Xinxin Wang

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Dive into the research topics where Xinxin Wang is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Profiles

3 Article
1 Chapter
1 internal PhD, WU

Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk-based monitoring of heavy metals
Wang, X., Bouzembrak, Y., Oude Lansink, A. G. J. M. & van der Fels-Klerx, H. J., 2 Mar 2023, (E-pub ahead of print) In: Risk Analysis. 13 p., 14120.
Research output: Contribution to journal › Article › Academic › peer-review

Open Access
Application of machine learning to the monitoring and prediction of food safety: A review
Wang, X., Bouzembrak, Y., Oude Lansink, A. & van der Fels-Klerx, H. J., 2022, In: Comprehensive Reviews in Food Science and Food Safety. 21, 1, p. 416-434
Research output: Contribution to journal › Article › Academic › peer-review

Open Access
29 Citations (Scopus)
Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning
Wang, X., 2022, Wageningen: Wageningen University. 167 p.
Research output: Thesis › internal PhD, WU

Open Access
Regional prediction of multi-mycotoxin contamination of wheat in Europe using machine learning
Wang, X., Liu, C. & van der Fels-Klerx, H. J., Sept 2022, In: Food Research International. 159, 111588.
Research output: Contribution to journal › Article › Academic › peer-review

Open Access
2 Citations (Scopus)
The Interplay Between Roots and Arbuscular Mycorrhizal Fungi Influencing Water and Nutrient Acquisition and Use Efficiency
Kuyper, T. W., Wang, X. & Muchane, M. N., 29 Apr 2021, The Root Systems in Sustainable Agricultural Intensification. Rengel, Z. & Djalovic, I. (eds.). Wiley, p. 193-220 28 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Open Access
10 Citations (Scopus)

Arbuscular mycorrhizal symbiosis increases P uptake and productivity of mixtures of maize varieties compared to monocultures
Feng, G. (Creator), Wang, X. (Creator), Hoffland, E. (Creator), Feng, G. (Creator) & Kuijper, T. (Contributor), 1 Jan 2020
DOI: 10.5061/dryad.s1rn8pk5g
Dataset

Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning
Wang, X., Oude Lansink, A., van der Fels-Klerx, I. & Bouzembrak, Y.
15/10/18 → 19/12/22
Project: PhD

Xinxin Wang, MSc

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Collaborations and top research areas from the last five years

Dive into details

Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk-based monitoring of heavy metals

Application of machine learning to the monitoring and prediction of food safety: A review

Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning

Regional prediction of multi-mycotoxin contamination of wheat in Europe using machine learning

The Interplay Between Roots and Arbuscular Mycorrhizal Fungi Influencing Water and Nutrient Acquisition and Use Efficiency

Arbuscular mycorrhizal symbiosis increases P uptake and productivity of mixtures of maize varieties compared to monocultures

Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning

Xinxin Wang, MSc

Fingerprint

Collaborations and top research areas from the last five years

Research output

Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk-based monitoring of heavy metals

Application of machine learning to the monitoring and prediction of food safety: A review

Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning

Regional prediction of multi-mycotoxin contamination of wheat in Europe using machine learning

The Interplay Between Roots and Arbuscular Mycorrhizal Fungi Influencing Water and Nutrient Acquisition and Use Efficiency

Datasets

Arbuscular mycorrhizal symbiosis increases P uptake and productivity of mixtures of maize varieties compared to monocultures

Projects

Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning