Food Analytical Methods

, Volume 12, Issue 1, pp 136–147 | Cite as

The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy

  • Dongyan Zhang
  • Lu Xu
  • Qingyan Wang
  • Xi Tian
  • Jiangbo LiEmail author


Soluble solid content (SSC) is one of the most important factors determining the quality and price of fresh fruits. However, the accurate assessment of SSC in some kinds of fruits with thick peel and large size is relatively difficult because it is important to select a suitable measurement position on this type of fruit. In this study, ‘Hami’ melon was used as the object of study, the visible and near-infrared (Vis-NIR) spectroscopy with spectral range of 550–950 nm was acquired from three positions (calyx, equator, and stem) of each sample to observe the effect of measurement positions on SSC assessment of whole ‘Hami’ melon. Three local models (calyx-region, equator-region, and stem-region models) and one global model based on the partial least squares (PLS) were developed with different preprocessing methods. Comparing all the established models, the results showed that the equator-region model and global model had the similar predictive performance which was better than ones of calyx-region and stem-region models. For improving the performance of models, the equator-region model and global model were further optimized based on different variable selection algorithms, including competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), combination algorithm CARS-SPA (successive projections algorithm), and combination algorithm UVE-SPA, respectively. And also, the linear multispectral PLS models and the nonlinear multispectral least squares support vector machine (LSSVM) models were established and compared using those selected characteristic variables, respectively. The results indicated that the performance of equator-region multispectral models was slightly superior to those of global multispectral models, and the optimal equator-region multispectral models were UVE-SPA-PLS (RP = 0.9143 and RMSEP = 0.8359) and CARS-SPA-LSSVM (RP = 0.9134 and RMSEP = 0.8958). The overall results indicated that it was feasible to develop the models using only the equator position information for detecting the SSC of whole ‘Hami’ melon. This study can provide some valuable references for building a fast and robust multispectral prediction model for SSC assessment in some kinds of fruits with thick peel and large size such as watermelon.


Vis-NIR spectroscopy Melon Soluble solid content (SSC) Optimal detection position selection 



This study received financial support provided by the Science and technology innovation ability construction project of Beijing Academy of agriculture and Forestry Science (Project No. KJCX20170417), Beijing Nova program (No. Z171100001117035), the National Natural Science Foundation of China (Project No. 31772052) and (Project No. 41771463), and the National Engineering Laboratory for Agri-product Quality Traceability (Project No. PT2018-21).

Compliance with Ethical Standards

Conflict of Interest

Dongyang Zhang declares that he has no conflict of interest. Lu Xu declares that he has no conflict of interest. Qingyan Wan declares that he has no conflict of interest. Xi Tian declares that he has no conflict of interest. Jiangbo Li declares that he has no conflict of interest.

Ethical Approval

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

Informed Consent

Not applicable.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Dongyan Zhang
    • 1
  • Lu Xu
    • 1
    • 2
  • Qingyan Wang
    • 2
  • Xi Tian
    • 2
  • Jiangbo Li
    • 2
    Email author
  1. 1.Key laboratory of intelligent computing & signal processing, Ministry of EducationAnhui UniversityHefeiChina
  2. 2.Beijing Research Center of Intelligent Equipment for AgricultureBeijingChina

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