The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rhizobacteria at the genus and species level. To develop the method, bacteria were inoculated on an agar medium and analyzed by near infrared (NIR) and ultraviolet-visible-NIR (UV-Vis-NIR) spectroscopy. Artificial neural network models applied to the UV-Vis-NIR spectra correctly identified the genus (species) of 70% (63%) of the lactic acid bacteria and 67% of the rhizobacteria on an independent prediction set of unknown bacterial strains. These results demonstrate the potential of UV-Vis-NIR spectroscopy to identify bacteria directly on agar plates.

农业工业感兴趣的生物过程涉及许多细菌种类。乳酸菌产生能够发酵食品并改变其感官特性的代谢物，促进植物生长的根际细菌可以作为农业中的生物肥料、生物刺激剂或生物防治剂。目前基于基因分型和表型的常规细菌鉴定技术方案需要特定的样品制备和破坏。本文介绍的工作详细介绍了一种在属和种水平上快速鉴定乳酸菌和根际细菌的方法。为了开发该方法，将细菌接种在琼脂培养基上，并通过近红外 (NIR) 和紫外-可见-近红外 (UV-Vis-NIR) 光谱进行分析。应用于 UV-Vis-NIR 光谱的人工神经网络模型在未知细菌菌株的独立预测集上正确识别了 70% (63%) 的乳酸菌和 67% 的根际细菌的属（种）。这些结果证明了 UV-Vis-NIR 光谱在直接在琼脂板上识别细菌的潜力。