Development of a prediction model for the pericarp CIE a* value of mature green tomato at different storage temperatures as a function of cumulative ethylene production

Ciptaningtyas, Drupadi; Kagoshima, W.; Iida, R.; Umehara, H.; Johkan, Masahumi; Nakamura, Nobutaka; Orikasa, Takahiro; Thammawong, Manasikan; Shiina, Takeo

doi:10.1016/j.jfoodeng.2020.109945

Cited by 10 publications

(13 citation statements)

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“…Cold-air storage is one of the most common and cost-effective storage methods for fresh tomatoes and is often used in research. Ciptaningtyas et al 8 developed a dynamic, nondestructive evaluation model to predict the change in pericarp color of tomatoes based on cumulative ethylene production at different storage temperatures, and the results correlated well with the experimental data. However, the present study focuses only on the ripening and color changes of mature green tomatoes stored at different temperatures without considering how the temperature and humidity combine to affect the tomato color in the different maturity stages.…”

Section: Introductionmentioning

confidence: 85%

“…Cold‐air storage is one of the most common and cost‐effective storage methods for fresh tomatoes and is often used in research. Ciptaningtyas et al 8 . developed a dynamic, nondestructive evaluation model to predict the change in pericarp color of tomatoes based on cumulative ethylene production at different storage temperatures, and the results correlated well with the experimental data.…”

Section: Introductionmentioning

confidence: 86%

“…Typical ranges of temperature and humidity fluctuations throughout the cold chain (i.e., precooling, transportation, storage, and marketing) are 5–25 °C and 50%–90% relative humidity (RH), 5,8 which are the ranges used in this study. A standardized form orthogonal table L 9 (3 3 ) was developed for the tomato ripeness, storage temperature, and humidity (see Table 1), and was used to determine different combinations of tomato storage conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Previous research shows that the evolution in tomato color is mainly due to the degradation of chlorophyll and carotenoid synthesis during ripening, which leads to the accumulation of pigments (e.g., lycopene and beta-carotene) in the peel. 8,28 At the light-red stage of tomato maturity, a* is maximal, which means that the tomato has a higher lycopene content than at the green stage or the turn stage. At the turn stage of tomatoes, the mean value of a* is −3.72; the negative value indicates a light green color.…”

Section: Comparison Of Tomato Color Of Different Maturitymentioning

confidence: 99%

“…The dramatic color change during ripening is visible to the naked eye, so color is considered as the most intuitive and convenient indicator of tomato ripeness, allowing tomatoes to be harvested at the correct time to satisfy market demand (e.g., considering sales distance, use, and consumer preference). 8 Given that visual stimuli are strong factors in the purchasing decision, tomato color is also an important quality parameter that influences the purchasing decision. The dynamic prediction of tomato color after harvest can help provide detailed information about the maturity and quality of tomatoes, thus helping to optimize control and decision making at each link (storage, transportation, and sales) in the cold chain (e.g., precision control of temperature and humidity, optimization of distribution path, maximal storage time, and best time to sell), improve sales volume, and satisfy the quality demands of consumers.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical model of postharvest variation in tomato color based on optimized response surface methodology

Han

Ren

et al. 2021

J Sci Food Agric

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BACKGROUND Manual inspection and instrumentation form the traditional approach to determining tomato color but these methods only determine tomato color at a given moment and cannot predict dynamically how tomato color varies during storage and transportation. Such methods thus cannot help suppliers and retailers establish good management practices for the flexible control of tomato maturity, accurate judgment of market positioning in the industry, or during distribution and marketing. To address this shortcoming, this work first investigates how tomato color parameters (a* and h°) evolve through the various stages of maturity (green, turn, and light red) under different storage conditions. Based on experimental results, it develops an optimized response‐surface model (RSM) by using differential evolution to predict how tomato color varies during storage. RESULTS Tomatoes are more likely to change color at high temperatures and under conditions of high humidity. Temperature affects tomato color more strongly than humidity. The accuracy of the RSM was confirmed by a good agreement with experiments. All determination coefficients R2 of the RSMs for a* and h° are greater than 0.91. The mean absolute errors for a* and h° are 3.8112 and 5.6500, respectively. The root mean square errors for a* and h° are 4.6840 and 6.9198, respectively. CONCLUSION This research reveals how storage temperature and humidity affect the postharvest variations in tomato color and thus establishes a dynamic model for predicting tomato color. The proposed RSM provides a reliable theoretical foundation for dynamic, nondestructive monitoring of tomato ripeness in the cold chain. © 2021 Society of Chemical Industry.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Comparison Of Tomato Color Of Different Maturitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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