19 Active packaging and colour control: the case of meat M. Jakobsen and G. bertelsen, The Royal Veterinary and Agricultural University, Denmark 1.1 Introduction The colour stability of meat products is influenced by a large number of factors some of biochemical nature, some due to handling during slaughter and processing, and others due to packaging and storage conditions. This chapter focuses on modelling how colour shelf-life is affected by the external factors applied during packaging and storage. However, meat from different sources shows different tendencies to undergo colour deterioration and this variation in internal factors influences the developed models. Therefore some consideration will also be given to discussing how internal factors like, e.g., muscle type and addition of nitrite in cured meat affect the models. modelling can be used to identify the most important factors/interaction of factors affecting quality loss during storage and to define critical levels of these factors, thereby forming the basis for proposing the optimal packaging and storage conditions or the best compromise if several deteriorative reactions need to be considered. Caution in choosing the optimal packaging and storage conditions can largely improve the colour shelf-life of meat products. Modelling of MAP systems shows great potential for optimising/tailoring storage and packaging parameters to maintain product quality, in this case a ood meat colour stability (Jakobsen and Bertelsen, 2000; Lyijynen et al 1998; Pfeiffer et al., 1999). It will be demonstrated how modelling can be sed to identify the most important factors affecting colour shelf-life. Multivariable experimental design is necessary to be able to investigate the large number of influencing factors on several levels as well as the interactions between factors
19.1 Introduction The colour stability of meat products is influenced by a large number of factors: some of biochemical nature, some due to handling during slaughter and processing, and others due to packaging and storage conditions. This chapter focuses on modelling how colour shelf-life is affected by the external factors applied during packaging and storage. However, meat from different sources shows different tendencies to undergo colour deterioration and this variation in internal factors influences the developed models. Therefore some consideration will also be given to discussing how internal factors like, e.g., muscle type and addition of nitrite in cured meat affect the models. Modelling can be used to identify the most important factors/interaction of factors affecting quality loss during storage and to define critical levels of these factors, thereby forming the basis for proposing the optimal packaging and storage conditions or the best compromise if several deteriorative reactions need to be considered. Caution in choosing the optimal packaging and storage conditions can largely improve the colour shelf-life of meat products. Modelling of MAP systems shows great potential for optimising/tailoring storage and packaging parameters to maintain product quality, in this case a good meat colour stability (Jakobsen and Bertelsen, 2000; Lyijynen et al., 1998; Pfeiffer et al., 1999). It will be demonstrated how modelling can be used to identify the most important factors affecting colour shelf-life. Multivariable experimental design is necessary to be able to investigate the large number of influencing factors on several levels as well as the interactions between factors. 19 Active packaging and colour control: the case of meat M. Jakobsen and G. Bertelsen, The Royal Veterinary and Agricultural University, Denmark
402 Novel food packaging techniques 19.2 Packaging and storage factors affecting colour stability Modified atmosphere packed meat is a complex and dyna several factors interact(Zhao et al., 1994). Models can be used to describe how the initial package atmosphere changes over time and how these changes affect product quality and shelf-life. The dynamic changes in headspace gas composition during storage can be modelled as a function of initial gas composition product and package geometry gas absorption in the meat. The knowledge of changes in gas composition can be combined with models on quality changes in the meat as a function of packaging and storage conditions · storage temperature · light exposure and predictions of product shelf-life can be made. Pfeiffer et al.(1999)developed simulations of how product shelf-life changes with different packaging and storage conditions for a wide range of food products(primarily dry products ). However, at present sufficient models for many quality deteriorative reactions are lacking and only few attempts have been made to model chemical quality changes in meat products, in contrast to modelling of microbial shelf-life, where extensive work has been performed(Mc Donald and Sun, 1999) 19.2.1 Modelling dynamic changes in headspace gas composition Permeability of the packaging film Headspace gas composition changes dynamically due to several factors. Gas exchange with the environment occurs over the packaging film if the partial pressure of a gas differs on the two sides of the film. The amount of gas that permeates over the film can be calculated from equation 19.1(Robertson, 1993) Q=P.△p·t·A 19.1 @= the amount of gas that permeates over the film(cm) P= the permeability of the packaging film(cm/m/24h/atm) the difference in gas partial pressure on the two sides of the film(atm) A=the area of the package(m2) Different gases have different permeability through the same film. For conventional films, the permeability of CO2 is generally 4-6 times larger than
19.2 Packaging and storage factors affecting colour stability Modified atmosphere packed meat is a complex and dynamic system where several factors interact (Zhao et al., 1994). Models can be used to describe how the initial package atmosphere changes over time and how these changes affect product quality and shelf-life. The dynamic changes in headspace gas composition during storage can be modelled as a function of: • gas transmission rates of the packaging material • initial gas composition • product and package geometry • gas absorption in the meat. The knowledge of changes in gas composition can be combined with models on quality changes in the meat as a function of packaging and storage conditions such as: • storage time • storage temperature • gas composition • light exposure and predictions of product shelf-life can be made. Pfeiffer et al. (1999) developed simulations of how product shelf-life changes with different packaging and storage conditions for a wide range of food products (primarily dry products). However, at present sufficient models for many quality deteriorative reactions are lacking and only few attempts have been made to model chemical quality changes in meat products, in contrast to modelling of microbial shelf-life, where extensive work has been performed (McDonald and Sun, 1999). 19.2.1 Modelling dynamic changes in headspace gas composition Permeability of the packaging film Headspace gas composition changes dynamically due to several factors. Gas exchange with the environment occurs over the packaging film if the partial pressure of a gas differs on the two sides of the film. The amount of gas that permeates over the film can be calculated from equation 19.1 (Robertson, 1993): Q P p t A 19:1 Q the amount of gas that permeates over the film (cm3 ) P the permeability of the packaging film (cm3 /m2 /24h/atm) p the difference in gas partial pressure on the two sides of the film (atm) t the storage time (24h) A the area of the package (m2 ) Different gases have different permeability through the same film. For conventional films, the permeability of CO2 is generally 4–6 times larger than 402 Novel food packaging techniques
Active packaging and colour control: the case of meat 403 that of O2 and 12-18 times larger than that of N2. The permeability of a plastic film is roughly proportional to the thickness of the film. Doubling film thickness approximately halves film permeability Permeability is also influenced by storage temperature and relative humidity Pfeiffer et al.(1999) found that the empirical equation 19.2 fitted well with literature data for oxygen permeability P(T, RH)=exp(co+c1/T+C2 RH +C3 RH) 19.2 P= the permeability of the packaging film storage temperature RH storage relative humidity 3 are experimental derived coefficients Gas exchange over the packaging film is of particular importance when the film needs to maintain a narrowly defined gas concentration as shown in the example in section 19.3.2, where the permeability of even small amounts of O2 into a package containing a cured meat product is considered a critical packaging arameter Gas absorption in the meat Headspace gas composition can also change due to gas absorption in the meat Packaging in elevated levels of CO2 can result in large amounts of CO2 absorbed in the meat(Jakobsen and Bertelsen, 2002; Zhao et al., 1994) and thereby large changes compared to the initially applied gas composition. Absorption of O2 and N2 is negligible compared to the absorption of CO2 (Jakobsen and Bertelsen 2002). Models for CO solubility as a function of packaging and storage parameters such as product to headspace volume ratio, temperature and initial CO2 level were developed by Zhao et al. (1995)and Devlieghere et al. (1998) Fava and Piergiovanni(1992)developed models of CO2 solubility as a function of different compositional parameters, aw, pH, protein, fat and moisture content The amount of absorbed CO2 ranges from 0-1. 8 L CO2/Kg meat, depending on the applied CO2 partial pressure, temperature, pH of the meat, etc (Jakobsen and Bertelsen 2002). As regards gas absorption, equilibrium is obtained during the first one or two days. Microbial or meat metabolism can also cause slight changes in gas composition by using O2 and producing CO 19.3 Modelling the impact of MAP When it is understood how the gas atmosphere can change from the initially applied atmosphere under different packaging and storage conditions, this knowledge can be used to evaluate the effect on quality deteriorating reactions Besides microbial growth, the primary concern when packaging both fresh meat and cured meat products is colour stability. The mechanisms of colour changes
that of O2 and 12–18 times larger than that of N2. The permeability of a plastic film is roughly proportional to the thickness of the film. Doubling film thickness approximately halves film permeability. Permeability is also influenced by storage temperature and relative humidity. Pfeiffer et al. (1999) found that the empirical equation 19.2 fitted well with literature data for oxygen permeability. P T; RH exp c0 c1=T c2 RH c3 RH2 19:2 P the permeability of the packaging film T storage temperature RH storage relative humidity C0ÿ3 are experimental derived coefficients. Gas exchange over the packaging film is of particular importance when the film needs to maintain a narrowly defined gas concentration as shown in the example in section 19.3.2, where the permeability of even small amounts of O2 into a package containing a cured meat product is considered a critical packaging parameter. Gas absorption in the meat Headspace gas composition can also change due to gas absorption in the meat. Packaging in elevated levels of CO2 can result in large amounts of CO2 absorbed in the meat (Jakobsen and Bertelsen, 2002; Zhao et al., 1994) and thereby large changes compared to the initially applied gas composition. Absorption of O2 and N2 is negligible compared to the absorption of CO2 (Jakobsen and Bertelsen, 2002). Models for CO2 solubility as a function of packaging and storage parameters such as product to headspace volume ratio, temperature and initial CO2 level were developed by Zhao et al. (1995) and Devlieghere et al. (1998). Fava and Piergiovanni (1992) developed models of CO2 solubility as a function of different compositional parameters, aw, pH, protein, fat and moisture content. The amount of absorbed CO2 ranges from 0–1.8 L CO2/Kg meat, depending on the applied CO2 partial pressure, temperature, pH of the meat, etc. (Jakobsen and Bertelsen 2002). As regards gas absorption, equilibrium is obtained during the first one or two days. Microbial or meat metabolism can also cause slight changes in gas composition by using O2 and producing CO2. 19.3 Modelling the impact of MAP When it is understood how the gas atmosphere can change from the initially applied atmosphere under different packaging and storage conditions, this knowledge can be used to evaluate the effect on quality deteriorating reactions. Besides microbial growth, the primary concern when packaging both fresh meat and cured meat products is colour stability. The mechanisms of colour changes Active packaging and colour control: the case of meat 403
404 Novel food packaging techniques in fresh meat and cured meat products are completely different as can be seen from the examples on modelling given in the following two sections When packaging fresh meat products an elevated oxygen partial pressure needs to be maintained to keep the meat pigment myoglobin in its oxygenated bright red state. By modelling a MAP system for fresh beef, the most critical external factors are identified to be storage temperature and gas composition Jakobsen and Bertelsen, 2000). By modelling a MAP system for cured meat products the most critical external factors are identified to be low availability of oxygen combined with exclusion of light to prevent degradation of nitrosylmyoglobin by photo oxidation( storage temperature was kept constant at 5.C)(Moller et al, 2003 ). However, low availability of oxygen is not ensured olely by reducing the residual oxygen level in the headspace during the packaging process. Other equally critical factors are a high product to headspace ratio and a packaging film of low oxygen transmission rate (OtR) of the packaging film(Moller et al., 2003) 19.3.1 Optimising colour stability of fresh beef Jakobsen and bertelsen(2000) and Bro and Jakobsen(2002)modelled colour ability of fresh beef under different packaging and storage conditions. In all cases colour measurements were performed with a Minolta Colorimeter using the L, a, b coordinates. Red colour was expressed as the a-value, the higher the a-value the redder the sample. When packaging fresh red meats elevated O partial pressures are used to stabilise myoglobin in its bright red oxygenated form (oxymyoglobin). However, elevated O2 levels may increase some deteriorative reactions e.g. lipid oxidation. Consequently it is interesting to investigate if a level of O2 exists that is acceptable when considering both colour stability and lipid oxidation. Jakobsen and Bertelsen (2000) investigated different packaging and storage conditions (Table 19. 1)and developed a egression model/response surface model predicting the colour a-value as a iunction of storage time, storage temperature and O2 level based on steaks of ongissimus dorsi muscles from four different animals The resulting model(equation 19.3)contains the main effects of the three actors plus two-way interactions and two squared effects. Interpretation of the model is best done by exploring the response surface plot(Fig. 19.1) a-value=o+ Bi Day + B2 Temp+B3. 02 64- Day.Temp+ B5 Day.O2+6·Temp·O2+ Day. Day+Bg·Temp.Temp 19.3 where the betas are regression coefficients temperature and O2 level, while keeping the third factor, storage time, constant at day no. 6. Figure 19. 1 also reveals an interval of approximately 50-80%O2 where the O2 level does not affect the colour a-value significantly(the nearly
in fresh meat and cured meat products are completely different as can be seen from the examples on modelling given in the following two sections. When packaging fresh meat products an elevated oxygen partial pressure needs to be maintained to keep the meat pigment myoglobin in its oxygenated bright red state. By modelling a MAP system for fresh beef, the most critical external factors are identified to be storage temperature and gas composition (Jakobsen and Bertelsen, 2000). By modelling a MAP system for cured meat products the most critical external factors are identified to be low availability of oxygen combined with exclusion of light to prevent degradation of nitrosylmyoglobin by photo oxidation (storage temperature was kept constant at 5ºC) (Møller et al., 2003). However, low availability of oxygen is not ensured solely by reducing the residual oxygen level in the headspace during the packaging process. Other equally critical factors are a high product to headspace ratio and a packaging film of low oxygen transmission rate (OTR) of the packaging film (Møller et al., 2003). 19.3.1 Optimising colour stability of fresh beef Jakobsen and Bertelsen (2000) and Bro and Jakobsen (2002) modelled colour stability of fresh beef under different packaging and storage conditions. In all cases colour measurements were performed with a Minolta Colorimeter using the L, a, b coordinates. Red colour was expressed as the a-value, the higher the a-value the redder the sample. When packaging fresh red meats elevated O2 partial pressures are used to stabilise myoglobin in its bright red oxygenated form (oxymyoglobin). However, elevated O2 levels may increase some deteriorative reactions e.g. lipid oxidation. Consequently it is interesting to investigate if a level of O2 exists that is acceptable when considering both colour stability and lipid oxidation. Jakobsen and Bertelsen (2000) investigated different packaging and storage conditions (Table 19.1) and developed a regression model/response surface model predicting the colour a-value as a function of storage time, storage temperature and O2 level based on steaks of Longissimus dorsi muscles from four different animals. The resulting model (equation 19.3) contains the main effects of the three factors plus two-way interactions and two squared effects. Interpretation of the model is best done by exploring the response surface plot (Fig. 19.1). a-value 0 1 Day 2 Temp 3 O2 + 4 Day Temp + 5 Day O2 6 Temp O2 7Day Day 8 Temp Temp 19:3 where the betas are regression coefficients. Figure 19.1 shows a response surface plot varying the two factors, temperature and O2 level, while keeping the third factor, storage time, constant at day no. 6. Figure 19.1 also reveals an interval of approximately 50–80% O2, where the O2 level does not affect the colour a-value significantly (the nearly 404 Novel food packaging techniques
Active packaging and colour control: the case of meat 405 Table 19.1 Packaging and storage conditions used in the models developed in Jakobsen and Bertelsen(2000) Modelling factor Abbreviat No of levels etting of levels Storage time(days) 2,4,6,8,10 O2 level (% 20,35,50,65,80 horizontal lines in this interval means that the a-value is depends only on the temperature). The borders of this interval change a little depending on the setting of the day. But it is evident that the O2 level can be reduced from the normal used 70-80% without adverse effect on the colour shelf-life The complexity of the interactions/squared terms in equation 19.3 called for further search for adequate models. A novel approach called GEMANOVA Generalized Multiplicative ANOVA)was therefore used in Bro and Jakobsen (2002). In this study the effect of different packaging and storage conditions (Table 19.2)on the colour stability of steaks of Longissimus dorsi muscles from three different animals was investigated The effect of light was evaluated as the time of exposure to a fluorescent tube commonly used for retail display (1000 ux at the package surface for 0, 50 or 100% of the storage time Even when considering only two factor interactions a traditional ANOVA 30) 623.9-255 8 O-level Fig. 19.1 Response surface plot of predicted a-values(average of four animals)after six days storage at different temperatures and different oxygen levels. ( Adapted from Jakobsen and bertelsen, 2000)
horizontal lines in this interval means that the a-value is depends only on the temperature). The borders of this interval change a little depending on the setting of the day. But it is evident that the O2 level can be reduced from the normally used 70–80% without adverse effect on the colour shelf-life. The complexity of the interactions/squared terms in equation 19.3 called for further search for adequate models. A novel approach called GEMANOVA (Generalized Multiplicative ANOVA) was therefore used in Bro and Jakobsen (2002). In this study the effect of different packaging and storage conditions (Table 19.2) on the colour stability of steaks of Longissimus dorsi muscles from three different animals was investigated. The effect of light was evaluated as the time of exposure to a fluorescent tube commonly used for retail display (1000 lux at the package surface for 0, 50 or 100% of the storage time). Even when considering only two factor interactions a traditional ANOVA Table 19.1 Packaging and storage conditions used in the models developed in Jakobsen and Bertelsen (2000) Modelling factor Abbreviation No. of levels Setting of levels Storage time (days) Day 5 2, 4, 6, 8, 10 Temperature (ºC) Temp 3 2, 5, 8 O2 level (%) O2 5 20, 35, 50, 65, 80 Fig. 19.1 Response surface plot of predicted a-values (average of four animals) after six days storage at different temperatures and different oxygen levels. (Adapted from Jakobsen and Bertelsen, 2000). Active packaging and colour control: the case of meat 405
406 Novel food packaging techniques Table 19.2 Packaging and storage conditions used in the models developed in Bro and Jakobsen(2002) modelling factor Abbreviation No. of levels Setting of levels Storage time(days) Day 4333 3,7,8,10 2.5.8 Light exposure (% Light O2 level (% 40.60.8 model for the experiment in Table 19.2 would look like equation 19.4(before removal of any insignificant effects a-value +1·Day+B2·Temp+B3· Light+B4·O2+Bs Temp+A6· Day. Light+Day.O2+B·Temp· Light+·Temp O2+A10· Light.O2 194 where betas are regression coefficients (before removal of any insignificant effects). The interpretation of the GEMANOVA model is much more simple than the ANOVA model as is discussed in detail in Bro (1997)and Bro and Jakobsen(2002) a-value= Day. Temp. Light. O2 Since the effect of the O2 level is insignificant in the interval between 40-80% O2, the resulting gEMANOVA model can be written as equation 19.6(Bro and Jakobsen 2002). The interaction term Day TempLightcoz describes deviations parameters can be performed from Fig. 192, and interpretation of the model 19.6 where a-valueo is the a-value at day 0 and co is a constant For all settings of the factors the a-value is simply calculated as the starting level of the a-value(a-valueo) plus the product of the four effects read from the ordinates in Fig. 19.2. Example: a-value a-valueo+ Day(10).Temp(2).Light(O) co2(constant)N a-valueo +(-2.3).1.1-1.7.1.9 N a-valueo -8.2, meaning that after ten days storage, at 2 C and no exposure to light the a-value has decreased by approximately 8.2 The interaction term is 0 on day o(the factor Day is 0) All changes in colour a-value during storage are negative(colour becomes less red) compared to the starting colour. The change is calculated as the
model for the experiment in Table 19.2 would look like equation 19.4 (before removal of any insignificant effects). a-value 0 1 Day 2 Temp 3 Light 4 O2 5 Day Temp 6 Day Light 7 Day O2 8 Temp Light 9 Temp O2 10 Light O2 19:4 where betas are regression coefficients. On the other hand, when applying the GEMANOVA model the interactions are modelled as one higher-order multiplicative effect, resulting in equation 19.5 (before removal of any insignificant effects). The interpretation of the GEMANOVA model is much more simple than the ANOVA model as is discussed in detail in Bro (1997) and Bro and Jakobsen (2002). a-value Day Temp Light O2 19:5 Since the effect of the O2 level is insignificant in the interval between 40–80% O2, the resulting GEMANOVA model can be written as equation 19.6 (Bro and Jakobsen 2002). The interaction term DayTempLightcO2 describes deviations from the a-value on day 0 in a very simple way, and interpretation of the model parameters can be performed from Fig. 19.2. a-value a-value0 Day Temp Light cO2 19:6 where a-value0 is the a-value at day 0 and cO2 is a constant. Interpretation: • For all settings of the factors the a-value is simply calculated as the starting level of the a-value (a-value0) plus the product of the four effects read from the ordinates in Fig. 19.2. Example: a-value a-value0 Day(10)Temp(2)Light(0)cO2 (constant) a-value0 +(ÿ2.3)1.11.71.9 a-value0 ÿ8.2, meaning that after ten days storage, at 2ºC and no exposure to light the a-value has decreased by approximately 8.2. • The interaction term is 0 on day 0 (the factor Day is 0). • All changes in colour a-value during storage are negative (colour becomes less red) compared to the starting colour. The change is calculated as the Table 19.2 Packaging and storage conditions used in the models developed in Bro and Jakobsen (2002) Modelling factor Abbreviation No. of levels Setting of levels Storage time (days) Day 4 3, 7, 8, 10 Temperature (ºC) Temp 3 2, 5, 8 Light exposure (%) Light 3 0, 50, 100 O2 level (%) O2 3 40, 60, 80 406 Novel food packaging techniques
Active packaging and colour control: the case of meat 40 1.15 Storage time(days) Temperature(C) .3 1.l5 Light exposure(%o) Oxygen level(%) Fig. 19.2 Parameter levels for the interaction term(Day-Temp Light-co2)in equation 19.6.(Adapted from Bro and Jakobsen, 2002) product of the four parameters Day, Temp, Light and Oz, which consist of one negative value(Day) and three positive values The changes are relative and the effect of the individual factors can be interpreted individually. For example when going from 2C to 8C the Temp loading increases from 1.2 to 2.4, meaning that regardless of all other factor he decrease in a-value at 8 C is twice the decrease at 2%C The effect of light is minor, although an increase in time of exposure to light seems to result in a decreased colour a-value The effect of the O, level is insignificant in the interval from 40-80% and is herefore contained in the model as a constant(the value of the constant co can be read from Fig. 19.2) The gemanova model confirms the results from jakobsen and bertelsen (2000)by emphasising the importance of keeping a low storage temperature and showing no effect of O2 level in the interval between approximately 40-80% However, the interpretation of the model is much more simple, since the effect of each factor can be interpreted individually. Likewise the GEManOVA model can be applied to the data set in Table 19. 1 resulting in equation 19.7 which is much more simple to interpret than equation 19.3 a-value= a-valueo +Day. Temp. O2 where a-valueo is the a-value at day no. 0
product of the four parameters Day, Temp, Light and O2, which consist of one negative value (Day) and three positive values. • The changes are relative and the effect of the individual factors can be interpreted individually. For example when going from 2ºC to 8ºC the Temp loading increases from 1.2 to 2.4, meaning that regardless of all other factors the decrease in a-value at 8ºC is twice the decrease at 2ºC. • The effects of storage time and temperature are most important. • The effect of light is minor, although an increase in time of exposure to light seems to result in a decreased colour a-value. • The effect of the O2 level is insignificant in the interval from 40–80% and is therefore contained in the model as a constant (the value of the constant cO2 can be read from Fig. 19.2). The GEMANOVA model confirms the results from Jakobsen and Bertelsen (2000) by emphasising the importance of keeping a low storage temperature and showing no effect of O2 level in the interval between approximately 40–80%. However, the interpretation of the model is much more simple, since the effect of each factor can be interpreted individually. Likewise the GEMANOVA model can be applied to the data set in Table 19.1 resulting in equation 19.7 which is much more simple to interpret than equation 19.3. a-value a-value0 Day Temp O2 19:7 where a-value0 is the a-value at day no. 0. Fig. 19.2 Parameter levels for the interaction term (DayTempLightcO2) in equation 19.6. (Adapted from Bro and Jakobsen, 2002). Active packaging and colour control: the case of meat 407
408 Novel food packaging techniques 0.8 Storage time Oxygen level Fig. 19.3 Parameter levels for the interaction term(Day.Temp. O2)in equation 19.7 From Fig. 19.3 the effect of the factors can be interpreted individually, and the stable interval between approximately 40-80%O2 is evident as an elbow in the It is rather surprising that 40%O2 is sufficient to ensure the stability of the bright red meat colour, as an O2 level of 70-80% is commonly used in the industry. The applied product to headspace volume ratio for the experiments in Tables 19.1 and 19.2 was approximately 1: 9. The large headspace volume might be the cause for only minor changes in headspace gas xyge partial pressure)taking place during storage. However, when packaging fresh meat products for retail sale, a large headspace volume is common. Furthermore, large amounts of oxygen have to permeate over the film or be used for meat/ microbial metabolism before a noteworthy change in oxygen partial pressure takes place and the meat colour will be affected The GEMANOVa model is an excellent tool for choosing the optimal setting for the individual factors The knowledge of the stable interval can be used when optimising headspace gas composition or the permeability of the packaging material as a reduction in the applied oxygen level leaves the possibility of using more carbon dioxide or nitrogen in the package headspace
From Fig. 19.3 the effect of the factors can be interpreted individually, and the stable interval between approximately 40–80% O2 is evident as an elbow in the plot. It is rather surprising that 40% O2 is sufficient to ensure the stability of the bright red meat colour, as an O2 level of 70–80% is commonly used in the industry. The applied product to headspace volume ratio for the experiments in Tables 19.1 and 19.2 was approximately 1:9. The large headspace volume might be the cause for only minor changes in headspace gas composition (oxygen partial pressure) taking place during storage. However, when packaging fresh meat products for retail sale, a large headspace volume is common. Furthermore, large amounts of oxygen have to permeate over the film or be used for meat/ microbial metabolism before a noteworthy change in oxygen partial pressure takes place and the meat colour will be affected. The GEMANOVA model is an excellent tool for choosing the optimal setting for the individual factors. The knowledge of the stable interval can be used when optimising headspace gas composition or the permeability of the packaging material as a reduction in the applied oxygen level leaves the possibility of using more carbon dioxide or nitrogen in the package headspace. Fig. 19.3 Parameter levels for the interaction term (DayTempO2) in equation 19.7 (unpublished data). 408 Novel food packaging techniques
Active packaging and colour control: the case of meat 409 Table 19.3 Packaging and storage conditions used in the models developed in Moller er a.(2003) Modelling factor Abbreviation No of levels etting of levels Storage time( days) 1,3,6,9,14 esidual O2 level (%% Reso2 53-3322 0.1,0.25,0.5 Measured O2 level (% MeasO2 ontinuously Oxygen Transmission OTR 0.5,10,32 Rate(ml/m /24h/atm) Volume ratio l:1,1:3,1:5 (product to headspace) Light intensity(Lux) Light Nitrite content(ppm) Nit 19.3.2 Optimising colour stability of cured ham When packaging cured meat products it is important to keep the O2 and light exposure at a minimum to prevent photo oxidation of nitrosylmoglobin. Moller et al.(2003)investigated the colour stability of cured ham under different packaging and storage conditions according to Table 19.3. Colour measurements were performed with a Minolta Colorimeter using the a-value to express the red colour of the product. The effect of light was evaluated as the light intensity from a fluorescent tube measured on the package surface The resulting model(after removal of insignificant effects)considering only two-factor interactions is shown in equation 19.8 a-value=+61·ResO2+B·Vol+ 63 Light+B4·Nit+Bs:Time 6· Measo2+所Res)2· Light+g·ResO2·Time+A·ResO2 Measo2+A1o·Vol· Measo2+B· Light· Measo2+B12:Time 19.8 where betas are regression coefficients As expected, the a-value decreases with increased time, increased residual O2 level, increased OTR, increased light intensity and decreased nitrite content. However the study also shows the importance of interactions between factors. The interaction between O2 level and product to headspace volume ratio is especially interesting. Normally, the focus is on the residual O2 level (%)in the package and it is commonly overlooked that also the total amount of available oxygen molecules is important. The total amount of oxygen molecules available for colour deteriorative reactions is determined by the residual oxygen level after packaging, the meat to headspace volume ratio, and the amount of oxygen that permeates into the package headspace in combination. It is not sufficient to keep a low O2 level in the package headspace. If the headspace volume is large there will still be plenty of oxygen molecules for colour deterioration
19.3.2 Optimising colour stability of cured ham When packaging cured meat products it is important to keep the O2 and light exposure at a minimum to prevent photo oxidation of nitrosylmoglobin. Møller et al. (2003) investigated the colour stability of cured ham under different packaging and storage conditions according to Table 19.3. Colour measurements were performed with a Minolta Colorimeter using the a-value to express the red colour of the product. The effect of light was evaluated as the light intensity from a fluorescent tube measured on the package surface. The resulting model (after removal of insignificant effects) considering only two-factor interactions is shown in equation 19.8. a-value 0 1 ResO2 2 Vol 3 Light 4 Nit 5 Time 6 MeasO2 7 Res)2 Light 8 ResO2 Time 9 ResO2 MeasO2 10 Vol MeasO2 11 Light MeasO2 12 Time MeasO2 19:8 where betas are regression coefficients. As expected, the a-value decreases with increased time, increased residual O2 level, increased OTR, increased light intensity and decreased nitrite content. However the study also shows the importance of interactions between factors. The interaction between O2 level and product to headspace volume ratio is especially interesting. Normally, the focus is on the residual O2 level (%) in the package and it is commonly overlooked that also the total amount of available oxygen molecules is important. The total amount of oxygen molecules available for colour deteriorative reactions is determined by the residual oxygen level after packaging, the meat to headspace volume ratio, and the amount of oxygen that permeates into the package headspace in combination. It is not sufficient to keep a low O2 level in the package headspace. If the headspace volume is large there will still be plenty of oxygen molecules for colour deterioration. Table 19.3 Packaging and storage conditions used in the models developed in Møller et al. (2003) Modelling factor Abbreviation No. of levels Setting of levels Storage time (days) Time 5 1, 3, 6, 9, 14 Residual O2 level (%) ResO2 3 0.1, 0.25, 0.5 Measured O2 level (%) MeasO2 – Continuously Oxygen Transmission OTR 3 0.5, 10, 32 Rate (ml/m2 /24h/atm) Volume ratio Vol 3 1:1, 1:3, 1:5 (product to headspace) Light intensity (Lux) Light 2 500, 1000 Nitrite content (ppm) Nit 2 60, 150 Active packaging and colour control: the case of meat 409
410 Novel food packaging techniques 3. 4.94 563529 Fig. 19.4 Contour plot of the interaction effect between volume ratio (product headspace) and measured O2 level (%)after nine days storage (Adapted from Moller et al, 2003) Figure 19.4 shows a contour plot of the interaction between'measured O2 level and 'volume ratio'(the remaining factors are fixed to the following settings residual O2 level =0.25%, light intensity 1000 lux, nitrite= 60 ppm, storage time =9 days). The measured O2 level is the actual O2 level measured during storage and therefore takes into account both the residual o, level right after packaging and the oxygen permeated into the package over the packaging material (OTR). The a-value of the product for a given combination of measured O2 level andvolume ratio can be found from the plot by reading the a-value from the orresponding contour line, e.g., applying 0.10%'measured O2 leveland a volume ratio of 1: 1.3 results in an a-value of 5.6 after nine days of storage. It appears that to maintain a high a-value, it is necessary to keep both the oxygen level and the headspace volume low (lower left corner of the plot), solely keeping he O, level low is not sufficient. The interaction between O, level and light intensity is also important(Moller et al, 2003)but more complex. It is evident that increased light intensity accelerates colour deterioration but the degree of colour deterioration is dependent on the amount of available oxygen in the package (which again is dependent on the three factors: residual O2 level,, volume ratio and OTR') Work is in progress to clarify matters on this issue. Modelling clearly demonstrates that the product to headspace volume ratio should be given far more attention when optimising MAP of cured ham 19.4 Pre-and post-slaughter factors The examples in section 19.3 clearly demonstrate the usefulness of modelling for identification of the most important factors/interaction of factors affecting
Figure 19.4 shows a contour plot of the interaction between ‘measured O2 level’ and ‘volume ratio’ (the remaining factors are fixed to the following settings: residual O2 level 0.25%, light intensity 1000 lux, nitrite 60 ppm, storage time 9 days).The ‘measured O2 level’ is the actual O2 level measured during storage and therefore takes into account both the ‘residual O2 level’ right after packaging and the oxygen permeated into the package over the packaging material (OTR). The a-value of the product for a given combination of ‘measured O2 level’ and ‘volume ratio’ can be found from the plot by reading the a-value from the corresponding contour line, e.g., applying 0.10% ‘measured O2 level’ and a ‘volume ratio’ of 1:1.3 results in an a-value of 5.6 after nine days of storage. It appears that to maintain a high a-value, it is necessary to keep both the oxygen level and the headspace volume low (lower left corner of the plot), solely keeping the O2 level low is not sufficient. The interaction between O2 level and light intensity is also important (Møller et al., 2003) but more complex. It is evident that increased light intensity accelerates colour deterioration but the degree of colour deterioration is dependent on the amount of available oxygen in the package (which again is dependent on the three factors: ‘residual O2 level’, ‘volume ratio’ and ‘OTR’). Work is in progress to clarify matters on this issue. Modelling clearly demonstrates that the product to headspace volume ratio should be given far more attention when optimising MAP of cured ham. 19.4 Pre- and post-slaughter factors The examples in section 19.3 clearly demonstrate the usefulness of modelling for identification of the most important factors/interaction of factors affecting Fig. 19.4 Contour plot of the interaction effect between volume ratio (product:headspace) and measured O2 level (%) after nine days storage. (Adapted from Møller et al., 2003). 410 Novel food packaging techniques
