Pons Marie-Noëlle, Le Bonté Sébastien, Potier Olivier
Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, 1 rue Grandville, BP 451, F-54001 Nancy cedex, France.
J Biotechnol. 2004 Sep 30;113(1-3):211-30. doi: 10.1016/j.jbiotec.2004.03.028.
Classical culture media, as well as domestic and/or industrial wastewater treated by biological processes, have a complex composition. The on-line and/or in situ determination of some substances is possible, but expensive, as sample collection and pre-treatment are often necessary with strict rules of sterility. More global methods can be used to detect rapidly "accidents" such as the appearance of an undesirable by-product in a fermentation broth or of a toxic substance in wastewater. These methods combine a "hard" part, for sensing, and a "soft" part, for data treatment. Among potential "hard" candidates, spectroscopy can be the basis for non-invasive and non-destructive measuring systems. Some of them have been already tested in situ: ultra-violet-visible, infra-red (mid or near), fluorescence (mono-dimensional, two-dimensional or synchronous), dielectric, while others, more sophisticated, such as mass spectrometry, coupled or not to pyrolysis, nuclear magnetic resonance and Raman spectroscopy, have been proposed. All these methods provide spectra, i.e. large sets of data, from which meaningful information should be rapidly extracted, either for analysis or fingerprinting. The recourse to data-mining techniques (the "soft" part) such as principal components analysis, projection on latent structures or artificial neural networks, is a necessary step for that task. A review of techniques, mostly based on spectroscopy, with examples taken in the bioengineering field in general is proposed.
传统培养基以及经过生物处理的生活污水和/或工业废水,其成分都很复杂。某些物质的在线和/或原位测定是可行的,但成本高昂,因为通常需要进行样品采集和预处理,且要遵循严格的无菌规则。可以使用更通用的方法来快速检测“事故”,例如发酵液中出现不良副产物或废水中出现有毒物质。这些方法结合了用于传感的“硬件”部分和用于数据处理的“软件”部分。在潜在的“硬件”候选方法中,光谱学可以作为非侵入性和非破坏性测量系统的基础。其中一些已经在原位进行了测试:紫外可见光谱、红外光谱(中红外或近红外)、荧光光谱(一维、二维或同步)、介电光谱,而其他更复杂的方法,如质谱(是否与热解联用)、核磁共振和拉曼光谱,也已被提出。所有这些方法都会提供光谱,即大量数据集,需要从中快速提取有意义的信息用于分析或指纹识别。为此,采用数据挖掘技术(“软件”部分),如主成分分析、潜在结构投影或人工神经网络,是必要的步骤。本文将对主要基于光谱学的技术进行综述,并给出一般生物工程领域的实例。