Analysis of Thermal Processing of Table Olives Using Computational Fluid Dynamics

Abstract

In the present work, the thermal processing of table olives in brine in a stationary metal can was studied through computational fluid dynamics (CFD). The flow patterns of the brine and the temperature evolution in the olives and brine during the heating and the cooling cycles of the process were calculated using the CFD code. Experimental temperature measurements at 3 points (2 inside model olive particles and 1 at a point in the brine) in a can (with dimensions of 75 mm × 105 mm) filled with 48 olives in 4% (w/v) brine, initially held at 20 °C, heated in water at 100 °C for 10 min, and thereafter cooled in water at about 20 °C for 10 min, validated model predictions. The distribution of temperature and F-values and the location of the slowest heating zone and the critical point within the product, as far as microbial destruction is concerned, were assessed for several cases. For the cases studied, the critical point was located at the interior of the olives at the 2nd, or between the 1st and the 2nd olive row from the bottom of the container, the exact location being affected by olive size, olive arrangement, and geometry of the container.

The check problem of food thermal processes: a mathematical solution

“…In a previous work [23], about the thermal processes design, an equation correlating g value with the thermo-physical and geometrical parameters of canned food (z, Jcc and fh) and the sterilizing value U, was obtained: (15) Where G is also a correction factor and it coincides with the product of two polynomials; the second polynomial is a function of (16) The equation (16), as a double polynomial, is a result of a careful analysis of the data in Stumbo's tables and as a consequence of a trial-and-error approach. By developing the product of two polynomials, the products of the coefficients a, b, c, d, A, B, C and D were obtained through a multiple analysis [26] (17) This because of, when , any tiny error of the first regression represented by equation (16), can easily produce, by equation 15, negative values of sterilizing value U. So, a second polynomial G' was arranged:…”

“…In last years, a third method was also proposed by using computational thermo-fluid dynamics. These CFD methods proved to be a valuable tool to ensure food safety and nutritional quality [15,16,17,18]. Nevertheless for this third numerical approach, a high computing power and long calculation times are necessary [19].…”

Technologies and Trends to Improve Table Olive Quality and Safety

“…On the other side, table olives act as a suitable substrate for the production of mycotoxins (especially citrinin) produced by filamentous fungi Penicillium, Aspergillus , and Monascus (Tokuşoglu et al, 2010 ). Nevertheless, thermal processing of table olives induces some quality deterioration, resulting in softening of olive tissue, loss of green color or change to brown, and development of cooking taste (Dimou et al, 2013 ; Abriouel et al, 2014 ). For this purpose, optimization of the HHP conditions is important for bioactive compounds stability and quality of table olives, as an alternative to thermal processing (Argyri et al, 2014b ).…”

Computational Fluid Dynamics (CFD) Modelling and Application for Sterilization of Foods: A Review

“…The presence of solids in liquid-solid foods has been found to influence the position of the SHZ, as well as the fluid motion inside the container. In contrast to the heat transfer in liquid foods, mixtures of liquid-solid food have been found to be heated by both convection and conduction [23,73,74]. In the simulation model for pineapple slices floating in sucrose solution, pineapple slices have been presumed to be heated up by pure conduction while the solution has been presumed to be heated by natural convection recirculating inside the can [75].…”