Work

Data-Driven Hybrid CNN-LSTM Neural Networks for Predicting Drying Kinetics of Green Tomatoes (Physalis ixocarpa) Integrating Mathematical Models

Year: 2025

Type: article

Abstract: This study investigates the drying kinetics of green tomatoes under controlled conditions, comparing 14 mathematical models including Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Two-Term, Wang and Singh, Thomson, Approximate Diffusion, Verma, Modified Henderson and Pabis, Two-Term Exponential, Simplified Fick’s Diffusion, and Midilli et al. The Verma, Two-Term Exponential, and Modified Page models demonstrated superior accuracy (R2 > 0.998, RMSE 0.01268 - 0.01345). A novel approach integrating a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) optimized drying process prediction, significantly enhancing moisture content prediction over time. This hybrid CNN-LSTM model, coupled with the accurate mathematical models (especially the Two-Term Exponential), offers a robust framework for predicting and optimizing food dehydration.

Source: IEEE Access

DOI: 10.1109/access.2025.3554492

Authors: Carolina Livier Recio-Colmenares, Roxana Berenice Recio-Colmenares, Robin Fernando Conchas-Cedano, Isaac Pilatowsky-Figueroa, César Alejandro García García

Institutions: Universidad de Guadalajara, Instituto Politécnico Nacional, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional

Language: English

Topic: Food Drying and Modeling

Subfield: Food Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,075