Postharvest quality assessment of tomato during storage: An experimental and ML fusion

被引:0
作者
Aftab, Rameez Ahmad [1 ]
Ahmad, Faizan [2 ]
Monish, Mohd [2 ]
Zaidi, Sadaf [2 ]
机构
[1] Aligarh Muslim Univ, Zakir Husain Coll Engn & Technol, Dept Petr Studies, Aligarh 202002, Uttar Pradesh, India
[2] Aligarh Muslim Univ, Fac Agr Sci, Dept Post Harvest Engn & Technol, Aligarh 202002, Uttar Pradesh, India
关键词
Tomato; Quality assessment; Storage; Overall quality index; Mathematical models; Machine learning; Experimental; MACHINE LEARNING TECHNIQUES; FRUITS; ACCUMULATION; PREDICTION; REGRESSION; PRODUCTS; MATURITY; MODELS; SYSTEM; INDEX;
D O I
10.1016/j.jspr.2025.102643
中图分类号
Q96 [昆虫学];
学科分类号
摘要
In the present study, tomatoes' physical and chemical qualities were assessed experimentally while they were being stored after harvest for 24 days at ambient storage conditions (27 +/- 2 degrees C). Every tomato quality attribute that was studied showed variations throughout storage. While the pH and sugar-acid ratio increased, firmness, ascorbic acid, acidity, and color change values decreased. The tomato's soluble solid content (SSC) increased and then reduced, ranging from 4.8 +/- 0.05 to 4.4 +/- 0.08 % Brix, while its pH and sugar-acid ratio fluctuated from 4.31 to 4.81 and 5.58-15.1 %, respectively. Moreover, overall quality index (OQI) models were formulated in terms of measured quality attributes and were validated with the sensory scores. The variations in the sensory overall quality scores were found to be consistent with the OQI predicted by Model M3. Moreover, the study's findings reveal that only 5 % of consumers preferred tomatoes when the quality index was assessed at 0.009. In contrast, 100 % of consumers rejected the tomatoes when the quality index was recorded at 0.004. Furthermore, 33 % of consumers expressed a favourable opinion towards tomatoes when the quality index reached 0.249. These results suggest that the optimal storage duration for tomatoes at ambient temperature is 12 days. Additionally, the overall quality index of the six distinct tomato cultivars was predicted using generalized machine learning (ML) models, which were shown to be successfully fitted (coefficient of determination, R-2 > 0.99) to the measured experimental data. These postharvest tomato quality prediction models could transform storage procedures by ensuring optimal conditions for increased shelf life and better-quality preservation.
引用
收藏
页数:11
相关论文
共 63 条
[51]   Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese [J].
Stangierski, J. ;
Weiss, D. ;
Kaczmarek, A. .
EUROPEAN FOOD RESEARCH AND TECHNOLOGY, 2019, 245 (11) :2539-2547
[52]   Application of nanoemulsion based edible coating on fresh-cut papaya [J].
Tabassum, Nazia ;
Aftab, Rameez Ahmad ;
Yousuf, Owais ;
Ahmad, Sadaf ;
Zaidi, Sadaf .
JOURNAL OF FOOD ENGINEERING, 2023, 355
[53]   Optimised ANN and SVR models for online prediction of moisture content and temperature of lentil seeds in a microwave fluidised bed dryer [J].
Taheri, Saeedeh ;
Brodie, Graham ;
Gupta, Dorin .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 182
[54]   Effects of variety on the quality of tomato stored under ambient conditions [J].
Tigist, M. ;
Workneh, Tilahun Seyoum ;
Woldetsadik, Kebede .
JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE, 2013, 50 (03) :477-486
[55]   Effects of Storage Duration on Physicochemical and Antioxidant Properties of Tomato (Lycopersicon esculentum Mill.) [J].
Tilahun, Shimeles ;
Park, Do Su ;
Taye, Adanech Melaku ;
Jeong, Cheon Soon .
HORTICULTURAL SCIENCE & TECHNOLOGY, 2017, 35 (01) :88-97
[56]   Impacts of harvesting stages and pre-storage treatments on shelf life and quality of tomato (Solanum lycopersicum L.) [J].
Tolasa, Makonnen ;
Gedamu, Fikreyohannes ;
Woldetsadik, Kebede .
COGENT FOOD & AGRICULTURE, 2021, 7 (01)
[57]   Some physicochemical and bioactive features of organically grown blackberry fruits (Rubus fructicosus L.) as inflluenced by postharvest UV-C and chitosan treatments [J].
Unal, Sevil ;
Sabir, Ferhan Kucukbasmaci ;
Sabir, Ali .
TURKISH JOURNAL OF AGRICULTURE AND FORESTRY, 2023, 47 (06) :907-+
[58]   Physico-chemical and postharvest quality characteristics of intra and interspecific grafted tomato fruits [J].
Walubengo, Dianah ;
Orina, Irene ;
Kubo, Yasutaka ;
Owino, Willis .
JOURNAL OF AGRICULTURE AND FOOD RESEARCH, 2022, 7
[59]   Machine learning for predicting chemical migration from food packaging materials to foods [J].
Wang, Shan -Shan ;
Lin, Pinpin ;
Wang, Chia-Chi ;
Lin, Ying-Chi ;
Tung, Chun-Wei .
FOOD AND CHEMICAL TOXICOLOGY, 2023, 178
[60]   Machine learning in materials science [J].
Wei, Jing ;
Chu, Xuan ;
Sun, Xiang-Yu ;
Xu, Kun ;
Deng, Hui-Xiong ;
Chen, Jigen ;
Wei, Zhongming ;
Lei, Ming .
INFOMAT, 2019, 1 (03) :338-358