Next Article in Journal
Local Oxidation Nanolithography on Metallic Transition Metal Dichalcogenides Surfaces
Next Article in Special Issue
Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce
Previous Article in Journal
Design of Cold-Formed Steel Screw Connections with Gypsum Sheathing at Ambient and Elevated Temperatures
Previous Article in Special Issue
Distinguishing Bovine Fecal Matter on Spinach Leaves Using Field Spectroscopy
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(9), 249; doi:10.3390/app6090249

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

1
College of Food Science and Technology, Nanjing Agricultural University, NO.1 Weigang Road, Nanjing 210095, China
2
College of Science, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
3
College of Life Science, Tarim University, Alar 843300, China
4
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kuanglin Kevin Chao
Received: 22 June 2016 / Revised: 28 July 2016 / Accepted: 1 September 2016 / Published: 6 September 2016
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
View Full-Text   |   Download PDF [1420 KB, uploaded 6 September 2016]   |  

Abstract

Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%–100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves. View Full-Text
Keywords: hyperspectral transmittance imaging; black heart; detection; chemometric analysis; successive projections algorithm hyperspectral transmittance imaging; black heart; detection; chemometric analysis; successive projections algorithm
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Song, D.; Song, L.; Sun, Y.; Hu, P.; Tu, K.; Pan, L.; Yang, H.; Huang, M. Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm. Appl. Sci. 2016, 6, 249.

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

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top