Luo Zhijie, Zhao Bin, Liu Wenjin, Zheng Jianhua, Chen Wenwen
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
Intelligent Agriculture Engineering Technology Research Centre, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
Micromachines (Basel). 2025 May 19;16(5):594. doi: 10.3390/mi16050594.
In recent years, emerging digital microfluidic technology has shown great application potential in fields such as biology and medicine due to its simple structure, sample-saving properties, ease of integration, and wide range of manipulation. Currently, due to potential faults in chips during production and usage, as well as high safety requirements in their application domains, thorough testing of chips is essential. This study records data using a machine vision-based digital microfluidic driving control system. As chip usage frequency rises, device degradation introduces seasonal and trend patterns in droplet motion time data, complicating predictive modeling. This paper first employs the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to analyze the droplet motion time data in digital microfluidic systems. Subsequently, principal component analysis (PCA) is applied for dimensionality reduction on the clustered data. Using the INFORMER model, we predict changes in droplet motion time and conduct correlation analysis, comparing results with traditional long short-term memory (LSTM), frequency-enhanced decomposed transformer (FEDformer), inverted transformer (iTransformer), INFORMER, and DBSCAN-INFORMER prediction models. Experimental results show that the DBSCAN-PCA-INFORMER model substantially outperforms LSTM and other benchmark models in prediction accuracy. It achieves an R of 0.9864, an MSE of 3.1925, and an MAE of 1.3661, indicating an excellent fit between predicted and observed values.The results demonstrate that the DBSCAN-PCA-INFORMER model achieves higher prediction accuracy than traditional LSTM and other approaches, effectively identifying the health status of experimental devices and accurately predicting failure times, underscoring its efficacy and superiority.
近年来，新兴的数字微流控技术因其结构简单、节省样品、易于集成和操作范围广泛等特点，在生物学和医学等领域展现出了巨大的应用潜力。目前，由于芯片在生产和使用过程中可能出现故障，以及其应用领域对安全性要求较高，对芯片进行全面测试至关重要。本研究使用基于机器视觉的数字微流控驱动控制系统记录数据。随着芯片使用频率的提高，设备退化会在液滴运动时间数据中引入季节性和趋势模式，这使得预测建模变得复杂。本文首先采用基于密度的带有噪声应用空间聚类（DBSCAN）算法来分析数字微流控系统中的液滴运动时间数据。随后，对聚类后的数据应用主成分分析（PCA）进行降维。使用INFORMER模型，我们预测液滴运动时间的变化并进行相关性分析，并将结果与传统的长短期记忆（LSTM）、频率增强分解变压器（FEDformer）、反向变压器（iTransformer）、INFORMER以及DBSCAN - INFORMER预测模型进行比较。实验结果表明，DBSCAN - PCA - INFORMER模型在预测准确性方面大大优于LSTM和其他基准模型。它的决定系数R为0.9864，均方误差MSE为3.1925，平均绝对误差MAE为1.3661，表明预测值与观测值之间拟合良好。结果表明，DBSCAN - PCA - INFORMER模型比传统的LSTM和其他方法具有更高的预测准确性，能够有效地识别实验设备的健康状态并准确预测故障时间，凸显了其有效性和优越性。
