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Predict Compositions and Mechanical Properties of Sugar Beet Using Hyperspectral Scattering

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Abstract

Sucrose, soluble solids, and moisture content and mechanical properties are important quality/property attributes of sugar beet. In this study, hyperspectral scattering images for the spectral region of 500–1000 nm were acquired, from which relative mean spectra were calculated. Prediction models were developed using partial least squares regression for both full spectra and selected wavelengths. The results showed that using relative mean spectra gave good predictions for the moisture, soluble solids, and sucrose content of beet slices with the correlations of 0.75–0.88 and the standard errors of prediction of 0.95–1.08 based on full-spectrum partial least squares regression (PLSR) models. PLSR models using wavelength selection with the uninformative variable elimination (UVE) method produced similar prediction accuracy. However, both modeling approaches gave poor predictions for the mechanical properties of beets with the correlation values of 0.46–0.63. The research demonstrated the potential of hyperspectral scattering imaging for measuring quality attributes of sugar beet.

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Acknowledgments

Authors Leiqing Pan, Qibing Zhu, and Kang Tu acknowledge the financial support from the Chinese National Foundation of Natural Science (31101282, 61275155), Special Fund for Agro-scientific Research in the Public Interest (201303088), and National Key Technology R&D Program (2015BAD19B03).

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Author notes

  1. Haiyan Cen

    Present address: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

Authors and Affiliations

  1. College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu, 210095, People’s Republic of China

    Leiqing Pan & Kang Tu

  2. USDA-ARS, Sugarbeet and Bean Research Unit, Michigan State University, 524 S. Shaw Lane, Room 224, East Lansing, MI, 48824-1323, USA

    Renfu Lu

  3. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, 1800 Lihu Road, 214122, Wuxi, People’s Republic of China

    Qibing Zhu

  4. Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, 48824, USA

    Haiyan Cen

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  1. Leiqing Pan
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Correspondence to Leiqing Pan.

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Pan, L., Lu, R., Zhu, Q. et al. Predict Compositions and Mechanical Properties of Sugar Beet Using Hyperspectral Scattering. Food Bioprocess Technol 9, 1177–1186 (2016). https://doi.org/10.1007/s11947-016-1710-5

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  • DOI: https://doi.org/10.1007/s11947-016-1710-5

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