Continuous monitoring of moisture loss of beef, beetroot, and banana slices during microwave vacuum dehydration by using THz-TDS combined with transformer-based neural network

Microwave vacuum dehydration (MVD) has emerged as a preferred alternative to conventional methods such as hot air drying, offering faster dehydration rates while better preserving product quality. Despite these advantages, challenges remain in implementing effective real-time monitoring systems and accurate dehydration prediction methods during the MVD process. This study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) to continuously monitor the drying kinetics of beef, beetroot, and banana slices during MVD without interrupting the dehydration process. Polytetrafluoroethylene (PTFE) was demonstrated as the most suitable airhose material among polyethene (PE), PTFE, and quartz with the highest transmittance of 0.824. Using the deep learning model of transformer-based neural network (TbNN) introduces the self-attention mechanisms to extract features at characteristic frequencies. It successfully correlated THz-TDS transmittance data with actual moisture loss of samples with a prediction accuracy of 0.96, which shows excellent generalisation capability of this TbNN model on such a small dataset size. Besides, the calibration strategy successfully improves the accuracy from 0.94 to 0.96, with a regression coefficient of R = 0.98328. The integration of these sensing and analytical techniques offers a valuable framework for improving industrial processing control while broadening the applications of THz-TDS technology across agricultural and food production sectors. • THz-TDS enables continuous, non-invasive monitoring of MVD drying kinetics. • TbNN achieve 0.96 prediction accuracy for moisture loss. • The calibration strategy improves model accuracy from 0.94 to 0.96 • TbNN shows excellent generalisation capability despite a limited dataset size. • Integration of THz-TDS and AI offers new potential for food processing control.

Authors

Institutions

Publication Details

Journal
Journal of Food Engineering
Published
2025-08-08
DOI
https://doi.org/10.1016/j.jfoodeng.2025.112774
Citations
3
Primary Topic
Food Drying and Modeling
Type
article
Field-Weighted Citation Impact
3.49

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Continuous monitoring of moisture loss of beef, beetroot, and banana slices during microwave vacuum dehydration by using THz-TDS combined with transformer-based neural network

Ying Fu, Da‐Wen Sun, Zhihang Zhang
3 citations
Journal of Food Engineering
Food Drying and Modeling
3.49
article

Continuous monitoring of moisture loss of beef, beetroot, and banana slices during microwave vacuum dehydration by using THz-TDS combined with transformer-based neural network

Ying Fu, Da‐Wen Sun, Zhihang Zhang
article en
3 citations

Abstract

Microwave vacuum dehydration (MVD) has emerged as a preferred alternative to conventional methods such as hot air drying, offering faster dehydration rates while better preserving product quality. Despite these advantages, challenges remain in implementing effective real-time monitoring systems and accurate dehydration prediction methods during the MVD process. This study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) to continuously monitor the drying kinetics of beef, beetroot, and banana slices during MVD without interrupting the dehydration process. Polytetrafluoroethylene (PTFE) was demonstrated as the most suitable airhose material among polyethene (PE), PTFE, and quartz with the highest transmittance of 0.824. Using the deep learning model of transformer-based neural network (TbNN) introduces the self-attention mechanisms to extract features at characteristic frequencies. It successfully correlated THz-TDS transmittance data with actual moisture loss of samples with a prediction accuracy of 0.96, which shows excellent generalisation capability of this TbNN model on such a small dataset size. Besides, the calibration strategy successfully improves the accuracy from 0.94 to 0.96, with a regression coefficient of R = 0.98328. The integration of these sensing and analytical techniques offers a valuable framework for improving industrial processing control while broadening the applications of THz-TDS technology across agricultural and food production sectors. • THz-TDS enables continuous, non-invasive monitoring of MVD drying kinetics. • TbNN achieve 0.96 prediction accuracy for moisture loss. • The calibration strategy improves model accuracy from 0.94 to 0.96 • TbNN shows excellent generalisation capability despite a limited dataset size. • Integration of THz-TDS and AI offers new potential for food processing control.

Journal of Food EngineeringVol. 406
University College Dublin (IE), National University of Ireland (IE)
China Sponsorship Council, China Scholarship Council
Openalex Percentile: Top 8%
Food Drying and Modeling
3.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.