TY - JOUR
T1 - Data-based prediction of microbial contamination in herbs and identification of optimal harvest parameters
AU - Anlauf, Stefan
AU - Haghofer, Andreas
AU - Dirnberger, Karl
AU - Winkler, Stephan
N1 - Funding Information:
The work presented in this article was sponsored by FFOQSI, the Austrian Competence Centre for Feed and Food Quality Safety and Innovation.
Publisher Copyright:
© 2021 Walter de Gruyter GmbH, Berlin/Boston.
PY - 2021/8/9
Y1 - 2021/8/9
N2 - 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).
AB - 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).
KW - data science
KW - herbs
KW - machine learning
KW - microbial contamination
KW - statistics
KW - data science
KW - herbs
KW - machine learning
KW - microbial contamination
KW - statistics
UR - http://www.scopus.com/inward/record.url?scp=85113302789&partnerID=8YFLogxK
U2 - 10.1515/ijfe-2021-0027
DO - 10.1515/ijfe-2021-0027
M3 - Article
SN - 1556-3758
VL - 18
SP - 171
EP - 183
JO - International Journal of Food Engineering
JF - International Journal of Food Engineering
IS - 3
M1 - 20210027
ER -