Evaluating Cooking Losses Of Meat Emulsions By Light Scatter

Transcription

1 Evaluating Cooking Losses Of Meat Emulsions By Light Scatter Gema Nieto a,c, Manuel Castillo b,c, Fred Payne c ; Youling Xiong d a Department of Food Technology, Human Nutrition and Food Safety. University of Murcia, Spain (gnieto@um.es) b Department of Animal and Food Sciences. Universitat Autònoma de Barcelona, Barcelona, Spain (manuel.castillo@uab.es) c Department of Biosystems and Agricultural Engineering. University of Kentucky, KY, USA (fred.payne@uky.edu) d Department of Animal and Food Sciences. University of Kentucky, KY, USA (ylxiong@ .uky.edu) ABSTRACT Frankfurters are the most popular comminuted products in the US and account for the 25% of all sausages sold (NHDSC, 2006). Base on an average cooking loss (weight, %) of 2.64 (optimum chopping conditions), the estimated economic loss resulting from non optimum emulsion stability during the cooking process was estimated to range between 0.2 and 1.65 billion dollar per year (only in the US). A number of off-line variables have been defined to characterize meat emulsions (emulsion capacity, emulsion stability, droplet size, etc.). However, no method is currently available to predict the emulsion stability during the chopping process, while corrective actions are still feasible. Improving process control and automation of the meat emulsification process will reduce the economical impact of emulsion breakdown in meat industry worldwide. The objective of this study was to determine if light backscatter intensity measured during the chopping process could be used to predict the susceptibility of the emulsion to loose weight during cooking. A completely randomized factorial design with two fat levels (15, 30%), two hydrolyzed potato protein (HPP) levels (0, 2.5%) and three replicates were used in this study to obtain a wide range of lipid oxidation susceptibility. HPP levels were included as a natural source of antioxidant agents. Adjusted proportions of pork lean and fat meats, HPP, NaCl, water and ice were chopped up to an emulsion temperature of 15 C. The optical properties of the raw emulsions were characterized by visible-nir light backscatter measurements. CIELAB colour coordinates of raw emulsions were determined using a portable colorimeter. Weight loss of cooked emulsions was also measured. The best single-, two-, three- and four-variable models for predicting cooking losses were selected using the SAS Maximum R 2 procedure. The variables taken into consideration in the selection procedure were the colour coordinates and the optical parameters obtained from the light scatter spectral scan. Several colour and light scatter parameters showed potential for online prediction of cooking loss. Keywords: Fiber optic; light scatter; prediction; frankfurters; cooking loss.

2 INTRODUCTION Meat emulsions such as frankfurters and bolognas are finely comminuted and cooked products composed of water, muscle proteins, fat particles, salt and small amounts of non-meat ingredients, where meat proteins serve as natural emulsifier. In this group of processed meat products, fat and protein concentration and their chemical interactions, especially those occurring during the emulsification process, exert a market impact on the quality of the final product as they affect emulsion stability. Meat emulsion stability and cooking losses depend on fat stabilization by proteins. According to Barbut [1], fat stabilization during chopping is due to the formation of a proteic film around the fat particles that allows retaining fat inside the protein matrix. During chopping, certain attractive forces contribute to hold the raw materials together and create a homogeneous matrix structure [2]. Excessive reduction of fat particles size and inadequate soluble protein extraction or fat to protein ratio could lead to reduced emulsification capacity and increased fat oxidation tendency. Finely comminuted meat products are an integral part of diet in developed nations like the US [3] or the EU and have great economic importance. Frankfurters and bolognas are the most popular comminuted products in the US and account for the 25% of all sausages sold [4]. Base on an average cooking loss (weight, %) of 2.64 (optimum chopping conditions), the estimated economic loss resulting from non optimum emulsion stability during the cooking process was estimated to range between 0.2 and 1.65 billion dollar per year (only in the US). Improving process control and automation of the meat emulsification process will reduce the economical impact of emulsion breakdown in meat industry worldwide. Currently, there is a lack of an effective inline technology to select the optimum duration of the chopping process during meat emulsification that results in maximum emulsion stability during the heat treatment. Control improvement will require the development of suitable sensor technologies to monitor the optimum level of emulsification that would maximize yield, quality and consistency of the finely comminuted meat products. The application of light scatter sensors for process control and optimization has been deeply studied in the dairy industry. Castillo et al. [5] designed an optical sensor for monitoring milk coagulation and curd syneresis to predict the changes in moisture while the curd is draining in the cheese vat. In meat, various methods have been proposed over the years to short and classify meat that could assist on preventing cooking losses induced by inappropriate meat emulsification i.e., to determine PSE meats [6], but there is a lack of an effective inline method to monitor meat emulsification. The use of a real-time meat emulsion stability sensor technology having the ability to determine the optimum chopping endpoint would significantly improve the current control over the chopping process preventing both under and over-chopping defects, which would result in evident final product yield, consistency and quality gain. According to Álvarez et al. [7], monitoring meat emulsification would require a sensor capable of providing a representative signal from the beginning of the process, when the sample is extremely heterogeneous, to the end of the process, when the sample is relatively homogeneous. The goal of this work was to establish whether light scatter measured at several radial distances from the light source in fresh pork emulsions having a range of lipid oxidation and emulsion stability tendencies could be used to predict susceptibility of the emulsion to phase separation during cooking. MATERIALS & METHODS Experimental design and meat emulsion manufacturing A completely randomized factorial design with two factors and three replications was used. Two different HPP levels (0 control (HPP) and 2.5% (2.5HPP) w/w) were tested within a range of emulsion breakdown and lipid oxidation tendencies that were induced by using two different levels of fat (15 and 30% w/w; i.e., fat/lean ratios of 0.18 and 0.43, respectively). A total of 12 tests (N = nab = 3 2 2) were conducted with this design. Cooking losses was determined in the cooked meat emulsions. Reflection photometry parameters (CIELAB colour coordinates) and light scatter measurements were collected from fresh emulsions. For furthers details regarding meat samples preparation, HPP preparation, meat emulsion manufacturing and measurement of mentioned final product quality indices see Nieto et al [8]. Cooking losses Once the chopping process was completed, cooking losses (C L ) of each emulsion sample was measured in triplicate. C L was calculated from the weight of the final cooked emulsion (W F ) and the initial weight (W 0 ) of the sample before cooking as follows:

3 CL WF / W 0 Light backscatter measurement of raw emulsions A dedicated laboratory optical sensor prototype was designed, built and tested [9] in the Food Engineering Lab (University of Kentucky) to measure light scatter of comminuted meats at different distances with the aim of identifying and detecting physical chemical changes occurring during chopping that may be correlated to emulsion stability. This optical sensor prototype was designed to set the radial distance between the emitting and detecting optical fibers by means of a micrometer. Two small plastic probes were built and configured such that light scatter from the sample could be detected using a High-Resolution Fiber Optic Spectrometer (Model HR4000, Ocean Optics, Inc., Dunedin, FL, USA). The light source utilized was a tungsten halogen ( nm) bulb (LS-1, Ocean Optics, Inc.). Fiber optic cables were manufactured using 600 µm diameter fibers (Spectran Specialty Optics, Avon, CN, USA). The terminating (i.e., measuring) ends of the two fibers were built into the plastic probes while the other two ends were connected, using an SMA connector, to the spectrometer and light source, respectively. The data acquisition system consisted of a PC connected by a USB port to the spectrometer and programmed for data acquisition with SprectraSuit Spectroscopy platform software (Ocean Optics, Inc.). Before each measurement, the terminating ends of the fibers were aligned vertically and horizontally to the same level. Emulsion samples were placed in a doublejacketed sample holder. The fiber tips were immersed into the emulsion sample up to a final depth of 12.7 mm from the surface of the sample. The temperature of the sample was controlled by means of connecting the sample holder to a water bath (Lauda Ecoline RE220. Brinkman Instruments Inc. NY. USA; ± 0.01 ºC of accuracy). The temperature of the emulsion was monitored using a Traceable Thermometer inserted into the emulsion. An opaque enclosure was used to isolate the sample from ambient light interferences. Light scatter intensity of the samples was measured at the target radial distances, and at an integration time (IT) ranging from 19 to 60 s, where IT was the detector light exposure time. The light scattering spectral scans, I(λ), were automatically processed by subtracting the respective dark spectral scans and dividing by the IT to give the light scattering normalized spectral scans, I(λ) (bits s -1 ). A number of optical parameters defined in Table 1 were obtained from the normalized spectral scans. Table 1. Definition of optical parameters derived from the normalized light scatter intensity spectral scan Peak position I P 1 ( nm) I 1 1 P 2 ( nm) I 2 2 P 3 ( nm) I 3 3 P 1, 2, 3 : peak 1, 2 y 3; I, normalized light scatter intensity;, wavelength Statistical Analysis For the analysis of data SAS software (SAS, version 9.1, , SAS Institute, Cary, NC, USA) was used. The best models for prediction of emulsion stability parameters were obtained using the variable selection procedure "maximum R 2 " of SAS [10]. RESULTS & DISCUSSION The best single-, two-, three- and four-variable models for predicting cooking losses were selected using the SAS Maximum R 2 procedure. The variables taken into consideration in the selection procedure were the colour coordinates (L*, a*, b*, C* ab, Hº ab) measured by a colorimeter and optical parameters obtained from the light scatter spectral scan (as defined in Table 1). Replication was included in the model as a class to account for possible differences between individual replicates of the same treatment (i.e., same composition but different emulsification process). Prediction of cooking losses. As shown in Table 2, the best single variable model (model I) contained the independent variable L* an intercept. This model explained 77% of the variability observed in cooking losses (C L ). Model II showed that the second best predictive parameter of C L was b*. The inclusion of b* in Model I increased the coefficient of determination (R 2 ), from 0.77 to The best three-parameter model (Model III) included the optical variables L*, the chromatic coordinate, b* and wavelength of peak 2 (λ 2 ) as C L predictors and had an R 2 value of Inclusion of I 2 on Model III did not increase the R 2 value (Model IV). Finally, inclusion of I 3 in

4 Model IV (Model V) increased the determination coefficient slightly, from 0.96 to According to these results, colour parameters, L*, b* and λ 2, show potential for online prediction of C L. Note that optical L*, b* can be directly calculated from the light scatter spectral scan measured using our proposed sensor, which will allow the use of color parameters in predictive equations without using a colorimeter. It should be also noted that our predictions only used optical parameters and there is no need for including compositional parameters or experimental conditions (e.g., temperature, ph, etc.) as predictors. A practical C L prediction model for industrial application would require at least two or three predictors measured inline during chopping by the sensor. A few studies have dealt with the use of inline instrumental devices to minimize C L during chopping and/or optimize the texture of the cooked product. Barbut [11] detected a correlation between L* and C L and proposed the use of a fiber optic probe as a quality control tool to monitor and predict C L in meat emulsions. Koolmes et al. [12] proposed conductimetry measurements to determine the stability of meat emulsion while Allais et al. [2] successfully predicted the final texture of cooked meat emulsions using front-face fluorescence spectroscopy. In the same line, Álvarez, et al. [13] suggested that light extinction spectroscopy could provide information about emulsion stability. Table 2. Models for the prediction of cooking losses in meat emulsions. Model β 0 β 1 β 2 β 3 β 4 R 2 I*** C L =β 0 + Lβ *** *** II*** C L = β 0 + L*β 1 + b*β *** *** 0.62 *** III*** C L = β 0 + L*β 1 + b*β β ns *** 0.72 *** ns IV*** C L = β 0 + L*β 1 + b*β β 3 + I 2 β ns ** 0.79 ** 0.34 ns ns 0.96 V*** C L = β 0 + L*β 1 + b*β 2 + I 1 β 3 + I 3 β ** ** 0.59 * * * , wavelength peak 2; I 1, normalized light backscatter intensity peak 1; I 2, normalized light backscatter intensity peak 2; I 3,, normalized light backscatter intensity peak 3; β 0, β 1, β 2, β 3, β 4 : regression coefficients; L*, Lightness; b*: Yellowness. *P<0.05, **P<0.01, ***P< CONCLUSION It could be concluded that the use of a fiber optic light scatter sensor to monitor meat emulsification during the chopping process allows successfully predicting cooking losses tendency in the cooked product using optical parameters alone. The proposed optical measurement system and obtained predictive equations strongly suggest that the development of an inline light scatter sensor for chopping time optimization is feasible. Chopping time optimization will maximize yield and final cooked sausage quality. Further research and development efforts are warranted to improve accuracy of the predictions. REFERENCES [1] Barbut, S. (1998). Use of a fiber optic probe to predict meat emulsion breakdown. Ital. Journal Food Science. 3, 253. [2] Allais I. Christophe V. Pierre A. & Dufour E A rapid method based on front-face fluorescence spectroscopy for the monitoring of the texture of meat emulsions and frankfurters. Meat Science, 67, [3] USDA (United States Department of Agriculture) Meat animal s production disposition and income 2004 summary. [4] NHDSC (National Hot Dog and Sausage Council) Vital hot dogs statistics. Washington, USA: AMI Foundation. [5] Castillo M. Payne F. A. & Shea A Development of a combined sensor technology for monitoring coagulation and syneresis operations in cheese making. In Proceedings of ADSA annual meeting. Cincinnati, Ohio, USA. [6] Bendall J. R. & Swatland H. J A review of the relationships of ph with physical aspects of pork quality. Meat Science, 24, [7] Álvarez D. Payne F.A. Castillo, M., & Xiong, Y.L Application of backscatter Light extintion tyo determine the stability of beef emulsions with different fat/lean ratios. ASABE, Annual International Meeting, Minneapolis, Minnesota, USA.

5 [8] Nieto, G., Castillo, M., Xiong Y. L. Álvarez, D. Payne F. A. & Garrido M. D Antioxidant and emulsifying properties of alcalase-hydrolyzed potato proteins in meat emulsions with different fat concentrations. Meat Science, 83, [9] Álvarez D. Castillo M. Payne F.A. Xiong Y.L A novel fiber optic sensor to monitor beef emulsion stability using visible Light scattering. Meat Science, 81, [10] SAS Statistical Analysis System, Rel Cary, NC: SAS Institute Inc. [11] Barbut S Use of fiber optic probe to predict meat emulsion breakdown. Italian Journal of Food Science, 3, [12] Koolmes P. A. Winjgaards G. Tersteeg M. H. G. & Van Logtestijn J. G Changes in the microstructure of a comminuted meat system during heating. Food Structure, 12, [13] Álvarez D. Castillo M. Payne F.A. Cox Y.L. Xiong Y.L Application of light extinction to determine stability of beef emulsions. Journal of Food Engineering, 96,