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Fast detection of water loss and hardness for cucumber using hyperspectral imaging technology

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Abstract

Hardness and water loss are the most important determining factors of the freshness of fruits and vegetables. In order to solve the defects of traditional detection methods, Hyperspectral imaging technology was investigated for fast determination of hardness and water loss of cucumber. The standard normal variate and Savitzky-Golay smoothing preprocessing methods were compared, and then optimal wavelengths were selected by competitive adaptive weighting sampling (CARS). 29 characteristic wavelengths for hardness and 42 characteristic wavelengths for water loss were selected by CARS, respectively. The partial least squares regression (PLSR) prediction models were developed based on the optimal characteristic wavelengths and the full spectrum, respectively. The results of the hardness and water loss PLSR model based on the optimal wavelengths (R2 = 0.9420 and RMSE = 19.5088; R2 = 0.8218 and RMSE = 1.0132) were better than those based on the full bands. Furthermore, visualized maps of hardness and water loss were built based on the generated model function, showing that the hardness and water loss change with prolonged storage time.

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Acknowledgements

This work is supported by the National Key R&D Program of China under Grant No.2017YFC1600802 and the Research on Key Technologies for Improving the Quality of Agricultural Products in the Machine Vision System of Henan Province Key Research Project under Grant No.172102210256.The authors acknowledge the support.

Funding

This work was supported by the National Key R&D Program of China and the Research on Key Technologies for Improving the Quality of Agricultural Products in the Machine Vision System of Henan Province Key Research Project (Grant Numbers [No.2017YFC1600802] and [No.172102210256]).

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Authors and Affiliations

  1. College of Food & Bioengineering, Henan University of Science & Technology, Luoyang, 471023, China

    Ying Li, Yong Yin, Huichun Yu & Yunxia Yuan

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  1. Ying Li
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  2. Yong Yin
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  3. Huichun Yu
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  4. Yunxia Yuan
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Contributions

YL: Methodology, data processing, and analysis, writing-original draft, experiment. YY: Experiment, supervision. HY: Methodology, writing-review and editing, supervision. YY: Writing-review and editing, data processing.

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Correspondence to Huichun Yu.

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Li, Y., Yin, Y., Yu, H. et al. Fast detection of water loss and hardness for cucumber using hyperspectral imaging technology. Food Measure 16, 76–84 (2022). https://doi.org/10.1007/s11694-021-01130-2

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  • DOI: https://doi.org/10.1007/s11694-021-01130-2

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