Determining the optimal shaking rate of a reciprocal agitation sterilization system for liquid foods: A computational approach with experimental validation

Shelf-Stable Sustainable Shrimp Thermally Processed With Reciprocal Agitation

“…When come-up-time credit was considered during cook (P t ), only three of the four agitation speeds had higher general method cook times than the Ball's formula method at a F 0 value of 3.5. Overall, the processor could reduce the cook time by 2 to 4 min to achieve an F 0 of 3.5; the reduced time at retort temperature can have a significant impact on improving product quality (Stoforos, 2010;Erdogdu et al, 2016;Singh et al, 2017Singh et al, , 2018.…”

Thermal inactivation of Listeria monocytogenes in the Shaka agitated reciprocal retort: Influence of food matrix rheology and fat content

“…For both types of rotation, forced convection is caused by an interaction of gravitational and centrifugal forces (Erdogdu et al, 2017;Pratap Singh et al, 2017a;Ramaswamy and Marcotte, 2005;Sarghini and Erdogdu, 2016). Recently, processes involving reciprocal agitation have gained more interest following the development of Shaka and Gentle Motion retorts (Clifcorn et al, 1950;Dwivedi and Ramaswamy, 2010;Erdogdu et al, 2016;Pratap Singh et al, 2018). Due to the combination of gravity and horizontal acceleration, heat transfer rates of reciprocal agitation processes are considerably higher than for rotational agitation processes (Walden and Emanuel, 2010).…”

“…Due to the novelty of the technique, there is a need for an improved understanding of parameters influencing heat transfer and microbial inactivation kinetics during reciprocal agitation thermal processes, both in relation to process operating parameters and food product related properties. Over the last decade, the influence of process operating parameters has already been studied rather extensively, e.g., reciprocation intensity and amplitude (Erdogdu et al, 2016;Ramaswamy, 2015, 2016), container placement orientation inside the retort (Pratap Singh and Ramaswamy, 2015), and container headspace (Pratap Singh and Ramaswamy, 2015;Pratap Singh et al, 2017b). Concerning food product related parameters, the influence of particle properties (e.g., size, shape, density) in particulate mixtures has been investigated to some extent (Cariño-Sarabia and Vélez-Ruiz, 2013;Singh and Ramaswamy, 2015), but research towards the influence of factors related to microstructural aspects of liquid food products has been scarce.…”

“…In this regard, food product rheology (e.g., viscosity) and the presence of fat droplets, and in relation to this, the fat content, are prominent examples of microstructural factors which significantly affect heat transfer rates and microbial inactivation kinetics in liquid food products (Chhabra et al, 1999;Fain et al, 1991;Juneja et al, 2001;Oliveira et al, 2018;Schultze et al, 2007;Sheridan and Shilton, 2002;Velliou et al, 2013). Concerning the role of food product rheology in reciprocal agitation thermal processes on the one hand, mathematical models have already been developed to determine the optimal reciprocation rate as a function of product viscosity (Erdogdu et al, 2016(Erdogdu et al, , 2017Pratap Singh and Ramaswamy, 2016). The resulting influence on microbial inactivation kinetics, however, was not taken into account in those studies.…”

“…Advantages related to the use of food model systems instead of real food products for experimental research include (i) a reduction in experimental workload, (ii) increased reproducibility, (iii) the absence of background microflora, and (iv) the possibility to systematically study independent factors (Baka et al, 2016). Concerning reciprocal agitation thermal processes, however, most studies related to the influence of food product parameters (e.g., rheology) on heat transfer were solely conducted in model systems which were not closely related to the composition and microstructure of relevant food products, e.g., distilled water (Erdogdu et al, 2016), carboxymethylcellulose (Singh and Ramaswamy, 2015), and glycerine Ramaswamy, 2015, 2016). Using artificial food model systems with compositional and microstructural properties more closely related to relevant food products, would improve applicability of findings for the food industry.…”