Quantitative Assessment of the Shelf Life of Fruit Juices

CHAPTER

QUANTITATIVE ASSESSMENT OF THE SHELF LIFE OF FRUIT JUICES

28

David Millan-Sango1,2 and Vasilis P. Valdramidis1,2 1

2

University of Malta, Msida, Malta National Centre for Technology and Food Safety, Navarra, Spain

28.1 INTRODUCTION Shelf life can be defined as a period of time after processing and packaging during which the food product maintains a minimum level of quality acceptable for consumption (Nicoli, 2012). The shelf life of a food product depends on several parameters which are related to the initial product quality, the conditions of processing, the properties of the packaging, and the storage conditions (Bacigalupi et al., 2013). These factors can affect the organoleptic properties, quality attributes, and microbial levels which determine the acceptability limit of the food product and therefore its shelf life. These acceptability limits can be determined by different approaches. On the one hand, sensorytrained panelists (e.g., Kou et al., 2014; Park et al., 2009) or untrained consumers (e.g., Fouladkhah et al., 2011; Manzocco et al., 2011) can assess different organoleptic attributes of the food product. On the other hand, new instrumental methods such electronic noses are being developed to determine flavor (and off-flavor) of the food product (e.g., Hong and Wang, 2014). Other physical and chemical analyses are also used to evaluate the key quality parameters of the food product along its shelf life (e.g., Valdramidis et al., 2009). These could also include the performance of microbial studies to determine the evolution of the microbial load (i.e., spoilage microorganisms and/or pathogens) of the food products during the shelf life (e.g., Patil et al., 2011). The outcomes obtained through these methods are used to determine the shelf life which is displayed on the labels by indicating two different concepts, i.e., the “best before” date or the “use by” date. The best before date is related to the deterioration of quality parameters such as flavor, texture, color, and nutritional components during the shelf life of the food product. Use by date refers to the safety (i.e., microbial or chemical) of the food product and therefore could constitute a health issue to the consumer if the product is consumed after the date (Kilcast and Subramaniam, 2000). The determination of a correct shelf life of the food product is a key factor for the food industry in order to guarantee food products with adequate levels of quality until its consumption without producing any health hazard. The parameters which affect the shelf life of fruit juices are directly linked with the microbial and chemical quality of the products. For example, quality deterioration could be related with the loss of vitamin C (ascorbic acid), cloud stability which is related with the activity of pectin Fruit Juices. DOI: https://doi.org/10.1016/B978-0-12-802230-6.00028-X © 2018 Elsevier Inc. All rights reserved.

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CHAPTER 28 QUANTITATIVE ASSESSMENT OF THE SHELF LIFE

methylesterase (PME) enzyme, the alteration of the color which is aligned with the generation of chemical components that affect the nonenzymatic browning or the enzymatic activity of polyphenol oxidase. Additionally, the presence of spoilage microorganisms (e.g., Saccharomyces cerevisiae, Issatchenkia orientalis) play an important role in the estimation of the shelf life of juices (e.g., Patil et al., 2011; Valdramidis et al., 2009).

28.2 IDENTIFYING THE LIMITS OF QUALITY INDICATORS AFFECTING THE QUANTIFICATION OF SHELF LIFE Ascorbic acid is one of the key parameters regarding the shelf life of juices, especially citric juices. The ascorbic acid content in orange juices range from 150 to 450 mg/L (Klimczak et al., 2007). However, ascorbic acid is a highly sensitive compound. The degradation of ascorbic acid can follow an aerobic or anaerobic pathway and depends on several factors, such as the presence oxygen in the headspace or dissolved in the juice, light, heat processing (Burdurlu et al., 2006; Tiwari et al., 2009). Aerobic degradation of ascorbic acid is produced during the processing of citrus juices, whereas anaerobic degradation of ascorbic acid occurs during storage (Johnson et al., 1995; Lee and Coates, 1999). Furthermore, thermal treatment during juice processing also has an impact on the ascorbic acid structure and can lead to nonenzymatic browning as a result of Maillard reactions. These also contribute to condensation between reducing sugars and amino acids, caramelization, and pigment destruction (Damasceno et al., 2008). The presence of hydroxymethylfurfural (5-HMF) which is a compound generated through the Maillard reactions, has been used as an indicator of the severity of the process treatment of fruit juices and it is applied as a quality deterioration parameter (Jovanov, 2003). Another parameter of importance in order to determine the shelf life of juices is the cloud stability. Cloudy juice is a result of a mix of different compounds such as proteins, hesperidin, hemicellulose, and pectin which are released into the juice from the endocarp of the fruit cells during the citrus juice mechanical extraction (Aghajanzadeh et al., 2016; Kimball, 1991). Along with these compounds, PME is also released. Cloudiness is a desirable parameter in juice quality. PME activity can result in the loss of the cloudiness and increase the product’s viscosity by de-esterification of the methyl groups on the galacturonic acid backbone of pectin. This reaction creates charged regions which with Ca21, form Ca21 pectate gels, precipitate, and clarify the juice (Croak and Corredig, 2006). Yeast, molds, and lactic acid bacteria are the main microorganisms present in juices, and therefore they are responsible for juice spoilage (Andr´es et al., 2001; Deak and Beuchat, 1993) which can determine the shelf life of the juice. Yeasts are the predominant microorganisms as they can grow at low pH, anaerobic conditions, in the presence of high sugar concentrations and low water activity conditions (Andr´es et al., 2001; Patil et al., 2011). Additionally, yeasts can generate certain metabolites which negatively affect the organoleptic quality of the juice (Gabriel, 2012). Moreover, some species of Penicillium, Aspergillus, and Byssochlamys can produce a mycotoxin called patulin which can generate some acute and chronic effects in humans (Sant’Ana et al., 2008). Therefore, a control of the evolution of the level of patulin is needed to extend the shelf life of a juice.

28.3 IMPACT OF PROCESSING AND POSTPROCESSING

559

Table 28.1 Limits of Acceptability for Quality Indicators in Shelf Life of Fruit Juices Quality Parameter

Limit of Acceptability

References

Ascorbic acid (vitamin C)

20 mg/100 mL orange juice

Hydroxymethylfurfural (5-HMF)

5
10 mg/L

Cloud stability

,36% of light transmission orange juice

Spoilage microorganism

5000 CFU/mL orange juice 106 CFU/mL 50 µg/L 10 µg/L (juices for infants)

Polydera et al. (2005), based on the Association of the Industry of Juices and Nectars from Fruits and Vegetables of the European Union International Federation of Fruit Juice Processors (IFFJP) Santini et al. (2014), Wagner and Seidler (2006) Carbonell et al. (2013), Cheng (2002) Huggart et al. (1951) Andr´es et al. (2001), Howard and Dewi (1995), Kimball (1991), Tran and Farid (2004)

Patulin

Codex Alimentarius 1425/2003 of the European Common Market

For all these quality attributes, limits of acceptability have been proposed which help in optimizing or identifying the shelf life of the fruit juice products (see Table 28.1).

28.3 IMPACT OF PROCESSING AND POSTPROCESSING ON QUALITY INDICATORS Processing technologies can be implemented to control the quality properties of the juices and consequently their shelf life. A traditional thermal process, such as pasteurization, in the range of 90
100 C for 15
60 s (Chen et al., 1993; Rivas et al., 2006) is commonly applied to juices. Application of heating treatments has been reported to be effective to kill vegetative cell of spoilage microorganisms and inactivate PME which results in microbial and cloud stability during the shelf life. In addition, the activity levels of PME after thermal treatment are used to evaluate the severity of the heat process as this enzyme is more heat resistant than the common spoilage microorganisms (Katsaros et al., 2010; Snir et al., 1996; Versteeg et al., 1980). PME of orange juice is usually inactivated by pasteurization at 90 C for 1 min (Nienaber and Shellhammer, 2001). However, heat treatments have a high impact on other quality parameters, leading to detrimental loss of ascorbic acid, and the formation of 5-HMF during the shelf life of the juice. Moreover, thermal processing is unable to inactivate patulin as it is relatively heat resistant, particularly in acidic environments (Saloma˜o et al., 2009). Patulin can also be destroyed during the course of yeast fermentation (Stinson et al., 1978). Alternatively, (new) nonthermal technologies, i.e., ultrasound, high hydrostatic pressure (HHP), pulsed electric fields, ultraviolet light, ozone, supercritical carbon dioxide, etc., can be used. These technologies have been proven to be effective under specific conditions to reduce the level of microorganisms while retaining the organoleptic properties of the juices. Therefore, the fruit industry has given a lot of attention to these technologies for extending the shelf life of the final product. Evidently, for all these processes different processing parameters

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CHAPTER 28 QUANTITATIVE ASSESSMENT OF THE SHELF LIFE

(refer to Valdramidis et al., 2012) should be considered in order to report on their impact on the shelf life of the fruit juices and its final quantification. Additionally, postprocessing aspects such as conditions of packaging, packing materials, and storage conditions, especially temperature, has to be taken also into account in order to quantify the shelf life of the juices. Throughout juice processing, especially during filling, the levels of oxygen should be as low as possible, as oxygen can have a negative impact on the levels of ascorbic acid. In order to avoid the presence of oxygen in the headspace of the package, the injection of an inert gas such as N2 can be applied immediately prior to sealing for removing O2 from the headspace (Ringblom, 2004). Moreover, the selection of an adequate packing material in order to avoid the permeation of oxygen from the environment to the juice during storage is a critical point regarding the determination of the shelf life (Ros-Chumillas et al., 2007). High oxygen barrier materials such as glass or gable-top cartons, laminated cartons, or plastic have been applied for juice packaging (Ros-Chumillas et al., 2007; Zerdin et al., 2003). Storage conditions, especially correct temperature of storage, is a crucial point in order to estimate the shelf life of a fruit juice as abuse storage temperatures can have a dramatic impact on the quality parameters of the juice, reducing notably its shelf life.

28.4 MODELING APPROACHES FOR THE QUANTIFICATION OF SHELF LIFE 28.4.1 MODELING THE KINETICS OF CHEMICAL INDICATORS Modeling the quality deterioration and consequently the shelf life of fruit juices is mainly based on the development of kinetic models. This requires the choice of the relevant quality attribute (refer to previous sections) based on which the process and storage parameters will be studied. Hereafter, the kinetics of a critical indicator, I, during storage can be described with the following function (Calligaris and Manzocco, 2000): I 5 gðt; θÞ

(28.1)

where t is the storage time and θ is the kinetic parameter of the model function. Consequently, the rate of the r of change of the critical indicator I (Taoukis et al., 1997) is defined as: r5

dI 5 k  In dt

(28.2)

k is the rate constant and n is the reaction order. The equation can be integrated in relation to I and depending on the n results in the forms presented in Table 28.2. Table 28.2 Integrated Kinetic Equations of Eq. (28.2) Reaction Order

Integrated Rate Function

n50 n51 n52 n 6¼ 1

I 5 kt 1 Io ln I 5 kt 1 ln Io 1/I 5 kt 1 1/Io I12n 5 (n 2 1)kt 1 Io12n

28.4 MODELING APPROACHES FOR THE QUANTIFICATION OF SHELF LIFE

561

Following a review of the shelf life modeling approaches that have been reported in the literature (see Table 28.3), it is evident that first-order kinetics are the most common to describe quality attributes such as vitamin C, HMF, and cloud stability of fruit juices. Other less-frequent kinetic descriptions include zero-order kinetics and the use of the Weibull-type model, while in the case of microbial indicators a number of different nonlinear growth models have been proposed. A general format of the Weibull-type model by using the same type of notations reads as follows: 
β  t I 5 Io exp 2 α

(28.3)

where I is the ascorbic acid concentration at time t, Io is the initial ascorbic acid concentration after treatment, α is a scale constant corresponding to the inverse of the reaction time constant and β is a shape constant. In some of these studies, the Arrhenius model was used in order to describe the temperature dependence of the inactivation kinetic parameters of the studied indicators. This is expressed under the form of the logarithm of the inactivation rate versus the reciprocal of absolute temperature. ln k 5 ln A 2

Ea RT

(28.4)

where k is the specific reaction rate (1/min), Ea represents the so-called activation energy of the reaction system (J/mol), T is the absolute temperature (K), R the universal gas constant (J/(mol K)), and A the so-called collision factor (1/min). The activation energy of food-related chemical reactions usually falls within the range 30
150 kJ/mol.

28.4.2 MODELING THE KINETICS OF MICROBIAL INDICATORS For the microbial kinetic studies, a number of different nonlinear models have been considered in the literature to describe the spoilage of fruit juices and quantify their shelf life (Table 28.3). These indicators are, e.g., yeasts, or mesophilic and psychrophilic microorganisms for which an acceptable level is predefined. In most studies the modified Gompertz equation, as originally described by Zwietering et al. (1990), was employed. It reads as follows:

μ e  y 5 Aexp 2 exp m ðλ 2 tÞ 1 1 A

(28.5)

where y is the relative population size at time t. A is the maximum relative population μm the maximal relative growth rate or increase in thermal power (1/day), and λ the lag time (days). In some cases, researchers extended this version of the model by incorporating a factor describing the microbiological acceptability limit which was estimated by setting the maximum microbial population to a fixed value, e.g., 6.0 log CFU/mL for mesophilic microorganisms, molds, and yeasts (Varela-Santos et al., 2012). The same authors correlated the parameters of the modified Gompertz model with the temperature by implying a power law and an Arrhenius-type equation. More recent studies employed the model of the Baranyi and Roberts model (Baranyi and Roberts, 1994) to estimate the shelf life of ozonated apple juice. The model reads as follows   eμmax AðtÞ 2 1 NðtÞ 5 Nð0Þ 1 μmax AðtÞ 2 ln 1 1 ðNmax 2Nð0ÞÞ e

(28.6)

Table 28.3 Representative Examples of Modeling Approaches With Relevance to Shelf Life Studies Shelf Life Indicators Vitamin C (ascorbic acid)

Type of Juice Orange
carrot juice Orange, lemon, grapefruit, and tangerine juice Orange juice

Hydroxymethylfurfural (5-HMF) Nonenzymatic browning Cloud stability Spoilage microorganism

Treatment Method Pulsed electric fields (fields at 25, 30, 35, and 40 kV/cm and five treatment times (from 30 to 340 µs) in each field) Not mentioned Juice producers in Turkey

Orange juice

Ultrasound (AED 5 0.30, 0.33, 0.36, 0.42, 0.47, 0.53, 0.61 and 0.81 W/mL) HHP (500 MPa, 1 min)

Lemon juice

Thermal treatment (65 C, 30 s)

Orange juice

Unpasteurized

Strawberry juice

Heat treatment (boiling water for 60 s)

Orange juice

Electroreduction (6 V) and electrooxidized pasteurization Thermal treatment (92 C for 30 s)

Mango juice Citrus juice concentrates Strawberry juice

HPP (600 MPa) for 4 min

Orange juice

Commercial pasteurized juice

Carrot juice Apple juice

Thermal treatment Ozone treatment (0.12 mL/L for 8 min at 33
40 µg/L of O3) HPP (350, 450, 555 MPa at 30, 90, 150 s) Chemical preservatives (citric acid, sucrose, ascorbic acid, potassium sorbate, and sodium benzoate)

Pomegranate juice Orange juice

Commercially processed

HHP, high hydrostatic pressure; AED, acoustic energy density.

Model Types (Equation)

Reference

2 C for 70 days and 10 C for 59 days

• First-order kinetics • Arrhenius

Torregrosa et al. (2006)

28 C, 37 C, and 45 C for 56 days

• First-order kinetics • Arrhenius

Burdurlu et al. (2006)

10 C for 30 days

• First-order kinetics • Weibull

Tiwari et al. (2008)

0 C, 5 C, 10 C, and 15 C for 30 days 25 C, 35 C, and 45 C for 120 days 12 weeks at 212 C, 4 C, and 28 C 4 C, 10 C, and 25 C for a period between 7 and 14 days 4 C, 21 C, and 37 C for 180 days 42 C for 8 weeks

• • • • • • • •

kinetics

Polydera et al. (2003)

kinetics

Al-Zubaidy and Khalil (2006) Amiri and Niakousari (2008) Derossi et al. (2010)

Storage Conditions 

28 C, 37 C, and 45 C for 8 weeks Up to 6 months at 4 6 2 and 25 6 2 C 4 C, 7 C, 10.6 C, 15 C, and 20 C for several days 8 C 4 C, 8 C, 12 C, 16 C for 30 days 4 C for 35 days 10 C for 35 days

First-order Arrhenius First-order Arrhenius First-order Arrhenius First-order Weibull

kinetics kinetics

• Weibull model

Fustier et al. (2011)

• Zero-order kinetics

Wibowo et al. (2015)

• Zero-order kinetics • Arrhenius • First-order kinetics

Koca et al. (2003)

• Gompertz model

Zanoni et al. (2005)

• Gompertz model • Baranyi and Roberts model • Modified Gompertz model • Gompertz model

Alklint et al. (2005) Patil et al. (2011)

Cao et al. (2012)

Varela-Santos et al. (2012) Andr´es et al. (2001)

28.4 MODELING APPROACHES FOR THE QUANTIFICATION OF SHELF LIFE

with eð2μmax tÞ 1 qo AðtÞ 5 t 1 ln μmax 1 1 qo 1

563

! (28.7)

while similarly to the Gompertz model the lag time can be estimated as λ 5 ln

11

1 qo

!

μmax

(28.8)

The notation is as follows: maximum specific growth rate (μmax) (1/days), lag phase (λ) (days), initial microbial population (N(0)) (log10 CFU/mL), maximum population density (Nmax) (log10 CFU/mL), q(0) (
) denotes the concentration of substance critical to the microbial growth and is related to the physiological state of the cells. The maximum specific growth rates can be further modeled as a function of the storage temperature by using the square-root model (Ratkowsky et al., 1982): pffiffiffiffiffiffiffiffiffi μmax 5 bðT 2 Tmin Þ

(28.9)



where b is a constant, T is the storage temperature ( ), Tmin is the theoretical minimum temperature for the growth of the organism.

28.4.3 CALCULATION OF THE Q10 VALUE The temperature quotient (Q10) is another indicator related to the shelf life that can be calculated following the studies on the kinetics. Q10 shows the effect of temperature on the shelf life and it is given as follows (Duyvesteyn et al., 2001; Labuza, 1982): Q10 5

Shelf life at T  C Shelf life at ðT 1 10 CÞ

(28.10)

This parameter was developed for a zero-order reaction when the influence of temperature on the reaction rate is described by using the Arrhenius relationship (Man, 2000). The Q10 value can be easily calculated by performing a regression between the ln shelf life (days) versus the temperature which yields a straight line. Consequently, Q10 5 exp (10k) with k the slope of the regression line. When the indicator is a microbial spoilage, the estimation of the time of the shelf life (ts) can be calculated considering that a microbial level (e.g., .106 CFU/mL) results in a failure (spoilage) of the product (see for similar examples in other products) (Al-Kadamany et al., 2002; Patil et al., 2011).

28.4.4 ALTERNATIVE APPROACHES 28.4.4.1 Logistic types of models Another approach to quantitatively report the shelf life of fruit juice could be by developing logistic types of models. These logistic models can be used to describe the probability of spoilage/no spoilage of the studied food products. Therefore, a correlation between the studied binary response

564

CHAPTER 28 QUANTITATIVE ASSESSMENT OF THE SHELF LIFE

Tstorage= 8oC, tstorage= 36 days

(A)

550 Pressure (MPa)

Pressure (MPa)

550 500 450 400

Tstorage= 12 oC, tstorage= 36 days

(B)

2/3 1/3

350

0

500 450

2/3 2/3

400 350

5

10 15 20 Pressure holding time (min)

25

0

1/3

5

20 15 10 Pressure holding time (min)

25

FIGURE 28.1 Example of spoilage/no spoilage studies data of Issatchenkia orientalis. Figures illustrate results of different storage times at different storage temperatures. Cross-sections are drawn at p 5 0.1 (upper curve), p 5 0.5 (middle curve), p 5 0.9 (lower curve). Data points p 5 0: open circles, p 5 1: closed circles, p A [0, 1]: diamonds, the cases (of the three separate experiments) of spoilage indicated. (A) 8 C, 36 days; (B) 12 C, 36 days. From Valdramidis, V., Graham, W., Beattie, A., Linton, M., McKay, A., Fearon, A., et al., 2009. Defining the stability interfaces of apple juice: implications on the optimisation and design of high hydrostatic pressure treatment. Innov. Food Sci. Emerg. Technol. 10 (4), 396
404. doi:10.1016/j.ifset.2009.02.006 with permission from Elsevier.

variable (in that case spoilage or no spoilage) and the set of the studied explanatory (i.e., processing, postprocessing) variables is established. The application of logistic types of models was successfully applied to assess the spoilage potential of apple juice that had been treated with HHP (Valdramidis et al., 2009). The selected spoilage indicator was the yeast of I. orientalis and the spoilage of the product was defined as the detection of more than 105 CFU/mL in the final product. In that study, a simple logistic model without interaction terms and a polynomial logistic equation were employed. An example of the outcome of this study is presented in Fig. 28.1 when a set of four parameters, i.e., high pressure amplitude (HHP), pressure holding time (tHHP), storage time after pressure (tstorage), storage temperature after pressure (Tstorage), are studied in order to describe quantitatively the spoilage/no interface of apple juice.

28.4.4.2 Dynamic modeling approaches Recent studies have shown that the microbial parameters estimated from the sets of dynamic experiments have a comparative advantage when compared with static experiments (Cattani et al., 2016; Dolan et al., 2013). These parameter estimates are expected to result in reliable predictions and have been reported to give the actual values of nonisothermal estimates as there may be more than one combination of parameters that give identical results (Dolan et al., 2013). Such approaches have also been applied during shelf life studies by Corradini and Peleg (2006). The studies examined the degradation dynamics of vitamin C in which nonisothermal nonlinear regression analysis

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565

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ACKNOWLEDGMENT This work was partly funded by MPNS COST Action CA15118 Mathematical and Computer Science Methods for Food Science and Industry (FoodMC).

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