利用微生物枪式元基因组学数据开发和评估统计与人工智能方法,作为食品生产中使用的非目标筛选工具。

IF 5 2区 生物学 Q1 MICROBIOLOGY mSystems Pub Date : 2024-10-10 DOI:10.1128/msystems.00840-24
Kristen L Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda
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引用次数: 0

摘要

随着人们对食品中与质量和安全有关的微生物生态学知识的不断增加,以及机器学习算法在多种情况下用于异常检测的实用性的确立,在食品生产系统中应用微生物组数据进行异常检测可能是一种有价值的方法,可用于食品系统中。这些方法可用于识别偏离其典型微生物组成的配料,这可能预示着食品欺诈或安全问题。本研究的目的是以液态奶为模型系统,评估将枪式测序数据作为异常检测算法输入的可行性。研究人员评估了对比主成分分析(PCA)、基于聚类的方法和可解释人工智能(AI),以利用液态奶的纵向元基因组图谱检测两类异常样本,并与在类似情况下收集的基线(BL)样本进行比较。传统方法(α和β多样性、基于聚类的对比 PCA、多维缩放和树枝图)无法区分异常样本类别;但是,可解释人工智能能够将异常样本与基线样本进行分类,并指出与抗生素使用相关的微生物驱动因素。我们利用更大规模的公开液态奶 16S rDNA 测序数据集验证了可解释人工智能对不同奶源进行分类的潜力,并证明可解释人工智能能够区分牛奶的储存方法、加工阶段和季节。我们的研究结果表明,人工智能的应用在微生物组数据分析领域仍大有可为,并为下游分析自动化提供了更多机会,从而有助于食品安全和质量:我们在一项专为测试这一假设而设计的研究中评估了利用人工智能方法对原奶进行非靶向元基因组测序以检测异常食品成分含量的可行性。我们还通过对公开的液态奶微生物数据的分析表明,我们的人工智能方法能够成功预测处于不同加工阶段的牛奶。这种方法有可能应用于食品行业的安全和质量控制。
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Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.

The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive principal component analysis (PCA), cluster-based methods, and explainable artificial intelligence (AI) were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline (BL) samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, multidimensional scaling, and dendrograms) failed to differentiate anomalous sample classes; however, explainable AI was able to classify anomalous vs baseline samples and indicate microbial drivers in association with antibiotic use. We validated the potential for explainable AI to classify different milk sources using larger publicly available fluid milk 16S rDNA sequencing data sets and demonstrated that explainable AI is able to differentiate between milk storage methods, processing stages, and seasons. Our results indicate that the application of artificial intelligence continues to hold promise in the realm of microbiome data analysis and could present further opportunities for downstream analytic automation to aid in food safety and quality.

Importance: We evaluated the feasibility of using untargeted metagenomic sequencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control.

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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
期刊最新文献
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