1. Introduction
Radish is one of the most consumed vegetables in Asia [
1]. It has dietary fibers and antioxidants such as vitamins, flavonoid pigments, and aromatic amines [
2,
3,
4,
5]. White radish is largely consumed as a soup in Asia. This soup is popular in food services and restaurants’ menus. For commercial purposes, white radish is processed through pickling, drying, and fermentation. However, chemical structures and nutritional values of raw radish are changed after processing [
6]. Recently, retort-processed ready-to-eat (RTE) or ready-to-cook (RTC) food has been in demand in processed food and food service industries due to its convenience. Although there are large demands for RTE or RTC white radish products with a long shelf life, commercial production is challenging because thermal processing causes a wide range of quality changes, such as color and texture changes. Especially, when a large package is required (for example, for food services or B2B products), the quality may change and the degree of sterilization may need to be optimized due to the long thermal processing time required for large size products. Ham and Yoon [
7] reported that the thermal processing time to reach the target
F0-value for pouched white radish broth is increased when the volume is increased from 500 to 5000 mL. When the thermal process time is increased, the browning index of white radish broth is significantly increased. Therefore, the retort process should be optimized before applying it to produce a long shelf-life white radish or white radish broth because of thermal degradation of product qualities, such as texture, color, and overall acceptability.
Retort processing is one of the most widely used thermal processes for commercial production of packaged food products with a long shelf life at room temperature [
6]. In general, a high temperature (>121.1 °C) and a pressure higher than the atmospheric pressure (1.3–1.5 atm) are used as reference processing conditions for the retort process [
8]. Most retort products are processed in pouches or tin cans with a vacuum or a minimum level of headspace. The heat transfer rate plays a major role in determining the degree of sterilization (i.e.,
F0-value) of the product. The cold point in the product must be determined before estimating the total thermal process time that is required to satisfy a certain degree of sterilization. Recently, there has been a demand for different sizes and shapes of retorted products. It is practically impossible to measure all temperature profiles at every point according to various sizes and shapes under high-temperature conditions [
9]. Especially, it is very difficult to predict temperature profiles of products under two-dimensional or three-dimensional heat transfer. Heat transfer occurring in retort pouches could be predicted with a relatively simple conduction heat transfer model because the pouch is tightly packed with a vacuum. However, with three-dimensional heat transfer in various sizes or shapes, it is difficult to evaluate the microbial safety based on the degree of sterilization at the cold point [
10]. Therefore, heat transfer rate changes depending on the package dimensions, suggesting that package dimensions are an important factor in retort processing.
Numerical simulation (NS) is a useful tool that provides a temperature gradient under various geometry and thermal processing conditions [
9]. NS is especially useful for analyzing the heat transfer phenomenon in a three-dimensional heat transfer, which is hard to predict with an analytical solution. The governing partial differential equations (PDE) for heat transfer by conduction can be numerically solved with either a finite volume method or a finite difference method [
11]. Solutions for PDE have been successfully used for predicting cold points of food products with complicated three-dimensional shapes [
11,
12,
13]. The total thermal processing time measured based on the cold point estimated based on NS can be used to evaluate the degree of sterilization of the product [
14,
15].
Heat-sensitive qualities, such as texture and color, must be considered before applying a retort process. A high temperature and a long time in the retort process can significantly change heat-sensitive qualities. Chemical reactions occurring during thermal processing often lower nutritional values. However, chemical reactions can also generate new favorable components, such as polyphenolic components and flavonoid compounds, during thermal treatment [
16]. For instance, the amount of physiologically active substances in several fruits and vegetables can be increased by certain levels of heat treatment [
17]. Antioxidant activities of defatted soybean and garlic extract are increased by thermal treatments [
18]. Antioxidant activities of ginseng, pear, and tomato are also increased after heat treatments because of increases of polyphenols and flavonoids [
17,
19,
20,
21]. Changes in active components in white radish extract products during thermal processing and those in white radish after long periods (30 days) of thermal treatments to produce white radish-based products have been studied [
22,
23]. However, effects of thermal processing temperature and time on quality attributes, such as color, texture, and antioxidant levels, in RTE and RTC white radish are currently unknown. Especially, effects of sterilization conditions on white radish with different package dimensions have not been reported yet.
Thus, the objective of this study was to investigate effects of thermal processing conditions on the quality of retorted white radish products with various package dimensions. To achieve this goal, this study estimated cold points of white radish products with various package dimensions using NS and evaluated effects of heating temperature and time on color, texture, and antioxidant activity of white radish.
2. Materials and Methods
2.1. Subsection
A bundle of fresh white radish (
Raphanus sativus L.) was obtained from a local farm and used for the experiment within one month after harvest. The white radish was sliced into a rectangular shape (30 × 30 × 15 mm) after washing and cleaning. The size of the slice was determined based on the average size of white radish slices used in commercial white radish soup products. Sliced radish samples were packed in a retort pouch (NY15/LLDPE70) with a vacuum. Various dimensions of rectangular parallelepiped packages were prepared to investigate the effects of dimensions on heat transfer rates by conduction during thermal processing. By stacking radish slices, the following four different rectangular parallelepiped packages were prepared: A = 0.120 × 0.360 × 0.015 m, B = 0.060 × 0.360 × 0.030 m, C = 0.120 × 0.180 × 0.030 m, and D = 0.120 × 0.090 × 0.060 m. Images are shown in
Figure 1I. The weight (5.87 × 10
−1 ± 0.01 kg) and the volume (6.48 × 10
−4 m
3) of each package were constantly maintained. The ratio of the surface area to unit volume was calculated with the following equation:
where
φ is the ratio of the surface area to volume,
A is the total area of the sample (m
2),
V is the volume of the sample (m
3), and
x is the unit calibration factor = 100 (m).
Samples were sterilized by retort (SR-240, TOMY, Osaka, Japan) as pressurized steam with an operating pressure range of 0–275 kPa. Packaged samples were placed in the retort chamber. The thermal processing temperature during the retort process varied at 121.1 (±0.2), 130 (±0.4), and 140 (±0.6) °C. F0-value of 6 min at 121.1 °C was defined as the target degree of sterilization for all thermal processes. A wireless temperature sensor (Tracksense pro, Ellab, Trollesmindealle, Hillerød, Denmark) was used to monitor temperature changes at the geometrical center of each package. To calculate the F-value for each temperature and dimension, the temperature profile at the geometrical center was measured. The thermal processing time at each temperature and dimension was varied to achieve the target F0-value.
2.2. Numerical Simulation
Transient heat conduction in the parallelepiped samples was simulated by solving the unsteady state Fourier’s equation as shown below with an assumption of constant thermal diffusivity (α) [
24]:
where
T is the initial temperature of the radish sample (K),
t is the time (s), and
α is the thermal diffusivity (m
2/s).
Thermal diffusivity of white radish was determined as:
where
k is the heat conductivity (W/m·K),
ρ is the density (kg/m
3), and
CP is the specific heat (kJ/kg·K).
Surface conditions of the sample were:
The sample had a uniform initial temperature. Thus, initial conditions were:
Symmetry boundary conditions on the axes were:
where
Ts is the temperature of the surface of the package (K),
T∞ is the temperature of the retort steam (K),
h is the convection heat transfer coefficient (W/m
2·K), and
x,
y, and
z are distances (m) of the
x-,
y-, and
z-axis, respectively.
Ansys Fluent (version 17.0, Ansys Inc., Canonsburg, PA, USA) was used to solve the Fourier equation with appropriate boundary conditions to simulate conduction heat transfer. As shown in the boundary conditions (Equations (3)–(5)), the heat transfer by convection occurring between the heating medium (steam) of the retort and the surface of the package was considered in the boundary conditions [
25]. Heat transfer rate from convection flow was assumed to be the same as that on the surface of the sample [
26].
2.3. Calculation of Heat Transfer Coefficient
Heat transfer coefficient (
h) for forced convection in the retort might be between 5700 and 28,000 (W/m
2∙K), derived from the forced convection current in the interior of a retort machine [
27]. Since the range of
h is too wide to be applied to the simulation, we estimated the
h with the two-step method used in the study by Hong et al. [
9]: (1) average
h value was estimated using dimensionless numbers, and (2) the
h value with the lowest root mean square error (RMSE) value estimated between the simulation value and the experimental value was then chosen for the simulation. All flow properties were approximated at the film temperature defined as follows:
where
TF was the film temperature (K),
TS was the temperature at the surface of the package (K), and
TR was the temperature of the heating medium (K).
The volume of the radish sample (6.480 × 10
−4 m
3) is very small compared to the volume of the retort chamber (4.449 × 10
−2 m
3). Thus, the geometry under forced convection can be simplified to a horizontal plate [
27]. Dimensionless numbers for estimating the
h value were: Reynold number (
NRe), Prandtl number (
NPr), and Nusselt number (
NNu). Values of
h were estimated from the following dimensionless numbers and empirical equations:
for the laminar region,
for the turbulent region,
where
μ is the viscosity (Pa·s),
v is the velocity (m/s), and
L is the average length of the longest side of the sample (m). Physical properties of the steam for calculating dimensionless numbers are summarized in
Table 1. The average flow rate of the steam was assumed to be
v = 0.0122 m
3/s. The thin layer of a pouch with a low thermal resistance was ignored in the simulation because the vacuum-packed white radish was sterilized by high-temperature steam [
28]. Physical properties of the radish and retort pouch for NS are presented in
Table 2.
2.4. Calculation of F-Value
The
F0-value indicating the degree of sterilization was calculated using the following equation, where a reference temperature of 121.1 °C and a
z-value of 10 °C were applied based on sterilization conditions for thermoresistant anaerobe
Clostridium botulinum (
C. botulinum) spores [
29]:
where
t0 is the time to initiate heating (min),
tf is the finish time of heating (min),
Tt is the temperature at time
t (K), and
Tref is the reference temperature (K).
According to Simpson et al. [
30], the inactivation of
C. botulinum spores can be calculated by a ratio between the final and initial bacteria ratio because the bacteria destruction during sterilization follows first-order kinetics:
where
is the inactivation percentage of
C. botulinum and
D is the D-value (min).
For
C. botulinum, the decimal reduction time (D-value), 0.21 min, was considered for thermal destruction at the reference temperature [
31].
2.5. Color Measurement
Changes in color of each white radish associated with the thermal process were evaluated by measuring
L* (lightness),
a* (redness), and
b* (yellowness) using a colorimeter (CR-300, Minolta, Osaka, Japan). A standard white plate with the following values was used:
L* = 93.60,
a* = 0.31, and
b* = 0.32. Locations chosen to measure the color are presented in
Figure 1II. Since these positions are adjacent to the center and the edge of each sample, these selected positions might be suitable for representing the pattern of color changes associated with heat treatment. Color measurements were performed in triplicate and the average value was used for analysis.
2.6. Extraction of Samples
White radish samples were freeze-dried for 48 h using a freeze-dryer. Samples were ground into a fine powder and kept at −70 °C prior to analysis. Powdered samples (50 mg) were extracted with 2 mL of 90% methanol containing 0.25% acetic acid for 30 min. The sample solution was then centrifuged at 3000 rpm for 10 min at 4 °C. The extraction was repeated three times. The supernatant was filtered through a 0.45 μm syringe filter (Whatman Inc., Maidstone, UK). The extract was appropriately diluted before it was used for total phenolic content analysis.
2.7. Total Phenolic Content (TPC)
TPC of white radish after thermal processing was determined using the Folin–Ciocalteu method. A 0.5 mL solution of the radish extract, 0.5 mL of Folin–Ciocalteu reagent, and 2 mL of 20% Na2CO3 solution were transferred to a glass test tube and mixed thoroughly using a vortex mixer. After 15 min of incubation at room temperature, 10 mL of distilled water was added and the absorbance was measured at 725 nm using a spectrophotometer. The TPC was presented as a gallic acid equivalent (GAE) per 100 g of dry matter (d.m.). All reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA).
2.8. Texture Measurement
Texture was measured with a compression test using a texture analyzer (CT3, Brookfield, Middleboro, MA, USA). Samples were cut into 0.030 × 0.030 × 0.015 m pieces. Locations of samples were taken to measure the texture, i.e., the center and the edge of each package, as described in
Figure 1II. Measurement conditions of the compression test were as follows: 0.007 m of plastic blade, test speed of 0.001 m/s, trigger force of 0.05 kg, and deformation rate of 30%. All measurements were performed in triplicate with different samples.
2.9. Statistical Analysis
Nodal temperature was estimated by simulating a model at 10 s intervals. Simulated data were compared to experimental data (temperature and
F-value) and root mean squared error (RMSE) was calculated using the following equation:
where
Te is the temperature of the sample at the cold point measured by experiment (K),
Tsim is the simulated temperature of sample at the cold point (K),
Fe is the
F-value calculated based on the experimental temperature (min),
Fsim is the
F-value calculated based on the simulated temperature (min), and
n is the number of data points.
Statistical significance of difference was evaluated by one-way analysis of variance (ANOVA) using MS-Excel 2016 (Microsoft, Redmond, WA, USA) at a confidence level of 95%.