The development of microalgae culture technology has been an integral part to produce biomass feedstock to biofuel production. Due to this, numerous attempts have been made to improve some operational parameters of microalgae production. Despite this, specialized research in cell growth monitoring, considered as a fundamental parameter to achieve profitable applications of microalgae for biofuels production, presents some opportunity areas mainly related to the development of specific and accurate methodologies for growth monitoring. In this work, predictive models were developed through statistical tools that correlate a specific micro-algal absorbance with cell density measured by cell count (cells∙per ml), for three species of interest for biofuels production. The results allow the precise prediction of cell density through a logistic model based on spectrophotometry, valid for all the kinetics analysed. The adjusted determination coefficients () for the developed models were 0·993, 0·995 and 0·994 for Dunaliella tertiolecta, Nannochloropsis oculata and Chaetoceros muelleri respectively. The results showed that the equations obtained here can be used with an extremely low error (≤2%) for all the cell growth ranges analysed, with low operational cost and high potential of automation. Finally, a user-friendly software was designed to give practical use to the developed predictive models.

中文翻译：

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基于具有生物燃料重要性的微藻物种的特定吸光度的快速生物量定量准确方法

微藻培养技术的发展已经成为生产生物燃料生产生物质原料的一个组成部分。因此，已经进行了许多尝试来改进微藻生产的一些操作参数。尽管如此，细胞生长监测方面的专业研究被认为是实现微藻在生物燃料生产中的盈利应用的基本参数，提供了一些主要与开发特定和准确的生长监测方法相关的机会领域。在这项工作中，通过统计工具开发了预测模型，这些工具将特定微藻吸光度与通过细胞计数（细胞数/每毫升）测量的细胞密度相关联，用于生物燃料生产的三个感兴趣物种。结果允许通过基于分光光度法的逻辑模型精确预测细胞密度，对所有分析的动力学都有效。调整后的决定系数（) 对于开发的模型，Dunaliella tertiolecta、Nannochloropsis oculata和Chaetoceros muelleri分别为 0· 993、0 ·995 和 0·994 。结果表明，对于所有分析的细胞生长范围，这里获得的方程可以以极低的误差 (≤2%) 使用，操作成本低，自动化潜力大。最后，设计了一个用户友好的软件，使开发的预测模型具有实际用途。