Advanced Modeling Approaches for Quality Assessment of Papaya Leather Dried in IoT‐Enabled IR‐Assisted Refractance Window Dryer

ABSTRACT The aim of this research is the comprehensive comparison of advanced modeling tools for predicting the quality parameters of IoT‐enabled IR‐assisted RW‐dried papaya leather. The models used in the current research were “response surface methodology (RSM),” “artificial neural network (ANN),”...

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Vydáno v:Journal of food process engineering Ročník 48; číslo 2
Hlavní autoři: Dadhaneeya, Harsh, Nema, Prabhat K., Warepam, Sophia Chanu
Médium: Magazine Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.02.2025
Témata:
adaptive neuro‐fuzzy inference system
artificial neural network
fuzzy logic
IoT‐enabled IR‐assisted refractance window dryer
leather
machine learning regression algorithms
neural networks
papaya leather
papayas
prediction
response surface methodology
ISSN:0145-8876, 1745-4530
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Shrnutí:ABSTRACT The aim of this research is the comprehensive comparison of advanced modeling tools for predicting the quality parameters of IoT‐enabled IR‐assisted RW‐dried papaya leather. The models used in the current research were “response surface methodology (RSM),” “artificial neural network (ANN),” “machine learning regression algorithms (MLRA),” and “adaptive neuro‐fuzzy inference system (ANFIS).” This comprehensive compression could be differentiated based on the model's fitness and prediction capabilities. The fitness of the model was assessed using statistical metrics such as “root mean square error (RMSE),” “mean square error (MAE),” and “coefficient of determination (R2).” While the prediction capability was evaluated by using tactics like predicting error, predicting accuracy, chi‐square, and associated p‐values. The results indicated that both the RSM and MLRA models had substantial predictive capabilities and achieved a prediction accuracy of 98.992 and 97.169, respectively. This study concluded that the model's fitness was getting excellent in the Alpha ANN (R2 = 0.9943) and ANFIS (R2 = 0.9903) models. However, when evaluating the prediction capabilities, the RSM and MLRA models outperformed the others. Fresh papaya was transformed into a high‐value product, like a papaya leather, by using an IoT‐enabled infrared‐assisted refractance window drying system. This innovative approach integrates modern drying technology with IoT monitoring for enhanced process control and product quality. Advanced modeling techniques, including response surface methodology (RSM), artificial neural networks (ANN), adaptive neuro‐fuzzy inference systems (ANFIS), and machine learning regression algorithm (MLRA), were applied. A comparative analysis of these models was conducted to identify the best model.
Bibliografie:The authors received no specific funding for this work.
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ISSN:0145-8876
1745-4530
DOI:10.1111/jfpe.70059