Food Analytical Methods

, Volume 11, Issue 5, pp 1518–1527 | Cite as

Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis

  • Qiang Liu
  • Ke Sun
  • Jing Peng
  • Mengke Xing
  • Leiqing Pan
  • Kang Tu
Article
  • 88 Downloads

Abstract

Mechanical bruise and fungi contamination are two typical defective features for strawberries, resulting in quick quality deterioration of the strawberries during transportation and storage. In this work, the approach of combined image processing with spectra analysis was successfully developed to identify defective strawberries (bruised and fungal infected) using hyperspectral reflectance imaging system. Hyperspectral image data was exploited by minimum noise fraction (MNF) transformation for strawberry defects distinguished by combining thresholding and morphology procedures, and defective regions were located and separated for spectra extracting. The linkages between quality parameters and spectra features were established based on the target defective regions of the fruit. After spectra normalization, three different spectral regions (400 to 600 nm, 650 to 720 nm, and 900 to 1010 nm) were identified for healthy, bruised, or infected strawberries, and eight optimal wavelengths were selected by the successive projection algorithms (SPA) from the whole range of wavelengths. Both linear and non-linear algorithms were developed to identify defective types in strawberries. The results indicated that based on full wavelengths, SVM model performed the highest overall identification accuracy, with the accuracy of 96.91% for calibration and 92.59% for prediction of the fruit. This work shows that hyperspectral reflectance imaging technology has the potential for identifying defective strawberries and provides theoretical basis for the development of online classification of different defected fruits.

Keywords

Strawberry Hyperspectral imaging Infection Bruise 

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (31671925) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and 2017 graduate students’ innovation project in Jiangsu province (2017-1520).

Compliance with Ethical Standards

Conflict of Interest

Qiang Liu declares that he has no conflict of interest. Ke Sun declares that he has no conflict of interest. Jing Peng declares that she has no conflict of interest. Mengke Xing declares that she has no conflict of interest. Leiqing Pan declares that he has no conflict of interest. Kang Tu declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

Supplementary material

12161_2017_1136_MOESM1_ESM.jpg (399 kb)
Fig. S1 Loading plot of the PLS-DA models. (a) full wavelengths; (b) selected wavelengths. (Lv = latent variable) (JPEG 399 kb)
12161_2017_1136_MOESM2_ESM.jpg (564 kb)
Fig. S2 Optimization grid results for SVM models. (a) full wavelengths; (b) selected wavelengths. (Black label ‘X’ stands for selected parameters; the number on contours = misclassification ratio; yellow color □ = 1 (100% misclassification); red color □ = 0 (0% misclassification)) (JPEG 564 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Qiang Liu
    • 1
  • Ke Sun
    • 1
  • Jing Peng
    • 1
  • Mengke Xing
    • 1
  • Leiqing Pan
    • 1
  • Kang Tu
    • 1
  1. 1.College of Food Science and TechnologyNanjing Agricultural UniversityNanjingChina

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