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Reliable and Rapid Identification of Listeria monocytogenes and Listeria Species by Artificial Neural Network-Based Fourier Transform Infrared Spectroscopy

机译:基于人工神经网络的傅里叶变换红外光谱法快速可靠地鉴定李斯特菌和李斯特菌种类

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摘要

Differentiation of the species within the genus Listeria is important for the food industry but only a few reliable methods are available so far. While a number of studies have used Fourier transform infrared (FTIR) spectroscopy to identify bacteria, the extraction of complex pattern information from the infrared spectra remains difficult. Here, we apply artificial neural network technology (ANN), which is an advanced multivariate data-processing method of pattern analysis, to identify Listeria infrared spectra at the species level. A hierarchical classification system based on ANN analysis for Listeria FTIR spectra was created, based on a comprehensive reference spectral database including 243 well-defined reference strains of Listeria monocytogenes, L. innocua, L. ivanovii, L. seeligeri, and L. welshimeri. In parallel, a univariate FTIR identification model was developed. To evaluate the potentials of these models, a set of 277 isolates of diverse geographical origins, but not included in the reference database, were assembled and used as an independent external validation for species discrimination. Univariate FTIR analysis allowed the correct identification of 85.2% of all strains and of 93% of the L. monocytogenes strains. ANN-based analysis enhanced differentiation success to 96% for all Listeria species, including a success rate of 99.2% for correct L. monocytogenes identification. The identity of the 277-strain test set was also determined with the standard phenotypical API Listeria system. This kit was able to identify 88% of the test isolates and 93% of L. monocytogenes strains. These results demonstrate the high reliability and strong potential of ANN-based FTIR spectrum analysis for identification of the five Listeria species under investigation. Starting from a pure culture, this technique allows the cost-efficient and rapid identification of Listeria species within 25 h and is suitable for use in a routine food microbiological laboratory.
机译:李斯特氏菌属中的物种分化对食品工业很重要,但到目前为止,仅有几种可靠的方法可用。尽管许多研究已经使用傅里叶变换红外(FTIR)光谱法来鉴定细菌,但是从红外光谱中提取复杂的图案信息仍然很困难。在这里,我们应用人工神经网络技术(ANN),这是一种先进的模式分析多元数据处理方法,可以在物种水平上识别李斯特菌的红外光谱。基于全面的参考光谱数据库,基于李斯特氏菌FTIR光谱的ANN分析,创建了一个分层分类系统,该数据库包括243个明确定义的单核细胞增生李斯特菌,无毒李斯特菌,伊万诺维氏菌,seeligeri和L. welshimeri的参考菌株。同时,开发了单变量FTIR识别模型。为了评估这些模型的潜力,收集了一组277种不同地理起源的菌株,但未包括在参考数据库中,并用作物种歧视的独立外部验证。单变量FTIR分析可以正确鉴定所有菌株的85.2%和单核细胞增生李斯特菌菌株的93%。基于人工神经网络的分析将所有李斯特菌物种的分化成功率提高到96%,包括正确鉴定单核细胞增生李斯特菌的成功率达到99.2%。还使用标准表型API李斯特菌系统确定了277菌株测试集的身份。该试剂盒能够鉴定88%的测试分离株和93%的单核细胞增生李斯特菌菌株。这些结果证明了基于ANN的FTIR光谱分析用于鉴定所研究的五个李斯特菌物种的高可靠性和强大潜力。从纯培养开始,此技术可在25小时内经济高效地快速鉴定李斯特菌,适用于常规食品微生物实验室。

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