Comparison of colorimetric methods for determination of phytic acid content in raw and oil extracted flour samples of maize

Journal Pre-proof Comparison of Colorimetric Methods for Determination of Phytic Acid Content in Raw and Oil Extracted Flour Samples of Maize Fatih Kahriman, Umut Songur, Mehmet S¸erment, S¸ule Akbulut, ¨ Cem Omer Egesel

PII:

S0889-1575(19)30801-4

DOI:

https://doi.org/10.1016/j.jfca.2019.103380

Reference:

YJFCA 103380

To appear in:

Journal of Food Composition and Analysis

Received Date:

30 May 2019

Revised Date:

31 October 2019

Accepted Date:

28 November 2019

Please cite this article as: Kahriman F, Songur U, S¸erment M, Akbulut S¸, Egesel C, Comparison of Colorimetric Methods for Determination of Phytic Acid Content in Raw and Oil Extracted Flour Samples of Maize, Journal of Food Composition and Analysis (2019), doi: https://doi.org/10.1016/j.jfca.2019.103380

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Comparison of Colorimetric Methods for Determination of Phytic Acid Content in Raw and Oil Extracted Flour Samples of Maize

Fatih KAHRIMAN1# Umut SONGUR1 Mehmet ŞERMENT1 Şule AKBULUT2 Cem Ömer EGESEL2

Çanakkale Onsekiz Mart University, Faculty of Agriculture, Department of Field Crops,

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17020, Çanakkale, Turkey

2

ÇanakkaleOnsekiz Mart University, Faculty of Agriculture, Department of Agricultural

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#

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Biotechnology, 17020, Çanakkale, Turkey

Corresponding Author’s E-mail: [email protected]

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Phone: +(90) 028621880018/1354

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Highlights:  Colorimetric determination methods for phytic acid had significantly differed in cost and duration of analysis.  Oil extraction had a significant effect on colorimetric determination of phytic acid in maize flour samples.  Methods may be sensitive to some biochemical compounds of the sample, in addition to oil.  A cut-off-point for oil content (%9.8) was determined to make a decision on selecting colorimetric method in the analyses of phytic acid in maize samples with variable oil content.

Abstract

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There are different colorimetric methods, with various analysis principles and phases, for the estimation of phytic acid content in agricultural products. Maize genotypes may possess a wide range of oil content, which is considered as a factor affecting the results of phytic acid analyses. Elaborative studies are needed to examine these methods to clarify the effect of oil content on the results, especially in the sample sets with varying oil concentrations. We utilized 4 different colorimetric methods; namely, AOAC, Wade, Chen and Haug-Lantzsch (H-L), to estimate phytic acid content in 19 maize genotypes, classified as having high (>7%,

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n=7) normal (3-5%, n=6) and low (<3%, n=6) oil content. Phytic acid determination was carried out on 2 groups of flour samples (raw: E1, and oil extracted: E2) using 3 replications. The results indicated that analysis methods yielded rather different phytic acid values. They

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also differed significantly in time and cost, with the Chen method being the cheapest and Haug-Lantzsch (H-L) the quickest. Oil extraction had significant effects on phytic acid

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results, and these effects varied across the analysis methods and the oil content of the

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genotypes. Our data suggest that either novel or improved colorimetric methods are necessary when analyzing phytic acid in special maize genotypes, considering the dissimilarity of the results from the current methods.

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Introduction

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Keywords: Phosphorus determination, Antinurtients, Zea mays, Specialty maize

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Phytic acid (inositol hexaphosphate) is a compound found in many different seeds at levels of 0.05-5% (Agostinho et al., 2016). It plays an active role in the storage of inorganic phosphorus in plant seeds. About 50-80% of the total phosphorus is stored as phytic acid in cereal grains (Coulibaly et al., 2011). Phytic acid has been associated with a variety of undesirable as well as desirable effects. Its ability to bind several nutritional factors in food/feed renders an anti-nutritional influence

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(Coulibaly et al., 2011; Adams et al., 2002). On the other hand, different researchers related phytic acid with positive health effects such as prevention of cancers (Liu et al., 2015) and lowering oxidative stress in seeds (Doria et al. 2009). Obviously, this is a compound needed to be effectively detected in many different agricultural products. Today, several analysis methods are available that utilize different instruments to estimate phytic acid content. These include the methods developed for spectrophotometry/colorimetry, liquid

chromatography,

gas

chromatography,

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Coupled

Plasma-Optic

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Spectroscopy (ICP-OES), Fourier Transform-Near Infrared (FT-NIR) instruments as well as titration, molecular fluorescence, graphene quantum dots, gold nanoparticles, turbidimeter, and enzymatic reactions (Agostinho et al. 2016). Among all, colorimetric methods are the

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most preferred ones since they are easy to use and cost effective. Especially, microplate-based methods make it possible to analyze a high number of samples at once, thereby making the

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these methods of choice.

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Colorimetric methods for phytic acid analysis are based on the indirect measurement of color change in the metal complex, or measurement of inorganic phosphorus composed of enzymatic or hydrolyzed compounds (Park et al., 2006; Mark et al., 1998). The Wade method,

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widely used and accepted as a reference method for food and seed analyses, measures the pink color, exposed by a complex that is formed by phytic acid entering a reaction with iron

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chloride and sulphosalicylic acid, at 500 nm (Wade and Morgan 1955). Another recognized

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colorimetric method, namely Haug-Lantzsch method, is based on the decrease of absorbance value at 519 nm, which originates as a result of the complex of phytic acid with iron and bipyridine (Haug and Lantzsch, 1983). Sureshkumar et al. (2015) developed a method using Chen solution (6N H2S04; 2.5% ammonium molybdate; 10% ascorbic acid; water) by measuring the color change of phosphomolybdate complex at 660 nm to determine phytic acid content on ground single seed samples of maize. This method, which makes it possible to

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detect phytic acid indirectly, has been suggested to replace any direct detection methods. Most of the colorimetric protocols found in the literature are the modified versions of abovementioned methods. Efforts are underway to introduce new reagents to bind phytic acid, to modify the current methods, or develop new ones. Different scientific studies have been carried out to compare colorimetric methods for determination phytic acid content (Raboy et al., 2017). Some others compared the colorimetric methods with High-Pressure Liquid Chromatography (HPLC), Anion Exchange

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Column (AEC) and Nuclear Magnetic Resonance (NMR) based protocols (Gao et al., 2007). It stands out that none of these studies considered oil extraction as a pretreatment, although it was suggested, especially for high oil samples, to improve the efficacy of phytic acid

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extraction (Thavarajah et al., 2014). From this standpoint, it is a necessity to clarify the effect of oil extraction pretreatment on phytic acid analysis, especially for high oil maize genotypes.

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Another important aspect is the possibility for the oil in the sample to react with the chemicals

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used for the analysis, which has not been addressed so far.

The objectives of this study are to (i) compare the colorimetric methods with regards to time and cost to determine the most advantageous method, (ii) clarify the effect of oil extraction

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pretreatment on phytic acid analysis, (iii) evaluate how the oil level of the sample interacts

methods.

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with phytic acid results from the raw and oil extracted samples in different colorimetric

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Materials and Methods Materials

Nineteen maize genotypes with a range of oil content were used as plant material (Table 1). These genotypes were classified into three groups based on their oil content. In scientific literature, high oil maize contains 6-7% oil in kernel (Singh et al., 2014), normal oil had %35 (Lambert, 2001), and low oil genotypes had belowe the 3% of oil content. Based on this

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classification, nineteen maize genotypes which are used in our maize breeding program were selected to conduct this study. About 50 g seed was milled (Fritsch pulverisette 14, Germany) from each genotype for laboratory analyses. Ground samples were divided into two and the first group (E1) was directly analyzed for phytic acid, while the samples in the second group (E2) were subjected to oil extraction before the analysis. A modified version of the Abbasi et al. (2008) method was utilized for oil extraction: About 10 g ground sample was kept in 100 mL dimethylether overnight to extract

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oil from flour. The solvent was evaporated using a rotary evaporator (Hannah, Kore), and the weight of the remaining crude oil was used to calculate the oil ratio of the sample. The samples free of oil (E2 group) were kept at room temperature to let the solvent evaporate

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before being used for the phytic acid analysis. Both groups (E1 and E2) were subjected to oven-drying for 24 h at 70 ℃ before weighing the flours for the colorimetric analyses.

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Colorimetric Methods

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Four different colorimetric methods were compared, which were explained in detail below. To make a cost comparison, the expenses of the chemicals and consumables were calculated (based on the Sigma website, https://www.sigmaaldrich.com). To make comparison with

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regards to the time spent, each step was timed separately for every method, and analysis time per sample was calculated.

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Method 1 (Wade Method): Five grams ground sample was shaken in 100 mL 3.5% HCl for

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an hour at room temperature. The extract mixture was centrifuged at 3000 g for 10 mins and the upper phase was transferred into a clean tube. From this sample, 5 mL upper phase was diluted in 25 mL distilled water. Then, 10 mL diluted extract was injected through a glass column stoppered with Teflon tape and a small piece of glass wool, packed with 0.5 g anion exchange resin (AG1 X8, 200–400 mesh, Sigma). Inorganic phosphorus and other interfering components were eliminated using 15 mL 0.1 M NaCl. The phytic acid in the column was

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eluted with 15 mL 0.7 M NaCl. 3 mL of this eluate was transferred into a clean glass tube, and 1 mL Wade solution (30 mg of FeCl3.6H2O and 300 mg of sulfosalicylic acid in 100mL distilled water) was added. It was vortexed and centrifuged at 3000 g for 10 mins, and the absorbance value at 500 nm was recorded against Wade solution as blank. Phytic acid content was determined by cross-reference to a calibration curve constructed by using absorbance values of the phytic acid standard (Makkar et al., 2007). Method 2 (Chen Method): A half gram of ground sample was extracted in 5 mL 0.4 M HCl

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for 12 h. Next, 100 µL extract and the same volume of Chen solution (Chen's reagent- 6N H2S04; 2.5% ammonium molybdate; 10% ascorbic acid; H2O- 1:1:1:2 v/v/v/v) were mixed. The mixture was left to react for 15 mins. When it turned blue, the absorbance value was

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recorded at 660 nm to detect the amount of free phosphorus. Phytic acid contents of the samples were estimated based on the curve that was created using dipotassium hydrogen

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phosphate as standard (Sureshkumar et al., 2015).

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Method 3 (Haug-Lantzsch Method): A half gram of ground sample within 10 mL 0.2 N HCI was placed in boiling water bath for 1 h. Then, 0.25 mL of extract was added into a test tube and 2.25 mL of 0.2 N HCI and 5 mL of iron solution were added. The tubes were

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incubated in a boiling water bath for 30 mins. After that, they were transferred to an ice bath till they reached room temperature. Then, they were centrifuged for 20 mins at 3000 g and

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100 µL of upper phase was taken and mixed with 150 µL H-L reagent (400 mg bipyridine +

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400 µL TGA + 40 mL distilled water). Absorbance values of the samples were recorded at 519 nm. With the help of the curve that has been constructed with the phytic acid standard, which was subjected to the same procedure, phytic acid values of the samples were determined (Raboy et al., 2017). Method 4 (AOAC Reference Method 986.11): A half gram of ground sample was shaken in 2.4% HCI room temperature for 3 hours. Equal volumes (1 mL) of the acquired extract and

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Na2EDTA-NaOH (10.23 g Na2EDTA and 7.5 g NaOH for 250 mL) solution were mixed and diluted to 25 mL. Then sample passed through an anion exchange column (0.7 mm x 30 mm) packaged with 0.5 g anion exchange resin (AG1 X8, 200–400 mesh, Sigma). The fraction containing phytic acid was eluted using 0.7 M NaCl and sample was digested using 0.5 mL H2SO4 and 3 mL HNO3 in a Kjeldahl system (Gerhardt, Germany). Digested sample was transferred to a 50 mL flask and cooled at room temperature. Then the sample was mixed with 2 mL molybdate reagent and 1 mL sulfonic acid reagent. After dilution the volume to 50 mL,

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colorimetric reading was done at 640 nm using a spectrophotometer. The KH2PO4 (80µg/mL) was used to create the standard curve (AOAC, 2012). Statistical Analyses

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The data were analyzed using R software (R development Core Team, 2012). Variance analysis was done on the combined data based on the following statistical model in order to

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evaluate the effect of phytic acid analysis methods and oil extraction. Yijkl = µ + αi + βj + γk + (αβ)ij + (αγ)ik + (βγ)jk + (αβγ)ijk + ril + εijkl

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where; Yijkl: observed value, µ: grand mean, αi: effect i. genotype (i=1, 2…..19), βj: effect of j. method (j = 1, 2, 3, 4), γk: effect of k. extraction (k = 1, 2), (αβ)ij: effect of genotype ×

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method interaction, (αγ)ik: effect of genotype × extraction interaction, (βγ)jk: effect of method × extraction interaction, (αβγ)ijk: effect of genotype × method × extraction interaction, rl: l.

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replication effect (l=1, 2, 3), εijkl: random error term. Variance analyses were done for each

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method separately and by excluding method effect and related interactions to more clearly understand the effect of interaction of G × E according to the analysis methods. Differences between E1 and E2 were compared using T test to evaluate the effect of oil extraction on the results of phytic analysis methods. The relations between the results obtained from raw and oil extracted samples with the same colorimetric method were analyzed using Pearson Correlation Analysis. The relations of the

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oil level of the sample and the difference between the results of raw and oil extracted samples were evaluated by using Regression and Decision Tree Analysis (Breiman et al., 1984).

Results and Discussion Comparison of the Methods for Analysis Cost and Duration The results of the calculations for analysis cost and duration per sample for each method are given in Table 2. In the extraction phase, the Chen method required the longest analysis time

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per sample set (720 mins), while the Wade method was the most expensive (0.15 €). Considering the total analysis cost, the most expensive method per sample was the AOAC method (28.60 €) while the cheapest method was Chen method (0.65 €). AOAC method

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offered some advantages for analysis time, but it was about 44 times as expensive as the Chen method (Table 2). Overall data suggested that one would opt for the Chen method in terms of

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expenses, while the Wade method would be the method of choice in terms of duration.

robustness are discussed later.

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Nevertheless, this evaluation takes only cost and time into consideration; precision and

Large differences among the methods for their duration stems from the procedure steps

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applied. The Chen method includes sample weighing, extraction, and colorimetric reading steps; while H-L method uses sample weighing, extraction, centrifuging, heating for

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sedimentation, centrifuging, and colorimetric reading steps in sequence. AOAC (sample

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weighing, extraction, NaCI cleaning, AEC column step, wet digestion and colorimetric reading), and Wade methods (sample weighing, extraction, NaCI cleaning, centrifuging, and colorimetric reading) use 6 and 5 steps, respectively. Although the Chen method includes the least number of steps, it is not the method of choice in terms of analysis duration since the extraction step lasts too long. The range of chemicals and consumables used in the procedure steps as well as their prices are determinant factors. The methods that require the use of

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columns are costly due to heavy use of extra chemicals and apparatus. Requirement of several types of chemicals in Wade and AOAC methods increase their cost of analysis. Variation of Phytic Acid Values Based on Analysis Methods Variance analysis results for individual methods as well as the combined data are presented in Table 3. According to the combined analysis; genotype, oil extraction, and the analysis method had significant effects on the variation of phytic acid obtained with colorimetric methods. Interactions of these main effects also affected the variation of phytic acid content.

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There were statistically significant differences among the AOAC method, Chen, and H-L methods (Fig 1). Considering the distributions on boxplot graphics, it is seen that the variation of phytic acid values in Wade and H-L methods is higher than the variation in the other

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methods. It can be said that oil extraction generally caused a significant (p=0.0092) increase in the determined phytic acid value (Fig 1). However, as can be seen from Figure 1, this is not

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observed in all genotypes. In other words, the effect of the extraction process on the phytic

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acid content varies depending on the analysis method used and the genotype of the sample being analyzed.

The variation between the results of the phytic acid obtained from the methods used in the

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study can be associated with the basic differences in the protocols. Firstly, in addition to using HCL at different concentrations for extraction, the extraction time is different for each

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method. The amount of sample weighed in the Wade method (5 g) is more than in the other 3

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methods (0.5 g). In addition, the amount of acid used per unit weight is also different in the four methods. Wade and AOAC methods require 20 mL, while Chen and H-L methods require 10 and 16.7 mL acid per gram sample, respectively. These two aspects may have a significant effect on the extraction efficiency of the methods. As a matter of fact, it was shown earlier that acid concentration, extraction time and extraction temperature affected the phytic acid extraction in rice (Canan et al., 2011). The other issue affecting the results stems

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from the theoretical approaches. Wade and AOAC are the methods of analysis based on the Anion Exchange Column (AEC) principle. On the other hand, in the Wade method phytic acid is determined based on the reaction of Fe-III and sulfosalicylic acid, so the determination of phytic acid is accomplished indirectly (Sivakumaran and Kothalawala, 2018). In the Chen method, the determination of phytic acid is carried out indirectly by measuring the total phosphorus content. In the H-L method, the estimation is performed based on the phosphorus content of the phytic acid standard without anion exchange column. These differences may

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have an impact on the analysis results. In fact, it has been reported that the AOAC method overestimates phytic acid values, which has been related with the reasons arising from the theory of the method (Lehrfeld and Morris, 1992). This supports our findings that phytic acid

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results obtained by the AOAC method were found to be higher than the other analysis methods. In a study analyzing an infant formula, Park et al. (2006) reported higher values

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from the AOAC method compared to Wade method. Another important point in the methods

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is the differences in protocol steps and the principle of analysis. Wade and H-L are methods based on the ability of the phytic acid in the sample to bind iron. The AOAC method, on the other hand, is based on the determination of inorganic phosphorus by means of wet ashing,

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which eliminates organic phosphorus in a sample. These differences may partly explain the variation of the results obtained in our study.

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One of the important findings from this study is the variable responses of maize genotypes

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with different biochemical properties (e.g., oil, protein quality) across the method of analysis. Since no study was encountered in the literature with a similar setup as the current study, making a literature-based discussion was not possible, except for comparing the results obtained from the genotypes. Queiroz et al. (2011) found a range of 0.48-1.04 mg/g for phytic acid content within 22 tropical maize lines. Lorenz et al. (2007) reported phytic acid values between 2.40-4.09 mg/g for 50 different maize lines, using modified Wade method. Although

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the results of this study are similar to the earlier studies in general, extreme values were detected for some genotypes. Genotypic effects and seed characteristics may be the reason for these differences. Because most of the phytic acid is located in the embryo (O’Dell et al., 1972), genotypes with large embryos may have higher phytic acid values. Raboy et al. (1989) showed that high oil genotypes from the Illinois Long Term Experiment possessed higher levels of phytic acid. The results obtained from high oil genotypes in our study are similar to these results (Table 4). However, the significant differences between the analysis methods

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were striking. AEC requiring methods such as AOAC and Wade did not meet this expectation. The phytic acid content of high oil genotypes was found to be relatively high compared to the other genotypes with Chen and H-L methods, which require no use of AEC.

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It was remarkable that H-L and Wade methods, which are based on iron precipitation, could not determine the phytic acid content in some genotypes when raw samples were used. Odd

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results were observed for genotypes with high oil content as well as opaque type samples

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when analyzed with these methods. Even though this study focused on the determination of phytic acid content in the samples with different oil concentrations, the results indicated that there is a need to carry out research to investigate the effect of other biochemical attributes of

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maize kernels on the phytic acid analyses. Park et al. (2006) reported that when the sample contained protein, it could react with the iron found in the Wade solution and cause

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precipitation. This may be a reason for obtaining low values from the opaque genotypes with

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Wade method in this study (Table 4). Effect of Oil Extraction and Oil Ratio on Phytic Acid Analysis Results The correlation analysis results among the phytic acid values from raw and oil extracted samples using different colorimetric methods are presented in Table 5. Correlation analyses were performed on the data sets of genotypes separated into three classes (high> 7%, normal= 3-5%, and low <3%) according to their oil content. The aim was to compare the values

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calculated over the combined data set to the values calculated from the three oil classes. The correlation coefficients for phytic acid values estimated from the raw and oil extracted samples varied across the colorimetric methods and oil classes (Table 5). For example, a significant negative correlation (r=-0.78; p<0.01) was found between the phytic acid results that were analyzed from oil extracted (E2) and raw (E1) samples of low oil (<3%) genotypes with AOAC method; while there was a positive relationship between high and normal oil containing genotype groups (r = 0.65; p< 0.01 for high, R = 0.44; p> 0.05 for normal). The

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correlations between the phytic acid results obtained from raw and oil extracted samples were not significant in all oil classes for Chen and H-L methods. It is remarkable that all correlation coefficients were positive (Table 5). Wade method yielded a positive and significant

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correlation between the raw and oil extracted samples of low oil genotypes (r=0.90; p<0.01), but for high and normal oil genotypes the correlation values were negative and nonsignificant

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(Table 5). These results suggest that there were significant differences in phytic acid values

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obtained from E1 and E2 groups when using the same method. Besides, these differences varied significantly depending on the method used and the oil classes of the genotypes. Studies in the literature have focused on the similarity of phytic acid results obtained by

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different methods, rather than the raw vs. oil extracted samples (Gao et al., 2007; Raboy et al., 2017). In this respect, a comparative discussion on the effect of oil extraction on the phytic

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acid analysis was not possible. Nevertheless, our results may provide some insight into this

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matter. Chen and H-L methods, which do not use AEC separation, showed a positive correlation between the phytic acid values from the raw and oil extracted samples; while, this was not the case in AEC using AOAC and Wade methods. It is noteworthy that the correlations calculated in low oil genotypes had the opposite signs and were found to be statistically significant when AOAC and Wade methods were used. In these methods, organic phosphorus is collected with 0.7 NaCI after passing the column, with the AOAC method

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using an additional combustion process, and the detection is carried out via total phosphorus with KH2PO4 standard. Such differences may have caused the calculated correlations with these two methods to be in opposite directions in low oil genotypes; and this may be valid for the other genotype groups just as well. The regression and decision tree analysis results, carried out to elucidate the effect of oil extraction on phytic acid analysis results, are highly informative. A model was developed to evaluate the relationship between the change in the amount of oil in a sample and the

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difference in the amount of phytic acid between the raw and oil extracted samples. In Figure 2, we tried to explain the relationship of the phytic acid difference of raw and oil extracted samples with oil content of the samples and the analysis method used. A total of 13 nodes

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were formed in the regression and decision tree. The average of the difference between the phytic acid values obtained from the samples (n = 76) is -0.089 mg/g. In these samples, the

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difference in the phytic acid values of the samples (n = 12) with an oil ratio of more than

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9.8% was negative (-0.66 mg/g). As these samples are classified only in terms of oil content, it is understood that the analysis method used has no effect on the difference in phytic acid analysis results. The difference between the phytic acid values of the samples (n = 64) with an

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oil content of less than 9.8% and classified under node 3 was found to be 0.018 mg/g. These samples are divided into two different groups according to the analysis method used, and

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phytic acid difference between the raw and oil extracted samples were calculated as 0.38 mg/g

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for those that were not analyzed with AOAC, Chen or H-L methods (n=16). In other words, in the case of using the Wade method in these samples, it can be stated that removing oil causes a difference in the phytic acid content estimated. In the samples classified under the third node, it was observed that the number of the samples analyzed by AOAC, Chen and H-L methods included 63% of all samples. A negative difference was observed in phytic acid values from raw and the oil extracted samples when these three methods were used. The

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samples classified at the sixth knot were assigned to two sub-classes based on the analysis method applied. The samples analyzed with Chen or H-L method (n=32) got a share of 42% within the total number of samples and the phytic acid difference of these samples were in a negative direction. Under the same knot, the differences between the phytic acid results were positive (0.055 mg/g) for the methods other than Chen or H-L methods. Considering the knots within this class, it could be said that the abovementioned group contains the samples analyzed with the AOAC method. Hence, we can argue that the method applied had a

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significant impact on the difference between the analysis results from raw and oil extracted samples. In addition, based on regression and decision tree analysis, it can be stated that an oil ratio above 9.8% in the samples has a notable effect on the phytic acid analysis result. Our

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results clearly indicate that the sample’s oil concentration affects the phytic acid analysis results, and this effect varies across the methods applied. Sivakumaran and Kothalawala

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(2018) stated earlier that phytic acid analysis should be performed after removing oil in the

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samples with oil content above 15%. Our findings agree with this earlier report in that there is critical value of oil content, while suggesting a lower cutoff point specifically for maize

Conclusion

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samples.

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The colorimetric methods compared here showed significant differences in terms of cost and

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duration of analysis. The AOAC method was the most expensive, while the Chen method offered the most cost-effective option. The data indicated that oil extraction had significant effects on phytic acid results. This effect varied based on the oil level of the genotype as well as the analysis method. Regression and classification tree analysis results suggest that the oil content over a limit value (9.8%) affect the results of phytic acid analysis. It was also found that the oil content of the sample interacts with phytic acid results from the raw and oil

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extracted samples in different colorimetric methods, as it can be deduced from the correlation analysis. Another point is that other biochemical components may also affect the phytic acid analysis and magnified the difference between raw and oil extracted samples based on the method used. It is considered that new methods should be developed, or existing methods should be revised in order to eliminate the deviation in the high oil samples exceeding a limit value. The different results obtained from the analysis methods may be due to the interaction of the extracted and unextracted samples with the seed content and the chemicals used in the

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method and the applications. The presence of oil in the sample may prevent the separation of phytic acid and phosphorus from the sample which allows indirect detection of this component in the extraction step. Since the methods used directly or indirectly yield results

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based on the amount of organic or inorganic phosphorus in the sample, this has changed the result of phytic acid in the degreased and unextracted samples.

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In conclusion, our results may help to select the appropriate method for phytic acid analysis

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when dealing with high, normal, or low oil maize genotypes. However, since all of these are spectrophotometric methods, it would be necessary to conduct comparative studies with alternative methods such as chromatography and NMR spectroscopy for decisive results.

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Further studies should also focus on optimization of the factors such as the acid volume and concentrations used in the extraction procedure and explore other agricultural products

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possessing significant variation in terms of the biochemical constitution.

Acknowledgment This work was supported by Çanakkale Onsekiz Mart University Scientific Research Coordination Unit, Project number: FHD-2018-2476.

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Table 1. The codes and classification of the genotypes used in the experiments Oil Content (%) 8.42 8.63 9.67 10.02 10.47 10.95 3.04 3.26 3.42 3.54 3.61 3.65 3.79 1.16 1.83 1.94 2.64 2.70 2.94

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Speciality for Oil Content High Oil (>7%) High Oil (>7%) High Oil (>7%) High Oil (>7%) High Oil (>7%) High Oil (>7%) Normal (3-7%) Normal (3-7%) Normal (3-7%), Opaque Normal (3-7%), Opaque Normal (3-7%) Normal (3-7%), Opaque Normal (3-7%), Opaque Low Oil (<3%) Low Oil (<3%) Low Oil (<3%) Low Oil (<3%) Low Oil (<3%) Low Oil (<3%)

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Genotype Code H1 H2 H3 H4 H5 H6 M1 M2 M3 M4 M5 M6 M7 L1 L2 L3 L4 L5 L6

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Table 2. Comparative time and cost assessment for phytic acid analysis methods

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Step 1-Extraction Step 2- Analysis Total Time Time Time Chemical Chemical Chemical requirement requirement requirement cost (€) cost (€) cost (€) (min) (min) (min) H-L Method 45 0.08 65 0.78 110 0.86 Chen Method 720 0.02 15 0.63 735 0.65 Wade Method 60 0.15 34 26.31 94 26.46 AOAC Method 180 0.05 86 28.55 266 28.60 Note: Cost calculations are based on the prices listed on https://www.sigmaaldrich.com as of 18.12.2018.

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Table 3. Mean squares for phytic acid estimations for different analysis methods Source of dfϯ variation Replication 2 Genotype (G) 18 Method (Y) 3 Extraction (E) 1 GxY 54 YxE 3 GxE 18 GxYxE 54 Error 302

Combined

dfϯ

0.0013 1.3606** 5.2348** 0.9132** 0.9097** 1.3028** 1.3203** 0.8607** 0,0073

2 18 1 18 74

H-L Method 0.0075 0.7136** 1.2898** -

Chen Method 0.0221 0.1969** 2.9981** 0.3507** 0.3422** 0.8321 0.9156

AOAC Method 0.0593** 4.6459** 0.0797** 3.0109** 3.5697** 0.1287 0.2188

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*Significant at the 0.05 probability level. **Significant at the 0.01 probability level. ϯdegrees of freedom.

Wade Method 0.0012** 2.9194** 0.4537** -

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H-L Method E2 Diff E1-E2 1.93 0.01 1.58 -0.35* 1.87 0.15 1.96 -0.26 2.54 -0.32 2.02 -1.58** 2.19 -0.19 1.28 -0.50** 1.59 -0.14 1.91 0.27** 2.95 -1.12** 1.80 -0.60** 1.60 0.11** 1.81 0.05 1.83 -0.07 1.54 0.24 1.76 -0.06 1.83 -0.10 1.54 0.42** 1.87 -0.21**

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Chen Method E2 Diff E1-E2 1.93 -0.95** 1.92 -0.38* 1.87 -0.74** 2.38 -1.00** 1.96 -0.56** 1.58 -0.67** 1.21 0.66** 2.03 -1.37** 1.88 -0.06 1.97 -0.13 1.76 -0.02 1.85 -0.23 1.60 -0.02 1.89 -0.20 1.56 -0.08 1.63 0.11 1.67 0.06 1.78 -0.04 1.76 -0.54* 1.80 -0.32**

E1 1.92 1.24 2.02 1.70 2.22 0.44 2.00 0.78 1.45 2.18 1.84 1.20 1.71 1.85 1.76 1.78 1.71 1.73 1.96 1.66

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E1 0.98 1.53 1.13 1.38 1.40 0.91 1.87 0.66 1.82 1.84 1.74 1.63 1.58 1.69 1.48 1.74 1.72 1.74 1.22 1.48

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E1 2.60 1.67 1.86 2.28 1.87 1.83 1.77 1.90 1.70 1.90 1.86 1.78 3.20 1.81 2.00 1.91 1.76 1.77 2.00 1.97

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Genotype H1 H2 H3 H4 H5 H6 L1 L2 L3 L4 L5 L6 M1 M2 M3 M4 M5 M6 M7 Mean

AOAC Method E2 Diff E1-E2 2.04 0.56** 1.56 0.11 1.82 0.05 1.98 0.30** 1.96 -0.08 1.92 -0.09 2.06 -0.29** 1.82 0.08 2.23 -0.52** 1.92 -0.02 1.88 -0.02 1.97 -0.19 1.97 1.23** 1.91 -0.10 1.94 0.06 1.97 -0.07 1.86 -0.10 1.89 -0.12 1.79 0.21* 1.92 0.05**

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Table 4. Phytic acid content (mg/g) of raw flour (E1) and oil extracted (E2) samples of genotypes in different analysis methods E1 2.44 1.39 2.53 0.27 0.03 0.93 2.03 0.71 0.62 2.30 1.94 1.91 2.50 1.10 2.45 2.50 0.35 0.08 2.40 1.50

Wade Method E2 Diff E1-E2 2.51 -0.07 0.00 1.39** 0.00 2.53** 2.42 -2.15** 0.00 0.03 2.46 -1.53** 2.19 -0.16** 1.12 -0.41** 0.42 0.19** 2.47 -0.17** 1.90 0.04 1.42 0.49** 1.57 0.93** 2.43 -1.33** 2.11 0.34** 0.00 2.50** 0.58 -0.22** 2.48 -2.40** 0.00 2.40** 1.37 0.13**

* Statistically significant at 0.05 level. ** Statistically significant at 0.01 level. Diff E1-E2 shows the differences between raw and oil axtracted sample of the same genotype.

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Table 5. Pearson correlation coefficients among phytic acid values from raw (E1) and oil extracted samples (E2) using different colorimetric methods.

AOAC Method Chen Method H-L Method Wade Method

Samples with Low Oil Content (<%3) -0.78** -0.45 0.62** 0.90**

Samples with Normal Oil Content (%3-5) 0.37 -0.09 -0.23 -0.38

Samples with High Oil Content (>%7) 0.65** 0.43 0.35 -0.04

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** Statistically significant at 0.01 level.

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Figure 1. Boxplots demonstrating the variation of phytic acid variation as affected by oil extraction (a) analysis methods (b) and genotypes (c).

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Figure 2. Regression and decision tree plot showing the effect of oil content and analysis

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method used on the phytic acid content differences between raw and oil-extracted samples.

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