Sartorius Corporate Research, Sartorius Stedim Biotech GmbH, Göttingen, Germany.
Sartorius Corporate Research, Sartorius Stedim Data Analytics AB, Umea, Sweden.
SLAS Technol. 2021 Aug;26(4):408-414. doi: 10.1177/24726303211008861. Epub 2021 Apr 19.
Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.
机器视觉是一项强大的技术，由于机器学习领域的快速发展，在过去十年中变得越来越流行和精确。大多数机器视觉应用目前都可以在消费电子产品、汽车应用和质量控制中找到，但生物处理应用的潜力是巨大的。例如，检测和控制泡沫的出现对于所有上游生物过程都很重要，但是缺乏强大的泡沫感测功能通常会导致泡沫溢出或消泡剂过量添加导致批次失败。在这里，我们报告了一种用于生物反应器应用的新型低成本、灵活和可靠的泡沫传感器概念。该概念应用了卷积神经网络（CNN），这是一种用于图像处理的最先进的机器学习系统。所实现的方法在二进制泡沫检测（泡沫/无泡沫）和泡沫水平的细粒度分类方面都具有很高的准确性。
