Department of Advanced Technology R&D, Nexcelom from PerkinElmer, Lawrence, MA 01843, USA.
Food Science and Human Nutrition, School of Food and Agriculture, University of Maine, Orono, ME 04469, USA.
J Ind Microbiol Biotechnol. 2023 Feb 17;50(1). doi: 10.1093/jimb/kuad007.
Mixed microorganism cultures are prevalent in the food industry. A variety of microbiological mixtures have been used in these unique fermenting processes to create distinctive flavor profiles and potential health benefits. Mixed cultures are typically not well characterized, which may be due to the lack of simple measurement tools. Image-based cytometry systems have been employed to automatically count bacteria or yeast cells. In this work, we aim to develop a novel image cytometry method to distinguish and enumerate mixed cultures of yeast and bacteria in beer products. Cellometer X2 from Nexcelom was used to count of Lactobacillus plantarum and Saccharomyces cerevisiae in mixed cultures using fluorescent dyes and size exclusion image analysis algorithm. Three experiments were performed for validation. (1) Yeast and bacteria monoculture titration, (2) mixed culture with various ratios, and (3) monitoring a Berliner Weisse mixed culture fermentation. All experiments were validated by comparing to manual counting of yeast and bacteria colony formation. They were highly comparable with ANOVA analysis showing p-value > 0.05. Overall, the novel image cytometry method was able to distinguish and count mixed cultures consistently and accurately, which may provide better characterization of mixed culture brewing applications and produce higher quality products.
混合微生物培养物在食品工业中很常见。各种微生物混合物已被用于这些独特的发酵过程中,以创造独特的风味和潜在的健康益处。混合培养物通常没有很好的特征描述,这可能是由于缺乏简单的测量工具。基于图像的细胞计量系统已被用于自动计数细菌或酵母细胞。在这项工作中,我们旨在开发一种新的图像细胞计量方法,以区分和计数啤酒产品中酵母和细菌的混合培养物。使用荧光染料和大小排除图像分析算法,Nexcelom 的 Cellometer X2 用于计数混合培养物中的植物乳杆菌和酿酒酵母。进行了三项实验进行验证。(1)酵母和细菌单培养物滴定,(2)不同比例的混合培养物,以及(3)监测柏林小麦白啤酒混合培养物发酵。所有实验均通过与酵母和细菌菌落形成的手动计数进行比较来验证。通过 ANOVA 分析,它们具有高度可比性,p 值>0.05。总的来说,新的图像细胞计量方法能够一致且准确地区分和计数混合培养物,这可能为混合培养酿造应用提供更好的特征描述,并生产出更高质量的产品。