Investigating Autoregressive and Machine Learning-based Time Series Modeling with Dielectric Spectroscopy for Predicting Quality of Biofabricated Constructs. (September 2022)

• Record Type: Journal Article
• Authors: Shohan, Shohanuzzaman; Hasan, Mahmud; Starly, Binil; Shirwaiker, Rohan
• Is Part Of: Manufacturing letters. Volume 33(2022)Supplement
• Page Start: 902
• Page End: 908
• Publication Date: 2022-09
• Subjects: Time Series Analysis -- Machine Learning -- Non-destructive Quality Monitoring -- Dielectric Spectroscopy -- Tissue Engineering; Manufacturing industries -- Periodicals; Production engineering -- Periodicals; Manufacturing industries; Periodicals; 670
• DOI: 10.1016/j.mfglet.2022.07.110
• Languages: English
• ISSNs: 2213-8463

Abstract

Abstract: Advances in biofabrication processes need to be complemented with appropriate nondestructive quality engineering techniques that can be integrated into scalable engineered tissue manufacturing systems. Previous studies have demonstrated the feasibility of dielectric spectroscopy (DS) as a inline, real time biological quality monitoring alternative. Time series modeling can help improve the efficiency and accuracy of quality prediction by analyzing trends in DS data as the biofabricated constructs mature over time. These models can help forecast potential future deviations in quality attributes and provide opportunities to take preemptive, corrective actions, leading to better yields and higher quality of final products. In this study, we investigated time series modeling of DS data to characterize the effects of two critical biofabrication parameters on constructs of gelatin methacryloyl (GelMA) hydrogel containing human adipose-derived stem cells (hASC) over 11 days of in vitro culture. The performance of standard autoregressive time series models (Exponential Smoothing, ARMA, ARIMA, SARIMA) and conventional sequence-based machine learning (ML) models (SVM, ANN, CNN and LSTM) were analyzed to forecast trends in Δɛ, a key DS metric that directly correlates to the volume of viable cells in constructs. The ML-based time series models, in general, showed superior performance in predicting future trends in Δɛ, with LSTM providing the lowest least mean square errors (MSE) in Δɛ forecasts. The outcomes of this study highlight the benefits of using DS and time series modeling synergistically for efficient quality monitoring in biofabrication.