International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
INTERNATIONAL JOURNAL OF AD...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
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20120130407002

  1. 1. 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
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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. 10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME 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

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