[1]仇逊超,张春越,张怡卓,等.流形学习在红松籽仁蛋白质含量近红外检测中的应用[J].江苏农业学报,2023,(01):246-254.[doi:doi:10.3969/j.issn.1000-4440.2023.01.028]
　QIU Xun-chao,ZHANG Chun-yue,ZHANG Yi-zhuo,et al.Application of manifold learning in quantitative detection of protein in Korean pine seed kernels using near-infrared quantitative detection[J].,2023,(01):246-254.[doi:doi:10.3969/j.issn.1000-4440.2023.01.028]
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流形学习在红松籽仁蛋白质含量近红外检测中的应用()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年01期
页码:
246-254
栏目:
加工贮藏·质量安全
出版日期:
2023-02-28

文章信息/Info

Title:
Application of manifold learning in quantitative detection of protein in Korean pine seed kernels using near-infrared quantitative detection
作者:
仇逊超1; 2; 张春越1; 张怡卓2; 曹军2
(1.哈尔滨金融学院计算机系,黑龙江哈尔滨150030；2.东北林业大学机电工程学院,黑龙江哈尔滨150040)
Author(s):
QIU Xun-chao1; 2; ZHANG Chun-yue1; ZHANG Yi-zhuo2; CAO Jun2
(1.Department of Computer Engineering, Harbin Finance University, Harbin 150030, China;2.College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)
关键词:
红松籽仁; 蛋白质; 流形学习; 近红外光谱
Keywords:
Korean pine seed kernel; protein; manifold learning; near-infrared spectroscopy
分类号:
TS255.6
DOI:
doi:10.3969/j.issn.1000-4440.2023.01.028
文献标志码:
A
摘要:
为研究检测红松籽仁蛋白质含量的近红外光谱分析技术，在用变量标准化校正+一阶导数+小波变换对原始光谱进行预处理的基础上，分别运用主成分分析、改进型局部线性嵌入、局部切空间对齐、黑塞特征映射进行光谱数据的降维处理，分别构建偏最小二乘、岭回归、支持向量回归、极度梯度提升数学模型。结果表明，改进型局部线性嵌入+支持向量回归法建立的参数优化模型质量最佳。其降维方法优化参数为：维度取4，邻域数取50；验证集均方差均值为0.568 1，验证集皮尔逊相关系数均值达0.940 8。可见，模型的预测结果是可靠的，能够实现对红松籽仁蛋白质含量的无损、准确检测。
Abstract:
To study the near-infrared spectroscopy for protein content detection in Korean pine seed kernels, principal components analysis (PCA), modified locally linear embedding (MLLE), local tangent space alignment (LTSA) and Hessian based locally linear embedding (HLLE) were used separately to reduce dimensions of the spectroscopic data, based on pretreatment of the original spectrum by standard normalized variate (SNV)+first derivative (1st-Der)+Symlet4 (SNV+1st-Der+Sym4) method. Partial least square (PLS), ridge regression (Ridge), support vector regression (SVR) and extreme gradient boosting (XGBoost) were adopted separately to establish mathematical models. The results showed that, the quality of the parameter optimization model established by MLLE+SVR method was the best. The optimized parameters for dimension reducing were as follows: the dimension (n-components) was four, the neighborhood number (n-neighbors) was 50, the mean value of mean squared error of validation (mean-MSEV) was 0.568 1, and the mean value of Pearson correlation coefficient of validation (mean-PCCV) was 0.940 8. Therefore, the prediction results of the model is reliable, and non-destructive, accurate and quantitative detection of protein in Korean pine seed kernels can be realized.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2022-02-16基金项目：黑龙江省省属本科高校基本科研业务费项目(青年学术骨干研究项目)(2021-KYYWF-019)；国家自然科学基金项目(31270757)；国家林业局948项目(2015-4-25)；中央高校基本科研业务费专项资金项目(2572020BK03)；黑龙江省省属本科高校基本科研业务项目（科研创新团队研究项目）（2020-KYYWF-E009）作者简介：仇逊超(1986-),女,黑龙江哈尔滨人,博士,讲师,主要从事农林产品无损检测、农林业机械化工程研究。(E-mail)ldqiuxunchao@126.com
更新日期/Last Update: 2023-03-21