尽管MALDI-TOF质谱(MS)被广泛认为是一种快速且具有成本效益的微生物鉴定参考方法,其商业数据库在准确区分双歧杆菌的特定亚种方面面临局限性。本研究旨在探索MALDI-TOFMS蛋白谱的潜力,再加上预测方法,区分长双歧杆菌亚种。婴儿(B.婴儿)和长双歧杆菌亚种。longum(B.longum)。调查涉及59个长芽孢杆菌菌株和41个婴儿芽孢杆菌菌株的质谱分析,导致识别五个不同的生物标志物峰,特别是在m/z2,929、4,408、5,381、5,394和8,817,使用循环特征消除(RFE)。为了便于根据质谱对长芽孢杆菌和婴儿芽孢杆菌进行分类,开发了机器学习模型,采用逻辑回归(LR)等算法,随机森林(RF),和支持向量机(SVM)。质谱数据的评估表明,RF模型表现出最高的性能,拥有令人印象深刻的AUC0.984。该模型在准确性和灵敏度方面优于其他算法。此外,在对多质谱数据采用投票机制进行应变识别时,RF模型达到了96.67%的最高精度。这项研究的结果具有商业应用的巨大潜力,使用MALDI-TOFMS与机器学习相结合,可以快速准确地辨别长B.此外,这项研究中提出的方法对各个行业都有重大影响,比如益生菌和药品,特定亚种的精确区分对于产品开发和质量控制至关重要。
Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.