*Article* **Effect of Postharvest Transport and Storage on Color and Firmness Quality of Tomato**

**Mai Al-Dairi 1, Pankaj B. Pathare 1,\* and Rashid Al-Yahyai <sup>2</sup>**


**Abstract:** Transport duration affects the vibration level generated which has adverse effects on fresh produce during transportation. Furthermore, temperature affects the quality of fresh commodities during storage. This study evaluated physical changes in tomatoes during transportation and storage. Tomatoes were transported at three distances (100, 154, and 205 km) from a local farm and delivered to the Postharvest Laboratory where vibration acceleration was recorded per distance. Tomato was stored at two different temperatures (10 ◦C and 22 ◦C) for 12 days. The physical qualities like weight loss and firmness of all tomato samples were evaluated. RGB image acquisition system was used to assess the color change of tomato. The results of vibration showed that over 40% of accelerations occurred in the range of 0.82–1.31 cm/s<sup>2</sup> of all transport distances. Physical quality analyses like weight loss and firmness were highly affected by transportation distance, storage temperature, and storage period. The reduction in weight loss and firmness was the highest in tomatoes transported from the farthest distance and stored at 22 ◦C. Lightness, yellowness, and hue values showed a high reduction as transport distance increased particularly in tomatoes stored at 22 ◦C. Redness, total color difference, and color indices increased significantly on tomatoes transported from 205 km and stored at 10 ◦C and 22 ◦C. The study indicated that the increase in transportation distance and storage temperature cause higher changes in the physical qualities of tomatoes.

**Keywords:** quality; vibration; tomato; transportation

#### **1. Introduction**

Consumers prefer high-quality fresh produce, which is primarily assessed based on their appearance and taste [1]. For this reason, the fresh produce provided to the market should meet the international standards of quality for freshness, firmness, and other quality characteristics [2,3]. Throughout postharvest operations like harvesting, handling processing, storing, and transporting, fresh produce is subjected to different external forces. Therefore, this reduces product quality and decreasing sale prices as well as losses to the orchardists and growers [4]. Transportation is an essential process during the postharvest supply chain of any fresh produce [5]. However, transportation could cause postharvest losses leading to high economic losses [6] if it is not probably managed. Several factors lead to postharvest losses during transportation including bad roads, non-refrigerated vehicles [7], the surrounding environment [8], and mechanical and physiological properties of fresh produce [6]. During transport, fruits and vegetables are often exposed to rough handling and transported over bad road conditions resulted in damage and mechanical injuries which could increase the losses over the supply chain [9].

The vibration produced by vehicles (truck beds) during road transportation has a significant effect on the damage process of agricultural products like fruits and vegetables [10]. Furthermore, it is one of the main reasons for causing external and internal damages to the fresh produce during the supply chain [11,12]. Transport distance is one

**Citation:** Al-Dairi, M.; Pathare, P.B.; Al-Yahyai, R. Effect of Postharvest Transport and Storage on Color and Firmness Quality of Tomato. *Horticulturae* **2021**, *7*, 163. https:// doi.org/10.3390/horticulturae7070163

Academic Editor: Elazar Fallik

Received: 18 May 2021 Accepted: 25 June 2021 Published: 28 June 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of the essential factors that directly correlate with vibration level where long-distance transportation time resulted in high vibration level. Çakmak et al. [13], Shahbazi et al. [14], and La Scalia et al. [15] recorded high vibration levels and great damage as transit time increased in fig, watermelon, and strawberry respectively. Furthermore, long transport times during fresh produce transportation can accelerate enzymatic and metabolic processes resulted in increasing the mechanical damage risk, therefore, reducing market value [6].

Different physical changes have been investigated on different fresh produce during transportation like an apple [16], fig [13], grape [17], kiwifruit [18], tomato [8,19,20], and strawberry [15]. Most of the results showed that the internal damages produced by vibration during transportation can generate a rapid degradation [21] which can directly affect ripening level and firmness [12]. The vibrated samples of strawberries had a lower shelf life compared to the control (non-vibrated) samples as reported by Dhital et al. [22]. Xu et al. [3] also recorded high color change on lightness in broccoli stressed to two hours vibration.

Fresh produce storage is another essential part of the postharvest supply chain [23]. The majority of microbial, physiological, and biochemical processes contributing to produce quality deterioration are temperature dependence [24]. A high increase in storage temperature rises the rate of respiration [25], transpiration, and the rate of ethylene production. However, a high reduction of storage temperature could be a reason for causing chilling injuries and quality reduction [26].

Tomato (*Solanum lycopersicum* L.) is one of the most widely grown and important fresh produce worldwide. Tomato is a vital source of many nutrients and other healthy minerals that benefit the human body [27]. Weight, firmness, and color are essential quality indicators influenced by postharvest operations [1,25]. These quality aspects affect consumer acceptance and market success [28]. Compared to other fruits and vegetables, tomato is a sensitive produce [29] which is highly perishable due to vibration and impact load during transportation, the market process, and storage temperature [25]. Vibration during transportation causes the tomato to rub and rotate against other products and the packaging containers resulted in softening and other mechanical damages [8,13]. Besides, storage becomes difficult to maintain particularly in high moisture content fresh produce like tomato [23]. Few studies investigated the effect of real-time transportation vibration generated from different distances coupled with varying storage conditions on tomato fruit that can resolve the critical post-harvest losses and problems in perishable commodities. Therefore, this study was performed to investigate the contribution of vibration generated from road transportation of three different distances (100, 154, and 205 km) and storage temperature (10 and 22 ◦C) in tomato quality aspects including weight, firmness, and color for 12 days storage period.

#### **2. Materials and Methods**

#### *2.1. Field Experiments*

To compare the influence of vibration level and distance during transit on the quality of fresh produce, a total of 27 wooden boxes (400 × 300 × 110 mm) of bright red color tomatoes with calyx ("Miral" variety) of one harvest were purchased from a farm located in Al-Suwaiq, Sultanate of Oman. Tomato boxes were then transported to three different distances (100 km, 154 km, and 205 km) using 1692 kg non-refrigerated pickup (Model: Hilux, Toyota, Samut Prakan, Thailand) to Postharvest Laboratory, College of Agricultural and Marine Sciences at Sultan Qaboos University, Oman. For each distance, the time required to transport tomato fruit boxes per distance was 75 min, 120 min, and 180 min for 100 km, 154 km, and 205 km, respectively. Tomato wooden boxes were covered by a blue plastic sheet to avoid direct exposure to the sun. This study was carried out during the summer season of June 2020.

#### *2.2. Vibration*

In this study, a three-axis USB vibration/acceleration data logger (Model: OM-VIB-101, Spectris plc, Connecticut, Norwalk, CT, USA) was used to record the data of obtained vibration during transportation. This sensor has a −16 to 16 g acceleration range, 0 to 60 Hz frequency range, and ±0.5 g accuracy. This sensor was attached in the top position of the tomato fruit container and was positioned vertically to get more vibration [30] inside tomato boxes. For sampling rate, vibration data were recorded every 1 s of road travel.

The generated signals were simplified on the sensor and then transferred to a personal computer to be later analyzed by shock application (vibration data logger, v2.3). Thousands of vibration signals were recorded in each distance during transit in milligal (mGal) which were later converted to cm/s2. Moreover, vibration analysis was applied to evaluate the number of obtained accelerations (time-domain) for each distance that occurs during transit. Subsequently, all time-domain signals above 0.13 cm/s2 were calculated using a histogram which was performed to identify the number of peaks of acceleration during transit for each distance.

#### *2.3. Sample Preparation and Laboratory Experiment*

A total of 195 tomato fruits from three distances with similar color, firmness, weight (0.487 ± 0.1 kg), and free from defects were selected for the study analysis. For each distance, tomato fruits were divided into two groups, where the first group stored at ambient room temperature (22 ± 1 ◦C with 65 ± 5% RH) and the other at refrigerated temperature (10 ± 0.5 ◦C with 95 ± 1% RH). Each storage condition consists of seven sub-groups where each group consists of five replicates to undergo some physical quality analysis (weight loss percentage, color, and firmness) for the evaluation of their postharvest changes due to transport vibration and storage for 12 days at 2 days interval. Daily monitoring of temperature and relative humidity in the laboratory were measured using a temperature meter (Model: TES 13604, TES Electrical Corp., Taipei, Taiwan).

#### 2.3.1. Tomato Weight Loss %

Weight loss (%) of tomato fruit transported from each distance stored at both conditions was determined for two days intervals to an accuracy of ±0.01 g by using an electric weight balance (Model: GX-4000, A & D Company, Tokyo, Japan). The results were calculated as a percentage of the initial recorded weight of the tomato group from the first day of the experiment.

#### 2.3.2. Tomato Color Change

A total of twenty-five external color readings (per one color parameter) were taken from 5 tomato samples per group at 2 days interval using RGB (Red, Green, Blue) image acquisition system (Figure 1). In this system, a cardboard box was used to cover the whole system to avoid backscattering effects. Furthermore, a white background (stage) was placed to provide high contrast between the tomato sample and the background. Tomato samples were illuminated using two 36 W long fluorescent tubes (Model: Dulux L, OSRAM, Milano, Italy) to provide light with uniform intensity over the sample. Light sources were fixed to be parallel to the tomato platform. Moreover, the image of tomato samples was captured using an RGB color camera (Model: EOS FF0D, Canon Inc., Tokyo, Japan) placed vertically in the center of the cardboard box at a distance of 0.26 m from the tomato sample. The digital camera was connected to the USB port of a personal computer where the images were stored for subsequent processing. A remote shooting software EOS Utility included in the camera was performed to acquire the image in the maximum possible resolution [31]. The captured images were stored in JPG format. Each sample was placed and oriented manually. For image processing, ImageJ software (v. 1.53, National Institute of Health, Bethesda, MD, USA) was applied. RGB mean values were obtained by using the histogram tool (Analyze/Histogram menu). RGB values were later converted to CIEL\*a\*b\* color space which was chosen as it is mostly applied in food quality studies [32]. Total color differences

(ΔE∗) that indicate the magnitudes of color change in the stored tomato (Equation (1)), chroma that represents the color intensity of stored (Equation (2)), a hue which indicates the purity of tomato color (Equation (3)), color index (CI) (Equation (4)), and tomato color index (COL) (Equation (5)) were also calculated to track red color development of stored tomato fruit transported from different distances using the following equations [33]:

$$
\Delta E = \sqrt{\Delta \mathbf{a} \ast^2 + \Delta \mathbf{b}^2 + \Delta \mathbf{L} \ast^2} \tag{1}
$$

$$\text{Clroma} = \sqrt{a \ast^2 + b \ast^2} \tag{2}$$

$$Hue = \tan^{-1} \left( \frac{\mathbf{b} \ast}{\mathbf{a} \ast} \right) \tag{3}$$

$$CI = a^\*/b^\* \tag{4}$$

$$COl = \left(\frac{2000 \times a^\*}{L^\* \times Clroma}\right) \tag{5}$$

**Figure 1.** A schematic of a typical RGB image acquisition system.

#### 2.3.3. Tomato Firmness

The firmness of tomato fruit was determined by a digital fruit firmness tester (Model: FHP-803, L.L.C., Franklin, ME, USA) [34] using an appropriate stainless-steel cylinder probe with a 3 mm diameter. Two measurements were taken at opposite positions on each tomato using a total of 5 samples from (10 readings per group) tomato fruit transported from each distance stored at both conditions and was expressed as N.

#### *2.4. Statistical Analysis*

SPSS 20.0 (International Business Machine Crop., New York, NY, USA) was used to study the impact of experimental variables, i.e., transport distance (100 km, 154 km, and 205 km), storage condition (10 ± 0.5 ◦C and 22 ± 1 ◦C), and storage duration on the physical (weight loss, color, and firmness) quality parameters of tomato by performing three-way analysis of variance (ANOVA) and the mean values were considered at 5% significance level (*p* < 0.05). Mean and standard deviation (S.D) were also reported for all

measured parameters. Tukey's range (HSD) test was applied to determine the significant differences between treatment means. Construction of all graphs was performed using GraphPad Prism software version 9.0.0 (GraphPad Software, Inc., San Diego, CA, USA).

#### **3. Results and Discussions**

#### *3.1. Vibration Level Analysis during Transit*

To determine vibration levels during transportation, continuous vertical, longitudinal, and lateral accelerations were measured. Minimum and maximum values of acceleration were the same in all three travel distances due to the continuous measurements of the same instrument during transportation. The results of the transport experiment showed that the vertical direction (Z) gave the maximum acceleration value in all three distances with 2.694 cm/s<sup>2</sup> followed by lateral (X) (1.314 cm/s2) and longitudinal (Y) (1.123 cm/s2). Therefore, all subsequent analysis was done using vertical vibration level data. Similar findings were observed by Soleimani and Ahmadi [35] who reported that vertical direction can generate high vibration compared to lateral and longitudinal directions.

A histogram of all time-domain vibration data higher than 0.13 g was used to identify the number of acceleration peaks that occurred per distance (Figure 2). The measured time-domain vibration signals were divided into intervals of 0.17 (0.13–2.69 cm/s2). The maximum number of peaks was highly found in the acceleration interval of 0.99–1.17 cm/s<sup>2</sup> with 1607, 2864, and 4121 peaks for 100, 154, and 205 km, respectively. It was followed by the acceleration interval of 1.17–1.34 cm/s<sup>2</sup> which was also higher during transit of the longest distance with 2505 peaks compared to medium (1935) and short (1365) distances.

**Figure 2.** The number of peaks (signals) generated during transport for each acceleration interval (cm/s2) from (**A**) 100 km (**B**) 154 km and (**C**) 205 km distances using a histogram.

Figure 3 indicates that over 40% of accelerations occurred in the range of 0.82– 1.31 cm/s2 of all transport distances. The percentage of this highest acceleration range was higher in the longest distance with 41% followed by medium 40% and short 38% distance. The maximum accelerations observed during transit (2.96 cm/s2) showed only 0.03% of acceleration occurrence in all three distances, particularly the shortest distance (100 km). Generally, increasing distance is responsible to increase the acceleration occurrence during transport. The result of the acceleration value measured in the research is close to that reported by Shahbaz et al. [14] who reported that the accelerations of over 97% of vibrations recorded on the transported bins had values below 2 g. They also reported that the intervals of 0.25–0.50 g and 0.50 0.75 g had the highest distribution percentages of vibration accelerations, where the values in these intervals were 35.06 and 23.59% respectively. Besides, increasing the vibration period to 60 min during simulated in-transit vibration resulted in a greater percentage of damage for watermelon with 0.7 g acceleration vibration compared with 0.3 g by 30 min. Wu and Wang [19] observed damage in tomatoes during simulated transit due to vibration when exposed to more than 1 g acceleration.

**Figure 3.** The occurrence percentage of the acceleration intervals (cm/s2) for 100, 154, and 205 km distances in the vehicle during transportation.

### *3.2. Effect on Physical Quality Characteristics of Tomato*

#### 3.2.1. Weight Loss (%)

Figure 4 shows that the weight loss of tomato fruit was affected by transport distance (*p* < 0.0001), storage temperature (*p* = 0.0052), and storage duration (*p* < 0.0072). An increasing trend in weight loss was observed in all tomato fruits transported from different distances stored at 10 and 22 ◦C for 12 days storage period. However, weight reduction percentage was significantly higher in tomato transported from the longest distance and stored at 22 ◦C with 6.91% followed by tomato transported from middle and shortest distances with 6.31% and 5.96%, respectively (Figure 4). Nevertheless, the weight loss of tomatoes transported from the long, medium, and short transport distances and subjected to cold temperature (10 ◦C) was 3.5%, 3.3%, and 3.09%, respectively over 12 days. Generally, increasing transportation distance and temperature storage at (22 ◦C) increased the weight reduction of tomato samples during storage. However, low-temperature storage reduced the effect of transportation distance on the weight loss of tomato fruit.

**Figure 4.** Weight loss (%) of tomatoes from three different distances stored at (A) 10 ± 0.5 ◦C (95 ± 1%RH) and (B) 22 ± 1 ◦C (65 ± 5%RH) for 12 days.

These results suggested that tomato fruit transported from the longest distance were stressed due to multiple vibrations compared to others. Exposure to external vibration resulted in a higher respiration rate leading to more weight reduction during storage [16]. Furthermore, Wei et al. [12] reported that fresh produce experienced high weight reduction due to the increment in transport vibration which accelerates water reduction of fresh produce as well as shriveling resulted from intracellular damage. Regarding storage, Endalew [36] stated that storage time and storage temperature had a great effect on the weight loss of tomatoes. Storage at ambient temperature increased the tomato weight loss due to transpiration, respiration [37], and dehydration [38] resulted in water loss leading to an increase in the physical barriers between a fresh produce with the surrounded air [36]. Transpiration occurs in response to the vapor pressure deficit of water that is a function of pressure, air temperature, and relative humidity. Moreover, respiration can prompt weight loss increment due to the alteration of carbon (C) atoms to atmospheric carbon dioxide (CO2) [7]. In tomato, a higher transpiration rate resulted from higher temperature storage condition compared to lower temperatures lead to shriveling and wilting, thus, reduce consumer acceptability and market-level [36]. Low relative humidity at ambient temperature can also reduce water quantity in the produce which accelerates water reduction [39]. In this study, low temperature storge at 10 ◦C declined weight reduction of tomato due to the direct impact on vapor pressure and water retention enhancement.

Similarly, Jung et al. [17] experienced 15% and 9% weight loss in the grape group exposed to vibration and control group during 30 days storage. Furthermore, Çakmak et al. [13] found higher mass loss of fig when affected by high vibration frequency and acceleration (16 Hz~2.54 m s−2) compared to fig exposed to 3 Hz~0.56 m s−<sup>2</sup> at different storage conditions. Wei et al. [12] also revealed a significant weight loss and shrinkage on kiwifruit affected by simulated vibration compared to the non-vibrated kiwifruit after 12 days storage at 25 ◦C and 75% RH. A progressive increase in weight loss was also reported during storage for 10 days at 34 ◦C [40] and 8 ◦C, 12 ◦C, 20 ◦C for 20 days [41]. The findings of this study were also in agreement with the findings of Pathare and Al-Dairi [42] who recorded a high percentage of weight reduction in tomato for 10 days storage at room temperature.

#### 3.2.2. Color Change

A significant difference was observed between tomato color lightness (L\*) values and transport distance (*p* = 0.0445), storage temperature (*p* = 0.0047), and storage duration (*p* = 0.0025) (Figure 5). During storage, tomato transported from 205 km and stored at 22 ◦C showed the highest decrease on L\* value from 44.30 to 18.10 on Days 0 and 12 respectively. This was followed by tomato transported from 154 km and 100 km from 44.57 and 45.50 to 18.23 and 24.62 respectively (Figure 5). The same scenario was observed on tomato fruit stored at 10 ◦C, where the highest L\* value reported on tomato transported from long followed by medium and short distances. On Day 12, the study reported a 45.88%, 59.08%, and 59.13% reduction in tomato lightness transported from short, medium, and long distances stored at 22 ◦C compared to only 23.44%, 34.80%, and 35.95% at 10 ◦C. This indicated that storage at 10 ◦C slowed down the lightness reduction of tomatoes that could be affected by vibration during different transportation distances. This could be attributed to the repeated vibration stresses during long-distance transportation of tomato resulted in increasing lightness reduction and alteration. Furthermore, the L\* value reduction during storage at 22 ◦C indicated the increment of tomato fruit darkening during storage due to carotenoids synthesis [36]. The slow reduction in lightness (L\*) at low storage temperature can occur due to normal ripening resulted from the inhibition of enzymatic activities [43]. The effect of transportation on the fresh produce was also studied by La Scalia et al. [15], where lightness was reduced during storage with no significant effect on vibration duration. Zhou et al. [44] confirmed that pears suffered from transport vibration during long transit time showed high changes on L\* stored at ambient temperature. Endalew [36] also stated that the lightness value of tomatoes decreased with the storage time.

**Figure 5.** L\* value of tomatoes from three different distances stored at (A) 10 ± 0.5 ◦C (95 ± 1% RH) and (B) 22 ± 1 ◦C (65 ± 5% RH) for 12 days. Error bars represent standard error (SE) of the mean values ±S.E. of 25 measurements (readings) of 5 tomato replicates. Bars with different letters (per day) are significantly different (*p* < 0.05) performed by the Tukey HSD test and numerical values of A, B, and C are *p*-values.

The increase of 'redness' and decrease of 'greenness' in tomato fruit was associated with the increment in a\* value. The results revealed that the red color (a\*) of tomato fruit was influenced by the independent variables like transport distance (*p* = 0.0343), storage temperature (*p* = 0.0093), and storage duration (*p* = 0.0014) (Figure 6). Tomato transported

from the longest distance and then stored at 22 ◦C recorded the highest alternation of a\* value from 20.94 on Day 0 to 33.39 on the last day of storage. However, the a\* value of tomato transported from the shortest distance increased from 20.05 to 32.21 on Day 0 and Day 12, respectively (Figure 6). Tomatoes stored at 10◦C after being transported from 100 km showed the least degree of red color development during storage from 20.05 to 25.74 on Days 0 and 12, respectively, while that from longest distance increased from 20.94 to 28.76 for 12 days storage (Figure 6). In the case of transportation distance, the repeated acceleration occurrence which was recorded from the longest distance could lead to an increase in the ripping process that accelerates redness development in tomato fruit. Tomato kept at room temperature can provide an optimal environment for the tomato to ripe resulted in increasing redness (a\*) compared to cold storage conditions [7]. Storage at 22 ◦C can cause an increase in a\* value of tomato due to chlorophyll degradation, lycopene accumulation, and ethylene biosynthesis [45]. Tigist et al. [46] also stated that high temperature tends to increase the red color of tomato compared to low temperature due to the increase in the ripening state of tomato during storage. Regarding transport, La Scalia et al. [15] recorded a small but significant influence of vibration duration on a\* value of strawberries. Similarly, Wu and Wang [19] found that tomatoes became redder when exposed to higher acceleration vibration during 60 min of simulated transport. An increase in a\* value was obtained during storage, particularly, at 20 ◦C with a delay in redness development recorded in tomato stored at 2 and 5 ◦C (Pinheiro et al., 2013). The same scenario was found by Guillén et al. [47], who reported a slow development on a\* value of tomato stored for 28 days storage.

**Figure 6.** a\* value of tomatoes from three different distances stored at (A) 10 ± 0.5 ◦C (95 ± 1% RH) and (B) 22 ± 1 ◦C (65 ± 5% RH) for 12 days. Error bars represent standard error (SE) of the mean values ±S.E. of 25 measurements (readings) of 5 tomato replicates. Bars with different letters (per day) are significantly different (*p* < 0.05) performed by the Tukey HSD test and numerical values of A, B, and C are *p*-values.

As shown in Figure 7, the alteration of tomato color yellowness was significantly affected by all three factors such as transport distance (*p* = 0.0100), storage temperature (*p* = 0.0032), and storage duration (*p* = 0.0004). The b\* value of all tomato samples transported from all distances decreased dramatically, particularly, at 22 ◦C with 58.18% followed by medium and short distances by 58.01% and 49.36% respectively for 12 days storage

period. However, the b\* value of tomato transported from short, medium, and long distances and stored at 10 ◦C was significantly lower than those stored at 22 ◦C with 30.92%, 41.98%, and 41.96%, respectively (Figure 7). The reduction in yellowness (b\*) during storage is mostly associated with red color development [36]. As highlighted by Khairi et al. [48], the yellowness (b\*) value of tomato was continually decreased as temperature and time increased. The yellow discoloration was also observed on tomato, which reached its minimum b\* value after 21 days at 5 and 10 ◦C [43].

The total color difference is considered as an outcome of alteration in L\*, a\*, and b\* values. Total color differences of tomatoes were statistically significant with transport (*p* = 0.0435), storage temperature (*p* = 0.0078), and storage duration (*p* = 0.0026) (Figure 8). Tomato transported from long distance and stored at ambient temperature had the highest change in total color differences which was ranging between 0 and 32.83 for 12 days storage compared to medium and short distances. Tomato transported from a short distance and stored at 10 ◦C recorded the lowest range of total color differences from 0 to14.99 (Figure 8).

**Figure 7.** b\* value of tomatoes from three different distances stored at (A) 10 ± 0.5 ◦C (95 ± 1% RH) and (B) 22 ± 1 ◦C (65 ± 5% RH) for 12 days. Error bars represent standard error (SE) of the mean values ±S.E. of 25 measurements (readings) of 5 tomato replicates. Bars with different letters (per day) are significantly different (*p* < 0.05) performed by the Tukey HSD test and numerical values of A, B, and C are *p*-values.

**Figure 8.** Total color differences (ΔE) value of tomatoes from three different distances stored at (A) 10 ± 0.5 ◦C (95 ± 1% RH) and (B) 22 ± 1 ◦C (65 ± 5% RH) for 12 days. Error bars represent standard error (SE) of the mean values ±S.E. of 25 measurements (readings) of 5 tomato replicates. Bars with different letters (per day) are significantly different (*p* < 0.05) performed by the Tukey HSD test and numerical values of A, B, and C are *p*-values.

This study showed a significant interaction between hue value and investigated factors like transportation distance (*p* = 0.0037), storage temperature (*p* = 0.0002), and storage duration (*p* = 0.0004) (Table S1). Hue value maximum reduction percentage was 64.48% by Day 12 in tomato transported from the longest distance and stored at 22 ◦C (Table 1). The percentage of hue value reduction of tomato transported from medium distance 63.65%, while it was 57.13% on tomato transported from the short distance. The least reduction (33%) was observed on tomatoes transported from the shortest distance and stored at 10 ◦C (Table 1). As expected, the reduction in hue value of tomato stored at 22 ◦C was greater than those stored at 10 ◦C due to the natural relationship found between the rate of biochemical reaction and temperature [49]. Long transport distance also showed a high increase in hue and chroma during storage at ambient storage temperature [44]. There was a significant effect of storage at 4, 20, and 30 ◦C for 16 days on the intensity (chroma) and purity (hue) of tomato color. In contrast to the current study, storage at a high temperature can retain the purity of color (hue) compared to low temperature [50]. However, there was no statistical difference (*p* > 0.05) between chroma and the investigated factors for 12 days of storage (Table 1 and Table S1). However, La Scalia et al. [15] recorded a significant reduction in chroma value of the vibrated fresh produce (strawberry) stored at cold storage temperature.

Color index (CI) was statistically influenced by transport distance (*p* = 0.0149), storage temperature (*p* = 0.0148), and storage duration (*p* ≤ 0.0001). Besides, a similar scenario was observed with tomato color index (COL) (Table S1). The dramatic increase in tomato CI and COL was mostly shown in tomato stored at 22 ◦C after it was transported from a 205 km distance (Table 1). However, 100 km transportation distance showed the lowest development of color indices for 12 days storage. According to Tadesse et al. [50], the increase in color indices can indicate the development of a dark red color in the investigated tomato. They recorded a high red color increment in tomatoes stored at 20 and 30 ◦C than tomatoes stored at low temperature (4 ◦C) which attributed to lycopene development associated with the internal membrane system of tomato.

**Table 1.** Chroma, Hue, CI, and COL changes in tomatoes transported from three distances (100 km, 154, and 205 km) stored at ambient (22 ◦C) and optimum (10 ◦C) for 12 days storage period. The data were expressed in mean ± standard deviation of 25 measurements (readings) of 5 tomato replicates. Mean values with different letters in a column (per parameter) differ significantly (*p* < 0.05) performed by the Tukey HSD test. Numerical values of A, B, and C are *p*-values. All *p*-values in bold are statistically significant (Tukey HSD test., *p* < 0.05).


ST, storage temperature; CI, color index; COL, tomato color index.

#### 3.2.3. Firmness

Tomato fruit firmness significantly influenced by transport distance (*p* = 0.0129), storage temperature (*p* = 0.0012), and storage duration (*p* = 0.0036) (Figure 9). Overall, firmness decreased drastically during the period of storage at both storage temperatures in all distances of transported tomato. In this study, as distance duration increases, the firmness of the tomato reduces. Firmness reduced by 50.82%, 51.44, and 58.39% for short, medium, and long-stored at 22 ◦C, respectively (Figure 9). However, the reduction in firmness at 10◦C was 28.36%, 33.69%, and 37.12% for the same distances, respectively. The results indicated that the maximum acceleration occurrence can affect the firm state of the longest distance transported tomatoes. The distribution of fresh produce during transportation can cause critical problems affecting ripening and firmness [12]. In terms of storage conditions, ambient temperature can cause a continuous reduction in tomato firmness due to moisture loss through transpiration and enzymatic changes [37] which can degrade tomato cell wall [45,51]. Texture/firmness reduction is attributed to different factors like losses in cell turgor pressure as well as the cell wall and polysaccharides degradation. Besides, the firmness state is closely correlated with the ripening stage of the fresh produce that causes a rapid increase in enzyme activity [16]. Furthermore, Zhou et al. [44] determined that firmness reduction and softening can occur due to the active state of pectic enzymes and cellulose on polysaccharides in the cell wall of the product. Jung and Park [16] experienced a firmness reduction on vibrated tomato with 33.2% compared to 21.9% of the control group for 30 days storage. Zhou et al. [44] also found that pear fruit exposed to less vibration stress retained higher firmness. Pear fruit exposed to high transit time and stored at ambient temperature had a higher softening rate. The findings of this study agreed with the findings reported by Munhuewyi [7], Park et al. [41], and Kabir et al. [25] who recorded similar reduction trends of et tomato firmness at cold and ambient temperature. Al-Dairi et al. [1] also recorded a 67.80% reduction in tomato firmness at ambient temperature for 12 days.

**Figure 9.** The firmness of tomatoes from three different distances stored at (A) 10 ± 0.5 ◦C (95 ± 1% RH) and (B) 22 ± 1 ◦C (65 ± 5% RH) for 12 days. Error bars represent standard error (SE) of the mean values ±S.E. of 10 measurements (readings) of 5 tomato replicates. Bars with different letters (per day) are significantly different (*p* < 0.05) performed by the Tukey HSD test and numerical values of A, B, and C are *p*-values.

#### **4. Conclusions**

The results of this study indicated that vibration generated from different transportation distances significantly affected tomato physical quality parameters. Storage temperature and duration were also found to have a significant impact on the physical quality attributes like weight, color, and firmness. Among all studied distances, long transportation distances highly increased weight and firmness reduction and produced greater color changes during storage. Moreover, storage at ambient temperature conditions (22 ◦C) accelerated all of these quality changes for 12 days storage period. Nevertheless, storage at a lower temperature (10 ◦C) showed slower reductions and enhancement of the studied parameters as affected by transport distance, storage time, and storage condition. The results of this study can help the industrial sector to avoid all the critical issues during the transport and storage of fresh produce. In this way, adequate packaging, transportation, and handling facilities, and storage temperature management, need to be available to reduce all expected damages due to transportation and storage.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/horticulturae7070163/s1, Table S1: the three-way analysis of variance (ANOVA) of chroma, hue, CI, and COL color parameters.

**Author Contributions:** M.A.-D.: data curation, formal analysis, writing—original draft; P.B.P.: conceptualization, supervision, formal analysis, funding acquisition, writing—review and editing; R.A.-Y.: conceptualization, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research leading to these results received funding from the Research Council (TRC) of the Sultanate of Oman under the Block Funding Program (TRC Block Funding Agreement No. RC/GRG-AGR/SWAE/19/01). We would like to thank Sultan Qaboos University for financial support for this project under the project code: IG/AGR/SWAE/19/03.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Effect of Simulated Vibration and Storage on Quality of Tomato**

**Pankaj B. Pathare \* and Mai Al-Dairi**

Department of Soils, Water and Agricultural Engineering, College of Agricultural & Marine Sciences, Sultan Qaboos University, Muscat 123, Oman; s124911@student.squ.edu.om **\*** Correspondence: pankaj@squ.edu.om or pbpathare@gmail.com; Tel.: +968-2414-1222

**Abstract:** The influence of simulated transport vibration and storage conditions for 10 days on tomato fruits quality (color, weight, firmness, total soluble solids, and headspace gases) were investigated. Better kinetic models for color changes, weight loss, and firmness of stored tomato fruits were selected. Tomato fruits were divided equally into two main groups where the first one was subjected to vibration at a frequency of 2.5 Hz for two hours and the other group was set as a control (with no vibration stress). Both tomato groups were stored for 10 days at 10 ◦C and 22 ◦C storage conditions. The results showed a reduction in total soluble solids, yellowness, weight, lightness in the tomato fruits subjected to vibration at 22 ◦C storage condition. Ethylene and carbon dioxide increased by 124.13% and 83.85% respectively on the same condition (22 ◦C). However, storage at 10 ◦C slowed down the investigated quality changes attributes of both tomato groups (vibrated and control) during storage. The weight loss change kinetics of both tomato groups at both storage temperatures were highly fitted with a zero-order kinetic model. Color and firmness kinetic changes of tomato groups stored at both conditions were described well by zero and first order kinetic models. To validate the appropriateness of the selected model, lightness, redness, yellowness, and firmness were taken as an example. The study revealed that the vibration occurrence and increasing storage temperature cause various changes in the quality attributes of tomatoes.

**Keywords:** quality; kinetic model; tomato; simulated vibration; storage; transport

#### **Practical Application**

The practical application of this research is the understanding of the main causes of damages and quality changes of tomatoes due to vibration generated from the simulated transport at a particular frequency. The use of optimal storage temperature and the other proposed temperature can help to minimize the resulted damages in tomatoes. Improving refrigeration storage conditions in the supply chain of tomatoes is required and very essential to reinforce the quality and shelf life of the product. The mathematical models used in our research with the presence of vibration and control data, storage temperatures, and storage durations helped to predict the effect of simulated vibration and control groups on the quality of tomatoes during the experimental time. Such valuable data can help to discover different strategies and technologies to minimize the deterioration of fresh produce like tomatoes during the supply chain.

#### **1. Introduction**

Tomato fruit (*Lycopersicon esculentum,* Mill) is one of the most common and significantly grown fresh produce worldwide and ranked second after potato in terms of area and amount of production as recently reported by Famuy˙ In˙ I and Sedara [1]. It is a vital source of nutrients and different beneficial minerals and considers as a source of income in most developing countries. The quality of any agricultural product is a significant factor for both the consumers and producers. The quality of tomatoes is highly categorized by weight, color, firmness, and flavor [2]. Tomatoes are climacteric fruits and their physiological attributes make them highly delicate agricultural products [3]. Wu and

**Citation:** Pathare, P.B.; Al-Dairi, M. Effect of Simulated Vibration and Storage on Quality of Tomato. *Horticulturae* **2021**, *7*, 417. https:// doi.org/10.3390/horticulturae7110417

Academic Editors: Maria Dulce Carlos Antunes, Custódia Maria Luís Gago and Adriana Guerreiro

Received: 31 August 2021 Accepted: 18 October 2021 Published: 20 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Wang [4] highlighted that tomatoes can be affected by postharvest factors like storage, handling, and transportation, etc. Besides, Cherono et al. [5] stated that postharvest losses in tomatoes are as high as 40%.

The quality of fresh produce is reduced during transportation due to biological and physical damages/changes caused by vibration [6]. Vibration generated from transportation caused different external and internal damages to fresh produce. Interior damage is most difficult to recognize via consumers as reported by Wei et al. [7]. Besides, vibration can consider as a critical problem influencing fruit and vegetable sugar content [8], ripening, firmness, browning, core breakdown [7], color redness [4], and headspace gases (O2, CO2, C2H4) [9]. Walkowiak-Tomczak et al. [10] found that the mechanical vibration of the simulated transport reduced the firmness of 'Gala' and 'Idared' apple by 9 and 13%, respectively, after 14 days of storage. Jung et al. [11] revealed that vibration stress increased the amount of ethylene concentration of packaged grapes (15.3 nL/g·h) compared to the initial stage, while about 9.8 nL/g·h for the packaged grapes with no vibration stress. Tao et al. [12] stated that the vibrated mushrooms showed higher changes in color browning index (89.4) compared to the controls (56.2). Besides, Xu et al. [13] reported that the soluble solids content of blueberries vibrated for 12, 24, and 36 h reduced by 12.9, 21.4, and 28.6%, respectively, and firmness decreased by 28.6, 57.1, and 78.6%, respectively, comparing with the control one. Vibration occurrence can induce both weight and water loss of fresh produce that led to shriveling, which is one of the major physical alterations and cause a direct effect on appearance. Therefore, increasing flesh of fresh tissues [7]. Also, Tao et al. [12] reviewed that mechanical damages generated due to vibration can accelerate the weight loss % in fresh produce, which directly affects the marketability of produce. Jung et al. [11] reported a weight loss of 15% in the vibrated grape group compared to 9% in the control grape group after 30 days storage period. The effect of transport vibration has been studied in the quality of different fresh produce including tomato fruit [4,14], kiwifruit [7], grape fruit [11], broccoli [15], strawberry [16] and mushroom [17].

The quality of fresh produce like tomatoes is highly correlated with storage temperature and storage time [5]. Storage temperature can greatly affect tomato firmness, color, and flavor [18]. Increasing the storage temperature of products can increase the processes of respiration, transpiration, and ethylene rates resulted in a high weight loss percentage [19]. Arah et al. [20] reviewed that tomato fruits contain a high amount of moisture contents; thus, it is difficult to keep and store them at ambient temperature for a long period. Recently, Al-Dairi et al. [2] recorded 16.60% weight loss on tomatoes stored for 12 days at ambient temperature. Low storage temperature condition is considered as a major factor applied for maintaining the quality of postharvest attributes of tomatoes. Furthermore, data on postharvest characteristics of fresh produce are significant and required as an input used for models to predict postharvest behavior and attributes [21].

Kinetic modeling is an essential tool for predicting and controlling the quality attributes alterations in fresh produce [22,23]. It has been highlighted that kinetic modeling has been applied to identify the changes in fresh produce quality characteristics like firmness, color parameters, weight [24], pigments, sugars, and acids [25]. The mathematical modeling depends on the reaction rate like zero-order kinetic models, first-order kinetic models, and higher was applied on different fresh produce [22,24].

This study was carried out to explore the influence of 2 h of simulated transport vibration and storage at 10 ◦C and 22 ◦C on tomato's quality attributes like weight loss, color parameters, firmness, TSS, and headspace gases for 10 days storage period. Kinetic models were also applied as a new contribution for predicting the weight loss, color, and firmness kinetic on the stored tomato groups as a function of time.

#### **2. Materials and Method**

*2.1. Plant Sample and Vibration/Storage Treatments*

'Roma' variety tomato fruits packaged in a recycled plastic container with a dimension of (365 × 255 × 155 mm) were purchased from the market and transported to Postharvest

Technology Laboratory of Sultan Qaboos University, Oman. The selected samples (*n* = 63) were similar in color, size, weight (177 ± 0.02 g), firm state, and free of defects and blemishes. Tomato fruits were divided equally into two main groups where the first one was subjected to vibration at 2.5 Hz frequency for two hours and the other group stressed no vibration stress.

The vibrated group were exposed to vibration using an orbital shaker (model: SM25, Edmund Bühler GmbH, Schleswig-Holstein, Germany) [16] to simulate the vibration generated during fresh produce transportation at 2.5 Hz frequency for 120 min at a speed of 150 revolutions per minute (r/min) (205 km distance). The plastic container was tightly fixed in the top of the shaker and 3-axis vibration/acceleration data loggers (Model: OM-VIB101, Spectris plc, Connecticut, Norwalk, CT, USA) were placed vertically inside the container (bottom, middle and top) to record the generated vibration (every 1 s) during simulated transport from three different positions. The resulted vibration signals were later transformed to a personal computer and a shock application (Vibration data logger v2.3) was applied for time-domain vibration analysis of signals. Also, a histogram was used to identify the peaks number generated per accelerometer fixed on each location during the simulated transport experiment.

After conducting the simulated vibration experiment, tomatoes with and without vibration stress were divided equally into two groups at 22 ± 1 ◦C (65 ± 5% RH) and 10 ± 0.5 ◦C (95 ± 1% RH). Further objective evaluations of tomato fruit were carried out such as weight loss, firmness, color, total soluble solids (TSS), and headspace gases to study the influence of vibration/control treatments and two storage conditions on the quality of tomatoes at two days intervals for 10 days. For day-0 analysis, three tomato fruits with no vibration were analyzed for all previously mentioned analyses. Besides, daily observations of bruising were recorded. In the current paper, a total of 3 tomato fruits replicates were utilized for each treatment.

#### *2.2. Physical and Physiological Quality Analysis*

#### 2.2.1. Weight Loss%

A batch of three tomato fruits for each treatment was weighed on day 0 and the weight loss percentage was recorded on days 2, 4, 6, 8, and 10, relative to day 0.

#### 2.2.2. Color Measurements

The color values of tomato fruits were measured using a computer vision system (Figure 1). A total of 5 readings were taken per sample for color measurements during the experiment at 2 days intervals (60 per day). The system includes a cardboard box utilized to cover the entire system and to avoid the backscattering effect. A lighting system including two long fluorescent lights (36 W) (Model: Dulux L, OSRAM, Milano, Italy) was placed above the sample at an angle of 45◦. An RGB digital camera (Model: EOS FF0D, Canon Inc., Tokyo, Japan) was fixed in the top of the cardboard box at 26 cm from the sample. The digital camera involves a remote shooting software EOS Utility used to acquire the image in the maximum required resolution. All captured images were transferred to a personal computer and stored in JPG format for subsequent analysis. ImageJ software (v. 1.53, National Institute of Health, Bethesda, MD, USA) was performed for image processing [6]. All obtained RGB values were transformed to CIEL\*a\*b\* color coordinates. The L\* value refers to darkness (0) and lightness (100), a\* value is used to donate redness (+) and greenness (-), and the value of b\* denotes yellowness (+) and blueness (-). The total color difference (TCD) (Equation (1)) from tomato samples was calculated. Chroma (Equation (2)), hue angle (Equation (3)), and tomato color index (CI) (Equation (4)) indicating color intensity, purity, and red color development index, respectively were also computed [26] as follow:

$$
\Delta \mathbf{E} \ast = \sqrt{\Delta \mathbf{a} \ast^2 + \Delta \mathbf{b} \ast^2 + \Delta \mathbf{L} \ast^2} \tag{1}
$$

$$
\Gamma \Gamma \Gamma \Gamma \vdash \sqrt{\mathbf{a} \ast^2 + \mathbf{b} \ast^2} \tag{2}
$$

$$Hue^{\circ} = \tan^{-1}\left(\frac{b^\*}{a^\*}\right) \tag{3}$$

$$CI = \left(\frac{a^\*}{b^\*}\right) \tag{4}$$

**Figure 1.** A schematic diagram of computer vision system.

#### 2.2.3. Firmness

To measure the force (N) needed to puncture the tomato surface, a digital fruit firmness tester (Model: FHP-803, L.L.C., Franklin, ME, USA) was used. Both sides were measured in each tomato sample at two days intervals.

#### 2.2.4. Total Soluble Solids (◦Brix)

Tomatoes juice was extracted and then analyzed by utilizing a digital refractometer (Model: PR-32 α, ATAGO Co., Ltd., Tokyo, Japan). Clear and pure drops of tomato juice were added to the prism surface of the refractometer and the readings were taken and expressed as ◦Brix.

#### 2.2.5. Headspace Gases (CO2, O2, and C2H4)

After the vibration treatment, eight plastic food containers (2.6 L) were prepared as gas collection containers. A total of 6 tomatoes (968.3 ± 25.2 g) were placed inside each container. Oxygen (O2) and carbon dioxide (CO2) concentrations were measured using O2/CO2 analyzer (Model: 90 2D, Quantek Instruments, Inc., Grafton, Australia). Ethylene (C2H4) (ppm) was determined using an ethylene detector (Model: SCS 56, Fricaval89, Valencia, Spain). Both instruments include a needle that is plunged inside the containers

and an electronically timed pump used to pull the needed amount of gases for further analysis. Besides, two replicates were used per treatment to determine O2, CO2 (%), and C2H4 (ppm) inside the containers for two days intervals.

#### *2.3. Kinetic Model*

To determine the physical quality changes of vibrated and non-vibrated tomatoes stored at different storage temperature conditions as a function of time, a kinetic model was applied. The rate of quality change factor was explained by (Equation (5)) [27]:

$$\frac{d\mathcal{C}}{dt} = -k\mathcal{C}^n\tag{5}$$

where *k* is the kinetic rate constant at a temperature *T*, *C* is the quality factor concentration at time t, and *n* is the order of the reaction. Most time-dependent relationships for most food materials are likely to be well fitted with the zero-order kinetic model (Equation (6)) or the first-order kinetic model (Equation (7)) follow [28]:

$$C = C\_0 \pm kt\tag{6}$$

$$\mathbf{C} = \mathbf{C}\_0 \times \exp\left(\pm kt\right) \tag{7}$$

where *C*<sup>0</sup> is the initial quality parameter value, *C* is the quality parameter value at a time and t is the time of storage. Regression analysis such as reduced chi-square (*X*2) (Equation (8)), determination of coefficient (*R*2) (Equation (9)), and root mean square error (RMSE) (Equation (10)) were done as the main standard to choose the best fit of the studied kinetic models to the current experimental data. Also, the model that effectively fitted tomato fruits quality parameters was defined with the maximum *R<sup>2</sup>* and lowest *X<sup>2</sup>* and *RMSE*. Besides, the following formulas were applied for the estimations of the parameters [23]:

$$X^2 = \frac{\sum\_{i=1}^{N} \left( MR\_{exp,i} - MR\_{pre,i} \right)^2}{N - n} \tag{8}$$

$$\mathcal{R}^2 = 1 - \frac{\sum\_{i=1}^{N} \left( MR\_{\text{prr},i} - MR\_{\text{exp},i} \right)^2}{\sum\_{i=1}^{N} \left( MR\_{\text{prr}} - MR\_{\text{exp},i} \right)^2} \tag{9}$$

$$RMSE = \sqrt{\frac{1}{N} \sum\_{i=1}^{N} \left( MR\_{pre,i} - MR\_{exp,i} \right)^2} \tag{10}$$

where, *MRpre,i* and *MRexp,i* are the *i*th predicted and experimental values of the quality parameters and *MRpre* is the average values of predicted quality parameters, *n* is the numbers of constant model and *N* is the number of observations. To validate the appropriateness of the selected model, some quality attributes were taken as an example.

#### *2.4. Statistical Analysis*

SPSS 20.0 (International Business Machine Crop., New York, NY, USA) was applied to study the influence of vibration/control treatments as well as storage temperature conditions (10 ◦C and 22 ◦C) on the physical and physiological attributes of tomatoes for 10 days. For statistical analysis, analysis of variance (ANOVA) was conducted at a 5% significance level. All resulted data were expressed in mean ± SD.

#### **3. Results and Discussions**

#### *3.1. Simulated Vibration Analysis*

The accelerometers placed in the three positions of the plastic container recorded thousands of vibration signals. Histogram analysis was applied for all time-domain vibration signals to obtain the peaks number of accelerations per accelerometer (Table 1). The middle position recorded the maximum number of peaks (1664 peaks) at 2.5 Hz for 120 min in the acceleration interval of 0.0275 to 0.0280 m/s2 with an acceleration occurrence

reached 22.75%. This was followed by the bottom position of the container which generated 1248 peaks in the acceleration interval of 0.0053 to 0.0055 m/s2. The acceleration interval of 0.0151 to 0.0156 m/s<sup>2</sup> of the top position recorded 896 peaks during the simulated transport experiment. The vibration recorded from each position of the tomato plastic container can indicate that tomato fruits can encounter several damages from each side of the packaging unit during transportation.

#### *3.2. Physiological Weight Loss (%)*

Figure 2 shows the weight loss (%) of both tomato groups stored at different storage conditions (10 and 22 ◦C) during the 10 days of storage. Tomato fruit weight loss was varied significantly (*p* < 0.05) between vibrated and control groups. Also, tomato weight loss was statistically influenced (*p* < 0.05) by storage condition and storage duration. Vibrated tomato fruits stored at ambient temperature (22 ◦C) had about 4.21% weight loss at the end of storage compared to the control tomato group that had 3.38% of weight loss at the same storage condition. The lowest tomato weight loss was recorded on the control group stored at 10 ◦C with 1.02% on day 10 of storage. While the vibrated tomatoes stored at 10 ◦C recorded a 1.39% weight loss on the last day of the study. As reported by Jung et al. [11], vibration can accelerate the increment of both respiration and ethylene rates resulted in a higher reduction in moisture content of the produce, consequently increasing weight reduction as storage duration increased. As stated by Xu et al. [13], vibration can prompt the process of ripening which is highly caused by the promotion of respiration rate and ethylene production. During ripening, an increase in weight loss can be observed due to water movement (water evaporation) from the produce to the surrounding environment. Also, Munhuewyi [29] confirmed that the rate of respiration is one of the main factors that contribute to weight alterations in fresh produce due to the conversion of carbon (C) atoms to atmospheric carbon dioxide (CO2).

**Figure 2.** Weight loss (%) of vibrated and non-vibrated (Control) tomato fruits stored at 10 and 22 ◦C.


**Table 1.** Vibration

accelerations

 data during simulated transport.

Ghazal et al. [30] also recorded higher weight loss on tomato fruit stressed to vibration compared to the control tomato group due to higher respiration rate and mechanical damages caused by the simulated transport vibration. According to Endalew [31] and Al-Dairi et al. [6], storage at ambient temperature resulted in a greater transpiration rate that leads to wilting, shriveling, and weight reduction in tomatoes. Also, Al-Dairi et al. [2] recorded a low weight loss percentage (3.18%) on tomato fruit stored for 12 days at low temperature (10 ◦C) which attributed to water retention that occurred at this condition. Regarding weight loss kinetic, Table 2 demonstrates that the zero-order kinetic model gave the highest *<sup>R</sup>*<sup>2</sup> (*R*<sup>2</sup> ≥ 0.9483) and the lowest values of *<sup>X</sup>*2*,* and *RMSE* for weight loss of both control and vibrated tomato groups stored at both storage conditions (10 ◦C and 22 ◦C).

**Table 2.** The statistical values of zero-order and first-order models of control and vibrated tomato groups were stored at 10 ◦C and 22 ◦C for 10 days storage period.


C indicates the control group; V indicates the vibrated group.

#### *3.3. Color*

Figure 3 shows the significant (*p* < 0.05) interaction effect of treatments (vibration/control groups), storage conditions, and storage duration on tomato L\* value. With storage time, a decreasing trend of the L\* value of all vibrated and non-vibrated tomatoes at both storage temperatures for the 12 days storage was observed due to a reduction in brightness. However, the results of this study showed a higher L\* value reduction (37.33) in tomato stressed to vibration compared to the control 39.83 group and stored at 22 ◦C. Besides, the non-vibrated tomatoes at 10 ◦C had a better (L\*) color value (46.71) than tomatoes stressed to vibration after 10 days of storage. More changes of lightness were observed on tomatoes exposed to vibration due to the repeated vibration motions generated during simulated transport. Besides, the reduction of the color change of the L\* value with storage time particularly at 22 ◦C is due to carotenoids synthesis which leads to tomato darkening [2]. Discoloration of fresh produce due to vibration results in enzymatic browning [32] like polyphenol oxidase (PPO) and peroxidase (POD) [33]. Lightness (L\*) change kinetics on Table 2 shows that the zero-order model produced a high *R*<sup>2</sup> for both control (*R*<sup>2</sup> = 0.9483) and vibrated (*R*<sup>2</sup> = 0.9720) tomato groups stored at 10 ◦C. However, the first-order model was considered the most appropriate model to represent the L\* value change for 10 days for both tomato groups stored at 22 ◦C. To validate the best model of the L\* value of both tomato groups stored at both temperature conditions, the predicted values with the experimental data values were presented and plotted in Figure 4. The predicted values were in a very good correlation with the experimental values (*R*<sup>2</sup> > 0.94) where the predicted data banded around the straight line for all storage conditions and groups which validate the suitability of the selected.

**Figure 3.** Lightness (L\*) value of vibrated and non-vibrated (control) tomatoes stored at (A) 10 ◦C and (B) 22 ◦C for 10 days storage. Error bars represent the standard deviation (SD) of the mean values ± S.D of 15 readings per 3 replicates.

**Figure 4.** Predicted and experimental results of L\* change kinetic of vibrated and non-vibrated (control) tomatoes stored at10 ◦C and 22 ◦C.

The redness (a\*) was differed (*p* < 0.05) significantly between tomato groups (control and vibrated), storage condition, and storage duration. The redness increased dramatically as storage temperature and duration increased. Besides, vibration showed a higher a\* value increment in tomatoes than those exposed to no-vibration. At the end of the 10 days storage period at 22 ◦C, the a\* value percentage of increase in tomato stressed to simulated vibration was 54.14%, while it was 38.66% in tomatoes with no vibration (Figure 5). However, the control tomato group stored at 10 ◦C showed the lowest percentage of increase in both groups. The increment in redness observed on vibrated tomato samples could be attributed to the high percentage of acceleration occurrence generated from simulated transport resulted in increasing the ripening process of the samples. Besides, higher ethylene and respiration are responsible for vibrated tomato color changes. Also, Dagdelen and Aday [32] indicated an increase in the a\* value of peach on the last day of storage due to the increase in respiration rate resulted from the mechanical vibration leading to fruit and color degradation. Wu and Wang [4] observed high red color development in tomatoes exposed to 60 min of simulated transport vibration. Furthermore, high temperature caused a rapid change in the redness value due to lycopene accumulation, rapid ripening, and chlorophyll degradation compared to low storage temperature with storage time [34]. The experimental data of a\* value color kinetic of vibrated and control tomato groups stored at 10 ◦C was highly described by the first-order model. The zero-order kinetic model provided the highest (*R*2) for the a\* value of vibrated and control tomato groups stored at 22 ◦C Table 2. The good agreement reported between the predicted values and the experimental values can validate the appropriateness of the models in a\* value color kinetic change (Figure 6).

**Figure 6.** Predicted and experimental results of a\* change kinetic of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C.

The yellowness (b\*) color from the two tomato groups decreased with storage temperature and storage period and a considerable b\* value difference (*p* < 0.05) between the vibrated and control tomato groups was observed (Figure 7). On the last day of storage, vibrated tomato stored at 22 ◦C showed 18.35% more b\* value reduction than the control group tomato stored at 10 ◦C that had the lowest reduction in the b\* value among all tomato groups stored at both conditions. Endalew [31] recorded a reduction in the b\* value of tomato at a higher temperature during storage due to red color increment. Table 2 shows that the b\* value of control tomato groups stored at 10 ◦C and 22 ◦C was highly described by the zero-order model. However, the first-order kinetic model was adequately fitted with the b\* value of vibrated tomato stored at 10 ◦C and 22 ◦C. Figure 8 indicated the correct selection of the kinetic models in the b\* color kinetic change which was resulted from the good agreement and relation between the predicted and the experimental data values.

**Figure 7.** Yellowness (b\*) value of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C for 10 days storage. Error bars represent the standard deviation (SD) of the mean values±S.D 15 readings per 3 replicates.

The color attributes of total color difference (ΔE), chroma, hue angle, and tomato color index were significantly (*p* < 0.05) varied with storage temperature condition and duration (Figure 9). Also, they were statistically (*p* < 0.05) differed between control and vibrated tomato groups, except with chroma, which had no pronounce (*p* > 0.05) significance between the tomato groups (vibrated and control). The total color difference value of tomato increased with storage time in all storage conditions and groups. The highest ΔE was observed in vibrated tomato group (22.87) followed by the control tomato group (17.86) stored at 22 ◦C (Figure 9A). On the last day of storage, the ΔE reached 15 and 11.16 on vibrated and control tomato groups stored at 10 ◦C, respectively (Figure 9A). The reduction in the hue◦ was higher in tomatoes exposed to vibration and stored at 22 ◦C with 54.99% than those exposed to no vibration at 10 ◦C (Figure 9B). Storage at 22 ◦C offered a faster reduction in hue angle caused due to the natural relation between chemical reactions and temperature that make tomato samples ripen rapidly and convert the green color of tomato to red [5]. During storage days, a fluctuation in chroma value was observed in tomato groups, particularly in those stored at 10 ◦C (Figure 9C). Despite this, the vibrated tomato group stored at 22 ◦C showed a dramatic increase in chroma for the 10 days storage period (Figure 9C). As tomato is exposed to vibration and stored at a higher temperature, more color index (CI) can be observed. The initial color index of tomato was 1.03 which later increased and reached 1.91 and 1.71 in vibrated and control tomato groups stored at

10 ◦C, respectively (Figure 9D). However, the increment was twice higher at 22 ◦C in the vibrated (2.29) and control (2.24) tomato groups (Figure 9D).

**Figure 8.** Predicted and experimental results of b\* change kinetic of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C.

Regarding model kinetics, Table 2 shows that the total color difference and tomato color index experimental data of all tomato groups stored at both storage conditions were better predicted by the first-order kinetic model. However, the zero-order model was found suitable to describe chroma and hue angle data of the control tomato group stored at 10 ◦C. To predict the kinetic changes in hue angle and chroma of vibrated tomato stored at both conditions and control tomato group at 22 ◦C, a zero-order model was selected (Table 2).

#### *3.4. Firmness (N)*

There was a significant difference (*p* < 0.05) in the firmness values between vibrated and control tomato groups. Besides, storage temperature conditions and storage duration were highly significant (*p* < 0.05) with firmness (Figure 10). The initial value of firmness in all tomato groups was 35.51 N. With storage time, the firmness reduced by 24% and 21.95% on vibrated and control tomato groups stored at 10 ◦C, respectively. When the vibrated and control tomatoes were stored at 22 ◦C, their firmness state became low with increasing storage duration. At the end of storage, tomatoes subjected to 2 h vibration and stored at 22 ◦C showed more reduction (44.82%) in firmness compared to those stressed no vibration (35.11%). As highlighted by Wei et al. [7], the vibration generated from simulated transport accelerated the ripening process, thus, reduced firmness with storage time. Dagdelen and Aday [32] reported that higher vibration during transportation can cause more damage to the produce cell wall, therefore, water loss and respiration increased due to structural degradation. In this study, firmness loss was observed in both control and vibrated tomato groups particularly at a storage temperature of 22 ◦C. This was attributed to the enzymatically controlled processes occurred at room temperature condition which is also link to other metabolic processes like respiration and transpiration as obtained by Cherono and Workneh [35]. Kabir et al. [21] and Al-Dairi et al. [36] found similar trends of firmness reduction at cold and ambient temperature conditions.

**Figure 9.** (**A**) Total color difference (ΔE), (**B**) hue angle (hue◦), (**C**) chroma, and (**D**) tomato color index (CI) value of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C for 10 days storage. Error bars represent the standard deviation (SD) of the mean values ± S.D 15 readings per 3 replicates.

The zero-order kinetic model was successfully fitted to experimental data of firmness reduction values of both vibrated to control tomato groups stored at 10 ◦C (Table 2). However, the first-order model gave the highest coefficient of determination (*R*2) and low chi-square (*X2*), and root mean square error (*RMSE*) of the firmness value of vibrated and non-vibrated tomatoes stored at room condition as shown in Table 2. Figure 11 illustrates the efficiency of the selected models. The straight line was banded by the predicted values of all tomato groups stored at both storage conditions. This can validate the suitability of the model chosen for this parameter.

#### *3.5. Total Soluble Solids (*◦*Brix)*

The amount of total soluble solids (TSS) was increased significantly (*p* < 0.05) with storage time and storage conditions. Also, it was varied statistically (*p* < 0.05) between the tomato groups (Figure 12). A higher magnitude of TSS increment was observed in tomatoes exposed to two hours vibration (4.70 ◦Brix) than in the non-vibrated one (4.48 ◦Brix) where the increase accelerated by storage at 22 ◦C. The increase in TSS was also observed in vibrated and control tomatoes stored at 10 ◦C with 4.51 ◦Brix and 4.41 ◦Brix, respectively. Increasing TSS in tomatoes with simulated vibration stress compared to control tomatoes is owing to the rapid ripening of stressed tomatoes under these conditions. During storage, TSS increased more rapidly, suggesting a more ripening resulted in pectin substance

degradation into more simple sugars e.g., Oligosaccharides [29]. More TSS was observed on samples subjected to vibration compared to the control samples by Dagdelen and Aday [32]. Similar results of significance on TSS between control and vibrated samples were also found on apples by Jung and Park [9]. A similar trend was observed by Kabir et al. [21] and Pathare and Al-Dairi [37], where increasing storage time can increase TSS contents. of fresh produce. Besides, Tigist et al. [34] recorded higher TSS content after the 32 days of storage at room temperature.

**Figure 10.** Firmness value (b\*) of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C for 10 days storage. Error bars represent the standard deviation (SD) of the mean values ± S.D of 6 readings per 3 replicates.

**Figure 11.** Predicted and experimental results of b\* change kinetic of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C.

**Figure 12.** TSS value (Brix◦) value of vibrated and non-vibrated (control) tomatoes stored at 10 ◦C and 22 ◦C for 10 days storage. Error bars represent the standard deviation(SD) of the mean values ± S.D 6 readings per 3 replicates.

#### *3.6. Headspace Gases*

Headspace O2 and CO2 concentration significantly (*p* < 0.05) declined over time at both storage temperatures. Headspace O2 was not varied significantly (*p* > 0.05) between the vibrated and non-vibrated groups Table 3. The average O2 concentration on day 2 was almost 16.85% and 16% in the control and vibrated stress group which reduced to reach 14.35% and 15.15% at 10 ◦C on day 8 respectively. More reduction was reported on O2% in the control and vibrated tomato groups stored at 22 ◦C. On day 8, the vibrated tomato group showed a reduction in O2 with 7.80% which later increased by 1% on day 10. Furthermore, more CO2 increment was observed in tomatoes stored at 22 ◦C. On day 8, the CO2 content reached 4.75 and 17.30% on the vibrated and control tomato groups respectively at 10 ◦C, while it was 4.55 and 17.75% respectively at 22 ◦C. The study suggested that both O2 and CO2 gases are correlated inversely during storage inside the gas collecting containers of tomatoes.

A significant increase was observed in ethylene (C2H4) at both storage conditions for 8 days storage period, which reduced on day 10 in all storage temperatures. There was no pronounce significance (*p* < 0.05) in C2H4 content between the vibrated group and the control tomato group. However, the vibrated tomatoes stored at room temperature recorded the highest content in C2H4 on day 8 with 3.25 ppm followed by the control tomatoes stored with 1.85 ppm compared to the initial value (1.45 and 1.25 ppm) respectively. Ethylene concentrations were 1.26 and 1.55 ppm in the control group and vibration stress group stored at 10 ◦C on day 8 respectively. All gases reached their equilibrium concentration on day 8 (Table 3). Low O2 concentration activates anaerobic metabolites. The slow change in O2 at 10 ◦C could result from the low rate of respiration at low-temperature storage conditions. Besides, the C2H4 production increased due to the continued ripening even after harvest. Therefore, C2H4 can accelerate the ripening of fresh produce [9].

#### *3.7. Subjective Quality Analysis/Visual Observation of Mechanical Damage*

The visual observation of the physiological damage and bruise incidence was mostly observed on the vibration stress tomato group at 22 ◦C (Figure 13) compared to the control group stored at both storage conditions. The damage on vibrated tomato at 22 ◦C reached 38.80%, while it was 5.50% on the control tomato group at the same temperature. No damage was observed on the control tomato stored in both conditions. Overall, the results of this study showed that vibration stress during simulated transport and storage at ambient accelerated the degradation of tomato with storage time.

**Table 3.** O2%, CO2% concentration, and C2H4 (ppm) production of control and vibrated tomato groups stored at 10 ◦C and 22 ◦C for 10 days storage period. Data are presented in mean values ± SD.


C indicates the control group; V indicates the vibrated group.

**Figure 13.** Physiological damages on the vibrated tomatoes stored at (**A**) 10 ◦C and (**B**) 22 ◦C after 10 days of storage.

#### **4. Conclusions**

The study investigated the effect of vibration stress generated from laboratory simulated transport and storage at two different storage conditions on the quality of tomatoes (weight loss %, color parameters, firmness, total soluble solids (TSS), and headspace

gases) for 10 days. Based on the obtained results, weight loss %, firmness, lightness (L\*), redness (a\*), yellowness (b\*), hue◦, and total color changes (ΔE) were highly dependent on all studied factors (storage duration, vibration, and storage temperature conditions). A high reduction in weight, L\*, b\*, firmness, O2 and hue angle, and increment in a\*, TSS, color index (CI), C2H4 content, and CO2 in the vibrated tomato fruits at room temperature 22 ◦C. Storage at low temperature (10 ◦C) reduced the quality changes occurrence of both control and vibrated tomato groups. The experimental data of weight loss, color, and firmness values were highly fitted to zero and first-order kinetic models. It was also found that the first-order kinetic model was the best model applied to represent the quality changes kinetic of both tomato groups at 10 and 22 ◦C. Proper technologies during transportation and storage are required to minimize the quality changes and degradation of tomatoes in the harvesting-consumption system.

**Author Contributions:** Conceptualization, P.B.P.; formal analysis, M.A.-D.; data curation, M.A.-D.; writing—original draft preparation, M.A.-D.; writing—review and editing, P.B.P.; supervision, P.B.P.; funding acquisition, P.B.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research leading to these results received funding from the Research Council (TRC) of the Sultanate of Oman under Block Funding Program (TRC Block Funding Agreement No. RC/GRG-AGR/SWAE/19/01). We would like to thank Sultan Qaboos University for their financial support under the project code: IG/AGR/SWAE/19/03.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article.

**Acknowledgments:** Help in conducting headspace gas experiment given by Adil Al-Mahdouri is thankfully acknowledged.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

