{'fraction_non_alphanumeric': 0.023835319609967497, 'fraction_numerical': 0.013001083423618635, 'mean_word_length': 5.282312925170068, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mathematical model of postharvest variation in tomato color based on optimized response surface methodology.

BACKGROUND
Although manual inspection and instrumentation form the traditional approach to determining tomato color, these methods only determine tomato color at a given moment and cannot dynamically predict how tomato color varies during storage and transportation. Such methods thus cannot help suppliers and retailers establish good management practices for flexible control of tomato maturity and accurate judgment of market positioning in the industry and 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, we develop an optimized response-surface model (RSM) by combining differential evolution to predict how tomato color varies during storage.


RESULTS
Tomatoes are more likely to change color at high temperatures and humidity, and temperature more strongly affects the tomato color than does 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. This article is protected by copyright. All rights reserved.