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Jan 9, 2018 - CONCLUSION: To our knowledge, this study presents the first statistical model for evaluating glucosinolate content, suggesting a.
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Statistical modeling for estimating glucosinolate content in Chinese cabbage by growth conditions

Running title: Statistical estimation of glucosinolate by growth conditions Do-Gyun Kim, Joon-Yong Shim, Myung-Jun Ko, Sun-Ok Chung, Milon Chowdhury and Wang-Hee Lee*

Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, 34134, Korea.

ABSTRACT BACKGROUND: Glucosinolate in Chinese cabbage (Brassica campestris L. ssp. pekinensis (Lour.) Rupr) has potential benefits for human health, and its content is affected by growth conditions. In this study, we used a statistical model to identify the relationship between glucosinolate content and growth conditions, and to predict glucosinolate content in Chinese cabbage. RESULT: Multiple-regression analysis was employed to develop the model’s growth condition parameters of growing period, temperature, humidity, and glucosinolate content measured in Chinese cabbage grown in a plant factory. The developed model was represented by a second-order multipolynomial equation with two independent parameters: growth duration and temperature (Adjusted R2 = 0.81), and accurately predicted glucosinolate content after 14 days of seeding. CONCLUSION: To our knowledge, this study presents the first statistical model for evaluating glucosinolate content, suggesting a useful methodology for designing glucosinolate-related experiments, and optimizing glucosinolate content in Chinese cabbage cultivation.

Keywords: Chinese cabbage, Glucosinolate, Growth condition, Multiple-regression analysis, Statistical modeling

*Corresponding author: W-H Lee, Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, 34134, Korea. E-mail: [email protected] This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jsfa.8874

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INTRODUCTION Cruciferous plants including Chinese cabbage (Brassica rapa subsp. pekinensis), cabbage (Brassica oleracea var. capitata), broccoli (Brassica oleracea var. italica), mustard (Brassica carinata), and cauliflower (Brassica oleracea var. botrytis) have been a food source used for medicinal or edible purposes. 1 Among them, Chinese cabbage (Brassica campestris L. ssp. pekinensis (Lour.) Rupr) has been a main ingredient in various foods such as salads and fermented foods (e.g., kimchi in Korea and suan-chi in China). It is the most consumed vegetable crop in Asia.2 The quality of Chinese cabbage is traditionally determined by indicators including color, firmness, sweetness, weight, and taste, which are all affected during cultivation, harvest, and post-harvest process. 3 For this reason, various studies have been performed to evaluate trim loss ratio and weight change by temperature,

4

pH-dependent color change of red cabbage, storage temperature and

duration, and ascorbic acid concentration. 5 Recently, functional components in Chinese cabbage have been highlighted to be beneficial to human health, such as glucosinolate inducing detoxifying enzymes, preventing cancer, and reducing oxidative stresses6,7; chlorophyll contributing to reduce cholesterol and boost antioxidant activity8; flavonol contributing to prevent the oxidation of LDL (low density lipoprotein) and reducing serum levels of glucose9; phenol related to lignin re-synthesis, biological membrane protection, and osmotic pressure regulation10; ascorbic acid

involved in

11

maintaining food quality and protecting lipid peroxidation ; various minerals, vitamins (A, B, C, E and K), lutein and carotenes12,13. Among these functional components, glucosinolate is unique to cruciferous vegetables 14 and has become a target for developing functional food. 2,15,16 The amount of glucosinolate in cruciferous vegetables is known to depend on genetic features conditions such as pH, temperature, humidity, and soil nutrients

21–23

17–20

and growing

. Until now, most of studies for

glucosinolates have mainly focused on physiological or metabolic characterization, while studies about relationship with growth condition were relatively less. In addition, only few studies considered more than 2 growth conditions at the same time. Statistical modeling is an effective tool for analyzing empirical data and explains a dependent variable as a function of explanatory variables determined by the statistical significance of their correlation. The model identifies relationships between variables and provides quantitative predictions of the target with a simple model structure compared to other types of models, i.e., mechanistic model .24 Statistical modeling has been widely applied in various fields ranging from humanities and social science to agricultural/food science and engineering. Regression analysis is one of the most popular methods used in developing a statistical predictive model. Various types of regression have been applied: linear, non-linear, multiple, and polynomial regression, which are determined by the characteristics of empirical data, types of mathematic formulas, and number of explanatory variables. 25

Because of its simplicity in model development and powerful predictive ability, regression analysis

was used to develop a thermal degradation model of glucosinolates in red cabbage as a function of

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processing temperature, 26 as well as drying models of golden apple and sultana grapes. 27,28 As aforementioned, most of the current studies regarding glucosinolate in cruciferous plants have concentrated on its biochemical properties, and relatively small number of studies has attempted to investigate the effect of single growth faction on it. However, quantitative evaluation of glucosinolate content by growth conditions is also important to propose practically useful information for cultivating functional Chinese cabbage. Unfortunately, such a study has not been reported yet. In this study, we hypothesized that an adequate form of model could estimate glucosinolate content in Chiness cabbage as a function of significant growth factors. Therefore, the objective of this study was to develop a statistical model for predicting glucosinolate content in Chinese cabbage in response to growth conditions. Specifically, we sought to identify effective growth factors affecting glucosinolate content, find optimal types of regression model, establish a statistical model for predicting glucosinolate content, and test effectiveness of the model. We expect the developed model can propose basic information for optimizing glucosinolate content in Chinese cabbage cultivation which is necessary for producing functional Chinese cabbage.

EXPERIMENTAL Chinese cabbage cultivation Chinese cabbage (Brassica campestris L. ssp. pekinensis (Lour.) Rupr.) was cultivated at a plant factory of Precision Agriculture Laboratory in Chungnam National University (Daejeon, Republic of Korea). Standard growth conditions were controlled as follows29: temperature (20 ± 1℃), humidity (65 ± 5%), CO2 (1000 ± 100 ppm), pH (6.0 ± 5), electrical conductivity (EC) of the nutrient solution (1.2 ± 0.1 dS·m-1), photoperiod (16/8 h [day/night]), photosynthetic photon flux density (PPFD, 200 μmol·m-2·s-1), and LED light source (Red: Blue: White = 11:4:3). Chinese cabbage was grown in 5 small rooms with the same environment for 28 days after seeding and then divided into 5 levels depending on the temperature, humidity, and carbon dioxide (CO2) (Table 1). The ranges of the main growth conditions of temperature, humidity, and CO2 were 14 to 26℃ with an interval of 3℃, 45 to 85% with an interval of 10%, and 400 to 1600 ppm by 100 ppm, respectively. Glucosinolate content was measured at 7, 14, 20, and 28 days after seeding.

Glucosinolate analysis To measure glucosinolate content, 3 healthy and normal-sized leaves were sampled from each of 16 Chinese cabbages separately cultivated in small rooms of the plant factory. As aforementioned, all the

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samples were collected at the same developmental stages which were 7, 14, 20, and 28 days after seeding. Sample leaves were freeze-dried and disrupted in liquid nitrogen to extract glucosinolte from Chinese cabbage leaves. Crude glucosinolates were extracted from freeze-dried products (100 mg) using 4.5 mL of 70% boiling methanol. Then, desulfated glucosinolates were firstly obtained by Diethylaminoethyl (DEAE) anion (GE Healthcare, Uppsala, Sweden) exchange columns, and they were eluted with 2.4 ml of distilled water. To analyze the eluates, the desulfated glucosinolates were filtered through a 0.20 µ of Teflon Polytetrafluoroethylene(PTFE) syringe filter, and the eluates were analyzed immediately by an High-performance liquid chromatography(HPLC) system (model: LC/MS2010A, Shimadzu, Kyoto, Japan) or stored at -20°C until analysis. A C18 column (250 x 2.1 mm, 5 µm, Inertsil ODS-3; GL Sciences, Tokyo, Japan) was used to separate desulfo-glucosinolate using a HPLC system equipped with a diode array detector for qualitative analysis. The HPLC conditions were set as the following: elution solution included water and acetonitrile, flow rate was 1.0 ml/min, UV-VIS detector wavelength was set at 227 nm. The chemical analysis was performed at Plant Environmental Physiology Laboratory in the department of Bio-Environmental Chemistry in Chungnam National University (Daejeon, Republic of Korea).

Database construction The raw database recorded glucosinolate content according to harvesting day, which includes time for growth, temperature, humidity, CO2, and other conditions such as pH, EC, and light source that were not considered in this study because they were fixed variables. Based on preliminary correlation analysis and variable type, we included only the main growth conditions in our model development. The selected explanatory variables to be further used in statistical modeling were growth duration (time for growing after seeding to harvesting), temperature, humidity, and CO2 due to their Pearson correlation coefficient (>0.7) and significance (p-value>0.05).

25

After the initial database was

constructed, data pre-processing was performed to convert raw data into workable data for model development. Independent variables were rounded off to remove errors in measurements and to clearly distinguish the levels of growth conditions. For example, temperature was re-grouped by 2℃unit (Table 2). Then, outliers which were not in the range from 1.5× the first quartile to 1.5× the third quartile were removed.

30,31

In total, four data points were removed, as they were outliers. Ten

data points were excluded from model development to be used in model validation, so the final number of data points used in the modeling was 76. Finally, log transform and standardization were applied to the final raw data to solve problems of irregularity of error and multi-collinearity between explanatory variables in the current database, and to consider the large variation that could occur in different agricultural experiments. 25,32 All analyses were performed using the SAS statistical software package (version 9.3, SAS Institute Inc., Cary, NC, USA).

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Modeling procedure using multiple regression analysis Multiple-regression analysis was used to develop a statistical model for predicting glucosinolate content by explanatory variables (Figure 1). Statistically, multiple-regression analysis is a method for developing an equation to predict dependent variable as a function of more than two explanatory variables. The model has a linear combination of explanatory variables with coefficients determined by least square fitting of empirical data.

25

When the model includes a high order of explanatory

variables, it is a polynomial regression model, but we should note that multicollinearity can be caused by explanatory variables.

33

Multicollinearity indicates an interdependency condition between

independent variables, causing inaccuracy in the estimating coefficient of the regression model. Because multicollinearity can drastically reduce the accuracy of model prediction, it is necessary to remove it. As aforementioned, this multicollinearity was solved by standardization, which was calculated by using mean value (μ) and standard deviation (σ) (Equation 1).

34

Consequently, all the

data were normalized and assigned a value between -1 and 1.

𝐙 = (𝒙 − 𝝁)/σ

(1)

where 𝝁 = mean of the data and σ = standard deviation of the data.

The overall process to develop the statistical model was composed of 5 steps: pre-processing of data, model estimation, multi-collinearity test, residual analysis, and model decision (Figure 1). In the pre-processing stage, data sets were pre-processed to remove outliers and to transform variables to meet criteria of normal distribution. This stage was devoted to constructing an adequate database for model development, as most of the empirical data were not suitable for modeling in its raw form. In the model estimation stage, a multinomial equation was estimated by combining explanatory variables that should satisfy the criteria of coefficient of determination (R2> 0.7) and significance (p-value > 0.05). Then, we checked multicollinearity between the estimated variables. In the residual analysis stage, the scatter plot between the residuals and predicted values by the model was used for diagnosing the linearity, homoscedasticity, and occurrence of outliers. Residuals have been known to randomly scatter without a specific pattern around 0.

25

Observation of a linear or quadratic form in

the residual plot is required to solve this problem. If the scatter plot shows a cone-shaped pattern, we need to transform the dependent variable. Also, in this stage, data over ±2 of the residual values were regarded as outliers and removed.

25

The last step of residual analysis was the normality test, which

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inspected whether the residuals’ distribution against its frequency followed normal distribution (p > 0.05). Satisfaction of this test guarantees that the sample was from a normal population and provides model reliability. In the model decision stage, the most appropriate model was determined using the previously performed statistical tests and the accuracy was validated. In general, the next step was performed as long as the previous step satisfied the statistical criteria (Table 3). When the step did not satisfy the criteria, we returned to the step where the problem occurred.

RESULTS AND DISCUSSION In the model development stage, CO2 was found that it was not a statistically significant and was eliminated from parameters, indicating that inclusion of CO2 could not significantly increase the model accuracy. This does not mean that CO2 is not important factor on plant growth, but statistically insignificant when predicting glucosinolate contents in this study. When plotting studentized residual against predicted value to test the adequateness of the model structure, the quadratic distribution pattern of predicted values was observed, suggesting quadratic terms of independent variables. We designed 3 scenarios that added the quadratic term of each independent variable (day, temperature, and humidity) into our model. The process of developing each model scenario and determining the optimal model structure is shown in Figure 2. In scenario 1, the quadratic term of day was added, but it caused multicollinearity between each day2. This issue was solved by standardization of variables. Humidity was eliminated in the model because of its low significance. The scatter plot of the residual showed a parabolic form, indicating another quadratic term of independent variable. Therefore, we rejected the first scenario. Scenario 2 also showed multicollinearity between the quadratic and linear terms of temperature, forcing standardization of variables. As in scenario 1, humidity was eliminated due to its insignificance, and the scatter plot of the residual having parabolic distribution indicated another quadratic form, resulting in the same failure of scenario 1. Scenario 3 included two quadratic terms—day2 and temperature2—and standardization of variables was exerted to solve the multicollinearity caused by adding two quadratic terms. Based on the significance of coefficient of independent variables, we excluded linear terms of humidity and temperature. Heteroscedastic form was observed in the scatter plot of the residual. Hence, we conducted log transform of dependent variable (glucosinolate) and the final residual plot showed random distribution, meaning the model structure was adequate. The final model included 3 standardized explanatory variables: day, day2, and temp2 for predicting log-transformed glucosinolate content. Finally, the normality test was performed, showing the model satisfies the normal distribution (p > 0.05). In conclusion, scenario 3 was the most adequate multiple regression model for evaluating

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glucosinolate content. The final model was composed of three independent variables: Day2, Day, and Temperature2 (Table 4). These variables corresponded with a previous experimental study where less than 10 days of rainfall and high temperature increased the glucosinolate content.

35

In addition, another study

concluded that high temperature and long day length correlated to the highest concentrations of total glucosinolate.

36

Similarly, the model showed that temperature and growth duration were the factors

that most affected glucosinolate content during cultivation in the plant factory. In particular, growth duration (coded as day) had the lowest p-value, suggesting glucosinolate content was significantly dependent on the time required for growth, and thus, growth duration could be a target for optimizing glucosinolate content. This was also confirmed by comparing the standardized estimate of explanatory variables, which addressed the effect of independent variables on dependent variable. The variable Day has 0.6780 of standardized estimate while other variables (Day2 and Temperature2) have 0.1851 and 0.2386, respectively. Overall, glucosinolate content linearly increased until the last day of this study (28 days). This result is consistent with a previous study where glucosinolate content increased in Chinese cabbages cultivated in a plant factory under various light conditions until the end of the experimental period (56 days of seeding), regardless of light source, period, and light intensity.

23

However, contrary studies reported that glucosinolate content in Brassica species decreased during growth stage. 37–40 This may be due to tissue expansion resulting in a dilution of glucosinolate content, 41

or a variation in glucosinolate concentration due to anatomy (head, stem, leaf, and root), and

environmental conditions. 37,38 In addition, all vegetables in these experiments were cultivated in soil, which differed from our study. This difference confirms that the glucosinolate in Chinese cabbage varies greatly with environmental conditions, suggesting further strictly controlled experiments to explore optimal growth conditions for functional Chinese cabbage. The final regression equation is a multiple-polynomial function with explanatory variables shown in Table 4. In general, the adjusted R-square value is used to evaluate a regression analysis model because R-square tends to increase with the addition of variables, regardless of their significance. In this study, both R-square and adjust R-square were greater than 0.8, indicating that the developed model suitably explained glucosinolate content as a function of growth duration and temperature. In addition, the p-value of the model was less than 0.05, making it significant for explaining variations in glucosinolate content. As dependent variables were logarithmically transformed during the modeling process, we refined the model to the developed equation as shown in Table 5. Additionally, we validated the developed model using the validation data that was excluded from model development. The main validation was based on relative error that could be calculated using Equation 2: ε = |(Y − X)|/X × 100[%] where, Y= the predicted value, X = the measured value

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(2)

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In the database construction step, eight data points were randomly extracted from each growth duration for model validation. The randomly selected data was transformed to be inserted in the model, and relative errors were calculated by growth duration. The model prediction against measured values and model validation using non-modeling data points are shown in Figure 3. Table 6 shows the average relative error by growth duration. The relative error for the whole duration was about 16%, meaning the model could predict glucosinolate content with 84% accuracy from 7 days to 28 days after seeding. In general, the prediction of glucosinolate content became more accurate as growth duration increased. The relative errors were very small (approximately 5%) after 14 days of growth, suggesting the developed model can accurately predict glucosinolate after 14 days of seeding with 95% accuracy. In contrast, the accuracy was relatively low before 14 days, increasing the total relative error of the developed model. This may be due to the fluctuation of total glucosinolate during the first growth stage. This is supported by a previous study showing total glucosinolate content from germination was greater after seven days than after eight days.

40

In

addition, initial growth rates and internal metabolism changed rapidly in the initial growth stage compared to those of the late growth stage, resulting in large variations even in the same Chinese cabbage. In the case of rapeseed, another member of the Brassica family, glucosinolate fluctuation was observed when individual glucosinolates in different anatomical regions decreased and then increased within 14 days because of its complex internal metabolism.

42

In this regard, the high

relative error before 14 days of this study was derived from rapid variations in the initial growth stage. After 14 days of growth, the overall growth rate stabilized, making it possible to validly predict glucosinolate content. Generally, stages of field grown Chinese cabbage is classified into growth stage and heading stage of which overall growth duration is 3~4 months.43 Although there was a similar characteristic that glucosinolate contents were fluctuated in early stage and stabilized in later, it is different between Chinese cabbage cultivated in the field and the plant factory because of cultivation period and environmental conditions. However, in spite of relatively short growth period in the plant factory, the amount of glucosinolate contents of Chinese cabbage was not much different. Hong et al. reported that total glucosinolate content of fully matured Chinese cabbage cultivated in the field was 17 µmol/g DW, which is similar with the total glucosinolate contents in this study.44 Furthermore, controlled growth conditions in the plant factory may be able to enhance synthesis of glucosionlate and boost growth rate, which leads to produce optimized Chinese cabbage in the plant factory than that in the field.45,46 Therefore, the developed statistical model for predicting glucosinolate content according to growth conditions can be a foundation of further researches. In the light of modeling, by considering bottom-up approaches about Chinese cabbage such as mapping of glucosinolate biosynthesis at gene-level47 and identifying related genes to glucosinolate synthesis48, various type of

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model can be developed as long as parameterization of genetic information is possible. For example, a predictive model for glucosionlate content as a function of coded genetic information from different cultivars can be combined with growth conditions. Also, as dynamic modeling has been applied to plant metabolism, metabolic-scale estimation of glucosinolate content would be possible based on unique characteristics caused by different genetic background of cultivars.49

CONCLUSION This study developed a statistical model for predicting glucosinolate content in Chinese cabbage cultivated in the plant factory in response to growth conditions. The results showed that the main factors affecting glucosinolate content were growth duration and temperature, and the developed model was able to explain the variation of glucosinolate content as a function of these factors. The model successfully integrated the experimental data under various conditions with evaluating glucosinolate content, identifying significant growth conditions, and developing a predictive model for glucosinolate content using an adequate statistical modeling framework, which had never been attempted before. Because of its powers in prediction and analysis with low cost and labor, this study suggests a platform of model-based investigation for agricultural products. We expect this study to provide a useful model for optimizing glucosinolate content in Chinese cabbage cultivation and proposing basic information of designing functional Chinese cabbage.

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Table 1. Growth conditions in terms of temperature, humidity, and carbon dioxide

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Temperature

Growth condition Humidity

CO2

Light source (LED color ratio)

R:B:W = 11:4:3

R:B:W = 11:4:3

R:B:W = 11:4:3

PPFD (µmol/m2·s)

200

200

200

EC (dS/m)

1.2 ± 0.1

1.2 ± 0.1

1.2 ± 0.1

pH

6.0 ± 0.5

6.0 ± 0.5

6.0 ± 0.5

CO2 (ppm)

1,000 ± 100

1,000 ± 100

400, 700, 1000, 1300, 1600 ± 100

Humidity (%)

65 ± 5

45, 55, 65, 75, 85 ± 5

65 ± 5

Temperature (°C)

14, 17, 20, 23, 26 ±1

20 ±1

20 ±1

Item

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Table 2.Mean glucosinolate concentrations (µmol/g DW) in Chinese cabbage Day after seeding a,b Glucosinolates

7 day

14 day

20 day

28 day

Glucoraphanin

0.26

0.29

0.86±0.5

6.15±2.6

Sinigrin

NDc

ND

ND

ND

Glucoalyssin

ND

ND

ND

ND

Gluconapin

0.12

0.1

0.18

ND

Glucobrassicanapin

0.24±0.1

0.21

0.7

1.61±0.5

Glucobrassicin

0.15

0.26

0.19

2.22±0.4

4-methoxyglucobrassicin

0.21

0.23

0.25

2.33±0.4

Gluconasturtiin

ND

ND

0.14

ND

Neoglucobrasscin

0.07

0.13

0.04

4.91±1.1

Total

0.94±0.2

1.11±0.1

2.37±0.5

16.69±2.7

a

Growth condition are on the Table 1.

b

Values are mean±standard deviation.

c

ND, not detected.

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Table 3. Parameter descriptions and pre-processing Parameter

Unit

Description

Pre-processing

Day

Unitless

Growth duration

-

Temp

°C

Temperature

Round up by 2

Humi

%

Humidity

Round up by 5

CO2

ppm

Carbon dioxide (CO2)

Round up by 100

Glu

µmol/g DW

Glucosinolate

-

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Table 4. Criteria used for qualifying 5 stages in multiple regression modeling Stage Pre-processing Model estimation

Detailed step Database modification

- Definition of variables

Estimating equation

- R-square of model - Significance of parameters

Multicollinearity Detecting multicollinearity test Residual analysis Model decision

Criteria

- Multicollinearity between variables

Analyzing scatter plot (studentized residual – predicted value) Normality test

- Necessity of order terms - Homoscedasticity of error - Finding and removing outliers

Determining model

- Accuracy of model

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Table 5. Significance of variables used in multiple-polynomial equation for predicting glucosinolate components Glucosinolate (log transformed)

Intercept

Coefficient 0.5705

P-value