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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Crop and Product Physiology
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1500819
This article is part of the Research Topic Non-Destructive Quality Assessment and Intelligent Packaging of Agricultural Products View all 4 articles

Detection of soluble solids content in tomatoes using full transmission Vis-NIR spectroscopy and combinatorial algorithms

Provisionally accepted
Letian Cai Letian Cai Liping Chen Liping Chen Yizhi Zhang Yizhi Zhang Zhonglei Cai Zhonglei Cai Ruiyao Shi Ruiyao Shi Li Sheng Li Sheng Jiangbo Li Jiangbo Li *
  • Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China

The final, formatted version of the article will be published soon.

    Soluble solids content (SSC) is an important indicator for evaluating tomato flavor, and general physical and chemical methods are time-consuming and destructive. This study utilized full transmittance visible and near infrared (Vis-NIR) spectroscopy for multi-posed data acquisition of tomatoes in different orientations. The role of two directions (Z1 and Z2) and four preprocessing techniques, as well as three wavelength selection methods in the exploitation of SSC regression models was investigated. After using the Outlier elimination method, the spectra acquired in the Z2 direction and the raw spectral data processed by preprocessing methods gave the best result by the PLSR model (𝑅 𝑝 = 0.877, RMSEP = 0.417 %). Compared to the model built using the full 2048 spectral wavelengths, the prediction accuracy using 20 wavelengths obtained by a combination wavelength selection: backward variable selection -partial least squares and simulated annealing (BVS-PLS and SA) was further improved (𝑅 𝑝 = 0.912, RMSEP = 0.354 %). The findings of this research demonstrate the efficacy of full-transmission visible-near infrared (Vis-NIR) spectroscopy in forecasting SSC of tomatoes, and most importantly, the combination of the packing method in wavelength selection with an intelligent optimization algorithm provides a viable idea for accurately and rapidly assessing the SSC of tomatoes.

    Keywords: Tomato, Online detection, Feature Selection, Internal quality assessment, Soluble solids content

    Received: 24 Sep 2024; Accepted: 24 Oct 2024.

    Copyright: © 2024 Cai, Chen, Zhang, Cai, Shi, Sheng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jiangbo Li, Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.