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食品与发酵工业  2020, Vol. 46 Issue (7): 218-224    DOI: 10.13995/j.cnki.11-1802/ts.022689
  分析与检测 本期目录 | 过刊浏览 | 高级检索 |
融合近红外光谱和颜色参数的草莓可溶性固形物含量定量分析模型构建
蔡德玲1*, 唐春华2, 梁玉英1, 曾川1, 彭碧宁1
1(中华人民共和国拱北海关技术中心,广东 珠海,519000);
2(珠海城市职业技术学院,广东 珠海,519090)
Establishment of quantitative analysis model for detecting the soluble solids content in strawberry by merging near infrared spectroscopy and color parameters
CAI Deling1*, TANG Chunhua2, LIANG Yuying1, ZENG Chuan1, PENG Bining1
1(Technical Center of Gongbei Customs District P.R., Zhuhai 519000, China);
2(Zhuhai City Polytechnic, Zhuhai 519090, China)
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摘要 草莓可溶性固形物(soluble solids content, SSC)含量是评价草莓内部品质的关键指标。为了实现对该指标的快速、无损评估,基于近红外光谱技术,构建了线性偏最小二乘(partial least squares, PLS)和非线性最小二乘支持向量机(least squares support vector machine, LS-SVM)模型,联合蒙特卡罗无信息变量消除和连续投影算法(Monte-Carlo uninformative variable elimination,successive projections algorithm,MC-UVE-SPA)从原始光谱4 254个变量中提取了27个有效变量,并构建了基于有效变量的定量分析模型。同时,考虑到草莓表面颜色的影响,基于草莓RGB图像各分量获取了颜色特征参数,进一步融合光谱和颜色特征构建了多参数融合PLS和LS-SVM模型。基于相同的校正集和预测集,比较了所有模型对草莓内部SSC的预测性能。结果表明,MC-UVE-SPA是一种有效的草莓光谱变量选择算法,且多参数融合非线性LS-SVM模型是草莓内部SSC定量预测的最优模型。针对预测集样本,该模型相关系数RP和预测均方根误差(root mean square error of prediction, RMSEP)分别为0.988 5和0.153 2。该研究为基于近红外光谱技术的草莓可溶性固形物含量检测便携式仪器和在线检测设备研发奠定了基础。
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蔡德玲
唐春华
梁玉英
曾川
彭碧宁
关键词:  草莓  近红外光谱  可溶性固形物含量  参数融合  建模分析    
Abstract: Soluble solids content (SSC) is a key index to evaluate the quality of strawberries. In order to achieve the non-destructive evaluation of SSC, the near infrared spectroscopy was used to build the linear partial least squares (PLS) and nonlinear least squares support vector machine (LS-SVM) models. 27 effective variables were selected from the original 4 254 variables by combining both Monte-Carlo uninformative variable elimination and successive projections algorithm(MC-UVE-SPA). The quantitative analysis models were then established by using the selected effective variables. At the same time, color feature parameters were obtained based on components of RGB images of samples considering the influence of surface color on strawberries, and the multi-parameter PLS and LS-SVM models were constructed by further integrating spectra and color features. Based on the same correction set and prediction set, the prediction performance of all models for SSC was compared. The results showed that MC-UVE-SPA was an effective spectral variable selection algorithm, and the multi-parameter fusion nonlinear LS-SVM model was the optimal model for the quantitative prediction of SSC in strawberries. For samples in the prediction set, the correlation coefficient (RP) and root mean square error of prediction (RMSEP) of the model were 0.988 5 and 0.153 2, respectively. This study lays a foundation for the development of portable instruments and online detection equipment for the detection of soluble solids content in strawberries based on near infrared spectroscopy.
Key words:  strawberry    near infrared spectroscopy    soluble solids content    parameter compensation    modeling analysis
收稿日期:  2019-11-03                出版日期:  2020-04-15      发布日期:  2020-05-19      期的出版日期:  2020-04-15
基金资助: 中华人民共和国拱北海关科研项目(ZH2017-29)
作者简介:  硕士,工程师(本文通讯作者,E-mail:caidelingkitty@163.com)
引用本文:    
蔡德玲,唐春华,梁玉英,等. 融合近红外光谱和颜色参数的草莓可溶性固形物含量定量分析模型构建[J]. 食品与发酵工业, 2020, 46(7): 218-224.
CAI Deling,TANG Chunhua,LIANG Yuying,et al. Establishment of quantitative analysis model for detecting the soluble solids content in strawberry by merging near infrared spectroscopy and color parameters[J]. Food and Fermentation Industries, 2020, 46(7): 218-224.
链接本文:  
http://sf1970.cnif.cn/CN/10.13995/j.cnki.11-1802/ts.022689  或          http://sf1970.cnif.cn/CN/Y2020/V46/I7/218
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