Investigating Autoregressive and Machine Learning-based Time Series Modeling with Dielectric Spectroscopy for Predicting Quality of Biofabricated Constructs
Investigating Autoregressive and Machine Learning-based Time Series Modeling with Dielectric Spectroscopy for Predicting Quality of Biofabricated Constructs
复制标题
使用介电谱研究基于自回归和机器学习的时间序列建模，以预测生物制造结构的质量
DOI：
10.1016/j.mfglet.2022.07.110
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发表时间：
2022
影响因子：
3.9
通讯作者：
Shirwaiker, Rohan
中科院分区：
文献类型：
--
作者：
Shohan, Shohanuzzaman;Hasan, Mahmud;Starly, Binil;Shirwaiker, Rohan
生物制造工艺的进步需要与适当的非破坏性质量工程技术相补充，这些技术可以集成到可扩展的工程化组织制造系统中。以前的研究已经证明了介电光谱(DS)作为一种在线、实时生物质量监测替代方案的可行性。时间序列建模可以通过分析DS数据中的趋势来帮助提高质量预测的效率和准确性，因为生物丰富的结构随着时间的推移而成熟。这些模型可以帮助预测未来质量属性的潜在偏差，并提供采取先发制人的纠正措施的机会，从而导致更高的产量和更高质量的最终产品。在这项研究中，我们研究了DS数据的时间序列建模，以表征两个关键的生物制造参数对体外培养11天的人脂肪来源干细胞(HASC)构建的明胶甲基丙烯酰基(GelMA)水凝胶的影响。分析了标准自回归时间序列模型(指数平滑、ARMA、ARIMA、SARIMA)和传统基于序列的机器学习(ML)模型(支持向量机、ANN、CNN和LSTM)的性能，以预测Δɛ的趋势。总的来说，基于ML的时间序列模型在预测Δɛ的未来趋势方面表现出了优越的性能，其中LSTM在Δɛ预测中提供了最低的均方误差。这项研究的结果突出了协同使用DS和时间序列建模在生物制造中进行有效质量监控的好处。爱思唯尔有限公司出版。保留所有权利。
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 sequencebased 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.© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.