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Food Analytical Methods

, Volume 12, Issue 4, pp 914–925 | Cite as

Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms

  • Lei Feng
  • Min ZhangEmail author
  • Benu Adhikari
  • Zhimei Guo
Article

Abstract

The aim of this study was to assess the applicability of a portable NIR spectroscopy system and chemometric algorithms in intelligently detecting postharvest quality of cherry tomatoes. The postharvest quality of cherry tomatoes was evaluated in terms of firmness, soluble solids content (SSC), and pH, and a portable NIR spectrometer (950–1650 nm) was used to obtain the spectra of cherry tomatoes. Partial least square (PLS), support vector machine (SVM), and extreme learning machine (ELM) were applied to predict the postharvest quality of cherry tomatoes from their spectra. The effects of different preprocessing techniques, including Savitzky–Golay (S-G), multiplicative scattering correction (MSC), and standard normal variate (SNV) on prediction performance were also evaluated. Firmness, SSC and pH values of cherry tomatoes decreased during storage period, based on which the tomato samples could be classified into two distinct clusters. Similarly, cherry tomatoes with different storage time could also be separated by the NIR spectroscopic characteristics. The best prediction accuracy was obtained from ELM algorithms using the raw spectra with Rp2, RMSEP, and RPD values of 0.9666, 0.3141 N, and 5.6118 for firmness; 0.9179, 0.1485%, and 3.6249 for SSC; and 0.8519, 0.0164, and 2.7407 for pH, respectively. Excellent predictions for firmness and SSC (RPD value greater than 3.0), good prediction for pH (RPD value between 2.5 and 3.0) were obtained using ELM model. NIR spectroscopy is capable of intelligently detecting postharvest quality of cherry tomatoes during storage.

Keywords

Cherry tomato Near infrared spectroscopy Partial least square Support vector machine Extreme learning machine 

Notes

Funding

This study was funded by the National Key R&D Program of China (No. 2018YFD0700303), Jiangsu Province (China) Key Project in Agriculture (Contract No. BE2015310217), National First-Class Discipline Program of Food Science and Technology (No. JUFSTR20180205), and Jiangsu Province Key Laboratory Project of Advanced Food Manufacturing Equipment and Technology (No. FMZ201803).

Compliance with Ethical Standards

Conflict of Interest

Lei Feng declares that she has no conflict of interest. Min Zhang declares that he has no conflict of interest. Benu Adhikari declares that he has no conflict of interest. Zhimei Guo 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

Not applicable.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Food Science and TechnologyJiangnan UniversityWuxiChina
  2. 2.Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and TechnologyJiangnan UniversityWuxiChina
  3. 3.School of Food Science and TechnologyJiangnan UniversityWuxiChina
  4. 4.School of Science-RMIT UniversityMelbourneAustralia
  5. 5.Wuxi Haihe Equipment Co.WuxiChina

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