20120130407002

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME

ENGINEERING AND TECHNOLOGY (IJARET)

ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 4, Issue 7, November-December 2013, pp. 10-19
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2013): 5.8376 (Calculated by GISI)
www.jifactor.com

IJARET
©IAEME

DETERMINATION OF STRUCTURAL DAMAGE DURING SLOW
FREEZING IN PORK CUTS (LONGISSIMUS DORSI)
Rosalía MELENDEZ-PEREZ1,2, Marta E. ROSAS-MENDOZA2,
Rodrigo R. VELAZQUEZ-CASTILLO1, José Luis ARJONA-ROMAN2
1

Universidad Autónoma de Querétaro, Facultad de Ingeniería. Centro Universitario, Cerro de las
Campanas s/n C.P. 76010, Santiago de Querétaro, Qro. México. Tel. 55 58 17 27 34
2
Facultad de Estudios Superiores Cuautitlán UNAM, Laboratorio de Análisis Térmico y Estructural
de Alimentos. UIM-L13. Carretera Cuautitlán Teoloyucan Km 2.5, Col. San Sebastián Xhala.
Cuautitlán Izcalli, Edo. Méx. C.P. 54714, México.

ABSTRACT
Meat freezing has been traditionally studied according to the effect of the storage, transport
and/or the display to the consumer. This study focused on the statistical analysis of the structural
damage in cuts of pork loin (Longissimus dorsi), during slow rate freezing process with fluctuations
of temperature and controlled relative humidity. Structural damage was seen as the area of the cavity
caused by the ice crystal’s formation, assessed by histological analysis. The associated behavior with
experimental errors was adjusted under a statistical protocol, to establish the dependence of the
damage growth depending on the time and temperature during the process, with an improvement in
the polynomial behavior of 86 to 97%. Structural damage was presented at the end of the process a
maximal area of 150.04 µm2 and temperature of -9.9 ° C. The representative rate of ice crystal
growth in meat had a high value at the beginning of freezing with 41.08 µm2/°C at -3.25°C and an
average value of 11.1 ± 5.5 µm2/°C until to the end of process. Final area is consistent with the
results presented by other authors.
Key words: Meat, Freezing, Structural damage, Statistical analysis.
1. INTRODUCTION
During freezing transport and storage, continual cycles of thawing - freeze occur because of
the temperature variation, which are very common in the retail, at home or a restaurant and bring the
deterioration in food products such as meat. Actually, these fluctuations in temperature have not been
10


studied throughout the freezing process, mainly at the moment of water phase change. The ice
crystals formed in meat structure during slow freezing promote morphological changes and cellular
destruction. The damage, as well as components degradation, can result in a texture change and the
expelled juices or exudates produced after thawing. These crystals are usually difficult to observe
due to their dynamic variations in morphology, size, configuration, color and transparency. Freezing
effect determination over the structural damage is not clear in most cases and there are not statistic
concepts applications, in order to have sufficient result´s reliability. Gormeley, Walshe, Hussey &
Butler (2002) and Ballin & Lametsch (2008) studied the temperature variation effect during process
and storage over physical alteration in relation to the binding capacity and water distribution during
freezing. The effect of environment temperature and door aperture on energy consumption has been
determined (Saidur, Masjuki & Choudhury, 2002). Few studies have been focused on the structural
damage (cavities), among them: Ngapo, Babare, Reynolds & Mawson (1999) studied freezing time
and temperature combinations, during thawing and frozen storage, in 150 cavities. Ishiguro &
Horimizu (2008); Qu, Komori & Jiang (2006), evaluated the three-dimensional behavior in frozen
and thawing cells, the variation in morphology, as well as the configuration of ice crystals without
establishing the amount of samples. Sifre, André & Coton (2009) determined the cavities uncertainty
in 50 images, obtained in flesh separated from the bone, using a destructuration indicator for fiber
muscle. Pawlas, Nyengaard & Jensen (2009), used information from other sources to determine the
cell volume with a strictly statistical approach, based on variance estimation; they considered the
variability and the error in the sample size (with a direct impact on the average and standard
deviation) and surface or area to be measured; these errors are independent and present a logarithmic
behavior. These last authors based their study on a stochastic process, used to characterize random
phenomena.
However, research on the temperature variation effects during meat freezing, requires a
further statistical approach to determine the uncertainty on the destructuration measurement, not only
by the variance determination, else by the distribution behavior and its adjustment, based on
statistical values and representative sample size choice.
The present study is focused in the use of different statistical techniques to improve the structural
damage evaluation, measuring the cavities areas through histological images obtained by microscopy
during the slow convection freezing process. The increasing in crystal represents a structural damage
in meat cuts are due to slow freezing and temperature variation by the aperture and close of the
freezer door.
2. MATERIALS AND METHODS
2.1 Sample preparation
Three pork loins were used (Longissimus dorsi) from York breed pigs (male six months age
healthy gelded, with 110 kg average weight), obtained under the same slaughter conditions. The
pieces were obtained 48 h after the slaughter and stored under controlled cooling conditions (4°C).
Each used loin weighed approximately 3.7 kg and acquired with a local provider.
2.2. Chemical composition analysis
Following analyses were carried out according to the AOAC (1995) official methods:
moisture content (950.46), protein (928.08), fat (976.21) and ash (920.153); pH with meat depth
meter HI99163 (Hanna Instruments, Romania). The determinations were made in triplicate.
2.3 Meat freezing
Pork loin was sectioned into 33 slices, 1 cm thick. A fresh slice meat at 23°C was used as
sample control. The samples were frozen without packaging (Anderson, 2007) in a vertical freezer
11


CV-16 (TOR-REY, México) with 1.7 m/s stream air, at -25°C ± 2°C temperature; average external
environmental conditions was 48.43% RH and 25°C during experimentation. A thermocouple type
"T" previously validated, placed in the meat slice center was used for register the freezing
temperature. At the same time, the freezer internal environment temperature was measured.
Temperature profiles were obtained with a data logger every 30 seconds using a digital indicator
SR630 (Stanford Research System, Sunnyvale, CA, USA) coupled to a computer. During the
freezing process, samples were taken at the fresh meat temperature, 5, 0 and -2°C (IFT), looking for
any damage during cooling. From -2°C temperature, sampling were carried out every 3 minutes
during phase change zone, until -10°C. The freezer chamber external environment conditions were
monitored, with an average of 48.4% RH and 25°C temperature.
2.4. Histological analysis
For the ice crystal´s growth, the damage determination was made in 1 cm3 subsamples of
each slice, were fixed in a Bouin mixture at 4°C for 24 h (Brancroft, Stevens & Turner 1990).
Histological analysis was performed by the paraffin routine inclusion method (Garrido, Cornejo,
Martínez, Reyes, Alba & Tórtora, 2007). Cuts of 4 µm in thickness were made using a microtome
RM2125RT (Leica Biosystems, Nussloch, Germany), and were stained using the technique of
hematoxilin-eosin routine. Observation and image capture was carried out with an optical
microscope Axioskop 40 (Carl Zeiss, Göttingen, Germany) coupled to a digital camera SSC-DC54A
(Sony Electronics Inc., NJ, USA). Three photomicrographs were taken of each sample, and the
images were evaluated with the Image-Pro Express 4 Analyzer; 15 cavities for photomicrograph
(total of 45 cavities) were analyzed to determine the area. The corresponding ice crystal growth area
was determined by the perimeter of each cavity, calculating the area in µm2.
2.5 Statistical analysis
In the damage analysis, areas were statistically analyzed to obtain central and dispersion
tendency measures, and to estimate the difference between areas in the photomicrographs; variance
analyses for each sampling condition were performed.
Confidence intervals were determined at 95% for the mean, median and standard deviation,
applying the Tukey test at the same confidence value. The residual values and normality graphics as
well as the histogram and ANOVA´s comparison were analyzed.
The corresponding adjustments with an appropriated sample size determination for atypical
values elimination and apply a good normality adjustment were made to improve the damage area
increase in function of time.
All data were analyzed using statistical software MINITAB 15 (Minitab Inc., State College,
Pennsylvania, US).
3. RESULTS AND DISCUSSION
The average values obtained from raw material analysis were as follows: pH= 5.4 ±0.65,
moisture= 75.3% ±1.19, fat= 1.867% ±0.90, protein= 21.83 ±2.54 and minerals 1.07 ±0.09. These
results are in agreed to the previously reported by other authors for pork loin. (Cannata, Engle,
Moeller, Zerby, Radunz, Green, Bass & Belk, 2010; Chiavaro, Rinaldi, Vittadini & Barbanti, 2009).
3.1. Thermal behavior of the freezing chamber
Figure 1 shows the meat freezing thermal profile, the chamber temperature behavior and its
coefficient of variation (CV). The total process time was 107 minutes, 23 minutes (21.4%)
corresponds to the cooling until the Initial Freezing Point (IFP), 72 minutes (67.29%) to the freezing
or phase change zone and ice crystals growth and 12 minutes (11.21%) to the subcooling. The
12


average beginning freezer temperature value was -11.30°C, with a confidence interval at 95% of 10.60 to -12.1°C and a CV between 0.40 and 26.98%. The door aperture and close frequency bring
about temperature variations and heat transfer coefficient changes.

Figure 1. Thermal profile and coefficient of variation for Meat Freezing
The freezing chamber relative humidity (RH) average was 2.21% with 73.36% CV; these low
values are related to meat surface dew temperature, which corresponds to 100% humidity or
saturation temperature (Lee & Ro, 2005). External environmental conditions promote the ice melting
and recrystallization over the meat cuts by the close and aperture of the freezer door, damaging the
cellular structure and affecting the quality. In this sense, Carballo, Cofrades, Solas & JiménezColmenero (2000) reported that there is a decrease of the meat protein’s functionality during
freezing, storage and thawing by denaturation; this modifies its aggregation state and cause water
losses and change in textural properties. However, the aforementioned changes in the freezer
conditions, lead the superficial frost formation, modifying the freezer thermal stability (Gormeley,
Walshe, Hussey & Butler 2002 and Qu, Komori & Jiang, 2006), as well as in meat, the linkage
ability, water distribution and some fatty compounds modification during the process.
3.2. Conventional statistical analysis of the ice crystals area
The measured area for each cavity is understood like the degree of the ice crystals growth
during the freezing process. Figure 2 shows that the increase in area, due to cutting damage during
sample preparation, was uniform with R = 98% for the cooling period (first 23 minutes) before IFP.
This behavior was also attributed to the chamber conditions homogeneity, allowing the temperature
stabilization. After 32 minutes of the onset freezing process (3 min after IFP) the damage area
measurements (Figure 2), showed significant fluctuations (pronounced crest and valley), caused by
the chamber temperature unsteadiness, the surface frost formation and the ice melting and
recrystallization process.

13


Figure 2. Behavior of the corrected and measured area during Meat Freezing
From the total values measured, during cooling until subcooling, the damage was from 3.90
to 206.96 µm2 in area. The central tendency measures, show a mean damage of 80.63 µm2 and a
median of 48.03 µm2; there is not a normality behavior, which is confirmed with the positive value
of the asymmetry coefficient (5.27) and the value of the Anderson-Darling test (P= 0.005). Also, was
observed a high dispersion, with S=114.73 µm2 and a CV of 142.29%. The confidence interval for
mean (74.70≤ µ ≥86.56) and median (46.03 ≤ µ ≥ 51.89) indicates that a lot area values were lower
than 100 µm2. Table 1 presents the prediction equations for measured and corrected data for damage
areas behaviors. The best response corresponds to a third order polynomial equation.
Table 1. Adjustment equation of measured damage area
Regresión

Original area (µm2)

Corrected area (µm2)

Equation

R2 (%)

Equation

R2
(%)

Lineal

y = 5.35x - 7.65

0.84

y = 3.66x - 0.25

0.90

Exponential

y = 12.21e0.094x

0.80

y = 11.13e0.0855x

0.81

Polinomial

y = 0.014x3 - 0.62x2 +
12.24x - 21.62

0.88

y = 0.007x3 - 0.34x2 +
7.94x - 11.66

0.92

A one-way ANOVA analysis for areas comparison at different sampling conditions, during
cooling and freezing, rejects the hypothesis of mean equality (F=12.78 and P =0.000) applying the
Tukey test at 95%. Figure 3 related to residuals, indicates a normality and symmetry unkindness, and
also shows atypical and abnormal values, which implies a lack of consistency in the variance.

14


Figure 3. Statistical residual analysis for the total damage area
3.3. Improvement behavior of the ice crystals size
Measurements of area for ice crystal were analyzed in more detail to determine the data set
out of expected behavior and eliminate the atypical values, establishing the appropriate sample size.
After ANOVA analysis, the box plot (not showed), represent the atypical values that should be
eliminated in each condition. However, before elimination, an appropriate and population
representative sample size was determined by the equation that considers sampling without
replacement in a finite population:
n=

(1)

NZ 2 σ 2
e 2 ( N − 1) + Z 2 σ 2

Where n is the sample size, N the population size, Z typical value at 90% confidence, σ2 the
population variance and e the permissible error.
Figure 4 presents, the behavior comparison and the statistical results for an 83 minute’s
process: in Figure 4a the measured data and Figure 4b the adjusted or corrected values. It is observed
an improvement in the normality behavior and in the confidence intervals for both the mean and
median; also, a greater concordance between the mean and median approaching to the normalized
curve. The standard deviation decreases to 41.63 and also the CV at to 43.5%, so that is observed a
greater homogeneity in the areas and an improvement in the adjusted behavior for the freezing effect
on the structural damage analysis.
15


a

b

Figure 4. Comparison of statistical behavior in sampling condition at 60 minutes: 4a. Measured
data, 4b. Corrected data
Photomicrographs corresponding to histological analysis of fresh and frozen meat at different
hotomicrographs
sampling times are shown in Figure 5
igure 5.

(5min) Fresh meat

(23 min) Meat at IFP

Meat at 29 min

Meat at 47 min

Meat at 56 min

Meat at 65 min

Meat at 83 min

Meat at 101 min

Meat at 104 min

Figure 5. Microphotograph (40X) for the damage determination at different sampling times
16


Is observed that the fresh meat image dose not present damage; the display cavities can be due to
manipulation during preparation. In the next pictures up to 29 minutes, the damage caused by freezing is
represented by differences in the meat surface structure; this observation is in good agree with the
presented by Do, Sagara, Tabata, Kudoh & Higuchi (2004), demonstrating that the variation in
morphology occurs during the freezing process and not only as a final effect, which is commonly
reported.
Martino, Otero, Sanz & Zaritzky (1998) reported that if temperature fluctuation exists in a
freezing process, a crystals melting exists for each Celsius degree increasing the temperature; also that
the rate of ice nucleation arose in approximately ten times. These same authors established that the
solutes concentration in the unfrozen phase increases gradually, decreasing the vapor pressure and
promoting a cellular dehydration by the water diffusion from cells and therefore, formation ice crystals of
great size. However, if the frost surface exists, it can help to minimize dehydration of food preventing the
juices exudates (Cheng & Cheng, 2001).
In fresh meat samples, at 35 and 42 minutes of the process, the cavities were homogeneous (less
than 10% CV); from the initial freezing temperature (-2.1°C at 23 minutes), the cavity area gradually
increased in size up to 35.76 µm2, representing an increase of 10.9% CV. From 38 to 80 minutes, during
the water phase change, the area increase to 51% CV in relation with the initial value, and a mean of
101.66 µm2. At 107 minutes and temperature of –9.9°C, the area had increased to 150.03 µm2. This
situation of drastic changes in the damage area is representative of the slow freezing effect (about
0.5°C/min) also to the recrystallization by the continuous freezer door aperture and close.
The area behavior adjustment during the process aforesaid; both equations and residual behaviors
of them can consider that any of the behaviors may be accepted for it high adjustment coefficient (R2).
However, the polynomial model defines in a more accurate way the damage growth, with an adjustment
increase of 4.4%; moreover, the axe interception is improved and close to zero as the initial area. All
these results confirm the convenience of statistical analysis application as ANOVA, non linear
regression, and appropriated sample size, as well as normality distribution verification, recommended by
Kozak (2009) in the sense that this type of analysis allows a better reliability on results.
Figure 6 shows the progressive modification of areas during the process time: the damage
maximal area value was 150.04 ± 14.9 µm2, that is in agree with Do, Sagara, Tabata, Kudoh & Higuchi
(2004) and Ngapo, Babare, Reynolds & Mawson (1999). Also can be seen applying this statistical
technique, that the rate of crystal growth is most important at the beginning of the freezing process close
to initial freezing point with a rate of 41.08 µm2/°C at -3.25°C and an average value of 11.1 ± 5.5 µm2/°C
until to the end of process. That mind in first approximation so a greater structural and cellular damage in
meat is expected close to IFP rather than a lower temperature.

Figure 6. Improvement behavior of damage area in function of temperature
17


4. CONCLUSION
The structural damage during the slow freezing rates on Longissimus dorsi meat cuts is
caused by the constant freezer door aperture and close, simulating commonly practice conditions at
domestic and/or industrial food preservation.
The damage measurement was established as an area and not as diameter or radius since the
ice crystal growth had not a spherical or uniform shape.
The one-way ANOVA analysis, with arbitrary data elimination, cannot explain well the effect
of temperature fluctuations and its consequences over the meat damage.
Improvement for the area damage analysis is the application of statistical techniques, like
appropriated sample size determination for atypical values elimination, in order to apply a good
normality adjustment. This analysis technique of ice crystal growth allows an acceptable reliability to
evaluate the structural damage by effects of low temperature application in meat cuts.
In agree, at high rate of ice crystal growth the area of the maximal structural damage in pork meat
cuts is near to IFP when the aperture and close freezer door are frequently.
ACKNOWLEDGEMENTS
The authors present their thanks to the financial support given by DGAPA-UNAM to the
PAPIIT key IN204506-2 project. To the Dr. Tonatiuh Cruz Sánchez of the Microbiology laboratory
(FES-Cuautitlán UNAM), and to Dr. Germán Garrido Fariña of the Histology and Biology
laboratory (FES-Cuautitlán UNAM) for their assistance in the experimental part implementation.
REFERENCES
1. Anderson, S. (2007). Determination of fat, moisture, and protein in meat and meat products
by using the FOSS Food Scan TM near-infrared spectrophotometer with FOSS artificial
neural network calibration model and associated database: collaborative study. Journal of
American Official of Analytical Chemists International, 90 (4), 1073–1083.
2. AOAC. (1995). Official Methods of Analysis of AOCC International (17th Ed.) Association
of Official Analytical Chemists, Washington, D.C.
3. Ballin, N. Z., & Lametsch, R. (2008). Analytical methods for authentication of fresh vs.
thawed meat – A review. Meat Science, 80, 151-158.
4. Brancroft, J. D., Stevens, A., & Turner, D. (1990). Theory and practice of histological
techniques. Churchill Livingstone, London.
5. Cannata, S., Engle, T.E., Moeller, S.J., Zerby, H.N., Radunz, A.E., Green, M.D., Bass, P.D.,
& Belk K.E. (2010). Effect of visual marbling on sensory properties and quality traits of pork
loin. Meat Science. 85, 428–434.
6. Carballo, J., Cofrades, S., Solas, M. T., & Jiménez-Colmenero, F. (2000) High
pressure/thermal treatment of meat batters prepared from freeze-thawed pork. Meat Science,
54, 357-364.
7. Cheng, Ch-H., & Cheng, Y-Ch. (2001). Predictions of frost growth on a cold plate in
atmospheric air. International Communications in Heat and Mass Transfer, 28, (7), 953-962.
8. Chiavaro, E., Rinaldi, M., Vittadini, E., & Barbanti, D. (2009). Cooking of pork Longissimus
dorsi at different temperature and relative humidity values: Effects on selected physicochemical properties. Journal of Food Engineering, 93, 158-165.
9. Do G-S., Sagara, Y., Tabata, M., Kudoh, K., & Higuchi, T. (2004). Three-dimensional
measurement of ice crystals in frozen beef with a micro-slicer image processing system.
International Journal of Refrigeration, 27, 184–190.
18


10. Garrido, F., Cornejo, C., Martínez, R., Reyes, E., Alba, H., & Tórtora, P. A. (2007). Study of
the process of apoptosis in animals infected with the contagious ecthyma virus. Veterinary
Microbiology, 129, 28-39.
11. Gormeley, R., Walshe, T., Hussey, K., & Butler, F. (2002). The effect of fluctuating vs.
constant frozen storage temperature regimes on some quality parameters of selected food
products. Lebensmittel-Wissenschaft und-Technologie, 35, 190–200.
12. Ishiguro, H., & Horimizu, T. (2008). Three-dimensional microscopic freezing and thawing
behavior of biological tissues revealed by real-time imaging using confocal laser scanning
microscopy. International Journal of Heat and Mass Transfer, 51, 5642–5649.
13. Kozak, M. (2009). Analyzing one-way experiments: a piece of cake or a pain in the neck.
Scientia Agricola, 66 (4), 556-562.
14. Lee, Y. B., & Ro, S.T. (2005). Analysis of the frost growth on a flat plate by simple models
of saturation and supersaturation. Experimental Thermal and Fluid Science, 29, 685–696.
15. Martino M. N., Otero, L., Sanz P. D., & Zaritzky N. E. (1998). Size and location of ice
crystals in pork frozen by High-Pressure-assisted freezing as compared to classical methods.
Meat Science, 50 (3), 303-313.
16. Ngapo, T. M., Babare, I. H., Reynolds, J., & Mawson, R.F. (1999). Freezing rate and frozen
storage effects on the ultrastructure of samples of pork. Meat Science, 53, 159-168.
17. Pawlas, Z., Nyengaard, J. R., & Jensen E. B. V. (2009). Particle sizes from sectional data.
Biometrics, 65, 216–224.
18. Qu, K., Komori, S., & Jiang, Y. (2006). Local variation of frost layer thickness and
morphology. International Journal of Thermal Sciences, 45, 116–123.
19. Saidur, R., Masjuki, H. H, & Choudhury, I.A. (2002). Role of ambient temperature, door
opening, thermostat setting position and their combined effect on refrigerator-freezer energy
consumption. Energy Conversion and Management, 43, 845–854.
20. Sifre, L., André, B., & Coton, J-P. (2009). Development of a system to quantify muscle fiber
destructuration. Meat Science, 81, 515–522.
21. Marta E. Rosas-Mendoza and Jose L. Arjona-Roman, “Ultrasound as Pre-Treatment for
Osmotic Dehydration of Mango (Mangiferaindica L.) Ataulfo”, International Journal of
Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 6, 2013,
pp. 142 - 152, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

19

20120130407002

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (20)

Similar to 20120130407002

Similar to 20120130407002 (20)

More from IAEME Publication

More from IAEME Publication (20)

Recently uploaded

Recently uploaded (20)

20120130407002