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Visualization of Protein in Peanut Using Hyperspectral Image with Chemometrics

文献类型： 外文期刊

作者： Yu Hong-wei 1 ; Wang Qiang 1 ; Shi Ai-min 1 ; Yang Ying 1 ; Liu Li 1 ; Hu Hui 1 ; Liu Hong-zhi 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Food Sci & Technol, Beijing 100193, Peoples R China; Minist Agr, Key Lab Agroprod Proc, Beijing 100193, Peoples R China

关键词： Hyperspectral imaging (HSI);Chemometrics;Peanut;Protein content;Visualization

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2017 年 37 卷 3 期

页码：

收录情况： SCI

摘要： The study aims to explore the potential of hyperspectral imaging (HSI) with chemometrics for rapidly and non-invasively visualizing the spatial distribution of protein content which can affect the quality of peanut products as a critical component of peanut. Spectral data contained in the region of interest (ROI) of the corrected hyperspectral images of peanut were extracted and protein contents were measured with conventional chemical method. By comparing different pretreatments and modeling algorithms, the second-order derivatives (2nd-der) on spectra is optimal pretreatment, and partial ceast square (PLS) is the best regression method. Based on the pretreatment spectra and the measured protein content model, a good performance model (R-c = 0. 91, SEC=0. 86; R-p =0. 86, SEP= O. 69) was built with full wavelengths. The fourteen optimal wavelengths were carried out based on the regression coefficients (R-c) of the established PLS model. Then, using optimal wavelengths built RC-PLS model which show resembling performance (R-c =0. 86, SEC=1. 03; R-p =0. 80, SEP= 0. 77). At last, an imaging processing algorithm was developed to transfer each pixel in peanut to protein content with the 2nd-der-RC-PLS model. There was no significant difference between Kjeldahl and HSI method by the paired test. The result demonstrated the capacity of HSI in combination with chemometrics for fast and non-destructively determining protein content in peanut.

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