Food Analytical Methods

, Volume 10, Issue 6, pp 1721–1726 | Cite as

Rapid Assessment of Tomato Ripeness Using Visible/Near-Infrared Spectroscopy and Machine Vision

  • Huishan Lu
  • Fujie Wang
  • Xiulin Liu
  • Yuanyuan Wu


A non-destructive assessment using visible/near-infrared spectroscopy and machine vision has been investigated for measuring tomato ripeness. Relationship between spectral wavelengths and green grayscale value was evaluated by application of chemometrics techniques based on partial least squares (PLS) regression. The tomatoes were divided randomly into two groups: 170 fruits for calibration and 71 for prediction. An accurate estimation, measured with a correlation coefficient of 0.992 and root mean square errors of prediction (RMSEP) of 9.92, was obtained when using the developed PLS model built with 550–750 nm spectral range. The accuracies of calibration and validation models based on data measured in this band were 90.93 and 90.05%. The prediction accuracy for 150 external independent samples was 90.67%. The results show that it is possible to realize detection standardization of tomato maturity based on only visible spectroscopy (550–750 nm) and machine vision technologies. This detection method does not depend on a visual grading or other maturity indices as a reference. It highlights the potential of the method to determine tomato ripeness and the optimum harvest time.


Detection standardization Tomato Ripeness Visible/near-infrared spectroscopy Machine vision 


Compliance with Ethical Standards


This study was funded by the Scientific and Technological Programs in Shanxi Province (No. 20150311023-2) and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi Province (No. 180012-117).

Conflict of Interest

Huishan Lu declares that he has no conflict of interest. Fujie Wang declares that he has no conflict of interest. Xiulin Liu declares that he has no conflict of interest. Yuanyuan Wu declares that she 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

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Huishan Lu
    • 1
  • Fujie Wang
    • 1
  • Xiulin Liu
    • 1
  • Yuanyuan Wu
    • 1
  1. 1.School of Mechanical and Power EngineeringNorth University of ChinaTaiyuanChina

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