Fuzzy Logic Decision Support System to Predict Peaches Marketable Period at Highest Quality

被引:7
作者
Magalhaes, Bianca [1 ]
Gaspar, Pedro Dinis [1 ,2 ]
Corceiro, Ana [1 ]
Joao, Luzolo [1 ]
Bumba, Cesar [1 ]
机构
[1] Univ Beira Interior, Dept Electromech Engn, Rua Marques de DAvila & Bolama, P-6201001 Covilha, Portugal
[2] C MAST Ctr Mech & Aerosp Sci & Technol, Rua Marques de DAvila & Bolama, P-6201001 Covilha, Portugal
关键词
computational tool; decision support system; artificial intelligence; fuzzy logic; marketable period; peaches; physical-chemical parameters; storage conditions; quality;
D O I
10.3390/cli10030029
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Food waste occurs from harvesting to consumption. Applying procedures and technologies, changing attitudes, and promoting awareness have positive social, economic, and environmental impacts that can contribute to reducing food waste. The paper presents a decision support system (DSS) to predict the quality evolution of fruits and vegetables, particularly of peaches, and estimate its commercialization period at the highest overall perceived quality by consumers, thus contributing to reducing food waste. The Fuzzy Logic DSS predicts the evolution of the physical-chemical parameters of peaches (hardness, soluble solids content, and acidity) depending on the cultivar (Royal Summer and Royal Time), storage time, and temperature. As the range of the values of these physical-chemical parameters of peaches that consumers perceive to be at their highest quality are known, the DSS predicts the marketable period in days. Case studies were developed to analyze the influence of each physical-chemical parameter on the commercialization days (number and time to start). It is concluded that temperature is the most important parameter for fruit conservation. A low value of conservation temperature allows for the significant extension of the time that peaches can be sold at the highest quality. Hardness is used to determine the harvest date since it is an index of fruit ripeness. The same conclusion is obtained for the influence of the soluble solids content. The influence of acidity on marketable days is less than the other physical-chemical parameters. This DSS helps retailers to sell their peaches at the highest quality with benefits for all parties. It also helps in the decision-making concerning the actions to take when fruits are reaching the end of their highest quality by predicting the range of the commercialization days. This formulation can be extended to other fruits and vegetables and in the last instance contribute to the reduction of food loss and waste, consequently promoting social, economic, and environmental aspects of our daily life.
引用
收藏
页数:23
相关论文
共 50 条
[31]   Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic [J].
Soufi, Mahsa Dehghani ;
Samad-Soltani, Taha ;
Vandati, Samad Shams ;
Rezaei-Hachesu, Peyman .
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2018, 114 :35-44
[32]   Fuzzy logic in decision support system as a simple Human/Internet of Things interface for shunt active power filter [J].
Jasinski, M. ;
Majtczak, P. ;
Malinowski, A. .
BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES, 2016, 64 (04) :877-886
[33]   Development of Decision Support System Using Mamdani Type Fuzzy Logic Clusters for Metabolic Syndrome Risk Assesment [J].
Birtane, Sibel ;
Canayaz, Emre ;
Altikardes, Zehra Aysun ;
Korkmaz, Hayriye .
2017 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO), 2017,
[34]   A Fuzzy Decision Support System for Management of Breast Cancer [J].
Saleh, Ahmed Abou Elfetouh ;
Barakat, Sherif Ebrahim ;
Awad, Ahmed Ebrahim Awad .
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2011, 2 (03) :34-40
[35]   Decision-support guideline in Obstetrical Nursing: application of Fuzzy Logic technique [J].
Marques, Isaac R. ;
Barbosa, Sayonara de Fatima ;
de Oliveira Basile, Anatalia Lopes ;
Marin, Heimar F. .
REVISTA BRASILEIRA DE ENFERMAGEM, 2005, 58 (03) :349-354
[36]   Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic [J].
De-Sousa, Karolini Tenffen ;
Deniz, Matheus ;
dos Santos, Mauricio Portella ;
Klein, Daniela Regina ;
do Vale, Marcos Martinez .
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2023, 67 (03) :475-484
[37]   Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic [J].
Karolini Tenffen De-Sousa ;
Matheus Deniz ;
Maurício Portella dos Santos ;
Daniela Regina Klein ;
Marcos Martinez do Vale .
International Journal of Biometeorology, 2023, 67 :475-484
[38]   Fuzzy Logic based decision support systems in variant production [J].
Karmarkar, A. U. ;
Gilke, N. R. .
MATERIALS TODAY-PROCEEDINGS, 2018, 5 (02) :3842-3850
[39]   Fuzzy Logic in Decision Support: Methods, Applications and Future Trends [J].
Wu, H. ;
Xu, Z. S. .
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2021, 16 (01) :1-28
[40]   A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices [J].
Kumar, Darshan ;
Singh, Jagdev ;
Singh, Om Pal ;
Seema .
MATHEMATICAL AND COMPUTER MODELLING, 2013, 57 (11-12) :2945-2960