Di Wu

Orcid: 0000-0002-4997-5082

Affiliations:
  • Zhejiang University, College of Biosystems Engineering and Food Science, Hangzhou, China


According to our database1, Di Wu authored at least 11 papers between 2006 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2022
Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds.
Sensors, 2022

2020
Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics.
Sensors, 2020

2018
E-Nose and GC-MS Reveal a Difference in the Volatile Profiles of White- and Red-Fleshed Peach Fruit.
Sensors, 2018

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of <i>Diaphania pyloalis</i> Larvae and Damage on Mulberry Leaves.
Sensors, 2018

2016
Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp.
Comput. Electron. Agric., 2016

2013
Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants.
Sensors, 2013

2009
Use of In-Situ Visible and Near-Infrared Spectroscopy for Non-invasive Discrimination of Spirulina Platensis.
Proceedings of the 2009 International Conference on Computational Intelligence and Security, 2009

2008
Application of Least-Square Support Vector Machines in Qualitative Analysis of Visible and Near Infrared Spectra: Determination of Species and Producing Area of Panax.
Proceedings of the Fourth International Conference on Natural Computation, 2008

2006
Selection of the Optimal Wavebands for the Variety Discrimination of Chinese Cabbage Seed.
Proceedings of the MICAI 2006: Advances in Artificial Intelligence, 2006

BP Neural Networks Combined with PLS Applied to Pattern Recognition of Vis/NIRs.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Fast Discrimination of Juicy Peach Varieties by Vis/NIR Spectroscopy Based on Bayesian-SDA and PCA.
Proceedings of the Intelligent Computing, 2006


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