Next Article in Journal
Secure Localization in the Presence of Colluders in WSNs
Next Article in Special Issue
Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images
Previous Article in Journal
Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
Previous Article in Special Issue
A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1894;

Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Institute of Quality and Standard for Agro-Products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Author to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 8 August 2017 / Accepted: 13 August 2017 / Published: 17 August 2017
(This article belongs to the Special Issue Sensors in Agriculture)
Full-Text   |   PDF [3360 KB, uploaded 18 August 2017]   |  


There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS–DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS–DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling. View Full-Text
Keywords: classification; NIR hyperspectral imaging; chemometrics analysis classification; NIR hyperspectral imaging; chemometrics analysis

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Feng, X.; Zhao, Y.; Zhang, C.; Cheng, P.; He, Y. Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis. Sensors 2017, 17, 1894.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top