Modelling Bathing Water Quality Using Official Monitoring Data

Džal, Daniela; Kosović, Ivana Nižetić; Mastelić, Toni; Ivanković, Damir; Puljak, Tatjana; Jozić, Slaven

doi:10.3390/w13213005

Cited by 10 publications

(8 citation statements)

References 28 publications

(44 reference statements)

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“…1 and 2 ). The feed-forward NN were trained by using the ‘nnet’ R package (version 7.3-17), with hyperparameters set as the same as Arbajian et al (2019) 65 and Džal et al (2021) 66 . The ‘nnet’ fits a feed-forward NN with a single hidden layer.…”

Section: Methodsmentioning

confidence: 99%

Alignment-free microbiome-based classification of fresh produce safety and quality

Liao¹,

Wang²,

Quon

2022

Preprint

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Small samples sizes and loss of up to 50-70% of sequencing data during the data denoising step of preprocessing can limit the statistical power of fresh produce microbiome analyses and prevent detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored an alignment-free analysis strategy using k-mer hashes to identify DNA signatures predictive of produce safety and produce quality, and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step. Random forests (RF)-based classifiers for fresh produce safety and quality using 7-mer hash datasets had significantly higher classification accuracy than those using the ASV datasets. We also demonstrated that the proposed combination of integrating multiple datasets and leveraging an alignment-free 7-mer hash strategy leads to better classification performance for fresh produce safety and quality. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing datasets for the application of machine learning in the area of microbial safety and quality of food.

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Section: Methodsmentioning

confidence: 99%

Alignment-free microbiome-based classification of fresh produce safety and quality

Liao¹,

Wang²,

Quon

2022

Preprint

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“…One of the examples is the replacement of a pyranometer (which is the important part of the smart building managing systems) with the low-cost set of sensors and software [17]. Another example is an early estimation of seawater quality, replacing a complex microbiological analysis of the seawater sample [18]. In many cases, they are not called "micromodels" explicitly, but the concept remains the same.…”

Section: B Related Workmentioning

confidence: 99%

In Search of Micromodels: Black-box Evaluation of Spatio-temporal Models in Normal and Extreme Conditions

Kosović

Mastelić

Sokol

et al. 2022

JCOMSS

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Spatio-temporal modelling is an emerging research area due to the increasing availability of sensor data collected across space and time. The models are build either with a modeldriven or data-driven approach. The former often results in complex monolith models that are not suitable for lightweight Edge deployment. The latter requires a vast amount of data and may not provide an overall good performance. Consequently, the data-driven approach is being used to substitute only parts of model-driven outputs, by creating micromodels that tackle specific scenarios. The main contribution of this paper is a definition and demonstration of the process for finding such scenarios for which a spatio-temporal model could be improved or replaced by a micromodel and deployed on Edge. The process is demonstrated on an example of a Numerical Weather Prediction model (NWP), namely its outputs of temperature and precipitation. NWP is evaluated using black-box testing considering the specificity of spatial and temporal components, in both normal and extreme conditions. The novelty of this process is its ability to highlight weaknesses of the existing expert models and suggest scenarios in which the models can be improved and deployed on the Edge.

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“…k values ranging from 1 to 10 were tested in order to identify k values with the best classification performance (Figures S1 and S2). The feed-forward NN were trained by using the 'nnet' R package (version 7.3-17), with hyperparameters set as the same as Arbajian et al (2019) [35] and Džal et al (2021) [36]. The 'nnet' fits a feed-forward NN with a single hidden layer.…”

Section: Classification Of Fresh Produce Safety and Quality Samplesmentioning

confidence: 99%

Alignment-free microbiome-based classification of fresh produce safety and quality

Liao

Wang

Quon

2022

Preprint

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Background: While next generation sequencing has enriched our knowledge about native microbial populations present in fresh produce, the loss of up to 50-70% of data during the alignment and denoising steps of data preprocessing may lead to the missing of important bacterial species information and decrease our ability to identify species associated with poor produce quality and contamination. Microbial studies are also often limited by small sample sizes, making generalization of results beyond individual studies difficult. Results: In this study, we explored separate strategies to mitigate the challenges of data preprocessing and small sample sizes. First, we explored an alignment-free analysis strategy using k-mer hashes to identify DNA signatures predictive of produce safety (contaminated vs. non-contaminated) and produce quality (good-quality vs. decreasing-quality), and compared it against the amplicon sequence variant (ASV) strategy that uses a typical alignment and denoising step. Random forests (RF)-based classifiers were trained on publicly available fresh produce microbiome datasets with data preprocessed using either the k-mer hash or ASV approach. RF-based classifiers for fresh produce safety and quality using 7-mer hash datasets had significantly higher classification accuracy than those using the ASV datasets, supporting the hypothesis that data preprocessing strategies that keep more data (k-mer hash) retain more useful information about bacterial species than approaches that lose data during preprocessing (ASV). We also demonstrated that integrating multiple datasets together also led to higher classification accuracy compared to those trained with individual datasets. Integrated datasets also enabled the identification of more consistent and generalizable biomarkers (ASV, 7-mer hash, or bacterial taxa) associated with fresh produce safety and quality. Conclusions:The proposed combination of integrating multiple datasets and leveraging an alignment-free 7-mer hash strategy substantially mitigates the loss of sequencing data due to the ASV denoising step and leads to better classification performance for fresh produce safety and quality. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing datasets (generated from different studies or different laboratories) for the application of machine learning in the area of microbial safety and quality of food.

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Modelling Bathing Water Quality Using Official Monitoring Data

Cited by 10 publications

References 28 publications

Alignment-free microbiome-based classification of fresh produce safety and quality

Alignment-free microbiome-based classification of fresh produce safety and quality

In Search of Micromodels: Black-box Evaluation of Spatio-temporal Models in Normal and Extreme Conditions

Alignment-free microbiome-based classification of fresh produce safety and quality

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