1. Introduction
Tomato (
Solanum lycopersicum L.) is an important horticultural product due to its attractive red color and nutritional value, containing high amounts of antioxidants [
1]. Tomato has a high consumption level all year round. However, a limitation of tomato in the supply chain is rapid postharvest ripening, which results in significant quality and market value losses. Thus, treatments and practices extending the shelf life and maintaining the quality of tomato after harvest are very necessary.
The application of 1-methylcyclopropene (1-MCP) successfully controls the ripening of fruits and vegetables after harvest by inhibiting the negative effects of ethylene. There have been many reports about the role of 1-MCP in delaying the ripening and retaining the texture, taste and appearance of fruits and vegetables [
2,
3,
4]. The 1-MCP treatment was shown to significantly delay the ripening of tomato [
5]; however, the level of efficacy depends on the physiological stages of tomato [
6,
7].
The color of tomato changes during fruit development and ripening; therefore, color measurement is a popular non-destructive technique in its quality assessment [
8]. The standard CIE Lab color parameters are typically measured with colorimeters (or chromameters), and the change in the a* color characteristic value, representing the color change along the green–red axis of the color space diagram, is used for tomatoes. It was found that on-vine and detached fruit behave differently, and especially sensitively react to storage temperature. Tomatoes kept at high temperature have increased β-carotene (orange) and decreased lycopene (red) content [
1]. Color is the primary quality parameter in cold storage below 8 °C, while firmness becomes more important above 13 °C [
9]. The USDA color grades [
10] of mature green (green, breaker), intermediate (turning, pink) and advanced (light red, red) describe ripeness, and this classification strongly correlates with internal quality attributes. Regarding color parameters, both colorimeter and machine-vision-measured hue angle and a*/b* achieved a strong linear relationship with total soluble solids (TSSs) for the tomato Master 100 hybrid [
11]. The comparison of different tomato genotypes also revealed the difference in the pigmentation (chlorophylls, carotenoids, anthocyanins) of genotypes, which may result in black and purple colors as well [
12]. It was found that native genotypes of different colors are rich in functional compounds, such as tocopherols, flavonoids and vitamin C. A digital phenotyping tool has been introduced to measure tomato color and its uniformity based on machine vision [
13,
14]. This Tomato Analyzer software (Tomato Analyzer ver. 4.0, Athens, GA, USA) saves the red, green and blue color components besides luminosity, converted a* and b*, hue angle and chroma. It was observed that measured red, green and blue intensity values differ significantly when a color difference is detected.
The results of several researchers and research groups [
15,
16] show that the photosynthetic activity of horticultural crops containing chlorophyll, i.e., freshness/ripeness, quality properties and shelf life, can be determined non-destructively, quickly, easily and relatively cheaply by chlorophyll fluorescence spectroscopy. According to Kasampalis et al. [
17], chlorophyll fluorescence measurements can be used innovatively and at least as efficiently and reliably as tristimulus colorimetry to classify tomatoes according to maturity. Ripe red tomatoes with different fluorescence values could be further subclassified. Both visible (Vis) and near-infrared (NIR) spectroscopy are non-destructive methods that can be used to describe chemical properties (color content, moisture, carbohydrate, water-soluble solids, starch, lycopene content, pH), as well as ripening and spoilage processes [
18,
19].
Li et al. [
20] investigated the relationship between reflected light and wavelength using reflectance spectroscopy for tomato at different stages of ripening, rather than absorbance and wavelength (
Figure 1). Using this method, they were able to distinguish tomatoes at different stages of ripeness in the range of 400–1100 nm.
The use of the non-destructive acoustic firmness measurement method is widespread, as it is excellent for testing the internal hardness and global firmness of spherical, homogeneous products such as apples, peaches, plums, pears, melons and tomatoes. The method relies on the propagation of mechanical waves. It is based on the acoustic sound response method, which is the study of the natural vibration caused by mechanical excitation (mechanical or manual low-pulse impact or vibration). Thus, the acoustic response (natural vibration) of a sample to the excitation gives comprehensive information about the hardness of the crop, while the resonance frequency carries information about the texture and its quality. The value of the resonance frequency is influenced by the hardness of the sample and its weight and shape, but not by the speed of the impact [
21,
22]. In vivo measurements on tomatoes found that the acoustic firmness coefficient of the tomato berry decreases as the ripening process progresses. The temporal variation in softness is not uniform, increasing significantly when the berry changes color from green to red [
22].
In addition, as a short summary, the most common methods used to monitor tomato ripening and their advantages and disadvantages are presented in
Table 1.
The aim of this study was to investigate the feasibility of the digital image processing method (within the hue spectra fingerprinting with PQS data compression technique) for monitoring tomato ripening and to evaluate the effect of the 1-MCP SmartFreshTM anti-ripening treatment on the maintenance of tomato quality during postharvest storage.
2. Materials and Methods
2.1. Materials
The tested tomatoes were freshly harvested according to color-related different maturity stages, as shown in
Figure 2, in Budapest, Hungary. Samples belonged to the Pitenza cultivar. Pitenza is a cluster tomato hybrid, which is widely used all over the world. The average mass of the berries is 100–120 g; the shape is rounded and dark red when ripe for consumption. The cultivar has excellent storage qualities and good yields under different growing conditions. It is one of the few varieties that can produce good quality cluster tomatoes even during the winter [
23].
After delivery and color classification, the tomatoes were assigned into 6 different maturity groups (
Figure 2). The color classification was based on the internationally accepted CTIFL (Centre Technique Interprofesionnel des Fruits et Légumes) scale of 1 to 12 [
24], where 1 indicates tomatoes that are ripe green and 12 indicates tomatoes that are fully ripe for consumption. The selected maturity stages are listed in
Table 2.
2.2. Implementation of Treatment
The applied anti-ripening treatment was the SmartFreshTM (SF) treatment, manufactured and marketed by AgroFresh Inc. (Philadelphia, PA, USA). The maturity regulator agent used was SmartFreshTM Protabs (Licence number: 04.2/1181-3/2017); the active ingredient of this agent is 2% 1-MCP gas (CAS registration number: 3100-04-7). The manufacturer’s recommendation for tomato treatment time is 12–24 h. After color grading and labeling, half of the samples (20 fruits per group) were randomly selected and treated for 12 h with SmartFreshTM, except for the red (F) group, which was composed of fully ripe tomatoes as the absolute control group. The treatment was carried out in an airtight plastic box (V = 0.5 m3) equipped with an internal fan, with a calculated amount of 1-MCP according to the manufacturer’s recommendation. The concentration of 1-MCP gas during treatment was approximately 625 ppb. The box was placed in a cooler at 15 °C for the duration of the treatment, while the control samples were stored at 15 °C. After the treatment was completed, the treated and control samples were stored in the same refrigerator at 15 °C for 2 weeks.
2.3. Color Measurements
The surface color changes in tomatoes were monitored using a portable Konica Minolta CR-400 colorimeter (Minolta Europe GmbH, Langenhagen, Germany). The instrument measures the CIE Lab color characteristics (L*, a*, b*, C* and h°). The measurements were carried out using the 8 mm diameter head of the instrument calibrated to the corresponding white etalon (No: 15033034; Y = 93.7, x = 3131, y = 3191) before starting the measurement. Measurements were taken at 2 points on each sample, along the maximum diameter of the tomato in a perpendicular position to the longitudinal axis, on two opposite sides of the berry.
2.4. Chlorophyll-Content-Related Maturity Stage Measurements
Changes in the chlorophyll-content-related maturity of the tomatoes and during storage were monitored using the Vis/NIR DA-meter
® type FRM01-F (Sintéleia s.r.l., Bologna, Italy). The chlorophyll content of the plant tissue was used to monitor maturity, which was determined by the instrument based on absorbance properties shown in Equation (1). Data acquisition was carried out by measuring the difference in absorbance between two different wavelengths. One of the measured wavelengths was the absorption peak of chlorophyll-a (670 and 720 nm), and the other was the reference wavelength during maturation to ensure minimum absorption. The chlorophyll content was determined using the DA-index
® ranging between 0 and 5 with an accuracy of 0.01. The higher the DA-index
® is, the more green the plant material is, together with the higher level of photosynthetically active chlorophyll content. The value of the DA-index
® decreases significantly as ripening progresses compared to the harvest stage [
25].
2.5. Image Processing
Color digital images with 3 × 8 bit/pixel were captured and saved in JPEG (Joint Photographic Experts Group) format. Fifteen samples were placed in front of the camera at the same time, 50 cm below the lenses. Tomato samples were illuminated by the laboratory ceiling-mounted, commercially available LED light panels with 3000 K color temperature. A white background was used, which additionally served as a color reference. Pictures were normalized to have the same white background so that potential fluctuations in illumination color could be managed.
The hue spectrum [
26,
27] was calculated for each picture with slight modification, as saturation was scaled in the range of 0–100%. Since saturations were summarized for the observed hue angles, the result was not a simple histogram of colors, but the colors were weighted with their vividness. Important colors appeared with peaks, and those peaks may have changed shape and position during ripening. The gray-scaled, low-saturation background and surface reflections were automatically ignored in the analysis. The resulting hue spectra were compressed with the Polar Qualification System (PQS) surface method [
28]. It transformed the spectra into polar data, and the gravity point of the visible graph was computed. The gravity point location was expected to change with different spectra shapes caused by color changes.
Reference color data as averages of red, green and blue color components and their normalized values were calculated as well. Normalization removes intensity differences; therefore, these indices only reflected changes in color. The normalized values were computed by following Equation (2).
where R, G and B represent the average intensity of red, green and blue color components on the surface, and R
N, G
N and B
N are the normalized values. High-saturation pixels were segmented as regions of interest (ROIs) by simple thresholding. The threshold was calculated based on the saturation histogram.
2.6. Acoustic Firmness Measurements
Changes in the texture of the samples were monitored with an Aweta AFS desktop firmness meter (AWETA AFS Desktop System, DTF V0.0.0.105, AWETA BV., Pijnacker, The Netherlands) connected to a computer. This instrument can be used for the acoustic and impact texture measurement of several types of crops with spherical or nearly spherical shapes. The acoustic firmness coefficient of a sample can be determined by the following formula (Equation (3)) based on De Ketelaere et al. [
29]:
where the terms are defined as follows:
S—acoustic firmness coefficient (g2/3s−2 or Hz2g2/3);
f—resonance frequency (Hz);
m—weight of the tested crop (g).
2.7. Data Analysis
Data were collected, pre-processed and plotted in graphs using routines in Microsoft® Excel® (version 2401). Statistical analysis was performed using SPSS (version 29.0.1.0, Armonk, NY, USA, 2022). A two-way analysis of variance (ANOVA) test was performed to detect significant effects. In addition to the main effects of treatment and maturity status, we included interaction effects in the models. The homogeneity of variances was assessed using the Levene test. Following the ANOVA test, parameters of homogeneous variances were further analyzed using the Tukey HSD post hoc test, while parameters of inhomogeneous variances were further analyzed using the non-parametric Games–Howell test. Significant differences were defined at p < 0.05. The relationship between the measured parameters was evaluated using both Pearson’s and Spearman’s rank correlation, due to the expected nonlinear behavior of pigment concentration. Data were plotted in graphs with mean ± standard deviation.
Collected pictures were processed with Scilab (version 6.1.1, Dassault Systèmes, Vélizy-Villacoublay, France) and the Image Processing and Computer Vision toolbox (IPCV 4.1.2).
4. Discussion
The red–green color parameter a* was relevant for our evaluation of the color measurement results, because the surface color of tomatoes changes from green to red during the transformation of the tomato skin pigments [
32]. According to the CIE a*, the effect of the SmartFresh
TM anti-ripening treatment was most effective for mature green and breaker tomatoes. This can be explained by the fact that the rise in ethylene production reached the climacteric peak for mature samples, and ethylene, as the trigger of the ripening process, accelerated color changes and softening [
33]. After the climacteric maximum, the ethylene production becomes exponential; thus, the ripening process accelerates exponentially [
34]. The respiration intensity of tomatoes treated before the climacteric maximum does not increase as significantly during ripening, significantly slowing the climacteric rate [
35]. A similar exponential increase in ethylene production during ripening after the inflection point and the subsequent slowing down along the saturation curve has been described for plum [
36]. Furthermore, the treatment did have a positive effect on all other sample groups (turning, pink, light red and red tomatoes), which could be significantly observed after 1 week of storage time. It could be explained by the decreased ripening speed via blocking ethylene receptors and inhibiting its hormonal action by 1-MCP [
2,
3,
4]. In general, the treatment was effective with different impacts for the sample groups. Changes in color with similar tendencies to the observation described in our study have been documented in several studies on climacteric fruits [
37,
38,
39,
40]. Moreover, softening following the same trend has also been reported [
38,
41].
The color change can be explained by the change in chlorophyll content, because the most typical tomato pigments are red, orange or yellow carotenoids (lycopene and β-carotene) and green chlorophyll. This is confirmed by the DA-index
® results, which also show that the mature green and breaker sample groups were the most affected by the SmartFresh
TM treatment. The turning sample group was also significantly affected by the treatment, but to a lesser extent. On the other hand, no significant effect was observed for the pink and red sample groups in terms of the DA-index
® as a reflection of chlorophyll content. The obtained results of the present study suggest that the measurement of the red–green color factor (a*) by reflection measurement is more sensitive to changes during ripening than the measurement of the chlorophyll content. Similar changes in the chlorophyll concentration of tomatoes have been reported in previous studies [
42,
43].
In the case of the acoustic firmness coefficient (S), SmartFresh
TM treatment induced a significant effect on each sample group. This effect was statistically significant but nominally not comparable to the effect on the red–green color factor (a*). Our observations also confirm that the mature green and breaker sample groups were most affected by the treatment, as explained above. Differences between the control and treated groups of samples became significant in each case from day 7 of storage. Thus, it can be stated that it is beneficial to use this treatment for storage longer than seven days. This may be important because previously, it has been found that tomatoes start to spoil or become unfavorable to consumers from the seventh day [
44,
45].
Several studies support the suitability of colorimetry for monitoring tomato ripening [
30,
31]. The results of digital image processing are in line with the results of color measurement and chlorophyll content measurement. During storage, the color of tomato samples changed. The effect of the treatment was primarily observed in the normalized green and normalized red values, which can also be explained by the previously described results. The method was found to be suitable for determining the ripening status of tomatoes, because the red–green color factor (a*), which reflects the ripening status, showed a strong correlation with the value of the normalized green and normalized red parameters and the PQS coordinates [
37,
38,
39,
40]. This creates the possibility of major improvement in methodology, since in the light of this knowledge, we may be able to determine the ripeness of tomatoes or other climatic fruits using a camera or even a cheaper imaging device instead of a reflectance colorimeter. The relationships determined from the results of the digital image processing method are linear functions, or saturation curves, which are most common in nature. According to these functions, the red–green color factor (a*) measured by color measurement and the DA-index
® can be calculated from the color parameters measured by digital image processing. With the exception of the DA-index
®, a linear correlation between all measured and derived parameters could be detected. Strong linear correlation was found, among others, between the red–green color factor (a*) and the normalized color parameters as well as the red–green color factor (a*) and the PQS coordinates. A strong but non-linear correlation was observed between the chlorophyll-content-related DA-index
® and normalized color parameters as well as the DA-index
® and PQS coordinates. This suggests that digital image analysis is suitable for monitoring tomato ripening.
The actual chlorophyll content of tomatoes was not determined in this series of studies (non-destructive methods were preferred); however, the relationship between total chlorophyll content and DA-index
® in tomatoes was previously investigated by Rahman et al. [
46], and a strong linear correlation was found (r = 0.91). Nevertheless, chromatography can be used to directly determine the actual chlorophyll content for reference.