Maize kernel color depends on the interaction between hardness and carotenoid concentration

Journal of Cereal Science 91 (2020) 102901

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Maize kernel color depends on the interaction between hardness and carotenoid concentration �s , Jos�e A. Gerde * Ezequiel Saenz , Lucas J. Abdala , Lucas Borra IICAR - CONICET, Consejo Nacional de Investigaciones Científicas y T�ecnicas, Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Campo Experimental Villarino S/N, S2125ZAA, Zavalla, Prov. de Santa Fe, Argentina

A R T I C L E I N F O

A B S T R A C T

Keywords: Kernel hardness Kernel color Vitreousness Carotenoids

Maize kernel color and carotenoid concentration are traits valued by the food industry to ensure the quality of their products. Correlations between color and carotenoid concentration have been extensively reported. Based on the concept that chromaticity is modified differently by opaque and translucent materials, we tested the hypothesis that maize kernel color is not only the result of total carotenoid concentration but also a consequence of kernel hardness. Kernel hardness (test weight, vitreousness, and floaters percentage), carotenoid concentra­ tion, and color (HunterLab) were measured in thirteen commercial hybrids. Genotypes showed significant dif­ ferences in all analyzed kernel hardness traits, carotenoid concentration (24.7–39.4 mg kg 1), and HunterLab color dimensions. Kernel color values and kernel hardness were correlated. Genotype differences in b (yellow­ ness) were observed in kernels with similar total carotenoid concentration but contrasting hardness. For a similar carotenoid concentration harder genotypes always showed lower b values. When whole kernels were milled and color was measured on the resulting flour, genotype differences in yellowness disappeared, further supporting that the kernel vitreous structure affects kernel color. Our results sustain the notion that the genotype capacity to form larger proportions of vitreous endosperm impacts color regardless of total carotenoid concentration.

1. Introduction Maize (Zea mays L.) is one of the most prevalent cereal crops, along with rice and wheat (Ranum et al., 2014). It accounts for part of the staple diet of millions of people in Latin America, Asia, and Africa (Ranum et al., 2014). In addition to being used as a minimally processed food and feed source at the household level, maize is feedstock to pro­ cesses yielding ingredients and products that include flour, cornmeal, grits, starch, snacks, tortillas, and breakfast cereals, among many others. The increasing interest in functional and healthy foods has drawn research focus into bioactive compounds derived from maize and their health properties (Luo and Wang, 2012). Maize is not only a source of macronutrients, it also contributes to the diet with various phyto­ chemicals, such as phenolic compounds, phytosterols, and carotenoids (Nuss and Tanumihardjo, 2010). Carotenoids, the most widespread organic pigments in nature, are well-known for their outstanding nutritional value. In humans, some carotenoids (β-carotene, β-cryptoxanthin, and α-carotene) are pre­ cursors of retinol or vitamin A, which is important in multiple biological

processes (Venado et al., 2017). Because of their antioxidant activity, carotenoids are major contributors to the reduction of free radicals. This characteristic results in positive effects on consumer health and chem­ ical and sensory quality food improvement by preventing lipid oxidation � �c et al., 2012). (Zili Total carotenoid concentration is highly variable among yellow and orange maize types, and commonly ranges from 16 to 30 mg kg 1 (dry weight basis; Kljak and Grbe�sa, 2015). Breeding efforts towards the development of high carotenoid maize genotypes are currently taking place in countries where maize is an important staple food crop (Menkir et al., 2017). Within the maize kernel, more than 70% of total carot­ enoids are located in the vitreous endosperm (the hard and translucent endosperm fraction), and the rest is distributed among floury endosperm (the soft and chalky endosperm fraction), germ, and bran fractions (Blessin et al., 1963). Higher proportions of vitreous endosperm found in hard kernels are commonly associated with an improved ability to store a high concentration of carotenoids. However, information connecting kernel hardness and total carotenoid concentration in maize is not available.

* Corresponding author. E-mail addresses: [email protected] (E. Saenz), [email protected] (L.J. Abdala), [email protected] (L. Borr� as), [email protected] (J.A. Gerde). https://doi.org/10.1016/j.jcs.2019.102901 Received 30 September 2019; Received in revised form 18 December 2019; Accepted 19 December 2019 Available online 23 December 2019 0733-5210/© 2019 Elsevier Ltd. All rights reserved.

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Journal of Cereal Science 91 (2020) 102901

In addition to the aforementioned benefits, carotenoids play a major role in yellow and orange pigmentation of the maize kernel. Several studies linked carotenoid concentration with reflectance color mea­ surements. Results showed consistent correlations between color satu­ ration and carotenoid concentration, suggesting that carotenoids determine color intensity in orange and yellow maize kernels (Kljak et al., 2014; Lozano-Alejo et al., 2007). The color of intact kernels and their derived products can be objectively measured with colorimeters. These instruments can define color in terms of the HunterLab three-dimensional (L, a, and b) color space. These coordinates determine lightness (L) and chromaticity (a defines red/greenness and b defines yellow/blueness) of samples (Choudhury, 2010). Measuring color using a three-dimensional color space is widely accepted throughout the agricultural industry for the assessment of visual quality (Dowell, 1998). From these coordinates, chroma (C, a measurement of color saturation) and hue angle (h, a representation of the actual perceived color) can be calculated. The optical perception of color is related to the amount and the spectrum of the light reflected from an object into the human eye (Hutchings, 1999). Besides pigment concentration, geometric features of the object modulate its color attributes and appearance (Hutchings, 1999). Chromaticity is modified differently by opaque and translucent materials; their differences in physical structure impact the way that light is scattered through them (Hunter and Harold, 1987). Maize kernel translucency is associated with the amount of vitreous (hard) endosperm relative to the amount of floury (soft) endosperm. Harder kernels pre­ sent a greater proportion of vitreous endosperm, and are characterized by higher vitreousness, higher kernel density, higher test weight, and lower flotation indices (Abdala et al., 2018; Caballero-Rothar et al., 2018). Vitreous endosperm is translucent because of its compacity, which results from starch granules being tightly packed within a thick and continuous protein matrix. The floury endosperm, where the protein matrix is thinner, has numerous air-filled spaces that prevent the light from passing through. These air pockets reduce translucency, resulting in an opaque appearance (Robutti et al., 1974). We hypothesize that in addition to the total carotenoid concentration kernel hardness has an impact on kernel color. Today, there is no information regarding the effect of kernel hard­ ness over maize kernel color. We are interested in understanding the relationship between maize kernel color, total carotenoid concentration, and kernel hardness. Our objective was to test how genotype differences in kernel hardness and total carotenoid concentration impact kernel color. We hypothesize that maize kernel color is not only the result of total carotenoid concentration, but also a consequence of kernel phys­ ical properties. To test this hypothesis, we grew a number of current commercial genotypes differing in kernel hardness, total carotenoid concentration, and color in different environmental conditions.

different intensities of yellow and orange hues. They all had colorless pericarp and aleurone layers. The selected hybrids represented the range of maturity groups used in the region (from 116 to 130), and the color and hardness of hybrids commercially available. Plots were four rows with 6 m long and 0.52 m of inter-row spacing. A uniform stand density of 8 plants m 2 was used, and plots were always overplanted and thinned at V2–V3 (Abendroth et al., 2011). All measurements were done using the two central rows. Soil samples (0–60 cm) were taken before sowing and analyzed for N–NO3. At sowing, monoammonium phosphate (MAP, 10–50–0, N–P–K) was applied at a rate of 80 kg ha 1 to all plots. The experimental area was fertilized with N using urea (46-0-0, N–P–K) to reach 160 kg N ha 1 total. Urea was broadcast manually over the plots from V4 to V6. Trials were kept free of weeds and pests throughout the growing season. Weeds were controlled by spraying commercially rec­ ommended maize herbicides and removed by hand whenever necessary. Insect pressure of D. saccharalis and S. frugiperda were specifically monitored and controlled with recommended products for minimizing any deleterious effect. At commercial maturity, the central two rows from each plot were manually harvested and used for determining kernel yield and average individual kernel weight. Yield was calculated and presented on a 14.5% moisture basis. Samples for color and total carotenoid analysis were stored at 22 � C in the dark. 2.2. Kernel weight and hardness Kernels from each individual plot replicate were analyzed for kernel weight, size, and hardness after a minimal sample cleaning. Individual kernel weight was determined by weighing two sets of 100 kernels per plot. The proportion of kernels sized over 8 mm was measured by using a Ro-Tap-like sieve shaker (Zonytest, Rey & Ronzoni, Argentina). A 100-g kernel aliquot was loaded on top of an 8-mm roundhole stackable standard sieve. The weight of the aliquots retained before and after the 8-mm sieve was determined after two minutes shaking. Duplicate measurements were performed for each field replicate and results were averaged. The proportion of kernels retained by the 8-mm sieve was reported as percentage (%; Abdala et al., 2018). Test weight was determined after kernel sample homogenization using a Schopper chondrometer (Cuenca, Rosario, Argentina). Results were expressed as kg hL 1. To determine vitreousness (%) 100 kernels per plot were longitudi­ nally dissected and visually inspected. The percentage of kernels that were not indented in the crown, had central floury endosperm completely surrounded by vitreous endosperm, and 50% or more of the endosperm was vitreous, were considered vitreous kernels. Values were reported as a percentage of total number of inspected kernels. Floaters percentage (%) was measured by introducing a 100-kernels aliquot in a NaNO3 solution (density: 1.25 g cm 3) at 35 � C and thor­ oughly shaking every 30 s for 5 min to eliminate any air bubbles. At the end of this time period, floating kernels were counted and reported as percentage of floating kernels. Duplicate measurements were conducted for each field replicate and results were averaged.

2. Materials and methods 2.1. Crop management Two field experiments were conducted at Campo Experimental Vil­ larino, Facultad de Ciencias Agrarias, Universidad Nacional de Rosario in Zavalla, Santa Fe, Argentina (33� 10 S, 60� 53’ W), during the 2017/ 2018 growing season. Experiments were planted on October 5 and December 27, 2017, two contrasting planting dates commonly used by farmers in the region (Abdala et al., 2018). These different planting dates allowed us to test all genotypes under contrasting growing envi­ ronments. In both cases the soil was a silty clay loam Vertic Argiudoll, �n series. Rolda Field experiments were arranged following a completely randomized design with four replicates. Thirteen yellow and orange temperate commercial genotypes differing in hardness were tested. Genotypes were selected based on local commercial relevance and contrasting kernel hardness, and kernel color variation. Their colors were within

2.3. Milling In order to study the effect of losing the vitreous structure over color, a 100-g aliquot of maize kernels from each field plot was milled without further conditioning on a CT 293 Cyclotec™ laboratory mill equipped with a 0.5-mm roundhole screen (FOSS, Denmark). Milling was done under gold fluorescent light to prevent carotenoid degradation and flour samples were kept in the dark and analyzed for color and total carot­ enoid concentration within the same day as milling. 2.4. Color analysis The color of kernels and flours was analyzed with a Minolta Chroma 2

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Journal of Cereal Science 91 (2020) 102901

Meter reflectance colorimeter (model CR-400, Minolta Co., Osaka, Japan) with a viewing angle of 0� that had the specular component included and preset with the 2� observer. The D65 standard illuminant was selected for the purposes of this study. The instrument was cali­ brated with a white tile initially and periodically throughout the anal­ ysis. Each sample was allocated on a color co-ordinate within the Hunter Lab three-dimensional (L, a, and b) color space. The lightness coefficient, L, ranges from 0 (black) to 100 (white). The origin of the color space (a ¼ 0, b ¼ 0) is achromatic (gray). On the a axis, positive or negative values indicate redness or greenness, respectively. On the b axis, positive or negative values indicate yellowness or blueness, respectively. In addition, chroma (C) and hue angle (h) were calculated from a and b. C, which indicates color intensity or saturation, was calculated as C ¼ (a2þ b2)½ and h was calculated as h ¼ tan 1 (b/a), where 0� or 360� ¼ red, 90� ¼ yellow, 180� ¼ green, and 270� ¼ blue (Bao et al., 2005). Kernels or flour were poured into a glass Petri dish (10 cm diameter, 2 cm depth) and scraped flat. The measuring head of the colorimeter was carefully placed on top of the Petri dish and a color measurement was taken. Ten color readings on different spots were done in each sample. Triplicate samples from kernels and flour from each field plot were averaged to reduce sampling errors resulting from lack of homogeneity of the measured surface (Black and Panozzo, 2004).

3. Results

2.5. Total carotenoid extraction and analysis

3.2. Kernel hardness

Total carotenoid extraction was done as described by Kurilich and Juvik (1999) with minor modifications. Briefly, all sample preparations and extractions were done under gold fluorescent light to minimize carotenoid degradation. A 0.600 g flour aliquot was treated with 6 mL of ethanol containing 0.1% butylated hydroxytoluene (BHT) for 5 min in a water bath at 85 � C. Afterwards, 0.5 mL of 80% potassium hydroxide was added and heated again in a water bath at 85 � C for 10 min for saponification. After 5 min, samples were mixed using a vortex blender for 20 s. Immediately after saponification, samples were cooled in an ice bath and 3 mL of cold distilled water was added, followed by 3 mL of hexane. After thoroughly mixing with a vortex blender, samples were centrifuged for 5 min at 1200 g. The upper hexane layer was transferred into a separate test tube, and the lower layer was extracted three addi­ tional times with hexane. The hexane from the combined supernatants was evaporated under nitrogen. Total carotenoid concentration was determined using the “method of mean” reported by Biehler et al. (2010). Solvent-free extracts were reconstituted in 10 mL of acetone and sonicated for 2 min for solubili­ zation. The absorbance of the carotenoid sample was measured at λ ¼ 450 nm with a spectrophotometer (Biotraza, China). Total carotenoid concentration was determined using a mean extinction coefficient ε ¼ 135,310 L mol 1 for a 1 cm path cuvette and an average molecular weight of 548 g mol 1 and presented as mg kg 1 on a dry weight basis (Biehler et al., 2010).

Kernel hardness was evaluated with test weight, kernel vitreousness, and floaters percentage. Significant differences in test weight were evident for genotype and environment main effects (p < 0.001; Table 1) and for the genotype � environment interaction (p < 0.05; Table 1). Genotype accounted for the largest proportion of the observed variation (37%; Table 1). Environment and genotype � environment interaction effects explained minor portions of the variation (10 and 14%, respec­ tively; Table 1). When averaged across environments, genotypes ranged from 77.6 to 80.5 kg hL 1 (Table 1). Significant differences in vitreousness were observed across geno­ types and environments and the interaction environment x genotype was also significant (p < 0.001; Table 1). However, most variation was related to differences among genotypes (98%, Table 1). Most genotypes had vitreousness higher than 60% and only three showed vitreousness levels below 10% in both environments (AX7761VT3P, DK7210VT3P, and AX7822VT3P). These three genotypes were also the highest yielding ones. Floaters percentage showed a significant genotype effect only (p < 0.001; Table 1), and mean values across environments ranged from 1 to 58% (Table 1).

3.1. Crop yield, kernel weight, and size Significant yield differences were evident for genotypes and envi­ ronments (p < 0.001; Table 1). These factors together accounted for 75% of the total explored yield variation. When averaged across environ­ ments, genotype yield ranged from 8434 to 12,221 kg ha 1 (Table 1). Higher yields were observed in the earlier environment (October 5 planting date) and the interaction genotype x environment was not significant (p > 0.05). Individual kernel weight showed significant genotype and environ­ ment effects (p < 0.001; Table 1). Genotype was the effect that accounted for the largest portion of individual kernel weight variation (63%; Table 1), ranging from 242 to 319 mg kernel 1 when averaged across environments (Table 1). Significant differences in 8-mm screen retention were observed for genotype, environment, and their interaction (p < 0.001; Table 1). Ge­ notype was the effect that accounted for the largest portion of the observed variation (63%) in screen retention, with mean retention values ranging from 10 to 53% (Table 1). Environment and genotype � environment interaction effects accounted for smaller portions of the variation (20 and 10%, respectively).

3.3. Total carotenoid concentration Significant differences were observed in total carotenoid concen­ tration for genotype, environment, and the genotype � environment interaction (p < 0.01; Table 1). The genotype effect accounted for most of the observed variation (71%) and ranged from 24.7 to 39.4 mg kg 1. Significant environment and genotype � environment interaction ef­ fects accounted only for 7% of the total explored variation each (Table 1).

2.6. Statistical analysis Results for all measured variables were analyzed by analysis of variance using R software with package agricolae (R Core Team, 2013). The model included genotype, environment, and their interaction. As­ sumptions of normality and homogeneity of variances were satisfied by all traits. Significance level was established at α ¼ 0.05. Percentage sum squares were calculated to estimate the contribution of particular effects to total variation. In order to test the effect of milling on color, L, a, b, C, and h of intact kernels and flours were analyzed as described previously but also including the milling treatment as a repeated measure (package nlme). Pearson coefficients were reported to assess the correlation be­ tween kernel hardness traits, total carotenoid concentration, and color dimensions in intact kernels and flour.

3.4. Kernel color The kernel color parameters L, a, and b were significantly affected by genotype, environment, and their interaction (p < 0.01; Table 1). Ge­ notype average L (lightness) across environments ranged from 46.4 to 54.6, a (redness) ranged from 6.5 to 10.5, and b (yellowness) from 14.7 to 20.2 (Table 1). For all parameters the genotype effect was the one explaining the largest portion of the explored variation (76, 77, and 80%, for L, a, and b, respectively). 3

E. Saenz et al.

Table 1 Genotype and environmental effects over yield, individual kernel weight, 8 mm screen retention, test weight, vitreousness, floaters, total carotenoid concentration, HunterLab color dimensions, chroma (C), and hue angle (h) for the 13 genotypes tested. All results are expressed on a dry weight basis, except yield and test weight that are expressed on a 14.5% moisture basis. Complete description of all genotypes grown in the two en­ vironments is available as Supplemental Information (Supplementary Data Table 1). Effect

Yield

Kernel weight

8-mm screen retention

Test weight

%

kg hL

Vitreousness

Floaters

Total carotenoid concentration

%

%

mg kg

Kernel color L

kg ha

1

mg kernel

1

1

a

Flour color b

C

1

h

L

a

b

C

�

h �

4

Genotype AX7761VT3P DK7210VT3P AX7822VT3P NT525 NK940TGPLUS Syn989 NT2610 EXP.PAC0402 ACA514 MILL522 NT426 ACAexp757 ACA530

12,221 11,614 10,997 10,446 10,415 10,351 9801 9717 9710 9610 9407 9353 8434

297 274 277 292 262 260 319 252 278 291 242 295 283

50 40 47 29 10 12 47 32 36 53 10 43 48

77.6 78.5 78.0 80.4 78.9 78.9 79.6 78.9 79.4 80.4 79.7 79.8 80.5

2 1 8 75 80 85 97 92 93 96 97 94 97

58 58 37 3 5 3 1 1 6 2 2 4 2

26.9 27.9 26.2 29.7 29.5 30.5 24.7 38.2 33.1 38.0 30.9 38.6 39.4

54.6 49.6 54.5 50.6 49.3 49.2 48.5 51.7 52.4 50.7 46.4 50.7 49.2

7.7 10.5 6.5 6.8 7.7 7.0 7.0 6.6 6.5 6.6 9.1 6.5 7.2

20.2 17.3 19.2 16.5 15.7 16.1 15.0 15.4 17.4 15.8 14.7 16.0 14.8

21.7 20.2 20.3 17.9 17.5 17.5 16.5 16.8 18.6 17.1 17.3 17.3 16.4

69.2 58.8 71.3 67.6 63.8 66.4 65.1 66.9 69.7 67.1 58.3 67.9 63.9

75.4 75.0 75.3 72.3 72.3 70.7 72.6 71.3 73.8 72.8 71.5 72.3 71.7

1.5 0.6 1.7 1.5 1.1 1.7 1.1 1.5 1.1 1.3 0.6 1.5 1.4

18.2 19.1 18.3 18.1 17.4 16.9 17.2 18.2 18.3 19.0 17.9 18.6 18.1

18.3 19.1 18.3 18.2 17.5 17.0 17.3 18.3 18.4 19.1 17.9 18.6 18.1

94.6 91.9 95.2 95.0 93.7 95.7 94.1 94.6 93.5 94.0 91.9 94.8 94.3

Environment Early Late

11,341 8949

288 268

44 27

79.7 78.8

68 73

13 15

30.2 33.4

50.9 50.2

7.6 7.1

16.9 16.0

18.6 17.6

65.6 66.1

72.6 73.0

1.2 1.3

17.8 18.4

17.8 18.5

93.9 94.2

*** (5)

***

***

***

n.s.

***

**

***

***

***

*

n.s.

*

n.s.

n.s.

n.s.

*** (12)

***

***

***

*** (9)

***

***

***

***

***

***

*** (9)

* (1.4)

*** (7)

n.s.

** (3.6)

*** (1)

*** (1.2)

** (1.3)

*** (1.7)

* (1.5) n.s.

% Variance E G ExG Residual

** (2.3)

* (1.6) n.s.

***

n.s.

*** (2.1) n.s.

***

ExG

*** (359)a *** (916) n.s. 45 30 5 20

15 63 5 17

20 63 10 8

10 37 14 39

0 98 1 1

0 86 2 12

7 71 7 14

2 76 7 16

4 77 8 10

6 80 6 8

8 77 4 11

0 84 10 6

0 44 8 48

2 64 11 23

2 25 3 69

2 25 3 69

1 52 11 36

Environment (E) Genotype (G)

* (1.5)

Journal of Cereal Science 91 (2020) 102901

*, **, and *** significant at p � 0.05, 0.01, and 0.001 respectively, n.s: non significant (p > 0.05). a Numbers in parentheses represent the least significant differences (LSD) of the means at p � 0.05.

** (0.4)

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Journal of Cereal Science 91 (2020) 102901

Kernel color intensity, defined as chroma (C), and hue angle (h) were calculated from color scalar coordinates to study color appearance. Their differences were also significant for genotype, environment, and their interaction (p < 0.05; Table 1). C of genotypes ranged from 16.4 to 21.7 and h ranged from 58.3 to 71.3� (Table 1). Genotype effect accounted for the largest portion of the observed variation in both pa­ rameters (77% for C, and 84% for h). In brief, significant differences were detected for all kernel color attributes. Genotype accounted for most of the observed variation (al­ ways more than 70%), while environment and the genotype � envi­ ronment interaction effects represented minor portions (always less than 10% each).

screen retention (p < 0.01) and floaters percentage (p < 0.05; Table 2). This result indicated that harder, more vitreous, and larger kernels tended to be darker. It also indicated that the flour from harder kernels was also darker. Positive and negative correlations (p < 0.05) between a and floaters and a and vitreousness, respectively, evidenced that harder kernels were less red. Similarly, positive correlations between b and floaters per­ centage and 8-mm screen retention and negative between b and test weight and vitreousness (p < 0.05) showed that harder and smaller kernels tended to be less yellow (Table 2). However, after kernel phys­ ical structure was lost (i.e. when comparing kernels vs. flour), almost all associations between a and b values with kernel hardness disappeared (Table 2). Regarding color intensity, C was negatively correlated with test weight and vitreousness (p < 0.05) and positively correlated with floaters percentage for both intact kernels and flour (p < 0.001; Table 2). Finally, neither h from intact kernels nor from flour was significantly related to any of the kernel hardness and size traits studied. Variation in total carotenoid concentration was related to significant changes in b for both kernels and flour (p < 0.05; Table 2). Reductions in b from kernels were associated with increases in total carotenoid con­ centration (Fig. 1A and Table 2). However, the linear relationship be­ tween flour b and total carotenoid concentration showed a positive trend. More yellow flour colors were related to higher carotenoid con­ centrations (Fig. 1B and Table 2). The opposite tendencies found in

3.5. Flour color Significant differences in L, a, b, C, and h were evident for the ge­ notype main effect (p < 0.05; Table 1). Significant differences in a value were also observed across environments (p < 0.05; Table 1). The ge­ notype � environment interaction was significant for a value and h (p < 0.05; Table 1). Unlike kernel color attributes, the residual component of the model accounted for an important proportion of the variation, especially for L, b, and C. However, for all flour color attributes the genotype effect accounted for a higher proportion of the explored variation when compared to the environment and the genotype � environment interaction effects (Table 1). These results indicated that when kernel structure was lost through milling, HunterLab color values changed (p < 0.01). Flours not only became lighter (>L) in color for the different genotypes than their intact kernels, but also had a narrower L range (from 46.4 to 54.6 and from 70.7 to 75.4 for kernels and flour, respectively). A similar effect was observed for genotype flour a values, which were within the negative side of the scale (greenness) ranging from 0.6 to 1.7 (Table 1). On the other hand, when compared with intact kernels, significant increases in flour b values were observed in genotypes with high vitreousness and total carotenoid concentration (Mill522, ACA530, ACAexp757, and EXP.PAC0403; p < 0.001). Flour C ranged from 17.0 to 19.1 across genotypes, and only one genotype (AX7761VT3P) showed a significant change (p < 0.01) when compared with the intact kernel. It is important to note that for h, all tested genotypes turned more yellow after milling (p < 0.05; values closer to 90� ; Table 1). 3.6. Kernel color and total carotenoid concentration as related to hardness traits Correlations between kernel color attributes, kernel hardness, kernel size, and total carotenoid concentration were studied in order to eval­ uate associations among them. Total carotenoid concentration was positively correlated with vitreousness (p < 0.01) and negatively correlated with floaters percentage (p < 0.05, Table 2). Thus, within the set of genotypes tested, harder kernels had a higher concentration of carotenoids. L value in intact kernels and flour was negatively correlated with test weight and vitreousness (p < 0.01) and positively correlated with 8-mm

Fig. 1. Relationship between kernel Hunter b (Fig. 1A) and flour Hunter b (Fig. 1B) with total carotenoid concentration (mg kg 1) for 13 genotypes grown in two environments. The equations of the linear regressions are: Y ¼ 0.158 X þ 21.5 (R2: 0.24; p < 0.01; n: 26; Fig. 1A), and Y ¼ 0.067 X þ 15.9 (R2: 0.17; p < 0.05; n: 26; Fig. 1B).

Table 2 Pearson correlation coefficients (r) between screen retention (%), test weight (kg hL 1), floaters (%), vitreousness (%), and carotenoid concentration (mg kg 1) and color attributes. Each correlation includes 26 data points (thirteen genotypes grown under two environments). Trait Screen retention Test weight Floaters Vitreousness Carotenoid concentration

Carotenoid concentration n.s. n.s. 0.43 * 0.52 **

Kernel color

Flour color

L

a

b

C

h

L

a

b

C

h

0.54 ** 0.41 * 0.49 * 0.55 ** n.s.

n.s. n.s. 0.44 * 0.41 * n.s.

0.46 * 0.46 * 0.74 *** 0.80 *** 0.49 *

n.s. 0.46 * 0.83 *** 0.88 *** 0.57 **

n.s. n.s. n.s. n.s. n.s.

n.s. 0.50 ** 0.75 *** 0.73 *** n.s.

n.s. n.s. n.s. n.s. n.s.

n.s. n.s. 0.41 * n.s. 0.43 *

n.s. n.s. 0.43 * n.s. 0.42 *

n.s. n.s. n.s. n.s. n.s.

*, **, and *** significant at p � 0.05, 0.01, and 0.001 respectively; n.s.: non significant (p > 0.05). 5

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Journal of Cereal Science 91 (2020) 102901

intact kernels and flours are evidence of the influence of the kernel physical structure on its color parameters. High and low b could be attained with similar carotenoid concen­ trations, especially for those treatments resulting on total carotenoid concentrations below 26 mg kg 1. In these treatment combinations we observed that harder kernels resulted in lower b, even at similar total carotenoid concentrations (p < 0.01; Fig. 1A and Supplementary Data). These differences decreased, or disappeared, when kernels were milled and lost their vitreous structure (Fig. 1B). In summary, these results indicated that kernel hardness was nega­ tively correlated to kernel chromaticity. Kernel vitreousness appeared as the main driver of this negative correlation. When kernel structure and translucency were disintegrated through milling, b increased with increased total carotenoid concentration. Translucency of the vitreous endosperm impacted color appearance, especially at lower kernel total carotenoid concentrations. Our results also indicated that total carot­ enoid concentration was higher in harder kernels.

kernel center (Gayral et al., 2016). Recently, Ortiz et al. (2016) found a higher concentration of provitamin A carotenoids in an experimental high provitamin A genotype with increased kernel bulk density. Further research is necessary to explore mechanistic interactions between kernel hardness and the ability to store specific carotenoids in yellow and or­ ange maize kernels. Colorimeters are commonly used as a fast and economical tool for the early selection of promising genotypes with enhanced carotenoid con­ centration (Venado et al., 2017; Owens et al., 2019). Maize processing industries, including dry milling and nixtamalization operations, also use colorimeters for raw material selection to guarantee some sensory quality attributes in their final food products (Ilo and Berghofer, 1999). Our findings reveal how kernel physical structure impacts on color appearance, modifying the effect of carotenoid pigments on color measurements. The effect of translucency over diffuse reflection and color perception was avoided when kernels were milled. However, the high variation observed in the residual component of the statistical model of flour color attributes suggest that there are other factors, not included in our model, that affect color measurement. Future studies should consider different milling products to analyze how particle size distribution affects color readings. Pursuing this new concept will improve the methodology for accurate color measurement and, conse­ quently, a better carotenoid estimation. These concepts could also be relevant for other cereal species with contrasting kernel hardness.

4. Discussion Our results showed that kernel hardness plays an important role on color appearance. It was partly associated with an improved ability to store a higher concentration of carotenoids, where genotypes with harder kernels had on average higher carotenoid concentrations than the softer ones. However, and most importantly, at similar total carot­ enoid levels, significant b differences existed among kernels with con­ trasting kernel hardness (Fig. 1A). All color parameters correlated positively with floaters and negatively with vitreousness, except for hue angle, which was not associated with any hardness trait (Table 2). Described relationships are showing that kernel physical characteristics are important drivers of kernel color differences found among genotypes with similar total carotenoids concentration. Little (1964) described the complexity of color measurements over translucent materials based on the interaction of absorption, trans­ mittance, reflectance, and light loss through internal scattering and trapping. Our results agree with this concept, where vitreous endosperm translucency hindered accurate kernel color measurement and carot­ enoid estimation through kernel colorimeter measurements. When kernels were milled and the effect of translucency disappeared, corre­ lations between kernel hardness traits and flour HunterLab values became weaker. Lozano-Alejo et al. (2007) reported better carotenoid estimations through flour color measurements than with intact kernels. When testing a larger set of genotypes and a wider range of hardness our results agreed with this previous report, because measurements of “yellowness” (b values) and color intensity (C) were positively corre­ lated with total carotenoid concentration. If the total carotenoid con­ centration is of interest, as in the poultry feed industry, a reasonable estimation could be made for whole kernel flour using b value and C from colorimeter readings. The total carotenoid concentrations explored in our study ranged from 23.7 to 42 mg kg 1 (dry weight basis), higher than values reported in other studies such as Kljak et al. (2015) ranging from 16 to 30 mg kg 1. However, in both studies higher total carotenoid concentrations were observed in genotypes with greater kernel hardness. The vitreous maize endosperm fraction contains more than 70% of total carotenes (α-carotene and β-carotene) and xanthophylls (β-cryptoxanthin, lutein, and zeaxanthin; Blessin et al., 1963). This might be related to carotenoid hydrophobic interactions with protein bodies (Larkins et al., 2017). Kernel hardness has traditionally been related to specific endosperm proteins, known as zeins (Dombrink-Kurtzman and Bietz, 1993; Cab­ allero-Rothar et al., 2018). Lutein fits into the core of an α-zein (19 kDa) and helps stabilize its molecular configuration (Momany et al., 2006). However, the relations of the other typical maize carotenoids with zeins and kernel hardness are not established. It is possible that carotenoids could follow a similar distribution pattern as zeins, which decrease in concentration from the harder endosperm kernel periphery to the softer

5. Conclusions Our results demonstrate that kernel hardness impacts maize kernel color regardless of total carotenoid concentration. This was tested in a set of commercial genotypes showing a range of kernel hardness and color attributes. HunterLab color dimensions were negatively correlated with vitreousness. Different colors were observed in kernels with the same total carotenoid concentration but contrasting hardness. This in­ dicates that assumptions of total carotenoid concentration solely based on kernel color may be misleading. It is necessary to consider the effect of endosperm vitreousness also. Genotypes with high kernel hardness had, on average, higher carotenoid concentrations when compared to softer ones. Total carot­ enoid concentration was positively related to vitreousness and nega­ tively correlated with floaters. Declaration of competing interest The authors declare no conflict of interest. CRediT authorship contribution statement Ezequiel Saenz: Formal analysis, Writing - original draft. Lucas J. �s: Writing - original draft, Abdala: Writing - original draft. Lucas Borra � A. Gerde: Formal analysis, Writing - original Formal analysis. Jose draft. Acknowledgements Authors wish to thank GR Rodriguez for lending the colorimeter. The study was funded by the Ministerio de Educaci� on, Cultura, Ciencia y Tecnología from Argentina (PICT 2016-0956) and CONICET (Scientific Research Council of Argentina, PUE 22920160100043). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jcs.2019.102901.

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Journal of Cereal Science 91 (2020) 102901

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