Quality Prediction of 'Sai Nam Pung' Tangerine After Truck Transportation Using Artificial Neural Network

Rithmanee, T.; Bumroonggit, G.; Boonprasom, Pichaya

doi:10.17660/actahortic.2008.802.50

Cited by 2 publications

(2 citation statements)

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“…Rithmanee and co-workers [48] predicted quality of tangerine after truck transportation from the packing house in Chiangmai to the wholesale market in Bangkok, as indexed by changes in vitamin C, titratable acidity (TA), pH, total soluble solids (TSS), TSS/TA; weight loss percentage, decay percentage, mechanical damage percentage and surface color change. The statistic treatment structure for the experiment was a 32 factorial design in RCBD with three replications (trips).…”

Section: Gg Tangerinementioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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Abstract-This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

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Section: Gg Tangerinementioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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“…Moreover, it has the ability to detect implicitly the complex nonlinear relationships between input variables and outputs during the training process. Therefore, there is no need for a high degree of human expertise to build up the model as the difficulty of the modeling process is no longer encountered (Rithmanee, Bumroonggit, & Boonprasom, 2008). Several research have revealed that ANNs show high performance for predicting food processing and production parameters such as the drying characteristics of potato, garlic, and cantaloupe under convective hot air dryers (Kaveh et al, 2018), the influence of agglomeration process parameters on the physical and chemical properties of cocoa powder mixtures (Benkovi c et al, 2015).…”

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confidence: 99%

Application of artificial neural networks for predicting parameters of commercial vacuum cooling process of baby cos lettuce

Kongwong

Boonyakiat

Pongsirikul

et al. 2021

J Food Process Engineering

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Artificial neural networks (ANNs) demonstrated sensitive results in predicting final temperature and weight loss percentage of commercial vacuum cooling process. According to the results for final temperature, ANNs showed better prediction performance than multiple linear regression in all criteria, including an adjusted R‐squared (R2adj) of .932 and root mean square error (RMSE) of 0.579. In addition, the predicted values of weight loss percentage from ANN models were in good agreement with all experimental data (Radj2 = .82 and RMSE = 0.286). The process parameters from proper ANN model was subsequently used to investigate the effect of vacuum cooling on the qualities of baby cos lettuce during storage compared with the non‐precooled samples. The results suggested that vacuum cooling was an effective method for extending shelf life of baby cos lettuce from 9 to 16 days at 4°C. Qualities of fresh lettuce vacuum cooled using selected process parameters simulated from proper ANN model were significantly better than the non‐precooled sample during storage (p ≤ .05). Practical Applications Vacuum cooling widely considered the best precooling technique for horticultural produce. However, vacuum cooling also has some disadvantages including weight loss and freezing injury occurring during the vacuum cooling process, due to the setting of inapplicable parameters before the vacuum cooling process such as final pressure and holding time. Experimental study of the optimum vacuum cooling parameters for baby cos lettuce in each season (winter or summer) takes a long time and is expensive. Therefore, the present study emphasized the effects of vacuum cooling parameters setting on final temperature and weight loss percentage of baby cos lettuce. Prediction methods using artificial neural network (ANN) allow for vacuum cooling processes based on experimental research to be recommended as feasible at an industrial scale.

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Quality Prediction of 'Sai Nam Pung' Tangerine After Truck Transportation Using Artificial Neural Network

Cited by 2 publications

References 0 publications

Artificial Neural Networks in Fruits: A Comprehensive Review

Artificial Neural Networks in Fruits: A Comprehensive Review

Application of artificial neural networks for predicting parameters of commercial vacuum cooling process of baby cos lettuce

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