Predict Compositions and Mechanical Properties of Sugar Beet Using Hyperspectral Scattering

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• 作者：Leiqing Pan ; Renfu Lu ; Qibing Zhu ; Kang Tu ; Haiyan Cen
• 刊名：Food and Bioprocess Technology
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：9
• 期：7
• 页码：1177-1186
• 全文大小：1,698 KB
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Food Science
  Chemistry
  Agriculture
  Biotechnology
  
• 出版者：Springer New York
• ISSN：1935-5149
• 卷排序：9

文摘

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.KeywordsHyperspectral scatteringSugar beetMoisture contentSoluble solids contentSucrose contentMechanical properties