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Effect of frying conditions on fatty acid profile and total polar materials via viscosity
Accepted Manuscript Effect of frying conditions on fatty acid profile and total polar materials via viscosity Xu Li, Xiaojing Wu, Ruijie Liu, Qingzhe Jin, Xingguo Wang PII: DOI: Reference:
S0260-8774(15)00306-4 http://dx.doi.org/10.1016/j.jfoodeng.2015.07.007 JFOE 8240
To appear in:
Journal of Food Engineering
Received Date: Revised Date: Accepted Date:
16 November 2014 30 June 2015 6 July 2015
Please cite this article as: Li, X., Wu, X., Liu, R., Jin, Q., Wang, X., Effect of frying conditions on fatty acid profile and total polar materials via viscosity, Journal of Food Engineering (2015), doi: http://dx.doi.org/10.1016/ j.jfoodeng.2015.07.007
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
Effect of frying conditions on fatty acid profile and total polar materials via viscosity Xu Li, Xiaojing Wu, Ruijie Liu*, Qingzhe Jin, Xingguo Wang State Key Laboratory of Food Science and Technology, Synergetic Innovation Center of Food Safety and Nutrition, School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu Province, PR China *Corresponding author. Tel.: +86 510 85876799, fax: +86 510 85876799. Email-address: [email protected] (R. Liu).
Abstract: This work investigates how the temperature dependence of soybean oil viscosity varies with frying conditions and chemical composition, to develop a model that predicts oil degradation directly from process parameters. The temperature dependence of frying oil viscosity is described using Lioumbas et al.(2012) non-linear model, which is confirmed with satisfactory accuracy (r2>0.9998). Two models, predicting viscosity separately from frying conditions and chemical composition, are established, both with an overall accuracy of greater than 95.0%. Using the viscosity parameter as a medium, multiple quasi-linear regression analysis was applied to derive an expression that predicts fatty acid composition and total polar materials from the average oil temperature and the frying time (r2=0.930). This final expression quantifies the relationship between the frying conditions and the chemical composition of the oil, which is convenient for on-site quality control. Keywords: oil; deep-fat frying; viscosity; fatty acid; polar materials; model
1.
Introduction Deep-fat frying has been widely used in the catering industry to prepare food. To ensure food
safety, the quality control of deep-fat frying oils has attracted increased attention. During the frying process, a number of chemical reactions are induced in the oil, such as hydrolysis, oxidation, cyclization, and polymerization. Most of the reaction products are non-volatile, but they accumulate in the oil, causing modifications of the physical and chemical characteristics, such as viscosity, fatty acid (FA) composition, and total polar materials (TPM) (Gertz, 2000). These modifications are also the result of the frying conditions, such as the oil temperature, the frying load, and the frying time (Ziaiifar et al., 2008). A correlation for predicting oil deterioration using frying conditions is important, especially for field quality control. Many investigations have tried to find a direct correlation between the frying conditions and
the chemical composition of the oil. Franke and Strijiwski (2011) performed experiments with various commercial fryers and showed that the frying conditions significantly influence the browning of food. Naghshineh (2010) also examined the effect of the oil type and the frying time on the alteration of oil composition. However, the frying conditions and the chemical composition of the oil contain numerous parameters, which should be analyzed and correlated with each other. This correlation analysis should not be conducted only by mathematical analysis, but should also take into account some principles of chemistry. The chemical principle of viscosity is applied in this analysis. The differences in viscosity between various oils are due to the FA composition and the molecular conformation of the triglyceride (TAG) molecules (Wang and Briggs, 2002). The former can be represented by the effective carbon number (ECN) in rheology, while the latter can be approximately described by the total polar materials (TPM) (Santos et al., 2005). ECN was first proposed by Geller and Goodrum (2000) to predict the viscosity of the triglyceride mixtures. It was calculated based on the total number of carbons and the number of double bonds in the acyl chain. The influence of double bonds was approximated by subtracting 1 for each double bond from the actual number of carbon atoms, to compensate for the reduction in viscosity. As a structural parameter, ECN was used by Wang and Briggs (2002) to prove a good linear relationship with fresh soybean oil viscosity. Besides ECN, TPM increases significantly during frying and it should not be neglected. Therefore, with this chemical principle, only several particular parameters like frying time, frying temperature, frying load, ECN and TPM, can be selected to analyze the potential correlation. The present study can be classified into three steps. First, the rheological properties of oil samples collected from specific frying conditions were evaluated. Further, the TPM and ECN of these oil samples were analyzed. Finally, with rheological parameter as a medium, a predicting equation from frying conditions to chemical indices was proposed, which is of great value to frying process optimization and on-site quality control in fast-food chain enterprises. 2.
Materials and methods
2.1. Frying equipment and experimental procedure Potato sticks (40×7.2×7.2mm) were donated by MacDonald and stored at -18ºC. These are commercial frozen pre-fried sticks with an initial fat content of 3%. The frying experiments were conducted with soybean oil, which is frequently used in traditional cooking by Asians families. The oil (donated by Wilmar), without additives, was stored at 4ºC until it was used. The frying was performed in a commercial fryer (FRYMASTER, BielA14) with a maximum oil capacity of 10 L and a nominal power, Q, 7000 W. The oil temperature was measured with a thermocouple
(OKAZAKI, Japan). As shown in Table 1, four frying experiments with different frying loads (F, kgpotatoes/Loil) and initial oil temperatures (Tin, ºC) were conducted. Three experiments were performed with F=1/100, 1/50 and 1/25 at Tin=168.3ºC, and one experiment was performed with F=1/100 at a lower temperature Tin=165.6ºC. All frying series began with 10 L of fresh oil in the fryer. The overall time that the oil was heated can be estimated as 50 h (12 h/day, total 250 frying batches) and each batch contains 3 min frying time (t1) and 9 min waiting time (t2). The oil was replenished after every 6 hours of frying. The oil samples (50 ml) were collected after every 2 hours and they were kept at −4ºC, in air-tight, dark-colored glass bottles. The selected oil samples frying for 4h, 8h, 12h, 16h, 20h, 24h, 30h, 34h, 38h, 42h, 46h and 50h were collected. Therefore, a set of 48 samples (4 frying series ×12 frying durations) was analyzed. The frying conditions, including the frying load, initial oil temperature and frying time, were selected to allow a considerable change in the chemical composition of the samples (Naz et al., 2005). Three frying loads from 1/100 to 1/25 encompassed the minimum and maximum values used in practical catering applications (Choe and Min, 2007). It must be stressed that the frying load was controlled with replenishment instead of reducing the mass of potatoes, which was more similar to actual situation in restaurants. Between two replenishments, the load was higher and higher with the biggest deviation of 8% due to sampling and absorption by the potatoes. Therefore, replenishment, as a crucial factor in practical operation, was taken into account in this experimental setup, which differs from previous work (Lioumbas et al., 2012, Kalogianni et al., 2009). 2.2.
Viscosity measurement Dynamic shear viscosity measurements were conducted using an MCR 301, TruGap®, Anton
Paar cone-plate rheometer (PP50, parallel plate diameter 49.957 mm, truncation 0.5 mm). The temperature dependence of oil viscosity was measured at a shear rate of 50 s−1 for a temperature range of 60ºC to 110ºC. A set heating rate of 5ºC/min was used, which showed a good agreement between the dynamic and steady measurements (Lioumbas et al., 2012). The sample temperature was controlled with a Peltier bottom plate and a thermally regulated hood accessory. All tests were performed twice to ensure reproducibility. The deviation among all replicates was less than ±1.0%. 2.3.
Determination of fatty acid composition According to IUPAC standard methods 2.301 and 2.302, the fatty acid composition was
analyzed by gas chromatography using Agilent 7820, equipped with a FID and a TRACE™ TR-FAME Column (60m×0.25μm, Thermo Fisher). The injection block temperature was set at 250ºC. The oven temperature was kept at 60ºC for 3 min, then programmed as follows, 60ºC to 175ºC at 5ºC/min, 175ºC for 15 min, 175ºC to 220ºC at 2ºC/min, and finally 220ºC for 10 min. The carrier gas was nitrogen with a flow rate of 25mL/min and the split rate was 1/100. 2.4.
Determination of total polar materials The EOPC flash chromatography system (Tianjin Bernard Agela Technology company) was
used to evaluate the TPM, as described by Cao et al. (2013).According to the AOCS Cd20-91 method, the eluent for non-polar material was miscible liquids (250 mL, petroleum ether/ethyl ether = 87/13). The solvent of collecting non-polar compounds eluent was dried using atmospheric pressure rotating evaporation at 60ºC. It was then put in the vacuum drying oven for 0.5 h at 40ºC. Finally, the percentage of TPM was calculated based on the weight of the remaining compounds. 2.5.
Statistical analysis The mathematical model analysis of the temperature dependence of oil viscosity was carried
out using Origin 8.0® software. Multiple quasi-linear regression analysis was then used to correlate the model coefficients with parameters converted from frying conditions and chemical composition of the oil. Finally, the regression analysis was again conducted excluding viscosity on the basis of two calculated equations. The statistical significance of each coefficient was tested using the t-test with P>0.95. For the determination of the best regression equation a stepwise backward elimination procedure was employed: the coefficient with the lowest significance below the threshold was removed and the approach was recalculated with the remaining coefficients. This procedure was repeated until the significance of each of the remaining coefficients in the approach was above the threshold. Regression analysis was conducted using the Microsoft® Excel™ plug-in Analyse-it®. 3. 3.1.
Experimental results Oil temperature measurement Fig. 1 presents soybean oil temperature profiles obtained at various frying conditions and the
results were shown in Table 1. Since temperature is not so much affected by gradual degradation of oil through repeated batches (Kalogianni et al., 2009), fresh oil was used. It is a combined result of temperature drop due to potatoes load and temperature increase due to fryer heating. Average oil temperature was calculated from three repetitions with average deviation ±2%.
3.2.
Check for Newtonian behavior Fig. 2 presents the apparent viscosity, μ, for soybean oil measured at 60ºC and 100ºC over a
shear rate, , range of 10 and 1000 s−1. The samples examined were those taken after 50 h of frying, where the highest degradation had occurred. The samples were from four experiments conducted using different frying conditions. It was found that μ was almost constant with varying from 10 to 1000 s−1, indicating that even the most polymerized oil samples present virtually Newtonian behavior under the tested condition. 3.3.
Viscosity measurement Fig. 3 (a) shows the dependence of viscosity on the measured temperature of soybean oil for
three different frying loads, for the 50 h frying duration. As machine measuring temperature (Tm) increased, the oil viscosity of all frying durations decreased non-linearly. Due to the increase in temperature, the attractive force reduction is more significant than the molecular interchange increase in the liquid, which is consistent with our results (Nik et al., 2005). Moreover, the temperature dependence of viscosity varied with frying duration. The highest viscosity was seen for a sample for 50 h, while the lowest was observed for fresh oil, probably due to differences in chemical composition (Santos et al., 2005). In further analysis, the influence of temperature (Tm) on viscosity (μ) can be expressed in an equation (Lioumbas et al., 2012): In a bInTm2 , (Tm / C)
(1)
μ is the oil viscosity, Tm is measured temperature, and coefficients (a, b) are determined by linear regression analysis. Comparing with the typical Arrhenius equation: In In 0
Ea , (Tm / C) R (Tm 273 .15)
(2)
Ea is the activation energy, R is universal gas constant, and μ0 is a constant. Higher ‘b’ absolute values (b is negative), like ‘Ea’ in Arrhenius model, indicate a more rapid change in viscosity with temperature. Otherwise, ‘a’ corresponds to the viscosity at Tm = 1ºC while ‘μ0’ is related to the limiting viscosity when Tm is extremely high. The equation (Lioumbas et al., 2012) was confirmed with satisfactory accuracy (r2>0.9998) and was used to fit our data. As can be seen in Fig. 2(b), ‘b’ almost decreases linearly with tp for all four frying series, which demonstrates a potential association between the viscosity parameter and the frying conditions. 3.4.
Chemical composition measurement
It has been suggested that the viscosities of various oils are chiefly related to the FA composition and the molecular conformation of the TAG molecules (Wang and Briggs, 2002). The FA composition was converted into a structural parameter, ECN, while the molecular conformation of the TAG molecules can be described by the TPM in general. The equation for calculating the ECN is as follows:
ECN Pi(Ci dbi)
(3)
where P is the percentage of each fatty acid, C is the number of carbon atoms, and db is the number of double bonds. As shown in Table 2, the increase in the ECN values from fresh oil to degraded oil seems to be noticeable with t-test. During degradation, the oil’s saturation increases with the decrease in linoleic and linolenic acids and the increase in palmitic, stearic, and oleic acids. This may be due to thermal oxidation with the free radical chain reaction (Min and Boff, 2002). However, replenishment of fresh oil in these four experiments has modified the frying oil properties including fatty acid profile. In addition, as oil temperature is high, some volatile reaction products like short chain fatty acids would not accumulate in the oil, which may be another reason for the result. The TPM of oil samples showed a marked increase after frying for 50 h, as shown in Table 2. Polar degradation materials were produced during deep-fat frying through hydrolysis, oxidation and polymerization. Unexpectedly, the TPM of the sample prepared using the highest frying load displayed the lowest value. The increasing frying load seems to inhibit the deterioration of oil, which is contrary to existing views. In an attempt to explain this unusual phenomenon, a further analysis was conducted focusing on the chemical reactions and the process operation. The oil temperature drops from its initial value as soon as the potatoes enter the hot oil, so a new concept of average oil temperature (Tave) was used to describe the intensity of the frying process. As demonstrated in Table 1, the larger frying load resulted in a lower average oil temperature, despite initial temperature being the same (average oil temperature 1/100: 168.4ºC; 1/50: 168.2ºC; 1/25: 167.1ºC). Further research with same low frying load at different initial oil temperatures (average oil temperature 1/100(Tin=165.6): 165.5ºC; 1/100(Tin=168.3): 168.4ºC) was conducted and the frying with lower initial temperature displayed the lower increase of TPM as expected. Therefore, the higher temperature throughout the whole process may be a primary factor, accelerating thermal oxidation and polymerization while simultaneously reducing the antioxidant ability (Gertz et al., 2000; Tyagi and Vasishtha, 1996). Frequent replenishment caused by the large frying load may be another key factor (volume of fresh oil added 1/100(Tin=165.6): 3.5L; 1/100(Tin=168.3): 3.7L; 1/50(Tin=168.3): 4.9L; 1/25(Tin=168.3): 6.7L), effectively decreasing the percentage of polar materials (Romero et al., 1998).
4.
Model Development Multiple quasi-linear regression analysis was used to test whether the parameters in Eq. (1)
depend on the frying conditions. For this, model parameters (a, b) were calculated from 48 frying oil samples (4 frying loads×12 frying times). The results showed that the parameters generated from fitting of experimental data could be correlated with the frying conditions to a satisfactory level. In particular, a depends linearly on b (Eq. (4)), while b depends linearly on F2, tp2, F*tp, F*Tave, tp*Tave and tp (Eq. (5)). a 0.5839 34.84 b r 2 0.972 * SE int ercept 0.1732 , SE b 0.8648
(4 )
b 0.1877 3.820 F2 1.714 10 6 tp 2 1.520 10 3 F * tp 1.429 10 3 F * Tave 1.998 10 5 t p * Tave 2.891 10 3 t p r 2 0.985 4 * SE int ercept 6.902 10 , SE F2 1.145, SE t 2 5.150 10 7 , SE F*t p 5.213 10 4 , p SE F*Tave 3.551 10 4 , SE t p *Tave 2.841 10 6 , SE t p 4.762 10 4
(5)
The viscosity values (μmod) estimated with Eqs. (1), (4), and (5) were plotted against the experimental values (μexp) in a parity plot (Fig. 4a). The deviation, μmod−μexp, of all samples was less than ±1 mPa.s (top inset in Fig. 4a) and the proposed equations were confirmed to predict the viscosity with better than ±5% accuracy for most of the examined cases (bottom inset in Fig. 4a). Multiple quasi-linear regression analysis was also used to test whether the parameters in Eq. (1) depend on the chemical composition of the oil. For this, the chemical composition was divided into two variables, ECN and TPM. The TPM can be obtained directly from detection while ECN is calculated from FA composition using Eq. (3). The results showed that the relationship between a and b is the same as Eq. (4), and the other equation is as follows:
b 0.5774 4.769 10 2 ECN 6.232 10 4 T PM r 2 0.969 2 3 * SE int ercept 8.819 10 , SE ECN 5.499 10 , SE TPM 3.347 10 5
(6)
Estimated using Eq. (1), (4), and (6), viscosity values (μmod) were plotted against the experimental values (μexp) in a parity plot (Fig. 4b). The deviation, μmod−μexp, was less than ±1 mPa.s (top inset in Fig. 3b) and the prediction accuracy was almost better than ±5% (bottom inset in Fig. 4b). Based on the above two sets of equations, another multiple quasi-linear regression analysis was used to correlate the frying condition with the chemical composition, excluding viscosity. The final equation is as follow: T PM 3.881 2.935 10 3 t p 2.852 F * t p 1.788 10 2 t p * Tave 2.410 t p r 2 0.946 * SE int ercept 0.7106 , SE tp 2 1.111 10 3 , SE F*t p 0.5379 , SE t p *Tave 5.604 10 3 , SE t p 0.9394 2
(7)
The TPM values (TPMmod) estimated with Eq.(7) were plotted against the experimental values (TPMexp) in a parity plot (Fig. 4c). The deviation, TPMmod−TPMexp, of all samples was less than
±2% (top inset Fig. 4c) and the proposed equations were confirmed to predict the TPM with better than ±10% accuracy for most of the examined cases (bottom inset in Fig. 4c). For further analysis, a great change that ECN was removed was observed in Eq.(7). When determining best regression equation, ECN was removed due to its lowest significance below the threshold. The insignificant increases in ECN during frying may be responsible for the result. Also, Tave has a positive effect while F has a negative effect on increasing TPM, which is consistent with experimental results. The effect degree of F is almost 160 times more than that of Tave. This equation offers the possibility of predicting oil quality from process parameters, which would be convenient for on-site quality control. However, some parameters have thresholds: t p should be greater than zero, F could not increase infinitely, Tave is near the initial oil temperature. These thresholds were not taken into consideration in this study, and the correlation between frying conditions and oil quality still demands further and more detailed research. 5.
Conclusions We verified that the temperature dependence of frying oil viscosity can be described by
Lioumbas et al. (2012) non-linear model with satisfactory accuracy (r 2>0.9998). The viscosity model parameters are linearly related to the frying conditions and the chemical composition of the oil, both of which achieved reasonable accuracy (±5%). With viscosity excluded, a direct expression was derived from the average oil temperature, frying load and frying time to total polar materials (r2=0.946). For catering industry especially fast-food chain enterprises, this study should be of great value to frying process optimization and on-site quality control. Acknowledgements This work was funded by “National Natural Science Foundation of China (31401525)”. The authors also wish to thank companies including MacDonald, Cargill and Wilmar International for continuous support throughout this work. References Cao, W.M., Zhang, K.Y., Xue, B., Chen, F.X., Jin, Q.Z., Wang, X.G., 2013. Determination of Oxidized
Triacylglycerol
Polymers
by
Preparative
Flash
Chromatography
and
High-Performance Size-Exclusion Chromatography. Asian J Chem. 25(16), 9189-9194. Choe, E., Min, D.B., 2007. Chemistry of deep-fat frying oils. J Food Sci. 72(5), R77-R86. Franke, K., Strijowski, U., 2011. Standardization of Domestic Frying Processes by an Engineering Approach. J Food Sci. 76(4), E333-E340. Geller, D.P., Goodrum, J.W., 2000. Rheology of vegetable oil analogs and triglycerides. J Am Oil Chem Soc. 77(2), 111-114.
Gertz, C., 2000. Chemical and physical parameters as quality indicators of used frying fats. Eur J Lipid Sci Tech. 102(8-9), 566-572. Gertz, C., Klostermann, S., Kochhar, S.P., 2000. Testing and comparing oxidative stability of vegetable oils and fats at frying temperature. Eur J Lipid Sci Tech. 102(8-9), 543-551. Kalogianni E P, Karastogiannidou C, Karapantsios T D. Effect of the presence and absence of potatoes under repeated frying conditions on the composition of palm oil[J]. J Am Oil Chem Soc. 2009, 86(6): 561-571. Lioumbas, J.S., Ampatzidis, C., Karapantsios, T.D., 2012. Effect of potato deep-fat frying conditions on temperature dependence of olive oil and palm oil viscosity. J Food Eng. 113(2), 217-225. Min, D.B., Boff, J.M., 2002. Lipid oxidation of edible oil. Food Sci Tech-New York: Marcel Dekker, pp. 335-364. Naghshineh, M., Mirhosseini, H., 2010. Effects of frying condition on physicochemical properties of palm olein-olive oil blends. J Food Agric Environ. 8, 175-178. Naz, S., Siddiqi, R., Sheikh, H., Sayeed, S.A., 2005. Deterioration of olive, corn and soybean oils due to air, light, heat and deep-frying. Food Res Int. 38(2), 127-134. Nik, W.B.W., Ani, F.N., Masjuki, H.H., Giap, S.G.E., 2005. Rheology of bio-edible oils according to several rheological models and its potential as hydraulic fluid. Ind Crop Prod. 22, 249-255. Romero, A., Cuesta, C., Sánchez-Muniz, F.J., 1998. Effect of oil replenishment during deep-fat frying of frozen foods in sunflower oil and high-oleic acid sunflower oil. J Am Oil Chem Soc. 75(2), 161-167. Santos, J.C.O., Santos, I.M.G., Souza, A.G., 2005. Effect of heating and cooling on rheological parameters of edible vegetable oils. J Food Eng. 67(4), 401-405. Tyagi, V., Vasishtha, A., 1996. Changes in the characteristics and composition of oils during deep-fat frying. J Am Oil Chem Soc. 73(4), 499-506. Wang, T., Briggs, J., 2002. Rheological and thermal properties of soybean oils with modified FA compositions. J Am Oil Chem Soc. 79(8), 831-836. Ziaiifar, A.M., Achir, N., Courtois, F., Trezzani, I., Trystram, G., 2008. Review of mechanisms, conditions, and factors involved in the oil uptake phenomenon during the deep-fat frying process. Int J Food Sci Tech. 43(8), 1410-1423.
Fig.1. Temperature of soybean oil during frying at 1/100, 1/50, 1/25 frying load (kgpotatoes/Loil), at 165.6 ºC and 168.3 ºC initial frying temperature and for the fresh oil (average deviation ±2% calculated from three repetitions).
Fig.2. Apparent viscosity, μ, measurement at 60 ºC and 100 ºC for soybean oil as a function of shear rate, , conducted at high(1/25), middle(1/50), low(1/100) frying load (kgpotatoes/Loil), at 165.6 ºC and 168.3 ºC initial frying temperature for samples after 50 h frying.
Fig.3. Viscosity, μ, as a function of temperature and model parameter, b, as a function of frying duration. Results are for soybean oil for frying loads 1/100, 1/50 and 1/25 kgpotatoes/Loil at 165.6 ºC and 168.3 ºC initial frying temperature and for 12 frying oil samples selected from different durations (0-50 h) and fresh oil.
Fig.4. Parity plots between the measured values and those predicted by the present model over all the examined frying conditions (a), chemical compositions (b) and TPM (c). The small inset plots show the actual difference in viscosity values (top) and the % deviation (bottom) between the measured values and the model predictions.
Table 1 Experimental settings taken into account for the computation of a and b parameters. Parameter
Value
Type of oil
Soybean oil
Fryer’s specific heating power, Q/Loil, kW/L
7/10
Frying load, F, kgpotato/Loil
1/100, 1/100, 1/50, 1/25
Initial oil temperature, Tin, ºC
165.6, 168.3, 168.3, 168.3
Average oil temperature, Tave,ºC
165.5, 168.4, 168.2, 167.1
Frying duration, tp, h
50 (12h/day)
Frying batch time, min
12 (t1=3min; t2=9min)
Volume of fresh oil added, L
3.5, 3.7, 4.9, 6.7
Table 2 FA compositions (mol%) of frying oils, calculated Effective Carbon Numbers(ECN) and their total polar materials(TPM). Tin/ºC
-
165.6
168.3
168.3
168.3
F/(kgpotatoes/
-
1/100
1/100
1/50
1/25
tp/h
0
50
50
50
50
Loil)
C14:0
0.08 ± 0.02
0.20 ± 0.00
0.25 ± 0.00
0.30 ± 0.00
0.37 ± 0.02
C16:0
11.07 ±
15.45 ±
16.08 ±
18.41 ±
22.29 ±
0.07
0.03
0.05
0.05
0.20
C16:1
0.08 ± 0.00
0.11 ± 0.01
0.11 ± 0.00
0.11 ± 0.00
0.11 ± 0.02
C18:0
4.29 ± 0.02
4.68 ± 0.01
4.61 ± 0.01
4.57 ± 0.01
4.59 ± 0.02
C18:1
25.22 ±
28.05 ±
30.79 ±
33.79 ±
30.64 ±
0.04 C18:2
51.97 ± 0.06
0.07 45.68 ± 0.02
0.05 42.71 ± 0.09
0.05 37.94 ± 0.09
0.07 37.29 ± 0.16
C18:3
5.62 ± 0.00
4.47 ± 0.02
4.03 ± 0.02
3.53 ± 0.02
3.50 ± 0.02
ECN
16.012 ±
16.107 ±
16.127 ±
16.171 ±
16.161 ±
0.03a
0.03b
0.03b
0.03c
0.08c
TPM/%
4.1 ± 0.05a
24.3 ± 0.26b
24.7 ± 0.14b
23.8 ± 0.05b
Values in the same line with different superscripts are significantly different (P<0.05).
21.3 ± 0.00c
Fig.1.
Fig.2.
Fig.3.
Fig.4.
Highlights 1. 2. 3.
Experimentally determined oil μ–T profiles during potato deep fat frying. Effective carbon number in rheology is used to represent fatty acid composition alteration in frying oil. Effect of fatty acid composition and total polar materials on μ(T) is examined. 4. An engineering expression is proposed for the prediction of oil deterioration from frying conditions.
S0260-8774(15)00306-4 http://dx.doi.org/10.1016/j.jfoodeng.2015.07.007 JFOE 8240
To appear in:
Journal of Food Engineering
Received Date: Revised Date: Accepted Date:
16 November 2014 30 June 2015 6 July 2015
Please cite this article as: Li, X., Wu, X., Liu, R., Jin, Q., Wang, X., Effect of frying conditions on fatty acid profile and total polar materials via viscosity, Journal of Food Engineering (2015), doi: http://dx.doi.org/10.1016/ j.jfoodeng.2015.07.007
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
Effect of frying conditions on fatty acid profile and total polar materials via viscosity Xu Li, Xiaojing Wu, Ruijie Liu*, Qingzhe Jin, Xingguo Wang State Key Laboratory of Food Science and Technology, Synergetic Innovation Center of Food Safety and Nutrition, School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu Province, PR China *Corresponding author. Tel.: +86 510 85876799, fax: +86 510 85876799. Email-address: [email protected] (R. Liu).
Abstract: This work investigates how the temperature dependence of soybean oil viscosity varies with frying conditions and chemical composition, to develop a model that predicts oil degradation directly from process parameters. The temperature dependence of frying oil viscosity is described using Lioumbas et al.(2012) non-linear model, which is confirmed with satisfactory accuracy (r2>0.9998). Two models, predicting viscosity separately from frying conditions and chemical composition, are established, both with an overall accuracy of greater than 95.0%. Using the viscosity parameter as a medium, multiple quasi-linear regression analysis was applied to derive an expression that predicts fatty acid composition and total polar materials from the average oil temperature and the frying time (r2=0.930). This final expression quantifies the relationship between the frying conditions and the chemical composition of the oil, which is convenient for on-site quality control. Keywords: oil; deep-fat frying; viscosity; fatty acid; polar materials; model
1.
Introduction Deep-fat frying has been widely used in the catering industry to prepare food. To ensure food
safety, the quality control of deep-fat frying oils has attracted increased attention. During the frying process, a number of chemical reactions are induced in the oil, such as hydrolysis, oxidation, cyclization, and polymerization. Most of the reaction products are non-volatile, but they accumulate in the oil, causing modifications of the physical and chemical characteristics, such as viscosity, fatty acid (FA) composition, and total polar materials (TPM) (Gertz, 2000). These modifications are also the result of the frying conditions, such as the oil temperature, the frying load, and the frying time (Ziaiifar et al., 2008). A correlation for predicting oil deterioration using frying conditions is important, especially for field quality control. Many investigations have tried to find a direct correlation between the frying conditions and
the chemical composition of the oil. Franke and Strijiwski (2011) performed experiments with various commercial fryers and showed that the frying conditions significantly influence the browning of food. Naghshineh (2010) also examined the effect of the oil type and the frying time on the alteration of oil composition. However, the frying conditions and the chemical composition of the oil contain numerous parameters, which should be analyzed and correlated with each other. This correlation analysis should not be conducted only by mathematical analysis, but should also take into account some principles of chemistry. The chemical principle of viscosity is applied in this analysis. The differences in viscosity between various oils are due to the FA composition and the molecular conformation of the triglyceride (TAG) molecules (Wang and Briggs, 2002). The former can be represented by the effective carbon number (ECN) in rheology, while the latter can be approximately described by the total polar materials (TPM) (Santos et al., 2005). ECN was first proposed by Geller and Goodrum (2000) to predict the viscosity of the triglyceride mixtures. It was calculated based on the total number of carbons and the number of double bonds in the acyl chain. The influence of double bonds was approximated by subtracting 1 for each double bond from the actual number of carbon atoms, to compensate for the reduction in viscosity. As a structural parameter, ECN was used by Wang and Briggs (2002) to prove a good linear relationship with fresh soybean oil viscosity. Besides ECN, TPM increases significantly during frying and it should not be neglected. Therefore, with this chemical principle, only several particular parameters like frying time, frying temperature, frying load, ECN and TPM, can be selected to analyze the potential correlation. The present study can be classified into three steps. First, the rheological properties of oil samples collected from specific frying conditions were evaluated. Further, the TPM and ECN of these oil samples were analyzed. Finally, with rheological parameter as a medium, a predicting equation from frying conditions to chemical indices was proposed, which is of great value to frying process optimization and on-site quality control in fast-food chain enterprises. 2.
Materials and methods
2.1. Frying equipment and experimental procedure Potato sticks (40×7.2×7.2mm) were donated by MacDonald and stored at -18ºC. These are commercial frozen pre-fried sticks with an initial fat content of 3%. The frying experiments were conducted with soybean oil, which is frequently used in traditional cooking by Asians families. The oil (donated by Wilmar), without additives, was stored at 4ºC until it was used. The frying was performed in a commercial fryer (FRYMASTER, BielA14) with a maximum oil capacity of 10 L and a nominal power, Q, 7000 W. The oil temperature was measured with a thermocouple
(OKAZAKI, Japan). As shown in Table 1, four frying experiments with different frying loads (F, kgpotatoes/Loil) and initial oil temperatures (Tin, ºC) were conducted. Three experiments were performed with F=1/100, 1/50 and 1/25 at Tin=168.3ºC, and one experiment was performed with F=1/100 at a lower temperature Tin=165.6ºC. All frying series began with 10 L of fresh oil in the fryer. The overall time that the oil was heated can be estimated as 50 h (12 h/day, total 250 frying batches) and each batch contains 3 min frying time (t1) and 9 min waiting time (t2). The oil was replenished after every 6 hours of frying. The oil samples (50 ml) were collected after every 2 hours and they were kept at −4ºC, in air-tight, dark-colored glass bottles. The selected oil samples frying for 4h, 8h, 12h, 16h, 20h, 24h, 30h, 34h, 38h, 42h, 46h and 50h were collected. Therefore, a set of 48 samples (4 frying series ×12 frying durations) was analyzed. The frying conditions, including the frying load, initial oil temperature and frying time, were selected to allow a considerable change in the chemical composition of the samples (Naz et al., 2005). Three frying loads from 1/100 to 1/25 encompassed the minimum and maximum values used in practical catering applications (Choe and Min, 2007). It must be stressed that the frying load was controlled with replenishment instead of reducing the mass of potatoes, which was more similar to actual situation in restaurants. Between two replenishments, the load was higher and higher with the biggest deviation of 8% due to sampling and absorption by the potatoes. Therefore, replenishment, as a crucial factor in practical operation, was taken into account in this experimental setup, which differs from previous work (Lioumbas et al., 2012, Kalogianni et al., 2009). 2.2.
Viscosity measurement Dynamic shear viscosity measurements were conducted using an MCR 301, TruGap®, Anton
Paar cone-plate rheometer (PP50, parallel plate diameter 49.957 mm, truncation 0.5 mm). The temperature dependence of oil viscosity was measured at a shear rate of 50 s−1 for a temperature range of 60ºC to 110ºC. A set heating rate of 5ºC/min was used, which showed a good agreement between the dynamic and steady measurements (Lioumbas et al., 2012). The sample temperature was controlled with a Peltier bottom plate and a thermally regulated hood accessory. All tests were performed twice to ensure reproducibility. The deviation among all replicates was less than ±1.0%. 2.3.
Determination of fatty acid composition According to IUPAC standard methods 2.301 and 2.302, the fatty acid composition was
analyzed by gas chromatography using Agilent 7820, equipped with a FID and a TRACE™ TR-FAME Column (60m×0.25μm, Thermo Fisher). The injection block temperature was set at 250ºC. The oven temperature was kept at 60ºC for 3 min, then programmed as follows, 60ºC to 175ºC at 5ºC/min, 175ºC for 15 min, 175ºC to 220ºC at 2ºC/min, and finally 220ºC for 10 min. The carrier gas was nitrogen with a flow rate of 25mL/min and the split rate was 1/100. 2.4.
Determination of total polar materials The EOPC flash chromatography system (Tianjin Bernard Agela Technology company) was
used to evaluate the TPM, as described by Cao et al. (2013).According to the AOCS Cd20-91 method, the eluent for non-polar material was miscible liquids (250 mL, petroleum ether/ethyl ether = 87/13). The solvent of collecting non-polar compounds eluent was dried using atmospheric pressure rotating evaporation at 60ºC. It was then put in the vacuum drying oven for 0.5 h at 40ºC. Finally, the percentage of TPM was calculated based on the weight of the remaining compounds. 2.5.
Statistical analysis The mathematical model analysis of the temperature dependence of oil viscosity was carried
out using Origin 8.0® software. Multiple quasi-linear regression analysis was then used to correlate the model coefficients with parameters converted from frying conditions and chemical composition of the oil. Finally, the regression analysis was again conducted excluding viscosity on the basis of two calculated equations. The statistical significance of each coefficient was tested using the t-test with P>0.95. For the determination of the best regression equation a stepwise backward elimination procedure was employed: the coefficient with the lowest significance below the threshold was removed and the approach was recalculated with the remaining coefficients. This procedure was repeated until the significance of each of the remaining coefficients in the approach was above the threshold. Regression analysis was conducted using the Microsoft® Excel™ plug-in Analyse-it®. 3. 3.1.
Experimental results Oil temperature measurement Fig. 1 presents soybean oil temperature profiles obtained at various frying conditions and the
results were shown in Table 1. Since temperature is not so much affected by gradual degradation of oil through repeated batches (Kalogianni et al., 2009), fresh oil was used. It is a combined result of temperature drop due to potatoes load and temperature increase due to fryer heating. Average oil temperature was calculated from three repetitions with average deviation ±2%.
3.2.
Check for Newtonian behavior Fig. 2 presents the apparent viscosity, μ, for soybean oil measured at 60ºC and 100ºC over a
shear rate, , range of 10 and 1000 s−1. The samples examined were those taken after 50 h of frying, where the highest degradation had occurred. The samples were from four experiments conducted using different frying conditions. It was found that μ was almost constant with varying from 10 to 1000 s−1, indicating that even the most polymerized oil samples present virtually Newtonian behavior under the tested condition. 3.3.
Viscosity measurement Fig. 3 (a) shows the dependence of viscosity on the measured temperature of soybean oil for
three different frying loads, for the 50 h frying duration. As machine measuring temperature (Tm) increased, the oil viscosity of all frying durations decreased non-linearly. Due to the increase in temperature, the attractive force reduction is more significant than the molecular interchange increase in the liquid, which is consistent with our results (Nik et al., 2005). Moreover, the temperature dependence of viscosity varied with frying duration. The highest viscosity was seen for a sample for 50 h, while the lowest was observed for fresh oil, probably due to differences in chemical composition (Santos et al., 2005). In further analysis, the influence of temperature (Tm) on viscosity (μ) can be expressed in an equation (Lioumbas et al., 2012): In a bInTm2 , (Tm / C)
(1)
μ is the oil viscosity, Tm is measured temperature, and coefficients (a, b) are determined by linear regression analysis. Comparing with the typical Arrhenius equation: In In 0
Ea , (Tm / C) R (Tm 273 .15)
(2)
Ea is the activation energy, R is universal gas constant, and μ0 is a constant. Higher ‘b’ absolute values (b is negative), like ‘Ea’ in Arrhenius model, indicate a more rapid change in viscosity with temperature. Otherwise, ‘a’ corresponds to the viscosity at Tm = 1ºC while ‘μ0’ is related to the limiting viscosity when Tm is extremely high. The equation (Lioumbas et al., 2012) was confirmed with satisfactory accuracy (r2>0.9998) and was used to fit our data. As can be seen in Fig. 2(b), ‘b’ almost decreases linearly with tp for all four frying series, which demonstrates a potential association between the viscosity parameter and the frying conditions. 3.4.
Chemical composition measurement
It has been suggested that the viscosities of various oils are chiefly related to the FA composition and the molecular conformation of the TAG molecules (Wang and Briggs, 2002). The FA composition was converted into a structural parameter, ECN, while the molecular conformation of the TAG molecules can be described by the TPM in general. The equation for calculating the ECN is as follows:
ECN Pi(Ci dbi)
(3)
where P is the percentage of each fatty acid, C is the number of carbon atoms, and db is the number of double bonds. As shown in Table 2, the increase in the ECN values from fresh oil to degraded oil seems to be noticeable with t-test. During degradation, the oil’s saturation increases with the decrease in linoleic and linolenic acids and the increase in palmitic, stearic, and oleic acids. This may be due to thermal oxidation with the free radical chain reaction (Min and Boff, 2002). However, replenishment of fresh oil in these four experiments has modified the frying oil properties including fatty acid profile. In addition, as oil temperature is high, some volatile reaction products like short chain fatty acids would not accumulate in the oil, which may be another reason for the result. The TPM of oil samples showed a marked increase after frying for 50 h, as shown in Table 2. Polar degradation materials were produced during deep-fat frying through hydrolysis, oxidation and polymerization. Unexpectedly, the TPM of the sample prepared using the highest frying load displayed the lowest value. The increasing frying load seems to inhibit the deterioration of oil, which is contrary to existing views. In an attempt to explain this unusual phenomenon, a further analysis was conducted focusing on the chemical reactions and the process operation. The oil temperature drops from its initial value as soon as the potatoes enter the hot oil, so a new concept of average oil temperature (Tave) was used to describe the intensity of the frying process. As demonstrated in Table 1, the larger frying load resulted in a lower average oil temperature, despite initial temperature being the same (average oil temperature 1/100: 168.4ºC; 1/50: 168.2ºC; 1/25: 167.1ºC). Further research with same low frying load at different initial oil temperatures (average oil temperature 1/100(Tin=165.6): 165.5ºC; 1/100(Tin=168.3): 168.4ºC) was conducted and the frying with lower initial temperature displayed the lower increase of TPM as expected. Therefore, the higher temperature throughout the whole process may be a primary factor, accelerating thermal oxidation and polymerization while simultaneously reducing the antioxidant ability (Gertz et al., 2000; Tyagi and Vasishtha, 1996). Frequent replenishment caused by the large frying load may be another key factor (volume of fresh oil added 1/100(Tin=165.6): 3.5L; 1/100(Tin=168.3): 3.7L; 1/50(Tin=168.3): 4.9L; 1/25(Tin=168.3): 6.7L), effectively decreasing the percentage of polar materials (Romero et al., 1998).
4.
Model Development Multiple quasi-linear regression analysis was used to test whether the parameters in Eq. (1)
depend on the frying conditions. For this, model parameters (a, b) were calculated from 48 frying oil samples (4 frying loads×12 frying times). The results showed that the parameters generated from fitting of experimental data could be correlated with the frying conditions to a satisfactory level. In particular, a depends linearly on b (Eq. (4)), while b depends linearly on F2, tp2, F*tp, F*Tave, tp*Tave and tp (Eq. (5)). a 0.5839 34.84 b r 2 0.972 * SE int ercept 0.1732 , SE b 0.8648
(4 )
b 0.1877 3.820 F2 1.714 10 6 tp 2 1.520 10 3 F * tp 1.429 10 3 F * Tave 1.998 10 5 t p * Tave 2.891 10 3 t p r 2 0.985 4 * SE int ercept 6.902 10 , SE F2 1.145, SE t 2 5.150 10 7 , SE F*t p 5.213 10 4 , p SE F*Tave 3.551 10 4 , SE t p *Tave 2.841 10 6 , SE t p 4.762 10 4
(5)
The viscosity values (μmod) estimated with Eqs. (1), (4), and (5) were plotted against the experimental values (μexp) in a parity plot (Fig. 4a). The deviation, μmod−μexp, of all samples was less than ±1 mPa.s (top inset in Fig. 4a) and the proposed equations were confirmed to predict the viscosity with better than ±5% accuracy for most of the examined cases (bottom inset in Fig. 4a). Multiple quasi-linear regression analysis was also used to test whether the parameters in Eq. (1) depend on the chemical composition of the oil. For this, the chemical composition was divided into two variables, ECN and TPM. The TPM can be obtained directly from detection while ECN is calculated from FA composition using Eq. (3). The results showed that the relationship between a and b is the same as Eq. (4), and the other equation is as follows:
b 0.5774 4.769 10 2 ECN 6.232 10 4 T PM r 2 0.969 2 3 * SE int ercept 8.819 10 , SE ECN 5.499 10 , SE TPM 3.347 10 5
(6)
Estimated using Eq. (1), (4), and (6), viscosity values (μmod) were plotted against the experimental values (μexp) in a parity plot (Fig. 4b). The deviation, μmod−μexp, was less than ±1 mPa.s (top inset in Fig. 3b) and the prediction accuracy was almost better than ±5% (bottom inset in Fig. 4b). Based on the above two sets of equations, another multiple quasi-linear regression analysis was used to correlate the frying condition with the chemical composition, excluding viscosity. The final equation is as follow: T PM 3.881 2.935 10 3 t p 2.852 F * t p 1.788 10 2 t p * Tave 2.410 t p r 2 0.946 * SE int ercept 0.7106 , SE tp 2 1.111 10 3 , SE F*t p 0.5379 , SE t p *Tave 5.604 10 3 , SE t p 0.9394 2
(7)
The TPM values (TPMmod) estimated with Eq.(7) were plotted against the experimental values (TPMexp) in a parity plot (Fig. 4c). The deviation, TPMmod−TPMexp, of all samples was less than
±2% (top inset Fig. 4c) and the proposed equations were confirmed to predict the TPM with better than ±10% accuracy for most of the examined cases (bottom inset in Fig. 4c). For further analysis, a great change that ECN was removed was observed in Eq.(7). When determining best regression equation, ECN was removed due to its lowest significance below the threshold. The insignificant increases in ECN during frying may be responsible for the result. Also, Tave has a positive effect while F has a negative effect on increasing TPM, which is consistent with experimental results. The effect degree of F is almost 160 times more than that of Tave. This equation offers the possibility of predicting oil quality from process parameters, which would be convenient for on-site quality control. However, some parameters have thresholds: t p should be greater than zero, F could not increase infinitely, Tave is near the initial oil temperature. These thresholds were not taken into consideration in this study, and the correlation between frying conditions and oil quality still demands further and more detailed research. 5.
Conclusions We verified that the temperature dependence of frying oil viscosity can be described by
Lioumbas et al. (2012) non-linear model with satisfactory accuracy (r 2>0.9998). The viscosity model parameters are linearly related to the frying conditions and the chemical composition of the oil, both of which achieved reasonable accuracy (±5%). With viscosity excluded, a direct expression was derived from the average oil temperature, frying load and frying time to total polar materials (r2=0.946). For catering industry especially fast-food chain enterprises, this study should be of great value to frying process optimization and on-site quality control. Acknowledgements This work was funded by “National Natural Science Foundation of China (31401525)”. The authors also wish to thank companies including MacDonald, Cargill and Wilmar International for continuous support throughout this work. References Cao, W.M., Zhang, K.Y., Xue, B., Chen, F.X., Jin, Q.Z., Wang, X.G., 2013. Determination of Oxidized
Triacylglycerol
Polymers
by
Preparative
Flash
Chromatography
and
High-Performance Size-Exclusion Chromatography. Asian J Chem. 25(16), 9189-9194. Choe, E., Min, D.B., 2007. Chemistry of deep-fat frying oils. J Food Sci. 72(5), R77-R86. Franke, K., Strijowski, U., 2011. Standardization of Domestic Frying Processes by an Engineering Approach. J Food Sci. 76(4), E333-E340. Geller, D.P., Goodrum, J.W., 2000. Rheology of vegetable oil analogs and triglycerides. J Am Oil Chem Soc. 77(2), 111-114.
Gertz, C., 2000. Chemical and physical parameters as quality indicators of used frying fats. Eur J Lipid Sci Tech. 102(8-9), 566-572. Gertz, C., Klostermann, S., Kochhar, S.P., 2000. Testing and comparing oxidative stability of vegetable oils and fats at frying temperature. Eur J Lipid Sci Tech. 102(8-9), 543-551. Kalogianni E P, Karastogiannidou C, Karapantsios T D. Effect of the presence and absence of potatoes under repeated frying conditions on the composition of palm oil[J]. J Am Oil Chem Soc. 2009, 86(6): 561-571. Lioumbas, J.S., Ampatzidis, C., Karapantsios, T.D., 2012. Effect of potato deep-fat frying conditions on temperature dependence of olive oil and palm oil viscosity. J Food Eng. 113(2), 217-225. Min, D.B., Boff, J.M., 2002. Lipid oxidation of edible oil. Food Sci Tech-New York: Marcel Dekker, pp. 335-364. Naghshineh, M., Mirhosseini, H., 2010. Effects of frying condition on physicochemical properties of palm olein-olive oil blends. J Food Agric Environ. 8, 175-178. Naz, S., Siddiqi, R., Sheikh, H., Sayeed, S.A., 2005. Deterioration of olive, corn and soybean oils due to air, light, heat and deep-frying. Food Res Int. 38(2), 127-134. Nik, W.B.W., Ani, F.N., Masjuki, H.H., Giap, S.G.E., 2005. Rheology of bio-edible oils according to several rheological models and its potential as hydraulic fluid. Ind Crop Prod. 22, 249-255. Romero, A., Cuesta, C., Sánchez-Muniz, F.J., 1998. Effect of oil replenishment during deep-fat frying of frozen foods in sunflower oil and high-oleic acid sunflower oil. J Am Oil Chem Soc. 75(2), 161-167. Santos, J.C.O., Santos, I.M.G., Souza, A.G., 2005. Effect of heating and cooling on rheological parameters of edible vegetable oils. J Food Eng. 67(4), 401-405. Tyagi, V., Vasishtha, A., 1996. Changes in the characteristics and composition of oils during deep-fat frying. J Am Oil Chem Soc. 73(4), 499-506. Wang, T., Briggs, J., 2002. Rheological and thermal properties of soybean oils with modified FA compositions. J Am Oil Chem Soc. 79(8), 831-836. Ziaiifar, A.M., Achir, N., Courtois, F., Trezzani, I., Trystram, G., 2008. Review of mechanisms, conditions, and factors involved in the oil uptake phenomenon during the deep-fat frying process. Int J Food Sci Tech. 43(8), 1410-1423.
Fig.1. Temperature of soybean oil during frying at 1/100, 1/50, 1/25 frying load (kgpotatoes/Loil), at 165.6 ºC and 168.3 ºC initial frying temperature and for the fresh oil (average deviation ±2% calculated from three repetitions).
Fig.2. Apparent viscosity, μ, measurement at 60 ºC and 100 ºC for soybean oil as a function of shear rate, , conducted at high(1/25), middle(1/50), low(1/100) frying load (kgpotatoes/Loil), at 165.6 ºC and 168.3 ºC initial frying temperature for samples after 50 h frying.
Fig.3. Viscosity, μ, as a function of temperature and model parameter, b, as a function of frying duration. Results are for soybean oil for frying loads 1/100, 1/50 and 1/25 kgpotatoes/Loil at 165.6 ºC and 168.3 ºC initial frying temperature and for 12 frying oil samples selected from different durations (0-50 h) and fresh oil.
Fig.4. Parity plots between the measured values and those predicted by the present model over all the examined frying conditions (a), chemical compositions (b) and TPM (c). The small inset plots show the actual difference in viscosity values (top) and the % deviation (bottom) between the measured values and the model predictions.
Table 1 Experimental settings taken into account for the computation of a and b parameters. Parameter
Value
Type of oil
Soybean oil
Fryer’s specific heating power, Q/Loil, kW/L
7/10
Frying load, F, kgpotato/Loil
1/100, 1/100, 1/50, 1/25
Initial oil temperature, Tin, ºC
165.6, 168.3, 168.3, 168.3
Average oil temperature, Tave,ºC
165.5, 168.4, 168.2, 167.1
Frying duration, tp, h
50 (12h/day)
Frying batch time, min
12 (t1=3min; t2=9min)
Volume of fresh oil added, L
3.5, 3.7, 4.9, 6.7
Table 2 FA compositions (mol%) of frying oils, calculated Effective Carbon Numbers(ECN) and their total polar materials(TPM). Tin/ºC
-
165.6
168.3
168.3
168.3
F/(kgpotatoes/
-
1/100
1/100
1/50
1/25
tp/h
0
50
50
50
50
Loil)
C14:0
0.08 ± 0.02
0.20 ± 0.00
0.25 ± 0.00
0.30 ± 0.00
0.37 ± 0.02
C16:0
11.07 ±
15.45 ±
16.08 ±
18.41 ±
22.29 ±
0.07
0.03
0.05
0.05
0.20
C16:1
0.08 ± 0.00
0.11 ± 0.01
0.11 ± 0.00
0.11 ± 0.00
0.11 ± 0.02
C18:0
4.29 ± 0.02
4.68 ± 0.01
4.61 ± 0.01
4.57 ± 0.01
4.59 ± 0.02
C18:1
25.22 ±
28.05 ±
30.79 ±
33.79 ±
30.64 ±
0.04 C18:2
51.97 ± 0.06
0.07 45.68 ± 0.02
0.05 42.71 ± 0.09
0.05 37.94 ± 0.09
0.07 37.29 ± 0.16
C18:3
5.62 ± 0.00
4.47 ± 0.02
4.03 ± 0.02
3.53 ± 0.02
3.50 ± 0.02
ECN
16.012 ±
16.107 ±
16.127 ±
16.171 ±
16.161 ±
0.03a
0.03b
0.03b
0.03c
0.08c
TPM/%
4.1 ± 0.05a
24.3 ± 0.26b
24.7 ± 0.14b
23.8 ± 0.05b
Values in the same line with different superscripts are significantly different (P<0.05).
21.3 ± 0.00c
Fig.1.
Fig.2.
Fig.3.
Fig.4.
Highlights 1. 2. 3.
Experimentally determined oil μ–T profiles during potato deep fat frying. Effective carbon number in rheology is used to represent fatty acid composition alteration in frying oil. Effect of fatty acid composition and total polar materials on μ(T) is examined. 4. An engineering expression is proposed for the prediction of oil deterioration from frying conditions.