Data-based prediction of microbial contamination in herbs and identification of optimal harvest parameters
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Stefan Anlauf
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Andreas Haghofer
Abstract
The quality of freshly harvested herbs is affected by several crucial factors, such as weather, tillage, fertilization, drying, and the harvesting process, e.g. Our main goal is to learn models that are able to predict spore contaminations in different types of herbs on the basis of information about the harvesting process, transport conditions, drying, and storage conditions. This shall enable us to identify optimal processing parameters, which will allow more effective and cost efficient contamination prevention. Using machine learning, we have generated ensembles of models that predict the risk for spore contamination on the basis of harvest processing parameters. The training information about contamination in herbs is given as results of laboratory analysis data. We applied different modeling algorithms (random forests, gradient boosting trees, genetic programming, and neural networks). In this paper we report on modeling results for yeast and mold contaminations in peppermint and nettle; e.g., for yeast contamination in peppermint we obtained models with 78.13% accuracy. Additionally, we use descriptive statistics to identify those parameters that have a statistically significant influence on the contamination; for example, our analysis shows that there seems to be a relationship between mold in peppermint and information about harrowing and the growth height (p = 0.001).
Acknowledgment
The work presented in this article was sponsored by FFOQSI, the Austrian Competence Centre for Feed and Food Quality Safety and Innovation.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Articles in the same Issue
- Frontmatter
- Editorial
- Special issue “Selected papers from the International Food Operations & Processing Simulation Workshop – sixth edition”
- Articles
- Data-based prediction of microbial contamination in herbs and identification of optimal harvest parameters
- Are consumers aware of products’ environmental impacts? Different results between life cycle assessment data and consumers’ opinions: the case study of organic Parmigiano Reggiano and its packaging
- Benefits and effectiveness of high pressure processing on cheese: a ricotta case study
- Simulation of different reordering policies for optimizing the inventory of perishable food: an Italian case study
- The vehicle routing problem in the dairy sector: a case study
Articles in the same Issue
- Frontmatter
- Editorial
- Special issue “Selected papers from the International Food Operations & Processing Simulation Workshop – sixth edition”
- Articles
- Data-based prediction of microbial contamination in herbs and identification of optimal harvest parameters
- Are consumers aware of products’ environmental impacts? Different results between life cycle assessment data and consumers’ opinions: the case study of organic Parmigiano Reggiano and its packaging
- Benefits and effectiveness of high pressure processing on cheese: a ricotta case study
- Simulation of different reordering policies for optimizing the inventory of perishable food: an Italian case study
- The vehicle routing problem in the dairy sector: a case study
