Benito-Altamirano Ismael, Moreno Sergio, Vaz-Romero David M, Puig-Pujol Anna, Roca-Domènech Gemma, Canals Joan, Vilà Anna, Prades Joan Daniel, Diéguez Ángel
Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.
eHealth Center, Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, Sant Martí, 08018 Barcelona, Spain.
Biosensors (Basel). 2025 Jan 12;15(1):40. doi: 10.3390/bios15010040.
In recent years, the wine industry has been researching how to improve wine quality along the production value chain. In this scenario, we present here a new tool, MicroVi, a cost-effective chip-sized microscopy solution to detect and count yeast cells in wine samples. We demonstrate that this novel microscopy setup is able to measure the same type of samples as an optical microscopy system, but with smaller size equipment and with automated cell count configuration. The technology relies on the top of state-of-the-art computer vision pipelines to post-process the images and count the cells. A typical pipeline consists of normalization, feature extraction (i.e., SIFT), image composition (to increase both resolution and scanning area), holographic reconstruction and particle count (i.e., Hough transform). MicroVi achieved a 2.19 µm resolution by properly resolving the G7.6 features from the USAF Resolving Power Test Target 1951. Additionally, we aimed for a successful calibration of cell counts for . We compared our direct results with our current optical setup, achieving a linear calibration for measurements ranging from 0.5 to 50 million cells per milliliter. Furthermore, other yeast cells were qualitatively resolved with our MicroVi microscope, such as, , or bacteria, like, , thus confirming the system's reliability for consistent microbial assessment.
近年来,葡萄酒行业一直在研究如何沿着生产价值链提高葡萄酒质量。在这种情况下,我们在此展示一种新工具——MicroVi,这是一种经济高效的芯片大小的显微镜解决方案,用于检测和计数葡萄酒样品中的酵母细胞。我们证明,这种新型显微镜设置能够测量与光学显微镜系统相同类型的样品,但设备尺寸更小且具有自动细胞计数配置。该技术依靠最先进的计算机视觉管道对图像进行后处理并对细胞进行计数。一个典型的管道包括归一化、特征提取(即尺度不变特征变换)、图像合成(以提高分辨率和扫描区域)、全息重建和粒子计数(即霍夫变换)。通过正确解析美国空军1951年分辨率测试靶标的G7.6特征,MicroVi实现了2.19微米的分辨率。此外,我们旨在成功校准细胞计数。我们将直接结果与当前的光学设置进行了比较,在每毫升0.5至5000万个细胞的测量范围内实现了线性校准。此外,我们的MicroVi显微镜还能定性分辨其他酵母细胞,如……,或细菌,如……,从而证实了该系统在一致的微生物评估方面的可靠性。