Keywords

1 Introduction

Food research and innovation during the last 50 years has significantly offered in the technological advancement. Significant effort has been targeted on the production of safer, of higher quality, of improved nutritional profile foods, as well as in the development of new-novel food products that could not be produced, or their safety and stability could not be ensured by the conventional equipment and knowledge. Novel Nonthermal technologies in food processing are among the technology advancements. Their aim is mainly to substitute conventional thermal treatment applied for the pasteurization and sterilization of foods, while simultaneously retaining their quality and nutritional characteristics. High pressure (HP), Pulsed Electric fields (PEF), Pulsed Electromagnetic Fields (PEMF), Cold Atmospheric Plasma (CAP) and Osmotic Dehydration (OD) are the most important novel technologies studied and (some of them) efficiently applied in an industrial environment.

Although the research activity on novel Nonthermal technologies has been continuously growing the last decades (Fig. 3.1) resulting in production of data, thus continuously growing application of some of the novel technologies, some scientific, technological, and technical issues should be answered. Food industry demands fully documented and validated answers with regards to the applicability and the benefits of all these technologies, as physical processes of food preservation. Process optimization (selection of most appropriate process conditions) for the production of safer, high quality, nutritious, cost effective and consumer acceptable products is essential. The implementation of the scientific achievements for an effective process is based on the kinetic approach of the destructive reactions of several factors that lead to spoilage or degradation of food, during the novel technologies’ treatments. A systematic study of the technical parameters would allow for food engineers to predict and evaluate for the process applied, its intensity and efficiency, enabling the use of corrective actions if needed, for production of safe, sustainable, and cost-efficient final food products.

Fig. 3.1
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Number of cited papers in the literature through years (keywords: novel and food and technology)

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Although the novel technologies are aimed to substitute conventional thermal treatment, there is still research being conducted even for the well-studied thermal processing that has only two process parameters (temperature and time). For all the other Nonthermal technologies, that the number of their variables is higher (Table 3.1), it is evident that for the production of data, increased number of experiments has to be conducted.

Table 3.1 Number of independent variables for each process nonthermal technology
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Depending on the product to be treated, the dependent variables need to be named and studied as a function of the independent variables of each technology applied. The most important quality and safety parameters to be studied are the pathogens inactivation, the spoilage microflora inactivation, the endogenous deteriorative enzymes inactivation and the non-alteration of colour, texture, pH value, nutritional characteristics and finally organoleptic acceptability. The mathematical description of the effect of independent variables (process parameters) on the safety and quality indices of the treated foods is of high importance since allows for the food engineer to optimize the process; mathematical equations describe the effect of the process parameters values on selected quality indices. Thus, food engineers save time and money by not applying the trial and error approach for the selection of the appropriate conditions for the Nonthermal treatment.

For all thermal and Nonthermal technologies, the mathematical description of the effect of process time on any food quality index is characterized as primary modelling. Within this approach, all other independent variables of each technology are kept constant, thus the effect on any chemical change, microbial population or enzyme activity is described by zero order, first order, second order or nth order kinetics (Table 3.2) (Labuza 1984; Saguy and Karel 1980). First-order kinetics is the one mostly applied in nonthermal processing for the estimation of the rate constant of a quality index alteration vs process time and will further be discussed within this chapter.

Table 3.2 Reaction order kinetics applied for the mathematical description of the effect of process time on any microbial, enzymatic or chemical index
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The primary models are useful when the processing conditions (values of the independent variables for each Nonthermal technology) are kept constant. For processing conditions that are changed, new experiments should be performed to estimate the corresponding new primary model parameters. For a number of different process conditions studied with simultaneous application of primary models for each alteration of the process conditions, new mathematical equations known as secondary models can be applied to express the independent variables parameters effect on the predicted primary model parameters. These equations are the result of theoretical considerations or empirical observations and in most cases are nonlinear.

2 High Pressure Processing

The initial interest of the scientific community in the application of High pressure (HP) technology included inactivation of microorganisms such as bacterial stem cells, yeasts and fungi (Hoover et al. 1989; Patterson et al. 1997; Smelt 1998). The main target was, through HP processing optimization, to increase foods shelf-life by inactivating spoilage and pathogenic bacteria (Bayindirli et al. 2006; Donaghy et al. 2007; Jofré et al. 2008). HP causes microorganisms cellular membrane damage and is mainly reported for pathogenic and spoilage bacteria vegetative forms when food is treated with pressures higher than 200 MPa, where irreversible protein/enzyme denaturation and intracellular content leakage occur. The effects of HP on enzymes (mainly pectin methylesterase, polyphenoloxidase and peroxidases) has also been reported in a large number of studies cited in the literature (Irwe and Olsson 1994; Seyderhelm et al. 1996; Butz et al. 1996; Weemaes et al. 1997; Ludikhuyze et al. 1996, 1998, 2000; Indrawati et al. 2000; Nienaber and Shellhammer 2001; Moatsou et al. 2008a, b; Eylen et al. 2008; Katsaros et al. 2006, 2017; Boulekou et al. 2010). The conclusion of the studies is that HP significantly affects enzymes and enzymatic reactions, allowing for the development of an alternative to thermal treatment preservation technique that leads to reduced undesirable changes in the texture and sensory characteristics of a food product. The enzyme inactivation is mainly attributed to changes in the secondary and tertiary structure of enzymes occurring at specific pressures (Giannoglou et al. 2016). HP process of pressurization is based on the principle of “Le Chatelier,” inducing a reduction in molecular volume and, consequently exponentially accelerating the occurrence of reactions favoured by pressure. Thus, the rates of the chemical or physical reactions resulting in lower volume products are accelerated by HP, whereas the reactions that result in an increase in the total volume are retarded. Based on the literature, the results show that different microorganisms and enzymes, exhibit different kinetic inactivation or activation with HP.

The observation from the researchers that the effect of HP processing, on a particular characteristic, over time follows a specific trend that could be kinetically expressed through mathematical modelling, led to the need mathematical models to be developed in order to predict the impact of HP processing on a specific characteristic (e.g., activity of enzymes, microorganisms etc). A mathematical model predicting the change of a target parameter as a function of time (primary model) is an essential tool when designing HP experiments and industrial processes. Furthermore, the application of a primary model can be extended if a secondary model describing the pressure dependence of the primary model parameters is available too. Below the most used primary and secondary mathematical models by various researchers in the field of HP processing are presented, to describe and predict the effect of HP on different microorganisms, enzymes and quality indices of various food systems.

2.1 Primary Mathematical Modeling

The main equations reported in the literature describing the effect of treatment time at constant pressure and temperature on the microbial, enzyme or any other quality index alteration are depicted in Table 3.3.

Table 3.3 Mathematical equations cited in the literature and used for the description of the effect of treatment time at any combination of pressure and temperature on the value of microbial, enzymatic or chemical reactions
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In Table 3.4, the effect of processing conditions range on the inactivation of indicative microorganisms and enzymes from various food products is depicted. The model used along with the model parameters estimated are also presented.

Table 3.4 Effect of High pressure processing conditions range on the inactivation of indicative microorganisms, enzymes and substituents from various food products as cited in the literature
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2.2 Secondary Mathematical Modeling

The primary models are useful when the processing conditions (pressure, temperature, pH, etc.) are kept constant. If any processing condition is changed, a new set of experiments must be performed to obtain new primary model parameters. To extend the application of primary models, mathematical expressions known as secondary models can be developed to estimate the pressure and/or temperature effect on the predicted primary model parameters. As in the case of primary models, secondary models can be obtained from theoretical considerations or empirical observations. Most of the secondary models here presented (Table 3.5) are nonlinear, reflecting complex biological behaviors under high-pressure/high-temperature conditions.

Table 3.5 The most common kinetic models used for secondary mathematical modeling for high pressure processing as cited in the literature
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In most references cited in the literature, the Bigelow model is used to describe the effect of temperature and pressure on the reduction of microbial load. As in the case of thermal treatment, the thermal resistance constant zT was developed is used, the analogous approach was established for pressure effect as well, estimating the pressure resistance constant zP. Both these values may be used for the description of the effect of pressure (zP) and temperature (zT) on the decimal reduction time of microorganisms. The parameter zP determines the pressure increase required to achieve a tenfold increase in the inactivation rate, while the zT determines the temperature increase required to achieve the tenfold increase in the inactivation rate constant.

The Eyring equation was also developed to mathematically describe the effect of pressure (P) on the inactivation rate constant (k). This effect was correlated to the Activation Volume value (ΔV). Similarly to the Arrhenius equation that is widely used for the mathematical description of temperature on the inactivation rate constant of a quality index. In this case, the Activation Energy (Ea) is estimated and is correlated to the effect of temperature on the inactivation rate constant. In High pressure treatments that pressure and temperature are the main process parameters apart from treatment time, the combined effect of Eyring and Arrhenius equations may be used. Thus, the effects of pressure and temperature may be expressed through the Activation volume and Activation energy, respectively. For more than one different treatment temperatures, the Activation volume value is estimated for each temperature and the effect of temperature on the ΔV values may be estimated (by plotting ΔV values vs treatment temperatures). The same approach may be followed for the Activation energy as well, that expresses the effect of temperature on the inactivation rate constant of an index. For more than one pressures studied, the Εa value is determined for each pressure and then the effect of pressure on the Εa value can also be determined by plotting the Ea values vs treatment pressures.

Secondary models for the Weibull parameters include a logistic-exponential expression for parameter b’, indicating that the inactivation rate constant will be close to zero until a critical pressure level (Pc) is reached. If the pressure level is increased beyond Pc, b′ will increase at a rate wP. An exponential decay model has been used to predict pressure effects on the Weibull parameter “n”, where the dimensionless parameter d0 is the value of n at pressures P approaching 0, and d1 is the rate at which n exponentially decays (Doona and Feeherry 2007; Peleg 2006; Doona et al. 2007).

3 Pulsed Electric Field Processing

For PEF treatment, the most critical processing factors are electric field strength, treatment time, shape and width of the pulse, frequency, specific energy, and preheating temperature (Barbosa-Canovas et al. 1999; Wouters et al. 2001). Depending on the conditions intensity, the treatment efficacy is affected leading to microbial population or enzyme activity decrease, causing cell disintegration. The development of mathematical models that can predict the death of microorganisms and inactivation levels of quality-related enzymes by PEF is a very useful tool to design safe and effective PEF processes.

3.1 Primary Mathematical Modeling

Many researchers have focused on developing mathematical models to understand the physiological mechanism of each quality or safety index alteration and how the treatment conditions affected microorganisms, enzymes, quality indices, retention of bioactive compounds and extraction of intracellular compounds from plant tissues. Kinetic models for studying the effects of PEF treatment on the activity of microorganisms or enzymes, have been extensively used in different process conditions and are presented in Table 3.6.

Table 3.6 Mathematical equations cited in the literature and used for the description of the effect of PEF treatment on the value of microbial, enzymatic or chemical reactions
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First-order kinetic model as a function of treatment time or electric field strength is the most widely mathematical equation used to describe successfully the effect of PEF treatment on inactivation rate of microorganism, enzymes or retention of health-related compounds in the foods. This model describes the dependency of residual activity of microorganisms or enzymes as a function of the PEF conditions intensity (Bendicho et al. 2002; Giner et al. 2001, 2002, 2003). In literature, the experimental data clearly show an exponentially decrease in residual activity as the intensity of PEF conditions (electric field strength or treatment time) is increased (Bendicho et al. 2002; Giner et al. 2002). Moreover, the effect of PEF on microbial or enzyme inactivation could also be explained by empirical models such as those of Hülsheger, Fermi’s and Weibull distribution model (Table 3.6). These models were first proposed for predicting microbial inactivation, and later to describe the destruction of enzymes by PEF (Giner et al. 2000; Min et al. 2003). Hülsheger’s and Fermi’s models describe the decrease of microbial or enzyme activity (RA) as a function of both electric field strength and treatment time.

PEF efficacy is crucial as a knowledge for accepting this technology as a pasteurization process equivalent of thermal treatment. Critical factors affecting microbial inactivation, enzymes inactivation as well as effect on other quality indices is necessary to be studied. The data received from these studies will establish optimized processes that are applicable under a wide range of conditions. Mathematical models applied target to fit microorganism and enzyme responses to processing factors and environmental variables. The fitting accuracy of the traditional first-order kinetics, Hulsheger’s, Fermi’s, and Weibull distribution models for microbial inactivation by PEF treatments has been reported in several studies (Table 3.7). Similarly to microbial inactivation, effort has been done on the mathematical description of PEF effect on enzymes inactivation. Indicative studies are presented, reporting the mathematical model they applied to describe their results in Table 3.8.

Table 3.7 Effect of PEF processing conditions range on the inactivation of indicative microorganisms from various food products as cited in the literature
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Table 3.8 Effect of PEF processing conditions range on the inactivation of indicative enzymes from various food products as cited in the literature
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Numerous papers report studies of quality parameters of foods and how are they affected by PEF treatment (Table 3.9). The results obtained are promising, since the health-related compounds and quality indices are retained better when compared to conventional thermal treatment (Min et al. 2003; Odriozola-Serrano et al. 2008a, b).

Table 3.9 Effect of PEF processing conditions range on the effect of quality indices of various food products as cited in the literature
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PEF technology led to retention or enhancement (due to increased extractability offered by PEF technology) of health-related compounds in juices, such as lycopene, vitamin C and polyphenols. Generally, more intense process conditions (higher electric field strength and treatment time), resulted in lower vitamin C (in strawberry as reported by Odriozola-Serrano et al. 2008c, 2009b in tomato reported by Odriozola-Serrano et al. 2007, 2008b, d, 2009a and in watermelon juice cited by Oms-Oliu et al. 2009) and in higher lycopene contents (Odriozola-Serrano et al. 2007) up to 146.2% for tomato juices.

Mathematical description of data obtained by PEF treatment of foods may improve the prediction of the variation of the health-related compounds and antioxidants as affected by key parameters involved in PEF treatments.

PEF was also used to assist and accelerate extraction of high added value compounds from by products, such as tomato peels (Andreou et al. 2020a; Luengo et al. 2014; Pataro et al. 2020), olive pomace (Andreou et al. 2020b), potato peels (Frontuto et al. 2019), grapes by-products (Corrales et al. 2008), orange peels (Luengo et al. 2013), and sesame cake (Sarkis et al. 2015). PEF-assisted extraction could lead to cell alterations or disruption of cell membranes resulting in higher recovery yields of valuable compounds while decreasing the extraction time or solvent volume.

There is limited data in literature about the modeling description of the effect of PEF treatment on the enhancement of mass transfer phenomena. Several researchers have used PEF as pretreatment to conventional extraction procedure and several mathematical models have been used to optimize the PEF conditions’ selection.

For instance, Andreou et al. (2020b) used a fractional fist order equation to describe the polyphenol and protein extraction assisted by PEF from olive pomace, and the extracted concentration of each compound was correlated with PEF energy input. Moreover, lycopene extraction kinetic from industrial tomato peels was well fitted by Peleg’s model (Pataro et al. 2020), allowing to select the optimal PEF conditions with low energy consumption.

3.2 Secondary Mathematical Modeling

Except on empirical models that are used as a first step to obtain optimum values of each factor with maximal reduction for every microorganism or enzyme, there is no secondary modeling approach proposed in the literature. This is mainly attributed to the numerous process parameters that counteract between them not allowing for development of secondary models.

4 Pulsed Electromagnetic Fields Processing

Pulsed electromagnetic fields (PEMF ) technology involves the generation and powerful direction of pulsed electromagnetic waves. The generated waves seem to react with the cells that come in contact changing the state of the electrons spin system (Pawluk 2015). PEMF is mainly studied for its use for human therapeutic purposes, indicating significant effects on cells, tissues and biological processes such as embryogenesis, regeneration, wound healing (Hammerick et al. 2010), as well as in cell migration, DNA synthesis and gene expression (Tsai et al. 2009; Luo et al. 2012; Kang et al. 2013). PEMF technology has hardly been studied for its effect on food products, however it is considered to be a promising technology for microbial inactivation (Tadevosian et al. 2006; Torgomyan et al. 2011; Giannoglou et al. 2020a, 2021). Extensive work on the effect of PEMF on a food system (whole fresh strawberries) has been performed by Giannoglou et al. (2021). PEMF processing did not appear to have a significant impact on the weight loss, the color, the total anthocyanin content and on the pH-value of the strawberries, after processing and during storage. PEMF processing led to a 16% decrease in the firmness of the strawberries immediately after processing compared to Control samples, which was also maintained during storage. A significant increase in the total phenolic content and in the free radical scavenging activity was observed for PEMF processed fruits immediately after processing. PEMF processed strawberries also presented higher peak values in the total phenolic content during storage compared to the untreated ones. The PEMF strawberries presented the highest values in ascorbic acid content after processing and also during storage, compared to Control. PEMF technology could be used for enhancement of nutritional value of fruits in addition to the quality retention related with safety and consumer perceived traits. Nevertheless, there has not yet been described the effect of PEMF on any quality index by mathematical equations. There is a lot of space for work towards this direction, since the equations applied for PEF treatments could be modified and applied also for PEMF treated food products.

5 Cold Atmospheric Plasma Processing

Cold atmospheric plasma (CAP ) is an emerging non-thermal processing method that attracts an ever increasing interest for future application in food industry (Schlüter et al. 2013; Niemira 2012; Giannoglou et al. 2020a, b). Plasma is a partially ionized gas consisting of a reactive mixture of charged particles, free radicals, excited species, and UV photons. Novel plasma reactor designs and electrical power supplies have enabled the generation of non-thermal, far from thermodynamic equilibrium, plasmas in atmospheric pressure (Pappas 2011; Brandenburg 2018). The high reactivity combined with the low temperature operation render plasma suitable for treatment of heat-sensitive food products, due to their ability to deactivate microorganisms (Kelly-Wintenberg et al. 1999; Moisan et al. 2002; Laroussi 2005; Puač et al. 2017; Dimitrakellis et al. 2021). In the field of fruits, the investigation mainly concerns the CAP effect on the microorganisms/enzymes and quality characteristics of fruit products such as orange juice (Xu et al. 2017), white grape juice (Pankaj et al. 2017), siriguela juice (Paixão et al. 2019), fresh-cut apples (Ramazzina et al. 2016; Tappi et al. 2014), fresh-cut melon (Tappi et al. 2016). A more limited number of studies concern the effect of this technology on whole fruit i.e. blueberries (Sarangapani et al. 2017) and cherry tomatoes (Misra et al. 2014). Studies on the effect of CAP processing on quality parameters of whole fruits during storage are limited to strawberries (Rana et al. 2020; Giannoglou et al. 2021) and mandarins (Won et al. 2017). Based on the reported results, CAP processing induced the inactivation of microorganisms mostly due to the interaction with reactive oxygen and nitrogen species (RONS) generated in the gas phase during processing. The effect of plasma treatment on the quality characteristics varied from significant to insignificant, depending on the investigated parameters, the product characteristics, as well as the intensity of the processing and the plasma source design. Indirect plasma treatment of strawberries through immersion in plasma activated water has also been proposed in the literature for efficient disinfection based on secondary RONS formed in liquid phase upon interaction with gas discharges (Ma et al. 2015).

In general, CAP can be used either as direct (direct processing of a food product by the ionized gas), semi-direct (surface dielectric barrier discharge used for the treatment of whole food products) or even indirect (production of “plasma activated water” and immersion of food products within this water rich in Reactive Oxygen Nitrogen Species with the antimicrobial effect) applications. In all cases, there is limited work done on the mathematical description of the data obtained. This is mainly attributed to the numerous process parameters for CAP treatment making it harder for the researchers to apply or develop appropriate mathematical equations. Nevertheless, the models applied in the limited works cited in the literature are depicted in Table 3.10. No secondary models have been applied or developed for the description of the effect of process parameters on the inactivation rate constants for microbial, enzymatic and chemical indices.

Table 3.10 Indicative mathematical equations cited in the literature and used for the description of the effect of CAP treatment on the value of microbial and enzymatic reactions
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The works having included the kinetic approach in data obtained by applying the Cold Atmospheric Plasma technology on microbial, enzyme and chemical indices are very limited and some of them are depicted in Table 3.11. The Weibull model is mainly used by the researchers both for microbial and enzymatic inactivation.

Table 3.11 Effect of CAP processing conditions range on the effect of indicative microorganisms, enzymes and quality indices of various food products as cited in the literature
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6 Osmotic Dehydration

Osmotic dehydration (OD ) has received greater attention in recent years as an important complementary treatment and food preservation technique in the processing of dehydrated foods. OD is a water removal process which is based on the implementation of foods, mainly fruits and vegetables, in a hypertonic solution, reducing its water content while increasing the soluble solid content. The raw material is placed in concentrated solutions of soluble solids with higher osmotic pressure and lower water activity. Diffusion phenomenon takes place with two countercurrent flows: a water flow from the food to the outer solution and a simultaneous flow of solute from the solution to the food (de Mello Jr et al. 2019), enriching its composition by enhancing its nutritional value. These mechanisms lead to water loss and solid gain in the food. OD process occurs at mild temperatures (<50 °C), thus low energy consumption and processing costs are required (Torreggiani 1993). Although OD process will not give a product of sufficiently low moisture content to be considered a shelf stable product and therefore, OD should be combined with other preservation techniques.

The kinetics of mass transfer is described using terms such as water loss (WL), solids or solutes gain (SG) and water activity (aw) reduction (Pękosławska and Lenart 2009). The most important variable markedly affecting the kinetics of mass transfer during OD is temperature (Mokhtarian et al. 2014). This enhances the removal of water and uptake of solids.

The most widely used model for OD processes at atmospheric pressure is the Crank’s model, which consists of a solution of non-steady Fick’s law and represents the diffusional mechanism. Crank’s model (Crank and Gupta 1975) consists of a group of analytical solutions of Fick’s diffusion law that were obtained by Crank for various geometries and several initial and boundary conditions. Other models can also be found in the literature trying to describe the effect of process parameters on the mass transfer phenomena and are presented in Table 3.12.

Table 3.12 Mathematical equations cited in the literature and used for the description of the effect of OD treatment on the value of mass transfer phenomena
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Many researchers used the Crank’s model in order to describe the mass transfer phenomena during OD treatment of various tissues such as apple (Sereno et al. 2001), strawberry (Dermesonlouoglou et al. 2017a), goji berry (Dermesonlouoglou et al. 2018), tomato (Dermesonlouoglou et al. 2017b), cucumber (Dermesonlouoglou et al. 2008), kiwi (Dermesonlouoglou et al. 2016), pumpkin (Dermesonlouoglou et al. 2020), apricot (Dermesonlouoglou et al. 2019a; Dermesonlouoglou and Giannakourou 2018), bananas (Mercali et al. 2010), etc. The effective diffusivities of water and solids were found to be in the range of 2.0 ± 1.0 × 10−7 to 0.7 ± 0.2 × 10−11 m2/s, depending on the food tissue and the OD treatment conditions applied.

OD is the process that can be used as pre-treatment for conventional drying procedures, such as air-drying, microwave-drying and freeze drying. High temperature and long drying time in conventional drying may change flavor, color and rehydration capacity of dried products (Garcia et al. 2007). In all cases the drying efficiency and energy demand is associated with drying time, which is highly related with volume of moisture in a material to be removed or the rate at which drying can be accomplished. As a pretreatment it is necessary for the food engineer to effectively predict the effect of OD on quality indices, so as not to over process the food product. Thus, kinetic modeling is essential and can provide useful information on the necessary treatment conditions, depending on the tissue to be treated. Several researchers have studied and mathematically modeled the use of OD as a pretreatment to air-drying for fruits and vegetables such as tomato and cucumber (Dermesonlouoglou et al. 2019b), goji berry (Dermesonlouoglou et al. 2018), apple (Mandala et al. 2005), banana (Fernandes et al. 2006), pumpkin (Garcia et al. 2007) and melon (Teles et al. 2006). The beneficial use of OD has also been demonstrated for dairy (Giannoglou et al. 2020c) and meat products (Andreou et al. 2018).

7 Models Application, Process Parameters Estimation and Validation

The development and application of predictive models for the description of the effect of Nonthermal process conditions on safety and quality indices is a very useful tool for the food engineers and food scientists in general. These models already developed by food scientists and researchers allow for the scaling up of Nonthermal processes. Industry food engineers can adopt these equations and effectively apply them for predicting the optimal process conditions for the products to be treated. This does not require any special skills from the food engineer, neither a special tool, apart from a common software (Excel, Sigmaplot, SYSTAT, Origin softwares etc.) already installed to most PCs.

The food engineer will have to transfer the equation to one of the softwares and by replacing the model parameters with the ones estimated by scientists after numerous experiments will be able to predict the total effect on microbial, enzymatic or chemical indices (depending on the dominant deterioration factor) allowing him to decide which process conditions are more appropriate and efficient for the production.

Of course, since the models are just equations, there is always an error in the predictions, thus the food engineer has to validate the results obtained in his/her PC by results obtained from the actual production considering the process parameters estimated by the models. The deviation between predicted and observed values (effect on safety and quality indices after the processing) must not be high otherwise the model cannot predict with high accuracy and cannot be used for the scale up of processes.

8 Application of Kinetic Modeling for Process Optimization and Case Studies

Two cases studies on how a food engineer could work and take advantage of the kinetic approach and the predictions by the kinetic models on selecting the optimal process conditions are presented below. Both case studies concern the application of High pressure technology on the cold pasteurization of orange juices of different varieties; the first one is Valencia var., while the second one is Navel var.

The dominant quality indices that were taken into consideration are the dominant microbial flora-Lactic acid bacteria, LAB (L. plantarum and L. brevis) and the endogenous enzyme pectinmethylesterase that causes cloud loss, thus quality degradation of the juices. For both cases, a wide range of experiments on inactivating these indices by high pressure was conducted. The data received were mathematically described by the combined Eyring-Arrhenius equation, used to predict the inactivation rate constants at any combination of pressure and temperature for both juices. By estimating the inactivation rate constants, the necessary processing time can be determined for achieving a stable final food product. Iso-reduction plots for achieving a seven microbial log reduction and 90% PME inactivation after High processing were developed.

8.1 Case Study 1: Valencia Orange Juice

The Fig. 3.2 depicts the necessary combination of pressure and temperature process conditions for achieving the seven microbial log reduction and 90% PME inactivation, after 2 and 5 min processing time, conducted by Katsaros et al. (2010). By the obtained results, the food engineer may select the necessary process conditions for optimal high pressure processing, avoiding over-processing and products degradation or not sufficient processing for pasteurization thus survival of degradation factors. Process conditions required for the simultaneous targeted inactivation of PME and LAB in 5 min are 325 MPa and 30 °C. More intense process conditions (360 MPa and 35 °C) are required for 2 min processing.

Fig. 3.2
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Microbial (LAB) and enzymatic (PME) iso-reduction plots for achieving a seven microbial log reduction and 90% PME inactivation after processing for 2 and 5 min. Dashed lines represent a 7D LAB destruction and solid lines represent 90% PME inactivation. Black lines show processing for 2 min, while grey lines show processing for 5 min (own data, as published to Katsaros et al. 2010)

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8.2 Case Study 2: Navel Orange Juice

Similarly to the case study 1, the authors of this current chapter have unpublished data showing the iso-reduction plots for achieving a seven microbial log reduction and 90% PME inactivation after processing for 3 and 5 min for another orange juice variety, the Navel cv. one. Lactobacillus plantarum appeared to be relatively sensitive to pressures above 300 MPa. Navel orange PME sensitivity is in accordance with the general statement that PMEs are generally more resistant than microorganisms and that treatment for PME inactivation is sufficient for juices pasteurization. For Navel orange juice pasteurization, inactivation of 90% of the pressure/temperature labile PME fraction was considered as process target. For LAB a 7D reduction of the most resistant strain was considered. The required processing times for pasteurization of Navel orange juice PME at different process pressures, at 25 and 30 °C are shown in Fig. 3.3. Processing at 25 and 30 °C requires longer times for the PME inactivation compared to the inactivation of LAB species. The necessary pressure and temperature process conditions for the inactivation of 90% PME and 7D LAB reduction were estimated for 5 min (milder conditions are required) and 3 min (more intense treatment conditions are required) processing time. The microbial and enzymatic iso-reduction contour plots for achieving 90% PME inactivation and seven microbial log reductions after processing for 5 and 3 min are depicted in Fig. 3.4.

Fig. 3.3
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Required processing time for the inactivation of PME and LAB as a function of pressure at 25 and 30 °C. Solid and dotted lines represent 90% PME inactivation at 25 °C and 30 °C, respectively. Dashed and dash-double dotted lines represent 7D LAB reduction at 25 °C and 30 °C, respectively

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Fig. 3.4
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Microbial (LAB) and enzymatic (PME) iso-reduction plots for achieving a seven microbial log reduction and 90% PME inactivation after processing for 3 and 5 min. Solid and dotted lines represent 90% PME inactivation after processing for 3 and 5 min, respectively. Dashed and dash-double dotted lines represent 7D LAB reduction after processing for 3 and 5 min, respectively

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According to the above results, the selection of HP processing conditions was based mainly on PME inactivation. A treatment of fresh Greek Navel orange juice at 600 MPa and 40 °C for 3 min can cause inactivation of the labile isoenzyme, leading to a remaining PME activity equal to approximately 10% of the initial activity of untreated juice. These conditions also exceeded process requirements for microbial stability of orange juice.

9 Conclusions and Future

In general, kinetic modeling is essential in nowadays for achieving an efficient processing in terms of producing food products of increased safety, quality, healthier profile while simultaneously being more cost effective. The scientists have done a lot of work towards producing data necessary for the development of mathematical equations for the description of the effect of process conditions with Nonthermal technologies on quality indices of new or improved food products. Nevertheless, there is space for more work on developing more reliable models with not significant errors, thus of higher accuracy when using them to scale up production from the labs to the industries. New measuring techniques providing more reliable values will boost the applicability and efficiency of kinetic models.

Nonthermal technologies are the future of food processing, but some issues related to products specificity have to be taken into consideration. Food engineers will have in their hands useful tools to predict optimal process conditions, thus evaluate for the process applied, its intensity and efficiency, enabling the use of corrective actions if needed, for production of safe, sustainable and cost-efficient final food products. Training and education has to be improved towards this direction enabling for the food engineers to efficiently take advantage of the tools-kinetic models available in the literature.