Kinetic, thermodynamic and optimization study of the corn germ oil extraction process

Kinetic, thermodynamic and optimization study of the corn germ oil extraction process

Journal Pre-proof Kinetic, thermodynamic and optimization study of the corn germ oil extraction process Olivera S. Stamenkovi´c, Milan D. Kosti´c, Mar...

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Journal Pre-proof Kinetic, thermodynamic and optimization study of the corn germ oil extraction process Olivera S. Stamenkovi´c, Milan D. Kosti´c, Marija B. Tasi´c, Ivica G. Djalovi´c, Petar M. Mitrovi´c, Milan O. Biberdˇzi´c, Vlada B. Veljkovi´c

PII:

S0960-3085(19)31062-4

DOI:

https://doi.org/10.1016/j.fbp.2019.12.013

Reference:

FBP 1203

To appear in:

Food and Bioproducts Processing

Received Date:

30 October 2019

Revised Date:

18 December 2019

Accepted Date:

31 December 2019

Please cite this article as: Stamenkovi´c OS, Kosti´c MD, Tasi´c MB, Djalovi´c IG, Mitrovi´c PM, Biberdˇzi´c MO, Veljkovi´c VB, Kinetic, thermodynamic and optimization study of the corn germ oil extraction process, Food and Bioproducts Processing (2020), doi: https://doi.org/10.1016/j.fbp.2019.12.013

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.

Original research paper

Kinetic, thermodynamic and optimization study of the corn germ oil extraction process

Olivera S. Stamenković,a Milan D. Kostić,a Marija B. Tasić,a, Ivica G. Djalović,b Petar M. Mitrović,b Milan O. Biberdžić,c Vlada B. Veljkovića,d,*

Faculty of Technology, University of Niš, 16000 Leskovac, Bulevar Oslobodjenja 124,

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a

Serbia

Institute of Field and Vegetable Crops, 21000 Novi Sad, Maksima Gorkog 30, Serbia

c

Faculty of Agriculture, University of Priština, 38210 Lešak, Serbia

d

The Serbian Academy of Sciences and Arts, Knez Mihailova 35, 11000 Belgrade, Serbia

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b

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E-mail addresses:

Olivera S. Stamenković: [email protected]

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Milan D. Kostić: [email protected] Marija B. Tasić: [email protected]

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Ivica G. Djalović: [email protected] Petar M. Mitrović: [email protected]

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Milan O. Biberdžić: [email protected] Vlada B. Veljković: [email protected]



Corresponding author: Vlada B. Veljković; Faculty of Technology, University of Niš, 16000 Leskovac, Bulevar Oslobodjenja 124, Serbia, Telephone: +381 16 247 203; Fax: +381 16 242 859; e-mail: [email protected].

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Highlights

Statistical analysis and optimization of the corn germ oil maceration were conducted



Physicochemical properties and fatty acid profile of the obtained oil were determined



The kinetics of the maceration was modeled using different kinetic models



The maceration was shown to be spontaneous, endothermic and irreversible

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Abstract

Corn germ oil (CGO) was recovered from the ground corn germ by maceration using n-

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hexane at the ranges of the temperature and the solvent:germ ratio (SGR) of 20-70 oC and 3:1-10:1 mL/g, respectively. The obtained CGO contained mainly the unsaturated fatty acids

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(87.09±0.37%) with linoleic and oleic acid as the most abundant while the main saturated fatty acid was palmitic acid. The CGO extraction yield was statistically analyzed, modeled

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and optimized combining the response surface methodology with the 33 full factorial design with replication. All individual process variables (maceration temperature, SGR and

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maceration time), the interaction of maceration temperature with SGR and the quadratic terms of maceration temperature and SGR had a statistically significant influence on the

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CGO yield. For the kinetic modeling of the CGO maceration, three models, namely the phenomenological model, the model that included instantaneous washing and diffusion and the diffusional model were tested. Although all models successfully described the kinetics of CGO extraction (MRPD values ≤2%), the diffusional model was less accurate. The thermodynamic analysis of CGO extraction showed that the extraction process was spontaneous, endothermic and irreversible. 2

Keywords: Corn germ; Extraction; Kinetics; Modeling; Response surface analysis; Thermodynamics.

Nomenclature - Modified pre-exponential factor (mLn/gnmin)

b0

- Constant regression coefficient

bi

- Linear regression coefficient

bii

- Quadratic regression coefficient

bij

- Regression coefficients of two–factor interactions (i, j, = 1, 2, 3)

C.V.

- Coefficient of variation

Ea

- Activation energy (J/mol)

F

- Fisher’s test value

f

- Corn germ oil fraction extracted by washing

(1-f)

- Corn germ oil fraction extracted by diffusion

K

- distribution coefficient

k1

- Rate constants of washing (1/min)

k2

- Rate constants of diffusion (1/min)

MRPD

- Mean relative percent deviation (%)

n

- Constant

p

- Probability values

q

- Corn germ oil yield (g/100 g)

qT

- Corn germ oil yield obtained at the temperature of T (g/100 g)

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- Corn germ oil yield obtained at the temperature of To (g/100 g) - Corn germ oil yield at equilibrium (g/100 g)

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q

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qTo

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A’

R

- universal gas constant (8.314 J/molK)

R2

- Coefficient of determination

2 Radj

- Adjusted coefficient of determination

2 Rpred

- Predicted coefficient of determination

s

- Solvent:germ ratio (mL/g)

T

- Absolute temperature (K) 3

- Maceration time (min)

X1

- Maceration temperature (oC)

X2

- Solvent:germ ratio (mL/g)

X3

- Maceration time (min)

Y

- Corn germ oil yield (%)

Go

- Gibbs free energy (J/mol)

Ho

- Enthalpy changes (J/mol)

So

- Entropy changes (J/mol K)

G

- Temperature extraction coefficient

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t

1. Introduction

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Corn (Zea mays L.) is a broadly cultivated crop all over the world due to its multiple uses, for

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instance, as a fodder crop, a raw material in the food and pharmaceutical industry and as a feedstock for biofuel production. This large industrial potential of corn is its kernels rich in

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starch and oil. The oil is predominantly located in the germ (about 80-84% of total kernel oil) (Rajendran et al., 2012), which can be separated from corn kernels by degerming processes.

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Although the corn oil is commonly obtained from the germ, it can also be recovered from whole kernels and by-product streams in ethanol production (Zabed et al., 2017). Corn germ

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oil (CGO) is used in food, pharmaceutical, and cosmetic productions. Recently, it has been considered a feedstock for biodiesel production, particularly in the integrated production with

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ethanol (Veljković et al., 2018). The major fatty acids of CGO are linoleic (39–63%), oleic (20–42%) and palmitic (9–17%) acid. CGO is also characterized by a high content of bioactive compounds, such as tocopherols, tocotrienols, and carotenoids (Navarro et al., 2016). Therefore, despite the high degree of unsaturation, the oxidative stability of CGO is good as a result of the high content of tocopherols, especially -tocopherol (Zheng et al., 2018). A remarkable antioxidant activity (Han et al., 2018) and high DPPH radical 4

scavenging activity of CGO was attributed to the presence of phenolic compounds (Esmat et al., 2018).

The various methods like pressing, solvent, aqueous, enzymatic and supercritical fluid extraction are used for CGO recovery. Among them, the solvent extraction is widely used for CGO production and the most used solvent is n-hexane (Esmat et al., 2018; Han et al., 2018; Li et al., 2015; Moreau and Hicks, 2005). The alcohols (ethanol and isopropanol) and their

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aqueous solutions were also employed as solvents for CGO extraction (Navarao et al., 2016; Ni et al., 2016). Supercritical CO2 extraction has been rarely used for CGO recovery

(Marinho et al., 2019). Crude CGO industrially extracted with n-hexane contains a higher

amount of tocopherols but a lower amount of carotenoids than the oils obtained by alcoholic

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extraction (Navarro et al., 2016). The CGO recovery may be considered an environmentally

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favorable process because of using the corn germ, a by-product of corn-based starch production (Rajendran et al., 2012). Although the use of n-hexane is undesirable due to its

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flammability and toxicity, its advantages as a special-use extracting solvent are numerous, such as good solubility and easy recovering, making this extraction method more suitable,

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compared to the other extraction methods (Sutar and Ghogare, 2017). According to Directive 2009/32/EC, this solvent may be used for lipid extraction in the production of food

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ingredients and foodstuffs. It is a highly efficient extracting solvent for oil dissolution, providing a high oil yield. Besides that, it is suitable for feedstocks with low and or medium

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oil content. Moreover, its reasonable volatility aids easy removal from oil, using low energy. Finally, the capital investment, equipment requirements and operational costs are low.

So far, the extraction of CGO using n-hexane has been studied with the aim of the oil characterization, the identification of the bioactive compounds and the determination of oxidative stability and antioxidant activity. The optimization, kinetic modeling and

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thermodynamic analysis of the CGO maceration have rarely been investigated. Up to date, only Karlović еt al. (1992) studied the influence of the moisture content and the extraction temperature on the kinetics of CGO extraction from corn germ flakes in a tube extractor (percolator). Therefore, the optimization and kinetics of the CGO maceration are needed for the reliable process design in terms of lower solvent and energy consumption that contribute to the sustainability and environmental issues.

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This paper deals with the oil recovery from ground corn germ by maceration using n-hexane as a solvent, providing an engineering analysis of the oil extraction. The fatty acid

composition and physicochemical properties of the obtained CGO were determined. The

extraction process was optimized using the response surface methodology combined with a 33

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full factorial design (FFD) with replication. Furthermore, the kinetic modeling and

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thermodynamic analysis of the CGO maceration were performed. The major goals were to evaluate the influence of the process variables, such as maceration temperature, solvent:germ

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ratio (SGR) and maceration time, on CGO yield, to select the optimal maceration conditions ensuring the highest CGO yield, to model the kinetics and to analyze the thermodynamics of

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the CGO maceration.

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2. Materials and methods

2.1. Materials

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The corn germ was purchased from a factory (ALMEX-IPOK, Zrenjanin, Serbia) producing starch and starch-related products. The germs were kept in paper bags in the darkened room. The moisture content in the corn germ, determined by drying germ to constant weight at 105 o

C, was 3.3 g/100 g. Before oil extraction, the corn germ was ground by a domestic grinder

(Braun, Germany) for 2 min. The CGO content of the corn germ determined by the Soxhlet

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extraction using n-hexane (HPLC grade; Lab–Scan, Ireland) at the SGR of 3:1 mL/g for 3 h was 45.35±0.32 g/100 g dried germ. 2.2. Extraction of corn germ oil 2.2.1. Maceration of corn germ The maceration of ground corn germ was performed in an Erlenmeyer flask (100 mL) equipped with a condenser. The flask was put in a water bath heated at the desired

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temperature (20, 45 or 70 oC). The corn germ (5 g) and an appropriate n-hexane volume (15, 32.5 or 50 mL) were added to the flask. The maceration was performed for 0.5, 1, 3, 5, 7.5

and 10 min with no mixing. The liquid phase was separated from the residual corn germ by

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filtration under vacuum. The corn germ cake was washed twice with the solvent (10 mL), and the filtrates were coupled and evaporated at 50 oC under vacuum to the constant mass. The

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experiments were performed in duplicate.

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CGO yield was outlined as g of oil obtained from 100 g of dry corn germ (g/100 g). All

2.2.2. Optimization of CGO maceration

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The 33 FFD with replication (overall 54 runs) was applied for the statistical analysis of the CGO maceration from the ground corn germ. The process factors included in the

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experimental design were maceration temperature, SGR and maceration time, as it is shown

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in Table 1. In order to compare the obtained results, the same maceration temperatures (20, 45 and 70 oC) and SGRs (3:1, 6.5:1 and 10:1 mL/g) were used as in the case of hempseed oil (Kostić et al., 2013) and white mustard seed oil (Stamenković et al., 2018) extractions. In this way, the effect of the operating temperature was tested in a wide range from the room temperature to close to the boiling temperature of n-hexane. The highest SGR, 10:1 mL/g, is commonly used for determining the oil content in the oilseed while the lowest SGR ensures

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soaking the ground seeds, dissolving the oil and reducing the solvent cost. The maceration times were chosen to include the periods of both fast and slow extraction, as well as the saturation (equilibrium) stage (1, 3 and 5 min). CGO yield was expressed as a function of the maceration temperature, SGR and maceration time by the Eq. (T1) (Table 2). The R-Project software (open-source, http://cran.us.r-project.org) was used for developing

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and testing the quadratic model, performing the analysis of variance (ANOVA) and optimizing the process factors. The model fit adequacy and the significance of the model terms were assessed at the confidence level of 95% (p < 0.05).

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.

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2.2.3. Kinetics of CGO maceration

The three models were used for modeling the kinetics of the CGO maceration:

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phenomenological, instantaneous washing accompanied by diffusion and diffusional model, which are in accordance with the observed extraction mechanism. Namely, the CGO

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extraction involves two mass transfer processes: an initial fast extraction (known as washing), followed by a slow extraction (diffusion). Among them, the phenomenological model is the

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most complex as it includes the dissolution of the CGO from surfaces of ground germ particles (known as washing or fast extraction) and mass transfer of CGO from particle inside

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into the liquid phase (known as diffusion or slow extraction). This model is given by Eq. (T2) (Kostić et al., 2014). The other two models are the simplified forms of the phenomenological model. Namely, if the washing of oil is instantaneous and accompanied by the diffusion of oil from the interior of the germ particles toward the external surface, i.e. k1 >> k 2 , Eq. (T2) is transformed into Eq. (T3). The diffusional model describes the extraction without the washing step ( f  0 ) and is presented by Eq. (T4). 8

The parameters of Eqs. (T2)-(T4) were calculated by the Levenberg-Marquardt nonlinear regression algorithm by the Polymath software using the CGO yields experimentally obtained during the maceration. 2.2.4. Thermodynamic of CGO maceration The changes of the enthalpy (Ho), entropy (So) and Gibbs free energy (Go) were calculated from Van 't Hoff equation, Gibbs free energy and the distribution coefficient by

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Eqs. (T6)-(T8), and the temperature extraction coefficient () was determined by Eq. (T9). 2.2.5. Statistical evaluation

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The models were statistically assessed by the coefficient of determination, R2, and the mean

i 1 n

p ,i

 (q i 1

MRPD 

p ,i

 qa ,i ) 2  qm )

2

100 n q p ,i  qa ,i  q n i 1 a ,i

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R2 

 (q

(1)

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n

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relative percent deviation, MRPD, which were determined by Eqs. (1) and (2):

(2)

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where q p ,i and qa ,i are the predicted and experimental CGO yields, qm is the mean value of

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2 CGO yield, and n is the number of experimental points. Additionally, the adjusted ( Radj ) and

2 predicted ( R pred ) coefficients of determination were calculated. All models’ equations were

evaluated by the Student’s t-test.

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2.3. Analytical methods The physicochemical properties of the CGO extracted under the optimal maceration conditions were analyzed. The CGO viscosity and density were determined at 20 oC using a viscometer (Fungilab S.A., Barcelona, Spain) and pycnometer, respectively. The fatty acid composition of the CGO was determined by gas chromatography as described elsewhere (Stanisavljević et al., 2009). Acid, iodine and saponification values were determined by the standard methods (AOCS, 1980).

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3. Results and discussion 3.1. Characterization of CGO

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The oil yield, physicochemical properties and fatty acid composition of the CGO obtained by the n-hexane maceration under optimal extraction conditions are given in Table 3, where are

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also included the yields and properties of CGO obtained by different extraction methods from

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various corn germ origin. The low acid value of the CGO (2.7±0.2 mg KOH/g) pointed out the presence of a small amount of free fatty acids in the oil (about 1.35 %). The iodine number value of the CGO of 127±1.3 g I2/100 g indicated that the unsaturated fatty acids

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were predominantly present in the oil as shown by the fatty acid composition of the CGO. The acid value of the CGO was in the range of the previously published values for CGO,

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while the iodine value was slightly higher. Based on the available data (Table 3), the

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extraction method, generally, had no significant influence on the iodine value while it slightly influenced the acid value. According to Han et al. (2018), the acid value of a CGO obtained by the aqueous enzymatic extraction was slightly higher than that of the oil obtained by the nhexane extraction, which was attributed to the acidic conditions of the enzymatic extraction. Esmat et al. (2018) reported that the acid value of a CGO obtained by the enzyme extraction

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was influenced by the enzyme type and was slightly lower than that of the oil obtained by the n-hexane extraction. The extraction efficiency of three extraction methods can be compared based on the reported relative CGO yields. Solvent extraction with the relative GCO yield of 67-91% appears the most efficient compared to aqueous enzymatic extraction (10-63%) and supercritical CO2 extraction with or without cosolvent (22-61%). Among the used solvents, n-hexane is the most effective, ensuring more than 90% recovery of the oil from the ground corn germs.

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However, the best extraction method must be selected based on the techno-economic analysis of the overall oil recovery process which will consider the advantages and drawbacks of each

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method.

The CGO consisted mainly (87.09±0.37%) of the unsaturated fatty acids among which the

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predominant were the polyunsaturated fatty acids (56.62±0.13%). The most abundant

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unsaturated fatty acid was linoleic acid (55.05±0.08%), followed by oleic acid (27.19±0.09%). The saturated fatty acids were present in much lower content (13.08±0.04%), and the most common was palmitic acid (10.04±0.16%). Comparing to the literature data,

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this CGO had small amounts of erucic, behenic and lignoceric acids, which have not been reported for CGOs so far, as it can be seen in Table 3. According to the t-test, there were no

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statistically significant differences among the fatty acid compositions of the reported CGO

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(p<0.05). The fatty acid composition of the CGOs appears not to be affected by the extraction method (Table 3). Small differences in the fatty acid content of the CGOs from different word’s regions could be influenced by the grain maturity, genetic variation and geographical origin of corn (Navarro et al., 2016). In general, CGO obtained by either solvent or various enzymes extraction consists mostly of the polyunsaturated fatty acids (total content in the

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range 48.45-56.62%) with linoleic acid as the major fatty acid and the monounsaturated acids (22.6-35.3%) with oleic acid as the most abundant. The CGOs from different regions had a similar total unsaturation degree (127.2-145.3). Also, the oleic-to-linoleic acid ratio (OLR; 0.43-0.74) and the linoleic-to-linolenic acid ratio (LLR; 35.06-105.96), were not affected by the extraction technique. The contents of oleic and linolenic acids in the Chinese CGO were the smallest, causing the lowest OLR value and the

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highest LLR value (Table 3). 3.2. Modeling, analysis and optimization of CGO maceration 3.2.1. Modeling of CGO maceration

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The quadratic model, Eq. (T1), was used for modeling the CGO maceration. The

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acceptability of the model was firstly examined by the sequential sum of squares, lack of fit and model summary statistic tests that aimed at the selection of the model with the highest

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2 2 order, insignificant lack-of-fit and maximized Radj - and R pred -values, respectively. Based on

these tests, the cubic model was disregard as being aliased while the quadratic model was

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2 2 accepted as it had the insignificant lack-of-fit and the highest R 2 -, Radj - and R pred -values

among the tested models (Tables S1-S3, Supplementary material). Consequently, the

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quadratic model was further evaluated by the ANOVA, and the results (Table 4) pointed out

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the significance of the quadratic model through its high F-value of 111.33, which was larger than the critical one (1.91), and its low p-value (<0.0001). The adequacy and good fit of the model were confirmed by the insignificant lack–of–fit (p < 0.05), the R 2 value (0.958) close 2 2 to 1 and the agreement between R pred (0.938) and Radj (0.949) values. Besides, the model

accuracy was established by a small coefficient of variation ( C.V . = 1.04%) and a high

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adequate precision (36.38 >> 4). The MRPD-value of only ±0.74% (based on 54 data) also verified the reliability of the quadratic model. The ANOVA results were validated by the normally distributed population of the experimental data and Cook's distance which was significantly below the limit value (0.94). After calculating the parameters of the quadratic model, the oil yield was related to the process variables (in actual and codded values) by Eqs. (3) and (4): -

Actual factors

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Y  32.653  0.074  X 1  0.733  X 2  0.505  X 3  0.0056  X 1 X 2  0.0005  X 1 X 3  0.0009  X 2 X 3  0.001  X  0.046  X  0.023  X 2 1

(3)

2 3

Coded factors

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-

2 2

Y  38.29  1.35  X 1  1.41  X 2  0.34  X 3 

(4)

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0.52  X 1 X 2  0.032  X 1 X 3  0.008  X 2 X 3  0.65  X 12  0.57  X 22  0.15  X 32

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3.2.2. Statistical analysis of CGO maceration

The ANOVA results indicated that all the individual process factors (X1, X2, and X3), the

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interaction of the maceration temperature with the SGR (X1 X2) and the quadratic terms of the 2

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maceration temperature and the SGR (X1 and X2 ) had the statistically significant influence

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on the CGO yield while the impacts of the other two interactions and the quadratic term of

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the maceration time were not statistically significant at the confidence level of 95%. Based on the F-values of the process factors, the greatest and almost the same impact on CGO yield had the SGR and the maceration temperature while the influence of the maceration time was quite smaller. The most significant influence of the SRG on oil yield has also been reported for the oil extraction from lavender croton (Jiyane et al., 2018) and white mustard (Stamenković et al., 2018) seeds. According to other authors, the greatest effect on 13

oil yield had the extraction temperature (Kostić et al., 2013; Sodeifian et al., 2018; Stroescu et al., 2013), the extraction time (Azmir et al., 2014), the particle size (Stanisavljević et al., 2007; Uzoh et al., 2014) and the moisture content of the extracted material (Karlović et al., 1992). The impact of the process variables on oil yield varies with the seed morphology and the applied ranges of the process variables. The influence of the individual process factors on CGO yield obtained from the ground germ by maceration could be considered in the examined design space from the perturbation plot

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(Figure 1). The perturbation plot presents the CGO yield as a function of all process factors by varying one process factor in its whole range while the others are kept constant. The slope of all factor’s curvature indicated their positive effect on CGO yield. The relatively flat curve

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of the maceration time showed its lower effect on CGO yield, suggesting that the CGO was

mostly recovered in the initial extraction stage. The curves corresponding to the maceration

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temperature and the SGR had a steep slope confirming the ANOVA results that their

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influence on CGO yield was more notable. The influence of the maceration temperature on CGO yield was less pronounced at lower temperatures and became more significant at the temperatures above 45 oC. CGO yield increased rapidly with increasing the solvent amount

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up to the SGR of 6.5:1.

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Based on the perturbation plot, the axes of the 3D response surface plot were selected (Figure 2). This plot presents the combined effect of the most influential individual factors,

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namely the maceration temperature and the SGR, on CGO yield at the maceration time of 10 min.

CGO yield increased with the increase of the maceration temperature. This effect was more pronounced at the larger solvent amount and higher temperatures, which could be attributed to the increased CGO solubility, reduced solvent viscosity and enhanced diffusion coefficient

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of the oil from the germ particles. The increase of the solvent amount generally enhanced CGO yield, especially at higher maceration temperatures, as a result of the positive influence of temperature on extraction efficiency and better oil dissolution in a higher solvent amount. However, at lower temperatures, CGO yield increased at smaller solvent amounts and practically remained constant because of the limited mass transfer of the oil from the germ particles. 3.2.3. Optimization of CGO maceration

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The optimal extraction conditions for attaining the maximum CGO yield were found by

solving Eq. (3). The software proposed 10 combinations of the extraction conditions with the desirability level of 1.0 that enabled reaching the CGO yield in a narrow range of 41.71-41.82

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g/100 g. The maceration temperature and SGR in all combinations were almost unchangeable

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and very close to 70 ◦C (range 69.8–70 oC) and 10:1 mL/g (range 9.8:1-10:1 mL/g), respectively while the maceration time was in the range of 8.19–10 min. Therefore, the

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optimal reaction conditions were as follows: the maceration temperature of 70 oC, the SGR of 10:1 mL/g and the maceration time of 10 min. Under these process conditions, the predicted

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maximum CGO yield of 41.82 g/100 g was close to the experimentally determined CGO yield of 41.34 g/100 g. The maximum CGO yield corresponded to 91.2% of the CGO yield

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achieved by the Soxhlet extraction. The lower extraction efficiency of the hempseed oil extraction by n-hexane compared to the Soxhlet extraction (86.7%) was reported by Kostić et

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al. (2013).

3.3. Kinetics of CGO extraction 3.3.1. The variation of CGO yield with maceration time The changes of CGO yield with the progress of the ground corn germ maceration by nhexane at different SGRs and maceration temperatures are shown in Figure 3. In the initial

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extraction period (up to 1 min) the CGO yield increased very fast, afterward it slowed down (up to approximately 3 min) and ultimately reached a plateau. The observed fast extraction or washing of CGO is typical for oil maceration from plant materials and is a consequence of effective breakage of germ structures (plumule, scutellum, and radicle) containing the oil by grinding. The lower extraction rate in the later extraction period (slow extraction or diffusion) was limited by oil diffusion from the germ particles and finally, the extraction reached saturation (equilibrium). The higher CGO yields were obtained at higher SGRs due to an

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increased capability of a higher solvent volume for oil dissolution. At a constant solvent amount, the higher CGO yield was achieved at a higher maceration temperature because of

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an increased solubility of the oil in n-hexane.

3.3.2. Phenomenological model

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According to the mechanism of the CGO maceration, the kinetics of the CGO extraction was

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first described by the most complex phenomenological model presented by Eq. (T2). The values of the kinetic parameters q , f , k1 and k 2 are shown in Table 5. The accuracy and

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significance of the kinetic model were verified by the R2- and p-values close to 1 and below 0.0001, respectively for all performed experiments. The validity of the developed kinetic

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model was confirmed by a very good agreement between the actual and predicted values of CGO yield as evidenced by the low MRPD values (±0.6-1.4%, Table 5).

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The influence of the maceration temperature and the SGR on the kinetic parameters are further analyzed. The CGO yield at saturation, q , as well as the washing and diffusion rate constants, k1 and k 2 , were higher at higher maceration temperatures and SGRs due to their positive effects on CGO solubility and mass transfer coefficient. However, the fraction of washable CGO, f , did not depend on these extraction conditions as its value was constant

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(0.8750.012). The similar effect of the maceration temperature and the solvent amount on the parameters of the phenomenological model was reported for the maceration of hempseed by n-hexane (Kostić et al., 2014).

The kinetic constants were correlated with the maceration temperature and the SGR using the modified Arrhenius equation, Eq. (T5) (Kostić et al., 2014). The values of the washing and diffusion rate constants ( k1 , k 2 ), were calculated by Eqs. (T2) and (T5), as well as the

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MRPD-values for the rate constants (Table 6). As can be concluded, the activation energy

and the n -values were higher for the diffusion than for the washing, thus indicating that both the maceration temperature and the SGR more significantly affected the oil diffusion. The

-p

obtained activation energy values were in the range already reported for the oil extraction

re

from other plant materials (Kostić et al., 2014; Stamenković et al., 2018).

lP

The accuracy of the model was checked by comparing the experimental CGO yield with the CGO yield calculated using Eqs. (T2) and (T5). In these calculations, the rate constants, k1

na

and k 2 , were calculated with the values of the parameters taken from Table 6 while f was 0.875. Figure 3 illustrates a good agreement of the model with the experiment. The MRPD-

Jo

model.

ur

value (based on 108 data) was ±1.0%, confirming the acceptability of the proposed kinetic

3.3.3. Simplified phenomenological model As can be concluded from Table 5, the washing rate constant was much larger than the diffusion rate constant, i.e. k1 k2 = 95-191, depending on the extraction conditions. Therefore, the phenomenological model could reasonably be simplified by considering the

17

washing step infinitely fast, i.e. instantaneous, leading to the three-parameter kinetic model, Eq. (3). The calculated values of the kinetic parameters are given in Table 7. The reliability of the kinetic model was confirmed by the R2- and p-values, as well as MRPD-values that were slightly higher (0.7-2.0%), compared to the phenomenological model (Table 5). The effect of the maceration conditions on the kinetic parameters (CGO yield at saturation, q , and the diffusion rate constant, k 2 ) was the same as in the case of the phenomenological

ro of

model. In all experiments, the fraction of washable CGO, f , was almost constant (average value: 0.8790.008). Kostić et al. (2014) have also reported a constant washable fraction for the hempseed maceration. The diffusion rate constant was correlated with the maceration

temperature and the SGR by the modified Arrhenius equation, Eq. (T5); the values of the

-p

model parameter are given in Table 6. The adequacy of this simplified kinetic model was

3.3.4. Diffusional model

lP

MRPD-value (1.1%, 108 data).

re

almost the same as that of the phenomenological model, which was confirmed with its similar

na

The diffusion model is simpler in comparison with the two previously presented kinetic models as it assumed that washing does not occur (f = 0), implying that CGO yield increased

ur

exponentially with time due to the oil diffusion through germ particles, Eq. (T4). The

Jo

parameters of this kinetic model are given in Table 8. The kinetic parameters increased with increasing both the maceration temperature and the SGR as was explained in the case of the phenomenological model. The calculated values of CGO yield at saturation (equilibrium) were like those obtained for the phenomenological model and the model that included instantaneous washing and diffusion and agreed with the experimentally obtained values. On the other hand, the diffusional model predicted the higher values of the diffusion rate constant

18

(6 to 17 times higher) compared to the two more complex kinetic models. The differences in k2 value were less pronounced at higher maceration temperatures and solvent amounts. The MRPD-value of the diffusional model (2.0%; 108 data) indicated a good fitting of the experimental CGO yield data but the R2-values were much lower (0.505-0.803) and p– values were higher, compared to the two more complex kinetic models.

ro of

3.4. Thermodynamic analysis of CGO maceration The thermodynamic parameters of the CGO maceration, such as the distribution coefficient (K), the changes of enthalpy (Ho), entropy (So) and Gibbs free energy (Go) and the

-p

temperature extraction coefficient (), were calculated based on the corn germ oil yield and

re

the overall CGO content in the ground germ using Eqs. (T6)–(T9).

The values of the distribution coefficient (or equilibrium constant) calculated at various

lP

maceration temperatures and SGRs, given in Table 9, indicated the increase of the

na

distribution coefficient with increasing both process factors.

The Ho and So were first calculated from the slope and the intercept of the linear

ur

dependence of ln K on 1/ T , respectively (Figure 4), and then the Go was determined by

Jo

Eq. (T6). The calculated values of Ho, So and Go are presented in Table 10.

The values of Ho and So for the CGO maceration using n-hexane were positive under the applied process conditions, indicating that the process was endothermic and irreversible, which was in line with the results of the other authors for oil extraction from different plant oily materials (Amarante et al., 2014; Nwabanne, 2012; Kostić et al., 2014; Stamenković et al., 2018; Sulaiman et al., 2013). The Ho for the CGO extraction increased with increasing 19

the SGR, pointing out that more energy was required for oil extraction with a higher solvent amount. The So-values for the CGO extraction agreed with those reported for castor cake (Amarante et al., 2014), hempseed (Kostić et al., 2014) and mustard (Stamenković et al., 2018) oil extraction, but lower than those for n-hexane oil extraction from pumpkin (Nwabanne, 2012) and Terminalia catappa (Menkiti et al., 2015) seeds. The Go-values for the CGO maceration were negative, indicating that the process was spontaneous and feasible. Additionally, the Go-values decreased with the increase of both the maceration temperature

ro of

and the SGR so the spontaneity of the CGO maceration was favored.

The temperature extraction coefficient,  , was calculated using Eq. 5, which was obtained by linearization of Eq. (T9):

The slope of the dependence of ln qT on

T  oC

-p

T ln  10

(5)

re

ln qT  ln qTo 

10

, presented in Figure 5, was used for the

lP

calculation of  ; the obtained values are given in Table 11. The CGO yield enhanced by a factor 1.011-1.019 for the temperature increase of 10 oC. The obtained values agreed with the

na

previously reported values for the oil extraction from white mustard seeds (1.011-1.040) (Stamenković et al., 2018), hempseed (1.012–1.027) (Kostić et al., 2014), olive cake (1.02)

ur

(Meziane and Kadi, 2008), rubber (1.02-1.10) and melon (1.05-1.12) (Ibemesi and Attah, 1990) seeds. The CGO yields at saturation calculated from the temperature extraction

Jo

coefficient were close to the experimentally obtained values.

4. Conclusions

CGO was obtained from ground corn germ by maceration using n-hexane. The physicochemical properties and fatty acid composition of CGO were close to the previously 20

reported. The most represented fatty acids of the CGO were unsaturated fatty acids with linoleic and oleic acid as predominant. The quadratic model, which adequacy was confirmed by statistical tests, was used for modeling and optimizing the maceration process. The optimum maceration conditions were determined to be the maceration temperature of 70 oC, the SGR of 10:1 mL/g and the maceration time of 10 min. These extraction conditions provided the maximum CGO yield (41.34 g/100 g) which was 91.2% of the CGO yield achieved by the Soxhlet extraction. The phenomenological model and the model including

ro of

instantaneous washing and diffusion were shown to be reliable for modeling the kinetics of the CGO maceration. The CGO maceration was endothermic, irreversible and spontaneous.

The CGO recovery is a suitable process regarding sustainability since the corn germ is a by-

-p

product of corn-based starch production. Despite the diverse aspects of using n-hexane as the extracting solvent, the techno-economic analysis is needed for a complete evaluation of the

lP

re

CGO recovery by maceration.

na

Declarations of interest: none

ur

Acknowledgement

Jo

This work has been funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Serbia (Project III 45001).

21

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http://dx.doi.org/10.1016/j.indcrop.2013.11.009

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surface methodology and its fatty acids constituents. Ind. Crop. Prod. 52, 405– 412.

Esmat, A.Y., Hassan, R.E., Abo-ElWafa, G.A., Abou-ElSoud, M.M., Megahed, M.G., 2018. Physicochemical characteristics and oxidative stability of Egyptian corn germ oil produced

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by aqueous enzymatic extraction, Grasas Y Aceites 69, e275. https://doi.org/10.3989/gya.0112181

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Han, C., Liu, Q., Jing, Y., Wang, D., Zhao, Y., Zhang, H., Jiang, L., 2018. Ultrasoundassisted Aqueous Enzymatic Extraction of Corn Germ Oil: Analysis of Quality and

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Antioxidant Activity. J. Oleo Sci. 67, 745-754. https://doi.org/10.5650/jos.ess17241 Ibemesi, J.A., Attah, J.C., 1990. Temperature effects on the extraction of rubber and melon

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seed oils. J. Am. Oil. Chem. Soc. 67, 443–445. https://doi.org/10.1007/BF02638958 Jiyane, P.C., Tumba, K., Musonge, P., 2018. Optimisation of Croton gratissimus oil extraction by n-hexane and ethyl acetate using response surface methodology. J. Oleo Sci. 67,

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Karlović, Dj., Sovilj, M., Turkulov, J., 1992. Kinetics of oil extraction from corn germ. J. Am. Oil Chem. Soc. 69, 471-476. https://doi.org/10.1007/BF02540952 Kostić, M.D., Joković, N.M., Stamenković, O.S., Rajković, K.M., Milić, P.S., Veljković, V.B., 2013. Optimization of hempseed oil extraction by n-hexane. Ind. Crop. Prod. 48, 133– 143. http://dx.doi.org/10.1016/j.indcrop.2013.04.028

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Kostić, M.D., Joković, N.M., Stamenković, O.S., Rajković, K.M., Milić, P.S., Veljković, V.B., 2014. The kinetics and thermodynamics of hempseed oil extraction by n-hexane. Ind. Crop. Prod. 52, 679–686. http://dx.doi.org/10.1016/j.indcrop.2013.11.045 Li, H., He, Y., Zheng, H., Chen, S., 2015. Study on the parameters of extrusion pretreatment of corn germ with semi-wet milling in solvent oil extraction. Cereal Chem. 92, 411-417. https://doi.org/10.1094/cchem-04-14-0084-r Marinho, C.M., Lemos, C.O.T., Arvelos, S., De Souza Barrozo, M.A., Hori, C.E., Watanabe, É.O., 2019. Extraction of corn germ oil with supercritical CO2 and cosolvents. J. Food Sci.

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Technol. 56, 4448–4456. https://doi.org/10.1007/s13197-019-03923-2 Menkiti, M.C., Agu, C.M., Udeigwe, T.K., 2015. Extraction of oil from Terminalia catappa L.: Process parameter impacts, kinetics, and thermodynamics. Ind. Crop. Prod. 77, 713–723. http://dx.doi.org/10.1016/j.indcrop.2015.08.019

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Meziane, S., Kadi, H., 2008. Kinetics and thermodynamics of oil extraction from olive cake.

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Navarro, S.L.B., Capellini, M.C., Aracava, K.K., Rodrigues, C.E.C., 2016. Corn germ-bran

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oils extracted with alcoholic solvents: Extraction yield, oil composition and evaluation of protein solubility of defatted meal. Food Bioprod. Process. 100, 185–194.

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http://dx.doi.org/10.1016/j.fbp.2016.06.009 Ni, S., Zhao, W., Zhang, Y., Gasmalla, M.A.A., Yang, R., 2016. Efficient and eco-friendly

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extraction of corn germ oil using aqueous ethanol solution assisted by steam explosion. J. Food Sci. Technol. 53, 2108-2116. https://doi.org/10.1007/s13197-016-2189-9. Nwabanne, J.T., 2012. Kinetics and thermodynamics study of oil extraction from fluted pumpkin seed. Int. J. Multidisc. Sci. Eng. 3, 11-15. Rajendran, A., Singh, R.N., Mahajan, V., Sapna, C.D.P., Kumar, R.S., 2012. Corn oil: an emerging industrial product. Technical Bulletin 8, Directorate of maize research, New Delhi.

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Sodeifian, G., Ardestani, N.S., Sajadian, S.A., 2018. Extraction of seed oil from Diospyros lotus optimized using response surface methodology. J. For. Res. 30, 709–719. https://doi.org/10.1007/s11676-018-0631-8 Stamenković, O.S., Djalović, I.G., Kostić, M.D., Mitrović, P.M., Veljković, V.B., 2018. Optimization and kinetic modeling of oil extraction from white mustard (Sinapis alba L.) seeds. Ind. Crop. Prod. 121,132-141. https://doi.org/10.1016/j.indcrop.2018.05.001 Stanisavljević, I.T., Lazić, M.L., Veljković, V.B., 2007. Ultrasonic extraction of oil from tobacco (Nicotiana tabacum L.) seeds. Ultrason. Sonochem. 14, 646–652.

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Comparison of techniques for the extraction of tobacco seed oil. Eur. J. Lipid. Sci. Technol. 111, 513–518. https://doi.org/10.1002/ejlt.200800232

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Stroescu, M.S., Stoica-Guzun, A., Ghergu, S., Chira, N., Jipa, I., 2013. Optimization of fatty acids extraction from Portulaca oleracea seed using response surface methodology. Ind.

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Crop. Prod. 43, 405-411. http://dx.doi.org/10.1016/j.indcrop.2012.07.051 Sulaiman, S., Aziz, A.R.A., Aroua, M.K., 2013. Optimization and modeling of extraction of

lP

solid coconut waste oil, J. Food. Eng. 114, 228–234. https://doi.org/10.1016/j.jfoodeng.2012.08.025

Devel. 4, 23-27.

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Sutar, M.S., Ghogare, A.B., 2017. Solvent extraction of oil and its economy. Int J. Sci. Res

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Uzoh, C.F., Onukwuli, O.D., Nwabanne, J.T., 2014. Characterization, kinetics and statistical screening analysis of gmelina seed oil extraction process. Mater. Renew. Sustain. Energ.

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3:38. https://doi.org/10.1007/s40243-014-0038-1 Veljković, V.B., Biberdžić, M.O., Banković-Ilić, I.B., Djalović, I.G., Tasić, M.B., Nježić, Z.B., Stamenković, O.S.,2018. Biodiesel production from corn oil: A review. Renew. Sust. Energ. Rev. 91, 531–548. https://doi.org/10.1016/j.rser.2018.04.024 Zabed, H., Boyce, A.N., Sahu, J.N., Faruq, G., 2017. Evaluation of the quality of dried distiller's grains with solubles for normal and high sugary corn genotypes during dryegrind

24

ethanol production. J. Clean. Prod. 142, 4282–4293. https://doi.org/10.1016/j.jclepro.2016.11.180 Zheng, L., Ji, C., Jin, J., Xie, D., Liu, R., Wang, X., Jin, Q., Huang, J., 2018. Effect of Moisture and Heat Treatment of Corn Germ on Oil Quality. J. Am. Oil. Chem. Soc. 95, 383–

Jo

ur

na

lP

re

-p

ro of

390. https://doi.org/10.1002/aocs.12032

25

re

-p

ro of

FIGURE CAPTIONS

lP

Figure 1. Perturbation plot for CGO yield (X1 – maceration temperature, X2 – SGR and X3 –

Jo

ur

na

maceration time).

26

ro of

-p

Figure 2. Response surface plot for CGO yield as a function of maceration temperature and

Jo

ur

na

lP

re

(X1) and SGR (X2) at the maceration time (X3) of 10 min.

27

ro of -p re lP na ur Jo

Figure 3. The variation of CGO yield with time at SGRs (mL/g): 3:1 (o), 6.5:1 (∆) and 10:1 (□) at maceration temperatures: a) 20 oC, b) 45 oC and c) 70 oC. Lines represent the phenomenological model, Eq. (T2).

28

ro of

Figure 4. The dependence ln K on 1/T at various SGRs (mL/g): 3:1 (o), 6.5:1 (∆) and 10:1

Jo

ur

na

lP

re

-p

(□).

Figure 5. The dependence ln qT on T 10 at various SGRs (symbols as in Fig. 4).

29

Table 1. The matrix of 33 full factorial design with replication and the CGO yield.* Matrix of experiments Coded factors

*

Uncoded factors

Actual

X2

X3

X1

X2

X3

Ser. 1

Ser. 2

-1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1

-1 -1 -1 0 0 0 1 1 1 -1 -1 -1 0 0 0 1 1 1 -1 -1 -1 0 0 0 1 1 1

-1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1

20 45 70 20 45 70 20 45 70 20 45 70 20 45 70 20 45 70 20 45 70 20 45 70 20 45 70

3 3 3 6.5 6.5 6.5 10.0 10.0 10.0 3 3 3 6.5 6.5 6.5 10.0 10.0 10.0 3 3 3 6.5 6.5 6.5 10.0 10.0 10.0

5 5 5 5 5 5 5 5 5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 10 10 10 10 10 10 10 10 10

35.86 36.08 36.93 37.75 37.78 40.17 37.24 38.61 41.29 36.37 36.52 37.68 37.54 38.27 40.00 37.87 39.16 41.66 36.52 36.33 37.67 37.75 38.19 40.20 38.45 39.10 41.34

35.35 36.05 37.16 36.59 37.18 40.42 37.59 38.83 40.58 36.02 36.60 37.40 37.23 37.61 41.20 37.55 39.84 41.68 36.13 36.75 38.22 37.91 38.11 41.35 38.32 39.61 41.70

na

ur

-p

re

Predicted (quadratic model) 35.62 35.83 37.35 37.07 37.80 39.84 37.39 38.64 41.19 36.13 36.31 37.79 37.59 38.29 40.29 37.92 39.13 41.65 36.34 36.49 37.95 37.81 38.48 40.45 38.15 39.33 41.82

ro of

X1

Jo

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Response (CGO yield, g/100 g)

lP

Run

X 1 - temperature, oC, X 2 - SGR mL/g, X 3 - maceration time, min,

30

oo

f

Table 2. Model equations used for statistical optimization, kinetics and thermodynamics. Model Equation Quadratic

Y  b0  b1 X1  b2 X 2  b3 X 3  b12 X1 X 2  b13 X1 X 3  b23 X 2 X 3  b11 X12  b22 X 2 2  b33 X 32

Kinetics b

Phenomenological

q  q 1  f  e k1t  1  f   e k2 t 

Instantaneous washing accompanied by diffusion

q  q 1  1  f   e k2 t 

Jo ur

Modified Arrhenius equation

Thermodynamics Van 't Hoff c equation

Gibbs free energy

e-

T1

T2

T3

Pr

q  q 1  e k2 t 

na l

Diffusional model

pr

Statistical optimization a

Numeration

 E  k  A ' s n  exp   a   RT 

T4 T5

H o S o  RT R

T6

Go  H o  T  S o

T7

ln K  

31

K

The temperature extraction coefficient

qT  qTo   T 10

T8

T9

pr

oo

f

q q0  q

X 1 - temperature, X 2 - SGR, X 3 - maceration time, Y - CGO yield (%), and b0 , bi , bii and bij ( i = 1, 2, 3 and i  j  3 ) - the constant, linear,

e-

a

The distribution coefficient

quadratic and two–factor interactions regression coefficients, respectively; b q - CGO yield (g/100 g), q - CGO yield at equilibrium (g/100 g), f - CGO fraction extracted by washing, 1  f  - CGO fraction extracted

Pr

by diffusion, k1 and k2 - the rate constants of washing and diffusion (1/min), respectively, t - the maceration time (min), A ' - the modified pre-

na l

exponential factor (mLn/gnmin), s - SGR (mL/g), n - constant, T - absolute temperature (K), Ea - the activation energy (J/mol) c K - the distribution coefficient, T - absolute temperature (K), R - the universal gas constant, Ho -the enthalpy changes (J/mol), So - the entropy changes (J/mol K), Go - Gibbs free energy (J/mol), qT and qTo - CGO yields (g/100 g) obtained at the temperatures of T and To (in oC),

Jo ur

respectively, -the temperature extraction coefficient.

32

Aqueous enzymatic extraction

Technology Maceration Oil yield, 41.34 g/100 g 91.2f

Ethanol Not specified -

Soxhlet 43.83 ±2.16 -

Isopropanol

Industrial Maceration extraction 10.4 ±0.4 83f 87f

Ethanol +Water

-

67f

-

-

-

-

-

-

-

-

5.6 ± 0.13 107.6 ±0.91

-

C17:0 C17:1 C18:0

0.09 0.05 2.33

C18:2 C18:3

nd nd 1.43 ±0.014 27.19 ±0.09 55.05 ±0.08 1.57 ±0.01

nd nd 1.95 ±0.01 34.71 ±0.03 48.3 ±0.2 0.97 ±0.01

0.13

33.0

52.55 0.95

Proteased

Proteasee

Cellulasec +proteased

Cellulasec +proteasee

Maceration

Laboratory scale unit

16.50 ± 1.79 38.0g

4.44 ± 0.30 10.2g

5.97 ± 0.32 13.8g

27.34 ± 0.71 63.0g

22.76 ± 1.51 52.5g

11.01

13.54

13.81

30.8h

44.8h

61.4h

-

1.90 -6.83f 6.3 -22.6f,g -

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

5.36 ±0.03 104.82 ±3.22

4.86 ±0.02 107.09 ±2.93

2.78 ±0.08 108.34 ±0.79

4.99 ±0.12 111.80 ±7.48

5.20 ±0.02 103.88 ±0.11

-

-

-

-

-

-

-

-

-

-

-

116.00 ±0.03

115.9 ±0.7

115.0 ±2.0

3.36 ±0.13 102.72 ±4.01

-

-

-

-

-

-

-

-

-

-

-

-

nd 12.80 ±0.27 0.17 ±0.01 nd nd 1.56 ±0.06 22.09 ±0.66 51.56 ±0.52 0.50 ±0.02

0.02 11.38

0.01 9.80

0.25 11.47

0.01 10.44

0.01 10.67

0.11

0.10

0.01

0.13

0.10

nd 11.7 -13.8 nd

nd 11.4 ±1.1 nd

nd 13.1 ±2.7 nd

nd 12.8 ±0.4 nd

0.02 0.16 2.07

0.09 0.19 2.26

0.01 0.35 2.04

0.09 0.16 2.32

0.09 0.19 2.21

nd nd 1.5-1.7

32.64

32.79

33.27

32.24

32.91

51.64

53.11

51.05

52.88

52.30

1.07

1.04

0.99

1.11

0.99

nd nd 1.2 ±0.0 34.8 37.3 -37.0 ±0.0 47.5-48.7 49 ±1.0 0.7-0.8 1.0 ±0.0

nd nd 1.6 ±0.5 35.7 ±1.7 48.7 ±1.7 1.7 ±0.1

nd nd 1.2 ±0.0 37.2 ±0.0 47.6 ±0.2 0.9 ±0.1

na l

0.01 10.34

Cellulasec

Supercritical CO2 extraction with/without cosolvent None Hexane Acetone Ethanol

51.7 -67.5f -

-

-

nd 12.7 ±0.2 nd

nd 14.0 ±0.1 nd

134.21 nd 14.27±0.03 14.0 ±0.7 nd nd

nd 14.0 ±1 nd

nd nd 1.8 ±0.1 32.4 ±0.5 51.1 ±0.7 2±1

nd nd 1.95 ±0.01 34.71 ±0.03 48.3 ±0.2 0.97 ±0.01

nd nd 1.93±0.01

nd nd 2.16 ±0.04 35.30 ±0.04 47.50 ±0.90 0.95 ±0.09

Jo ur

C18:1

-

83f

Pr

Density (20 921.3 o C), kg/m3 ±0.2 Viscosity 92.8 (20 oC), ±0.8 mPas Acid value, 2.7 mg KOH/g ± 0.2 Iodine 127 116.1 value, g ±1.3 ±0.3 I2/100 g Moisture 0.1 content, % ± 0.02 Fatty acid composition, % C14:0 nd nd C16:0 10.04 14.0 ±0.16 ±0.1 C16:1 nd nd

116.1 ±0.3

Isopropanol Cellulaseb +Water Maceration

pr

n-Hexane

Relative oil yieldf, %

oo

Solvent extraction

e-

Property

f

Table 3. The physicochemical properties and fatty acid composition of CGO.a

nd nd 2.1 ± 0.02 34.46±0.02 34.9 ±0.4 48.32±0.04 47.91 ±0.02 1.02±0.02 1.1 ±0.01

33

C24:0 SFA MUFA PUFA ALC TUD OLR LLR Reference

nd

nd

nd

0.02

nd

0.33 ±0.01 0.34 ±0.01 nd

nd

0.01

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

15.59 ±0.11 34.71 ±0.03 49.27 ±27 17.7 134.3 0.72 49.79 Han et al. (2018)

13.30

14.5±0.3

33.19

32.4±0.5

53.50

53.1±1.7

17.8 141.1 0.63 55.31 Esmat et al. (2018)

17.8 141.4 0.61 47.6 Navarro et al. (2016)i

15.59 16.2 ±0.11 ±0.04 34.71 34.46 ±0.03 ±0.02 49.27 49.34 ±27 ±0.06 17.7 17.7 134.3 134.2 0.72 0.71 49.79 47.37 Navarro et al. (2016)j

16.1 ±0.72 34.9 ±0.4 49.01 ±0.03 17.7 134.0 0.73 43.55

16.16 ±0.14 35.30 ±0.04 48.45 ±0.99 17.7 133.2 0.74 50

0.61

0.54

0.62

0.52

nd

nd

nd

nd

f

nd

oo

nd

0.87

0.01

0.01

0.01

0.01

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

14.7 ±4.5 35.7 ±1.7 50.4 ±1.8 17.9 138.2 0.73 28.65

14.0 ±0.6 37.2 ±0.0 48.5 ±0.3 17.7 135.1 0.78

pr

C22:1

0.53

nd

nd

nd

nd

nd

nd

nd

14.69±0.48

13.50

12.77

14.31

13.49

13.50

33.78

33.09

33.73

32.54

33.21

52.71

54.15

52.04

53.99

53.29

17.8 17.8 140.3 142.4 0.63 0.62 48.26 51.07 Esmat et al. (2018)

17.8 138.7 0.65 51.56

17.8 141.6 0.61 47.64

17.8 140.8 0.63 52.83

13.2-15.5 12.6 ±0.5 34.8-37.0 37.3 ±0.0 48.2-49.5 50.0 ±1.4 17.6 17.8 134.35 138.3 0.75 0.76 64.13 49.00 Marinho et al (2019)

e-

C22:0

nd

Pr

C20:1

0.49 ±0.0 0.88 ±0.0 0.43 ±0.04 2.40 ±0.08 0.50 ±0.01 13.08 ±0.04 30.47 ±0.24 56.62 ±0.13 18 145.3 0.49 35.06 This work

na l

C20:0

22.6 ±0.96 52.06 ±0.76 15.8 127.2 0.43 103.12 Han et al. (2018)

Abbreviations: ALC − average length chain, LLR − linoleic/linolenic ratio, MUFA - monounsaturated fatty acids, OLR − oleic/linoleic ratio, nd – not determined, PUFA − polyunsaturated fatty acids, SFA - saturated fatty acids, and TUD − total unsaturation degree. b Ultrasound pretreatment. a

c

Jo ur

Trichoderma reesei. Bacillus licheniformis. e Bovine pancreas. f Depending on the extraction conditions. g Oil extracted by solvent/hexane-extracted oil by the Soxhlet method × 100. h Oil extracted by supercritical CO2 and cosolvent/ oil extracted by the Soxhlet method using pure cosolvent × 100. i Industrially extracted oil obtained from Caramuru S.A. (Brazil). j Oil extraction from corn germ-bran pellets. d

34

Table 4. The results of ANOVA.

Jo

ur

na

lP

re

Model 157.07 X1 65.64 X2 71.63 X3 4.13 X1 X2 6.43 X1 X3 0.025 X2 X3 0.0015 2 5.09 X1 2 3.87 X2 2 0.26 X3 Residual 6.90 Lack-of-fit 2.95 Pure error 3.95 Corrected total 163.96 2 2 R2  0.958, Radj  0.949, Rpred

Degree Mean F-value p-value of square freedom 9 17.45 111.33 < 0.0001 1 65.64 418.72 < 0.0001 1 71.63 456.94 < 0.0001 1 4.13 26.33 < 0.0001 1 6.43 41.00 < 0.0001 1 0.025 0.16 0.693 1 0.0015 0.0096 0.922 1 5.09 32.48 < 0.0001 1 3.87 24.68 < 0.0001 1 0.26 1.65 0.205 44 0.16 17 0.17 1.19 0.336 27 0.15 53  0.938, C.V .  1.04%, Adeq.Precision  36.38

ro of

Sum of squares

-p

Source

35

Table 5. The values of the phenomenological model parameters for CGO maceration by nhexane, Eq. (T2). fa

q∞ a, g/100 g

re, oC

m ratio, ml/g

Calculate Experimenta

20

3:1

d

l

36.760.

35.94±0.11

05 6.5:1

37.970.

36.63±0.06

40 10:1

37.640.

37.71±0.23

11 45

3:1

37.240.

36.63±0.17

66 6.5:1

38.110.

38.19±0.11

16 10:1

39.160.

39.35±0.37

3:1

38.070. 36

6.5:1

40.250. 11

41.040.

40.26±0.23

41.52±0.25

na

10:1

37.54±0.19

lP

70

50

MRP

0.8690.0

51.502.

0.2690.0 0.9990.0 0.7

12

12

67

0.8770.0

70.502.

0.4330.1 0.9990.0 1.0

01

12

88

0.8600.0

86.500.

0.9100.0 0.9990.0 0.9

03

71

14

0.8720.0

58.500.

0.3160.1 0.9990.0 0.9

10

71

49

73.001.

0.4400.0 0.9990.0 0.6

00

01

0.8840.0

01

01

04

41

97

0.8590.0

92.502.

0.9350.0 0.9990.0 0.7

09

12

21

0.8900.0

63.002.

0.3450.0 0.9990.0 1.0

04

83

64

0.8840.0

75.001.

0.7560.0 0.9980.0 1.4

08

41

63

0.8820.0

97.500.

0.9370.0 0.9980.0 1.1

01

71

28

00

00

01

02

01

Value of a parameter ± standard deviation; b p < 0.0001.

Jo

ur

a

R2, a,b

D, %

re

29

k1 a, min-1 k2 a, min-1

ro of

Solvent:ger

-p

Temperatu

36

Table 6. The parameters of the modified Arrhenius equation. Kinetic model

Rate constant, min-1

A’, mln/min gn

Ea, kJ/mol

n

R2,a

MRPD, %

Phenomenological model

k1

83.85

2.14

0.381

0.937

3.5

k2

0.674

4.88

0.909

0.798

12.5

k2

3.785

7.47

0.474

0.830

5.1

k2

8.093

1.51

0.393

0.667

1.1

Simplified phenomenological model Diffusional model p<0.001.

ro of

a

Table 7. The values of the parameters of the kinetic model involving instantaneous washing followed by diffusion for CGO maceration by n-hexane, Eq. (T3). q∞ a

o

ratio, ml/g

calculated,

C

g/100 g 3:1

36.670.07

6.5:1

37.470.11

10:1 45

3:1

%

0.2700.069

0.9640.006

0.7

0.8840.008

0.4630.072

0.9150.048

1.3

0.8790.011

0.5160.134

0.9490.006

0.9

36.830.08

0.8720.011

0.4270.007

0.9130.108

0.7

37.940.38

0.8790.001

0.5610.046

0.9540.028

0.7

39.260.45

0.8820.008

0.6680.143

0.9400.027

0.7

3:1

38.450.59

0.8870.008

0.4510.001

0.7870.062

2.0

6.5:1

40.420.36

0.8780.004

0.6250.105

0.9160.047

0.9

10:1

41.270.33

0.8760.015

0.8400.042

0.8770.121

1.1

Value of a parameter ± standard deviation; b p < 0.001.

Jo

a

MRPD,

10:1

ur

70

R2, a,b

38.010.02

na

6.5:1

k2 a, min-1

0.8750.005

lP

20

fa

-p

Solvent:germ

re

Temperature,

37

Table 8. The values of the diffusional model parameters. Solvent:germ ratio, q∞ a (g/100 g) ml/g 3:1 35.370.28 6.5:1 36.950.13 10:1 37.290.18 45 3:1 35.930.14 6.5:1 37.320.31 10:1 37.901.04 70 3:1 37.170.03 6.5:1 39.750.21 10:1 40.680.46 a b Mean value  standard deviation. p < 0.01. b p < 0.001.

k2 a (min-1)

R2, a

4.5450.346 4.6490.058 4.9070.150 4.7840.293 4.8500.005 4.9420.100 5.0600.014 5.1000.141 5.2700.113

0.5930.042b 0.8030.010c 0.6510.055c 0.7650.134c 0.7210.000c 0.7410.182c 0.5050.056b 0.6140.176c 0.6210.284c

MRPD (%) 2.5 1.5 1.9 1.5 1.7 2.7 1.9 2.3 1.9

ro of

Temperature, o C 20

Table 9. The CGO yield and the oil content in used corn germ, both at saturation, and the

q∞a, g/100 g 35.61±0.36 36.64±0.16 37.54±0.19 36.64±0.06 38.19±0.11 40.26±0.23 37.71±0.23 39.36±0.36 41.52±0.25

lP

Ka 3.66±0.17 4.20±0.10 4.80±0.14 4.20±0.04 5.33±0.10 7.92±0.40 4.94±0.18 6.58±0.46 10.87±0.79

Jo

ur

na

qsa, g/100 g 9.75±0.36 8.72±0.16 7.82±0.19 8.72±0.06 7.16±0.11 5.09±0.23 7.64±0.23 6.00±0.36 3.83±0.25

re

T, oC 20 45 70 6.5:1 20 45 70 10:1 20 45 70 a Parameter valuestandard deviation Solvent:germ ratio (mL/g) 3:1

-p

distribution coefficient.

38

Table 10. The thermodynamic parameters of the CGO extraction. Solvent:germ ratio (mL/g) 3:1

T (oC) 20 45 70 20 45 70 20 45 70

6.5:1

10:1

So (J/mol K) 26.27

10.48

47.47

13.04

57.43

Go (kJ/mol) -3.14 -3.79 -4.45 -3.43 -4.61 -5.80 -3.79 -5.22 -6.66

R2,a 0.953

0.958

0.943

p < 0.001.

ro of

a

Ho (kJ/mol) 4.56

Table 11. Temperature extraction coefficient for CGO maceration.

1.011 1.019 1.019

0.948 0.991 0.979

MRPD, % ±0.1 ±0.1 ±0.1

-p

Ra

re



Jo

ur

na

lP

Solvent:germ ratio, mL/g 3:1 6.5:1 10:1 a p<0.001

39