O 45 2 N Q N Q N Q N Q N Q N Q N Q N Q N Q R IL 27 8 28 .3 ± 9. 2 N Q N Q N Q N Q N Q 11 .0 ± 1. 7 N Q N Q D 63 B 73 N Q N Q 6. 2 ± 1. 0 N Q N Q 4. 1 ± 0. 8 N Q N Q N Q C O 44 1 N Q N Q N Q N Q N Q N Q N Q N Q N Q C O 44 9 N Q N Q 5. 0 ± 0. 7 N Q N Q 3. 4 ± 0. 8 N Q N Q N Q R IL 27 8 N Q N Q 15 .1 ± 1. 6 N Q N Q 5. 3 ± 1. 2 10 .4 ± 2. 1 N Q 22 .6 ± 3. 4 A bb re vi at io ns :C FA ,c af fe ic ac id ;C G A ,c hl or og en ic ac id ;8 8T H F- D iF A ,8 ,5 ′ di fe ru lic ac id be nz of ur an fo rm ;N Q ,n ot qu an tif ie d; p- C A ,p -c ou m ar ic ac id ;t -F A ,t -f er ul ic ac id ;S PA ,s in ap ic ac id ;S Y G ,s yr in gi c ac id ;V N A ,v an ill ic ac id . a D 11 ,D 20 ,D 30 ,a nd D 63 ;h ar ve st in g tim e, 11 ,2 0, 30 ,a nd 63 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2172 HADINEZHAD ET AL.Crop Science T A B L E 9 H ig h- pe rf or m an ce liq ui d ch ro m at og ra ph y an al ys is of ph en ol ic s co m po si tio n in th e co nj ug at ed fr ac tio n of m ai ze ge no ty pe s ha rv es te d in 20 18 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic co m po un d, μg /g dr ie d an d de fa tte d sa m pl e V N A C FA SY A VA N p- C A t-F A SP A 88 TH F- D iF A D 11 B 73 19 .7 ± 2. 7 53 6. 1 ± 70 .4 N Q N Q 34 .0 ± 2. 3 20 5. 9 ± 10 .0 37 .6 ± 1. 7 10 8. 5 ± 12 .8 C O 43 0 22 .5 ± 2. 5 17 74 .6 ± 59 .8 N Q N Q 12 2. 3 ± 12 .7 56 8. 1 ± 16 .7 54 .9 ± 3. 0 87 .2 ± 6. 1 C O 43 3 25 .7 ± 1. 7 20 8. 0 ± 4. 3 N Q N Q 76 .8 ± 2. 7 36 0. 4 ± 21 .9 28 .5 ± 2. 6 N Q C O 44 1 34 .8 ± 4. 6 53 0. 5 ± 56 .8 8. 3 ± 1. 0 N Q 71 .6 ± 3. 9 36 2. 8 ± 15 .7 86 .2 ± 3. 8 48 .1 ± 5. 1 C O 44 9 N Q 59 8. 5 ± 46 .3 N Q N Q 54 .9 ± 2. 2 25 9. 7 ± 18 .1 28 .7 ± 2. 9 39 .4 ± 2. 8 C O 45 2 27 .4 ± 3. 4 22 1. 9 ± 14 .7 6. 9 ± 0. 5 N Q 76 .6 ± 9. 4 30 7. 5 ± 17 .4 84 .2 ± 8. 3 10 9. 7 ± 9. 7 R IL 4 29 .0 ± 2. 8 32 7. 3 ± 22 .4 N Q N Q 17 .3 ± 2. 5 20 2. 3 ± 9. 5 43 .8 ± 2. 8 N Q R IL 27 8 N Q 37 2. 0 ± 3. 8 N Q N Q 8. 6 ± 0. 4 17 5. 2 ± 14 .2 22 .1 ± 2. 0 27 .7 ± 1. 6 D 20 B 73 N Q 20 .9 ± 1. 7 N Q N Q 6. 3 ± 0. 9 34 .3 ± 1. 6 42 .1 ± 4. 4 55 .0 ± 1. 5 C O 44 1 N Q 12 .7 ± 1. 3 8. 8 ± 0. 6 4. 7 ± 1. 0 14 .0 ± 1. 0 49 .7 ± 2. 5 59 .2 ± 3. 9 12 .2 ± 0. 8 C O 44 9 N Q 31 .2 ± 2. 1 8. 3 ± 1. 1 N Q 21 .8 ± 0. 9 62 .1 ± 4. 0 88 .6 ± 3. 8 15 .1 ± 1. 9 C O 45 2 N Q N Q 6. 0 ± 0. 7 7. 2 ± 1. 0 22 .3 ± 1. 5 10 6. 4 ± 5. 1 68 .6 ± 3. 5 10 3. 1 ± 6. 4 R IL 27 8 N Q 10 .7 ± 1. 3 9. 5 ± 0. 7 N Q 7. 2 ± 0. 6 66 .0 ± 3. 9 86 .1 ± 3. 2 45 .6 ± 3. 2 D 30 B 73 N Q N Q 8. 4 ± 0. 9 N Q 6. 8 ± 1. 0 51 .4 ± 3. 0 80 .8 ± 3. 5 77 .4 ± 1. 1 C O 44 1 N Q N Q 10 .1 ± 0. 7 N Q 14 .2 ± 1. 1 61 .5 ± 8. 1 76 .2 ± 3. 1 23 .2 ± 1. 8 C O 44 9 N Q N Q 10 .5 ± 0. 2 N Q 16 .8 ± 1. 2 51 .1 ± 3. 8 74 .2 ± 5. 4 23 .8 ± 1. 4 C O 45 2 N Q N Q N Q N Q 7. 2 ± 1. 2 44 .4 ± 2. 3 76 .4 ± 3. 7 17 .8 ± 1. 6 R IL 27 8 N Q N Q 7. 9 ± 0. 8 N Q 5. 9 ± 0. 7 55 .2 ± 1. 0 10 6. 5 ± 9. 8 56 .8 ± 2. 5 D 63 B 73 N Q N Q N Q N Q 8. 6 ± 0. 3 36 .7 ± 1. 9 59 .8 ± 5. 0 12 .9 ± 1. 9 C O 44 1 N Q N Q 7. 7 ± 0. 5 N Q 17 .1 ± 1. 8 93 .7 ± 4. 1 27 .8 ± 2. 2 10 .3 ± 1. 2 C O 44 9 N Q N Q 6. 4 ± 0. 5 N Q 29 .6 ± 3. 3 86 .0 ± 6. 4 41 .8 ± 1. 5 12 .9 ± 1. 1 R IL 27 8 N Q N Q 7. 5 ± 1. 3 N Q 24 .1 ± 3. 3 12 2. 5 ± 8. 4 84 .2 ± 5. 7 19 .6 ± 1. 1 A bb re vi at io ns :C FA ,c af fe ic ac id ;C G A ,c hl or og en ic ac id ;8 8T H F- D iF A ,8 ,5 ′ di fe ru lic ac id be nz of ur an fo rm ;N Q ,n ot qu an tif ie d; p- C A ,p -c ou m ar ic ac id ;t -F A ,t -f er ul ic ac id ;S PA ,s in ap ic ac id ;S Y G ,s yr in gi c ac id ;V N A ,v an ill ic ac id . a D 11 ,D 20 ,D 30 ,a nd D 63 ;h ar ve st in g pe ri od ,1 1, 20 ,3 0, an d 63 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2173Crop Science T A B L E 10 H ig h- pe rf or m an ce liq ui d ch ro m at og ra ph y an al ys is of ph en ol ic s co m po si tio n in th e bo un d fr ac tio n of m ai ze ge no ty pe s ha rv es te d in 20 18 M ai ze ge no ty pe /h ar ve st in g da ya Ph en ol ic co m po un d, μg /g dr ie d an d de fa tte d sa m pl e VA N p- C A t-F A SP A 55 -D IF A 8O 4- D iF A 85 -D iF A -B F D 11 B 73 20 .0 ± 3. 8 15 9. 2 ± 18 .8 41 23 .1 ± 28 4. 4 15 4. 2 ± 4. 2 N Q 72 .5 ± 8. 9 49 .2 ± 5. 4 C O 43 0 N Q 12 8. 8 ± 16 .4 49 91 .2 ± 36 5. 4 16 9. 9 ± 9. 8 N Q 99 .3 ± 14 .5 61 .5 ± 9. 3 C O 43 3 43 .2 ± 5. 6 11 22 .3 ± 95 .7 66 86 .6 ± 54 8. 3 16 6. 9 ± 12 .2 N Q 91 .3 ± 6. 5 59 .9 ± 3. 9 C O 44 1 40 .3 ± 7. 7 11 55 .5 ± 11 6. 5 88 80 .3 ± 55 5. 9 20 5. 9 ± 9. 9 N Q 16 6. 9 ± 8. 8 11 9. 5 ± 4. 7 C O 44 9 N Q 13 8. 3 ± 23 .2 42 00 .0 ± 44 5. 8 16 0. 2 ± 7. 8 N Q 69 .9 ± 6. 2 55 .0 ± 7. 3 C O 45 2 18 .3 ± 2. 8 24 1. 3 ± 10 .8 45 71 .0 ± 26 8. 9 16 1. 7 ± 23 .2 N Q 10 2. 6 ± 13 .3 67 .8 ± 6. 8 R IL 4 26 .1 ± 1. 9 18 9. 9 ± 30 .6 59 73 .5 ± 62 5. 4 15 1. 4 ± 12 .6 N Q 87 .8 ± 3. 7 57 .4 ± 5. 7 R IL 27 8 N Q 18 9. 0 ± 21 .0 32 80 .7 ± 36 7. 2 14 4. 7 ± 16 .5 N Q 71 .2 ± 8. 0 57 .0 ± 5. 6 D 20 B 73 N Q 48 .2 ± 1. 5 23 61 .6 ± 24 9. 3 70 .8 ± 11 .3 34 .7 ± 2. 1 52 .6 ± 4. 1 41 .2 ± 6. 8 C O 44 1 N Q 37 2. 9 ± 66 .5 42 26 .6 ± 29 4. 8 12 3. 3 ± 14 .1 53 .9 ± 9. 2 83 .6 ± 5. 9 52 .1 ± 7. 9 C O 44 9 N Q 15 1. 4 ± 27 .2 33 38 .2 ± 32 5. 5 11 9. 4 ± 6. 4 46 .1 ± 3. 7 79 .3 ± 8. 5 47 .8 ± 11 .4 C O 45 2 N Q 79 .5 ± 5. 7 18 10 .3 ± 10 5. 1 N Q N Q N Q N Q R IL 27 8 N Q 51 .7 ± 5. 8 13 17 .3 ± 15 7. 9 N Q N Q N Q N Q D 30 B 73 N Q 15 4. 3 ± 20 .2 64 66 .6 ± 62 6. 8 82 .0 ± 14 .9 13 0. 3 ± 14 .3 16 0. 0 ± 9. 9 99 .8 ± 13 .5 C O 44 1 N Q 18 7. 0 ± 22 .1 30 64 .2 ± 31 0. 8 N Q 61 .2 ± 7. 9 83 .9 ± 10 .7 53 .9 ± 7. 7 C O 44 9 N Q 81 .4 ± 9. 6 24 08 .1 ± 21 2. 3 N Q 50 .2 ± 2. 9 66 .1 ± 4. 3 43 .3 ± 4. 6 C O 45 2 N Q 78 .0 ± 6. 6 25 17 .2 ± 21 2. 0 N Q 44 .2 ± 3. 0 53 .3 ± 2. 7 46 .3 ± 3. 5 R IL 27 8 N Q 10 5. 2 ± 9. 7 38 12 .4 ± 23 3. 2 N Q 73 .5 ± 10 .1 12 2. 8 ± 10 .9 87 .0 ± 3. 1 D 63 B 73 N Q 78 .7 ± 5. 1 21 40 .6 ± 22 9. 4 59 .3 ± 4. 6 15 0. 2 ± 20 .5 17 6. 6 ± 16 .1 87 .9 ± 4. 1 C O 44 1 N Q 82 .3 ± 9. 9 21 16 .0 ± 16 5. 7 N Q 12 5. 2 ± 3. 4 14 9. 5 ± 7. 0 71 .5 ± 2. 1 C O 44 9 N Q 15 9. 3 ± 11 .6 22 14 .4 ± 11 0. 4 N Q 12 8. 7 ± 6. 1 16 8. 0 ± 7. 3 79 .3 ± 10 .0 R IL 27 8 N Q 10 9. 9 ± 4. 2 18 97 .8 ± 20 9. 1 N Q 11 0. 7 ± 5. 8 15 3. 9 ± 15 .1 81 .6 ± 3. 0 A bb re vi at io ns :C FA ,c af fe ic ac id ;C G A ,c hl or og en ic ac id ;8 8T H F- D iF A ,8 ,5 ′ di fe ru lic ac id be nz of ur an fo rm ;N Q ,n ot qu an tif ie d; p- C A ,p -c ou m ar ic ac id ;t -F A ,t -f er ul ic ac id ;S PA ,s in ap ic ac id ;S Y G ,s yr in gi c ac id ;V N A ,v an ill ic ac id . a D 11 ,D 20 ,D 30 ,a nd D 63 ;h ar ve st in g pe ri od ,1 1, 20 ,3 0, an d 63 da ys af te r se lf -p ol lin at io n, re sp ec tiv el y. 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2174 HADINEZHAD ET AL.Crop Science vectors was done using the “factoextra” package; confidence region ellipsoids on biplots were set at 95%. 3 RESULTS 3.1 Total phenolic content, 2016 Phase one of this study (2016 season) analyzed phenolic pro- files in kernels during the blister stage of kernel development (D11 and D15 after pollination) (Abendroth et al., 2011). The TPC values, measured by the Folin–Ciocalteu assay, showed significant differences among genotypes, harvesting dates, and phenolic fractions. The overall contribution of phenolics among fractions for different genotypes and harvesting dates was in the range of 6%–17%, 6%–28%, and 63%–87% for free, conjugated, and bound fractions, respectively (Table 3). A strong and significant (p < 0.0001) negative correlation was observed between soluble conjugated and bound phenolics (r = 0.93); the higher the percent of conjugated phenolics, the lower the percent of bound phenolics. The highest conjugated phenolics % (and the lowest bound phenolics %) were seen for CO430 at D11, and vice versa for RIL4 at D15. A significant (p < 0.0001) negative correlation was also observed between free and bound phenolics (r = 0.61). However, the free and conjugated phenolics did not show any significant correlation. The total content of phenolics (sum of the three fractions) ranged between 4.07–8.34 and 3.50–5.64 mg GAE/g sample for D11 and D15, respectively. For both days, CO441 and RIL278 had the highest and lowest values, respectively. For all fractions, the TPC values showed a decrease from D11 to D15 for most genotypes (Figure S1). A two-way ANOVA followed by Tukey multiple comparison test was used to investigate the interaction between genotypes and har- vest dates and rank the significant decreases from D11 to D15. All fractions (free, conjugated, and bound) and the sum of TPC values showed a significant interaction (Table S1). For all genotypes, the free and bound phenolic fractions showed the largest and smallest decreases in TPC from D11 to D15, respectively, which can be related to the transfer of some of the free phenolics in D11 into conjugated and bound forms by D15. The genotypes that showed a statistically significant decrease in the free fraction TPC values from D11 to D15 include CO433, CO441, CO449, and RIL24 (p < 0.0001), CO430 (p < 0.001), B73, CO452, and RIL278 (p < 0.01). From D11 to D15, the TPC values in the free fraction were reduced by 61% and 60% for CO433 and CO449, respec- tively. Similar to the free fraction, the TPC reduction in the conjugated fraction from D11 to D15 was statistically signifi- cant for most genotypes; CO441, CO433, CO430, RIL251 and RIL24 (p < 0.0001), RIL53 (p < 0.001), and B73 (p < 0.05). For the conjugated fraction, the decreases in the TPC values, from D11 to D15, were 46% for RIL251, 43% for CO441, and 42% for RIL226, RIL24, and CO430. The decrease in the bound fraction TPC from D11 to D15 was statistically significant for CO441 (p < 0.0001), CO433 (p < 0.001), and CO430 and CO452 (p < 0.01). The highest decrease in TPC values in the bound fraction was observed for CO430 (31%) and CO441 (30%), and interestingly, RIL226 showed an increase of 29% in TPC on D15 compared to D11. The sum of TPC values (Table 3) also showed a reduction from D11 to D15, which was statistically significant for all inbred lines including CO430, CO433, CO441, and CO449 (p < 0.0001), and CO452 (p < 0.01); however, B73 and all the recombi- nant inbred lines did not show a significant decrease (RIL226 showed an increase in sum of TPC values from D11 to D15). The TPC heritability was also calculated for all fractions at each harvest date, separately (Table S2). The absolute TPC of all fractions, their distribution percentages, and the sum of TPC values showed very high and significant heritability, ranging from 0.84% to 0.98% (p < 0.0001), which supports the accuracy of the TPC values (Hallauer et al., 2010). 3.2 Total phenolic content, 2018 Based on the results obtained from 2016, which was focused on the blister stage of kernel development and the diver- sity/similarity of phenolics results observed between geno- types, as well as previous research showing a significant decrease in phenolic content by kernel physiological maturity (Giordano et al., 2017), a decision was made to extend the sampling period up to physiological maturity with a smaller number of genotypes (those which showed more diverse TPC results). Four harvesting times were chosen including D11 (blister stage), D20 (milk stage), D30 (dough/dent stage), and D63 (physiological maturity) (Abendroth et al., 2011). Due to the dry weather in 2018, not all genotypes produced sufficient ears to analyze all harvest dates. The genotypes that were only analyzed on D11 include CO430, CO433, and RIL4. Three harvest dates (D11, D20, and D30) were analyzed for CO452. Table 4 shows the 2018 TPC results for maize genotypes at different harvesting dates. The extension of harvesting to maturity resulted in a broader overall distribution of phenolics among fractions for different genotypes and developmental stages compared to what was observed in 2016. In 2018, the phenolics distribution of free, conjugated, and bound frac- tions were in the range of 4%–18%, 8%–36%, and 47%–84%, respectively (Table 4). Similar to 2016, a significant nega- tive correlation was found in 2018 between free and bound phenolics (r = 0.80, p < 0.0001), and a stronger negative correlation was observed between conjugated and bound phe- nolics (r = 0.94, p < 0.0001). Similar to 2016, in 2018 on D11, CO430 significantly showed the highest conjugated TPC contribution% (36%), and the lowest bound TPC contribu- tion% (47%) among all tested genotypes. In 2018, a small but 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2175Crop Science significant positive correlation was obtained between free and conjugated phenolics (r = 0.54, p < 0.0001) which indicates the increases in bound phenolics resulted in decreases in both free and conjugated fractions. Similar trends in the relative percentages of all three frac- tions and the total phenolic content of the maize kernels were seen in 2018 compared to 2016, although the weather conditions were quite different (Table 2). Expanding the developmental period up to maturity revealed more dras- tic changes in the phenolic content of the lines examined. The TPC values of all phenolic fractions and for all geno- types at four harvest dates are presented in Figure S2. The two-way ANOVA statistical analysis showed a significant interaction between genotypes and harvest dates for all phe- nolics fractions as well as sum of TPC values (Table S1). Regardless of genotype, D11 showed significantly higher phe- nolic content than the rest of the harvest dates in all three phenolic fractions as well as sum of TPC values. As for geno- type effect, CO441 and CO449 were the top two genotypes with higher phenolic content for all fractions and harvest dates. Phenolic reduction from D11 to D20, D30, and D63 was most pronounced for the free phenolics. Free phenolics in CO499 decreased 86% from D11 to D30 (p < 0.0001), and the decrease was 79% for RIL278 from D11 to D20 (p < 0.0001). In the conjugated fraction, similar significant reductions were observed from D11 to D20, D30, and D63 but to a lesser extent compared to the free fraction. The greatest reduction was for CO441 and B73 (78% and 77% from D11 to D63 and D11 to D20, respectively). The trend was not consistent for the bound phenolics as some genotypes such as CO449 and B73 showed no significantly different content over the devel- opmental period, while RIL278 showed 73% reduction in the bound phenolics from D11 to D20 (p < 0.001). The sum of TPC values (all three fractions) ranged from 4.21 to 7.46, 1.15 to 3.91, 1.97 to 3.22, and 2.59 to 2.90 mg GAE/g for D11 (blister stage), D20 (milk stage), D30 (dough stage), and D63 (physiological maturity), respectively. The range was much narrower at maturity compared to D11. The range of sum of TPC values at D11 was very similar for 2016 and 2018. Comparing D11 and D15 in 2016 with D11 and D20 in 2018, CO441 had the highest , and RIL278 had the lowest sum of TPC values (Table 4). For all genotypes tested at all four harvesting periods (B73, CO441, CO449, and RIL278), the content of total phenolics decreased sig- nificantly (p < 0.0001) from D11 to D20 and D20 to D30; however, the changes from D30 to D63 were not statistically significant. The TPC heritability values for phenolics fractions, their contribution percentages, and sum of TPC values were high and significant for most traits, ranging from 0.65 to 0.99 (Table S2). The lowest heritability values with no genotype significant results include the conjugated TPC contribution% at D30 (H2 = 0.65), the bound TPC contribution% at D30 (H2 = 0.67), and the sum of TPC values at D63 (H2 = 0.73). 3.3 Phenolic composition (HPLC analysis), 2016 Eight phenolics were detected in the free fraction (Table 5); CGA was the predominant compound in all genotypes and harvesting dates, followed by SYG. The three genotypes with the highest content of these two main phenolics (CGA and SYG) combined were CO449, RIL24, and CO430, all at D11. A diferulic acid compound, 8,8′ (tetrahydrofuran) difer- ulic acid (88THF-DiFA), was also detected in most maize genotypes, and its content was calculated as t-FA equivalent. Interestingly, this was the only peak identified in the free frac- tion for CO441 and CO452 at D15. On the other hand, CGA was the only detected phenolic in RIL251 at D11 and D15, and RIL278 at D15. For all phenolics in all maize genotypes, D11 showed higher content of phenolics compared to D15. The most significant decrease in the free fraction was observed for CGA in RIL24 (from 193.0 μg/g in D11 to 16.5 μg/g in D15; 91.4% reduction) and CO449 (from 339.1 μg/g in D11 to 34.2 μg/g in D15; 89.9% reduction). In the conjugated fraction, seven phenolic compounds were detected including 88THF-DiFA (Table 6). Overall, there were higher amounts of phenolics in the conjugated fraction compared to the free fraction. For all genotypes/harvesting dates, the two main compounds in the conjugated fraction were CFA and t-FA. At D11, CO430, RIL251, and B73 showed the highest amount of CFA and CO430, and RIL24 contained the highest t-FA levels. While CFA and t-FA con- tent decreased from D11 to D15, the amount of 88THF-DiFA increased and was more pronounced in the conjugated fraction compared to the free fraction, especially for B73, CO441, and RIL278. In the bound phenolic fraction, five phenolic compounds were quantified including CFA, VAN, p-CA, t-FA, and SPA (Table 7); t-FA was the main phenolic compound, and CO433 and CO441 showed the highest amount at D11 (6606.5 ± 559.2 and 6684.7 ± 625.8 μg/g, respectively), while RIL278 showed the lowest amount of t-FA at D11 (3196.1 ± 540.4 μg/g). For most genotypes, the content of p-CA and t-FA showed a broad reduction range (from as low as 1.3% to as high as 68.9%) from D11 to D15. Similar to TPC results, RIL226 showed significant increases in both p- CA and t-FA contents from D11 to D15 (131.2% and 25.6%, respectively). Two DiFAs were quantified in the bound fraction based on their retention times and UV spectra, including 8-O-4ť difer- ulic acid (8O4-DiFA) and 8,5ť diferulic acid benzofuran form (85-DiFA-BF), and their contents were calculated as μg t- FA equivalent/g sample (Table 7). CO441 had the highest 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2176 HADINEZHAD ET AL.Crop Science content of both diferulic acids among genotypes. Interest- ingly, no quantifiable amount of either of these two DiFAs was found for CO430, CO433, RIL4, RIL24, RIL53, RIL226, and RIL251 for either harvesting date. There was a slight decrease from D11 to D15 with regard to DiFAs content. 3.4 Phenolic composition (HPLC analysis), 2018 The phenolic composition of the different fractions at differ- ent harvesting dates in 2018 was similar to 2016; however, the amount and developmental profile of each phenolic was different. In the free fraction at D11, CGA was the main phenolic compound (similar to 2016); however, t-FA was the second highest phenolic for most genotypes (Table 8). The highest amount of CGA was observed in CO430, and the highest t-FA was from CO433. The other phenolics quantified in the free fraction at D11 were SPA, SYA, VNA, VAN, p-CA, and CFA. Phenolic content and profile over the developmental stages were quite genotype dependent in the free fraction. For CO430 and CO449, the amount of SPA at D11 was significantly higher than in other genotypes, while VNA was the highest for CO430 and CO433. The phenolic compound 88THF-DiFA was also present above the LOQ in most genotypes at D11. The 2018 conjugated fractions presented a similar pheno- lics profile as was observed in 2016, with CFA and t-FA exhibiting the highest concentration (Table 9). CO430 had the highest amount of CFA and t-FA at D11. The conjugated phenolics content decreased by D20, D30 and maturity, with VNA and CFA being below LOQ on D30 and at maturity (D63). Interestingly, a small quantity of VAN was found only in CO441 and CO452 at D20. At D11, the level of 88THF- DiFA was greater in 2018 compared to 2016, ranging from 27.7 ± 1.6 μg/g for RIL278 to 109.7 ± 9.7 μg/g for CO452; CO433 and RIL4 did not possess quantifiable amounts of this diferulic acid. The significant increase observed for 88THF- DiFA from D11 to D15 in 2016 was not seen in 2018; there was a decrease in most genotypes by D20, and a more grad- ual reduction by D30 and at maturity (D63). This could be explained by the transformation of this diferulic acid into the bound fraction during kernel development. In the bound fraction, four phenolic compounds were quantified (Table 10) with t-FA and p-CA being the main components. At D11, CO441 and CO433 showed the highest content for both phenolics. In our study, the change in t-FA content over the grain fill- ing period was not consistent across the genotypes. B73 and RIL278 had a decrease in t-FA by D20, a sharp increase at D30, and then a sharp decrease by maturity, while a decrease was observed over the whole period for CO441 and CO449, being much sharper for CO441 than CO449. No VNA was quantified after D11, and the phenolic SPA also disappeared for most genotypes during kernel development. Three diferulic acids were identified in the bound fraction, including 55-DiFA, 8O4-DiFA, and 85-DiFA-BF (Table 10). The 55-DiFA was absent at D11 (similar to 2016); however, its content increased significantly by maturity (D63). For most genotypes, the diferulic acids 8O4-DiFA and 85-DiFA-BF both increased significantly by maturity after a slight decrease from D11 to D20; at maturity, the 8O4-DiFA accumulation in B73, CO449, and RIL 278 was 2.44-, 2.40-, and 2.16-fold higher relative to D11, respectively. 3.5 Principal component analysis A PCA was done to visualize and summarize the phenolic composition profile for different fractions, in both growing seasons and all development stages. The individual princi- pal component scores and the loading vectors were visualized using biplots. All quantified phenolics were included for free, conjugated, and bound fractions in both 2016 and 2018 (Figure 1). The first two PCAs (DIM1 and DIM2) accounted for most variations observed for free, conjugated, and bound pheno- lics in 2016 and 2018 (66.7%, 73.0%, and 90.4% and 76.6%, 67.3%, and 74.7%, respectively). In these biplots, an angle smaller than 90˚ between vec- tors indicates a positive correlation (the smaller the angle, the higher the correlation). On the other hand, an angle of 180˚ indicates the highest negative correlation. An angle of 90˚ is an indication of zero correlation. Another feature of biplot is the length of vectors, which is related to the varia- tion of the trait across genotypes, and longer vectors indicate better representation of the phenolics in the biplot. All biplots showed appropriate variation for all phenolic fractions in both growing seasons. In 2016, a well-separated clustering was observed between D11 and D15 for all three phenolic fractions, except bound for phenolics that show some degree of overlap (Figure S3). On the other hand, with a broader range of harvesting days in 2018, phenolic fractions showed different patterns of clus- tering. However, for all fractions, there was a distinctive clustering between D11 and the rest of harvesting times. Regarding individual phenolics and their correlations, the following relationships were observed (Figure 1). In 2016, the free phenolics t-FA, CGA, and VNA were positively corre- lated, and p-CA, SPA and CFA were positively correlated. SYG and 88THF-DiFA were correlated negatively. In 2018, in the free fraction, a different pattern of clustering was observed as three groupings of p-CA, CGA, VAN, VNA; SYA, t-FA, SPA; and CFA, 88THF-DiFA were all positively correlated. For conjugated phenolics, a positive correlation was found in 2016 with three sets of phenolics grouping together as CFA 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2177Crop Science F I G U R E 1 Principal component analysis (PCA)-biplots of phenolic composition of all genotypes for free (a and d), conjugated (b and e), and bound (c and f) fractions in 2016 and 2018, respectively. and t-FA; SYA, p-CA, and VNA; and SPA and 88THF-DiFA. The trend for 2018 was similar with vectors being further apart. For bound phenolics, 2016 and 2018 showed different pat- terns indicating a more significant environment effect on phenolics in this fraction as well as the effect of kernel devel- opmental time on phenolic composition of the bound fraction. In 2016, the two DiFAs and SPA showed very high cor- relation. On the other hand, CFA, p-CA, VAN, and t-FA correlated to each other in a group with no correlation to the former group. In 2018, three groupings of phenolics, namely TPA and SPA; VAN and p-CA; 8O4-DiFA and 55-DiFA, were positively correlated. The PCA-biplots were redone to include the GER rating and evaluate the PCA scores and clustering pattern (Figure 2). The top resistant genotypes (CO430, CO433, CO441, CO449, and RIL53) and the top susceptible genotypes (B73, RIL251 and RIL278) were included in the biplots. All phenolic frac- tions for both growing seasons showed high scores for the first two PCAs combined (ranged between 68.6% and 92.1%), indicating the suitability of the parameters to cover the most observed variability. 4 DISCUSSION 4.1 TPC analysis Phase one of this study (2016 season) analyzed phenolic pro- files in kernels during the blister stage of kernel development (D11 and D15 after pollination) (Abendroth et al., 2011). In 2018 (phase two), the harvesting dates were extended to maturity, which resulted in a broader overall distribution of phenolics among fractions for different genotypes and developmental stages compared to what was observed for 2016. Our results showed that genotypes with the highest or low- est TPC% in different phenolic fractions did not necessarily have the highest or lowest sum of TPC values, which might suggest that the total accumulation of phenolics in maize 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2178 HADINEZHAD ET AL.Crop Science F I G U R E 2 Principal component analysis (PCA) biplots, highlighted for disease rating; resistant (CO430, CO433, CO441, CO449, and RIL53) and susceptible (B73, RIL251, and RIL278). The PCA is the same as Figure 1 for all genotypes in free (a and d), conjugated (b and e), and bound (c and f) fractions in 2016 and 2018, respectively. kernels is not a governing factor in the distribution pattern of phenolics observed among three fractions. There was a significant decrease in TPC values as grain filling advanced. This could partly be related to the mass:phenolic ratios since most phenolics are concentrated in the kernel outer layers, and their content will be diluted as grains become larger (Abendroth et al., 2011; Giordano et al., 2017; Hu & Xu, 2011; LeClere et al., 2007). However, the decrease was significantly different among genotypes in all three fractions, which indicates that genetic variability plays an important role. On the other hand, our results showed that most phenolics would be stabilized in maize grains by four weeks after self-pollination, and further grain filling (D30–D63) had a minimum impact on TPC. Among three phenolic fractions, the TPC values for the conjugated fraction were higher than the free fraction for most genotypes, indicating that the conjugated fraction contribu- tion to the phenolic profile of maize kernels is similar or even higher than the free fraction. In addition, strong correlations were found between TPC values of conjugated and bound phenolics for both years. These results indicated that conju- gated and bound phenolics were strongly related throughout the entire developmental stages and grain filling. In cere- als, the majority of phenolics are concentrated in the bound form (Ndolo & Beta, 2014; Sosulski et al., 1982; Van Hung, 2016; Mariana Horvat et al., 2020; Zavala-López et al., 2018); however, there is not much information regarding the distri- bution of different phenolic fractions, especially during early stages of kernel development. To our best knowledge, this is the first report of changes in conjugated phenolics dur- ing maize kernel development and their relation to bound phenolics. Although the bound phenolics were the highest fraction for all harvest dates in both years, the %contribution of bound 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2179Crop Science phenolics from D11 to D63 was significantly different. In 2018 at D11, the bound phenolics were as low as 2.8 times the amount of free phenolics for CO430, and as high as 6.7 times for CO452. At maturity, however, the levels of bound phenolics were between fourfold and 13-fold higher, relative to free phenolics for the tested genotypes. This is a broader range but lower magnitude than the 10- to 17-fold reported by Giordano et al. (2017) for different open-pollinated and hybrid maize genotypes (mature kernels) tested in their research. There are several studies investigating the phenolic con- tent of maize kernels, as well as comparison to other cereal grains in the literature; however, due to the different extrac- tion methods and fractionation strategies employed, a direct comparison of results is not prudent. Most researchers reported higher overall phenolics in maize grains than other cereals such as wheat, barley, oats, and rice (Adom & Liu, 2002; Atanasova-Penichon et al., 2016; Hor- vat et al., 2020; Sosulski et al., 1982; Stuper-Szablewska & Perkowski, 2019; Van Hung, 2016). In addition, a maize geno- type dependency for TPC has also been observed (Ndolo & Beta, 2014). Mahan et al. (2013) screened 84 maize hybrids (from 11 inbred lines carrying different kernel colors) in four different environments for their diversity in phenolic content using the TPC method. They reported a high heritability of 0.84 for total phenolics, and purple maize lines showed the highest phenolics. In that study, the total phenolic in the top colored hybrid was more than twice that of the top yellow hybrid. In our study, the heritability values for the TPC values in different fractions and also sum of TPC values in both years and different harvest dates were significantly high in a range of 0.73–0.99 (Table S2). Our results are consistent with Giordano et al. (2017) who reported genotype-dependent changes in free and bound phe- nolics for open-pollinated and hybrid maize kernels during kernel development stages. They also observed significant decreases in free and bound phenolics at maturity compared to earlier developmental stages and reported the highest total phenolic content for blister stage (close to D11 in our study). Hu and Xu (2011) reported a range of 0.24–3.88 mg GAE/g dry weight of TPC at maturity for different colored and waxy maize genotypes with black maize showing higher phenolics than white and yellow genotypes. In their study, the TPC assay was performed on a single fraction extraction. Adom and Liu (2002) reported a phenolic distribution of 85% bound and 15% free (conjugated phenolics were assumed part of free frac- tion) with a total phenolic content of 15.55 μmol GAE/g of maize kernels (equal to 2.28 mg of GAE/g kernels). Their findings regarding the distribution and total phenolic content are similar to the values obtained in our study for mature maize kernels (Table 4); however, in our study a clear and significant difference in the distribution of phenolics between free and conjugated fractions for different genotypes was also demonstrated. There has not been much focus on conjugated phenolics because of the low percentage of this fraction, and it was often considered part of free fraction. Although our findings are aligned with the literature in terms of low per- centage contribution of conjugated fraction in mature maize kernel phenolics (similar contribution for free and conjugated phenolics), our results also demonstrate the important role of this fraction in the phenolic profile makeup of maize ker- nels during early kernel development. Our previous study (Hadinezhad & Miller, 2019) on wheat rachis also demon- strated the significance of conjugated phenolics in immature wheat rachis after inoculation of florets with F. graminearum. Maize genotypes used in our research were compared for their GER disease rating based on available data from 2010 to 2019 (Blackwell et al., 2022; Kebede et al., 2016). The three most susceptible genotypes were B73, RIL251, and RIL278, and the most resistant genotypes were CO430, CO433, CO449, CO441, and RIL53 (Table 1). In line with findings of Giordano et al. (2017) and Atanasova-Penichon et al. (2012), in our study, it seems that higher phenolic content is related to higher disease resistance. 4.2 Phenolic composition In general, the free phenolic fraction showed the highest number of phenolic compounds, which is in agreement with Atanasova-Penichon et al. (2012). In both 2016 and 2018, eight phenolic compounds were quantified in the free frac- tion with CGA showing the highest content, while in the conjugated fraction, seven phenolic compounds were quanti- fied with CFA and t-FA being the main ones. In the bound fraction, five compounds were measured (with t-FA as the main phenolic acid) including three DiFAs: 55-DiFA (only detected in 2018), 8O4-DiFA, and 85-DiFA-BF. Overall, there was a higher amount of phenolics present in free frac- tion in 2018 compared to 2016. Environmental differences between the 2016 and 2018 experiments, including drier conditions, may have influenced the phenolic biosynthetic pathways, which were genotype dependent. In 2016, for all three fractions, D11 phenolic compounds showed higher content compared to their content at D15 (consistent results with the TPC values described before). However, the phenolic decreases in the conjugated fraction were less pronounced compared to the free fraction. In 2018, as kernels developed to maturity, most free phenolics dis- appeared (below the LOQ of the HPLC detector). CFA and p-CA were the only two compounds quantified in some geno- types at maturity (D63). Our results agree with a previous study (Atanasova-Penichon et al., 2012) reporting six main phenolic compounds including CGA, VNA, CFA, p-CA, t- FA, and SPA in the free fraction of immature kernels from two maize hybrids. They reported CGA as the main pheno- lic acid in the free fraction, and the highest concentration 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2180 HADINEZHAD ET AL.Crop Science was observed at silking to milk stage (85% and 56% of total soluble phenolics for the two genotypes tested). All free phe- nolics showed a significant decrease in their concentration over kernel development except for p-CA, which showed a stable level. Interestingly, when these hybrids were inocu- lated with F. graminearum, a sharp increase in CGA content occurred (Atanasova-Penichon et al., 2012), suggesting that CGA may play a role in GER resistance as well as toxin accu- mulation. Giordano et al. (2017) also reported CGA and t-FA as the main phenolic acids in the free fraction of kernels from six open-pollinated maize hybrids with a significant decrease in their amount from silk stage to kernel maturity. The other phenolics detected in that study include VNA, SPA, and p-CA which all showed a genotype dependency in their content and profile over the developmental stages. Giordano et al. (2017) suggested that at the beginning of kernel development, phe- nolic acids may play a role in inhibition of deoxynivalenol (DON) metabolism. In particular, a strong negative correla- tion was reported between CGA in the free fraction at the end of silking stage and DON contamination at harvest maturity. CGA and CFA (a hydrolysis product of CGA) were reported by Gauthier et al. (2016) to negatively impact F. graminearum growth and mycotoxin production. They suggested that the fungus degrades the CGA into CFA that is more toxic to the fungus and more effective at inhibiting mycotoxin production. Not much information has been reported in the literature for the composition of conjugated phenolics. A study inves- tigating the susceptibility of different maize genotypes to gibberella stalk rot and the role of phenolics in stalk tis- sue (Santiago et al., 2007) reported a genotype-dependent response for two measured phenolics, t-FA and p-CA. For most genotypes, the t-FA content in the soluble ester-bound fraction (equivalent to the conjugated fraction in our study) increased significantly over four time points after F. gramin- earum inoculation, while the p-CA content did not change significantly. In agreement with this finding, in our study, the content of t-FA and p-CA in the conjugated fraction for a GER-susceptible line (RIL278) in 2018 at D11 was as low as 175.2 ± 14.2 and 8.6 ± 0.4 μg/g sample, respectively, compared to their contents in a GER-resistant line (CO441), which was as high as 362.8 ± 15.7 and 71.6 ± 3.9 μg/g sample, respectively (Table 9). In addition, for both pheno- lic compounds, the content decreased over the grain filling period. The bound phenolic composition in 2016 and 2018 showed some differences. In contrast to 2016, CFA was not detected in 2018 in the bound phenolics. On the other hand, the expres- sion of SPA was more pronounced in 2018 compared to 2016. Perhaps environmental differences between the 2016 and 2018 experiments, including drier conditions, may have stimulated the biosynthesis of more SPA at the expense of CFA (a precursor of SPA in the phenylpropanoid pathway) (Stuper-Szablewska & Perkowski, 2019). A previous study (Giordano et al., 2017) also reported significant decreases in CFA content in the bound fraction of maize kernels from blis- ter stage to maturity; it was detected at maturity only in some of the genotypes tested. It is well established that t-FA is the main phenolic compound in maize kernels, and it is mainly concentrated in the bound form (kernel outer layer). Adom and Liu (2002) reported 98.9% of t-FA in the bound frac- tion, 1% in conjugated form and 0.1% in free form in a mature maize kernel. Our results at D63 (maturity) showed a similar distribution for B73 (98.3% in bound, 1.7% in conjugated, and 0% in free fractions). However, the distribution was also geno- type dependent; for RIL278, 93.5% of t-FA was in the bound fraction, 6.0% was in conjugated fraction, and 0.5% was in the free fraction. Regarding the DiFAs in bound fraction, our results are con- sistent with those of Atanasova-Penichon et al. (2012) who reported four DiFAs in two maize kernel genotypes with 8O4- DiFA and 85-DiFA-BF being the main components, followed by 55-DiFA and an opened form of 85-DiFA. However, in their study, the DiFAs content increased from silking to milk stage and remained stable until maturity for one genotype but decreased for the other one. It has been reported that higher levels of DiFAs in cell walls fortifies the polysaccharides and decreases the susceptibility of these polymers to pathogen- produced degrading enzymes (Bily et al., 2003; García-Lara & Bergvinson, 2014). Santiago et al. (2007), García-Lara et al. (2004), and Santiago et al. (2006, 2007) suggested that DiFAs play an important role in gibberella stalk rot especially dur- ing the early post-infection stage. In their study, using a set of similar inbred genotypes as used in our study, three similar DiFAs were found in the bound phenolic fraction. A signif- icant negative correlation was reported for all three DiFAs and the disease rating at four days after inoculation, while the correlation for t-FA and p-CA was not significant. Another similar study (Bily et al., 2003) found the strongest correla- tion between GER disease rating and total DiFAs content and suggested that individual DiFAs participate equally in resis- tance. However, the two DiFAs with the highest content were 8O4-DiFA and 55-DiFA, which were primarily considered to be responsible for the resistance observed. 4.3 Phenolic clustering profile The PCA biplots showed different clustering patterns for phe- nolics in different fractions and harvest dates. However, there was a clear clustering pattern for phenolics in all three frac- tions between D11 and the rest of the harvest dates. This might indicate that the changes in phenolic profiles in early stages of grain development are indicative of later stage phenolic profiles. On the other hand, the different pattern of groupings observed for individual phenolic compounds in different 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense HADINEZHAD ET AL. 2181Crop Science phenolic fractions between 2016 and 2018 might indicate environmental effects on phenolics composition in addition to the genetic variability. The difference was more pronounced for the bound phenolic fraction. There is limited information on phenolic profile (asso- ciation/clustering) for maize genotypes at different kernel development stages and in different phenolic fractions. Zavala-López et al. (2020) investigated the compositional variation between some phenolic acids and DFAs in 24 maize hybrids of B73 × landraces and 24 maize hybrids of B73 × NAM and reported a positive correlation (r = 0.466, p = 0.001) between p-CA and t-FA in soluble phe- nolics. However, the testing was done only on the grains harvested at maturity. Bernardi et al. (2018) also performed a comprehensive phenolic profile analysis on three inoculated maize genotypes (two pigmented and one Purple B73, har- vested at maturity) using a non-targeted metabolic approach (UHPLC-QTOF) and carried out a hierarchical cluster anal- ysis on the detected metabolites. They found that the three pigmented genotypes had 30 common phenolic compounds with coumaric and hydroxycaffeic acids at the highest lev- els. They also reported a completely differentiated phenolic profile clustering for the pigmented maize genotypes and the Purple B73. They reported 42 phenolic compounds that could be considered the main contributing variables in class dis- crimination; hydoxycinnamics were the most abundant for the phenolic acids subclass. In our study, the PCA biplots between genotypes and the GER rating revealed different clustering patterns in 2016 and 2018. Compared to the 2018 biplots, the 2016 biplots showed a clearer clustering pattern between resistant and suscepti- ble genotypes especially for bound and conjugated phenolics, which suggests the importance of phenolic composition at an early stage of kernel development to determine the fate of a maize kernel against GER disease. In terms of genotypes, in 2016, the free phenolics of CO449 (resistant line) and B73 (susceptible line) showed the most diversity, while the bound phenolics of CO433 (resistant line) and RIL278 (susceptible line) were most different. Bily et al. (2003) reported highly positive correlations between the DiFAs content of the maize cell wall at maturity and GER resistance; however, there was no significant corre- lation between DON content and those phenolic compounds. The content of t-FA in maize kernel cell walls was reported to be significantly correlated to F. graminearum resistance (Ass- abgui et al., 1993). They also measured the ability of t-FA to inhibit the growth of F. graminearum in vitro and reported an EC50 value of 0.65 mg/g, which is within the range of this phenolic concentration in a typical maize kernel. Atanasova- Penichon et al. (2012) demonstrated that the main phenolic acid preventing the growth of F. graminearum and DON pro- duction at the beginning of maize ear development is CGA and to a lesser extent t-FA, and they suggested the use of CGA as a resistance biomarker. A negative correlation was reported by Giordano et al. (2017) between DON contamination at maturity and the content of free phenolics including CGA, t-FA, and VNA as well as bound phenolic t-FA compound at early stages of kernel development. They also showed a strong and significant positive correlation (ρ = 0.713, p < 0.01) between severity of GER and DON accumulation at maturity. The primary access route for maize kernel infection by F. graminearum is through the silks, which are most sus- ceptible during the first 6 days after their emergence (Reid et al., 1996). This could suggest the importance of a maize phenolics profile at an early stage of development in playing a role in GER resistance capacity. Overall, this study presents a comprehensive analysis of phenolics in various maize genotypes with differing levels of resistance to GER, harvested at different kernel development stages. The TPC data and HPLC results are in agreement, confirming the trends observed for three different phenolic fractions. The TPC values show high heritability for all frac- tions, their contribution%, and the sum of TPC results. The soluble conjugated phenolic fraction accounted for 7%–36% of total phenolics found at the blister stage with a high diver- sity of phenolic compounds. This fraction is often ignored since it has a minimum contribution on the phenolics makeup of mature grains; however, our findings clearly demonstrate that this fraction plays a significant role in the phenolics pro- file of a maize kernel at early stages of development. Different clustering patterns were observed in PCA-biplots for phenolic associations in different fractions. A clear clustering between resistant and susceptible genotypes in bound and conjugated phenolics at the blister stage demonstrates the importance of maize phenolics in defending against GER. This study pro- vides a useful resource of maize inbred phenolics profiles that can be applied in future breeding efforts. AU T H O R C O N T R I B U T I O N S Mehri Hadinezhad: Conceptualization; data curation; formal analysis; investigation; methodology; validation; writing—original draft; writing—review and editing. Linda J. Harris: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; supervision; writing—review and editing. Susan Shea Miller: Concep- tualization; data curation; formal analysis; investigation; methodology; supervision; writing—review and editing. Danielle Schneiderman: Data curation; formal analysis; methodology; software; validation; writing—review and editing. A C K N O W L E D G M E N T S The financial support of Agriculture and Agri-Food Canada’s Genomics Research & Development Initiative is gratefully acknowledged. The authors also wish to thank Dr. Lana Reid for providing pedigree and disease rating information for the 14350653, 2023, 4, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/csc2.21004 by C anadian A griculture L ibrary, W iley O nline L ibrary on [22/04/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 2182 HADINEZHAD ET AL.Crop Science chosen genotypes for this study and the CEF corn crew with field preparation and planting, Anne Johnston and Whynn Bosnich for their assistance in the 2016 maize harvest, Dr. B.A. Blackwell for providing access to the HPLC instrument, Aparna Haldar for her assistance in 2018 maize phenolic analysis, and Dr. Aida Kebede for her support in the TPC heritability analysis. C O N F L I C T O F I N T E R E S T S T AT E M E N T The authors declare no conflict of interest. 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