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Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine

机译:基于近红外光谱和最小二乘支持向量机快速检测总酸含量并对不同类型的醋进行分类

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摘要

More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (N1R) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ = 0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (R_p) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar.
机译:中国每天消耗超过320万升醋。中国有许多类型的醋。如何控制醋的质量是一个问题。应用近红外光谱(N1R)传输技术来实现此目的。收集了来自中国11个省的14个产地的95个醋样品。它们分为成熟醋,芳香醋,米醋,水果醋和白醋。经过主成分分析(PCA)后,在NIR的二维主成分空间中将水果醋和白醋与其他传统类别分开。本研究首先将最小二乘支持向量机(LS-SVM)作为模式识别技术,用于识别成熟醋,芳香醋,米醋。通过主成分分析(PCA)提取了前两个主要成分(PC)作为LS-SVM分类器的输入。使用σ= 0.8的径向基函数(RBF)LS-SVM分类器可获得最佳实验结果。三种传统醋类别的鉴定准确率均超过85%。与反向传播人工神经网络(BP-ANN)方法相比,LS-SVM算法对识别结果具有很好的推广性。由于总酸含量(TAC)与醋的质量高度相关,因此使用NIR来预测样品的TAC。 LS-SVM被用于建立基于频谱传输速率的TAC预测模型。与偏最小二乘(PLS)模型相比,LS-SVM模型在预测TAC方面具有更高的精度和准确性。 LS-SVM模型的预测确定系数(R_p)为0.919,预测均方根误差(RMSEP)为0.3226。这项工作表明,近红外光谱技术与LS-SVM结合可以用作醋的质量控制方法。

著录项

  • 来源
    《Food Chemistry》 |2013年第1期|192-199|共8页
  • 作者单位

    School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013 Zhenjiang Jiangsu, China;

    School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013 Zhenjiang Jiangsu, China,Key Laboratory of Modern Agricultural Equipment and Technology, 301 Xuefu Rd, 212013 Zhenjiang, Jiangsu, China;

    School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013 Zhenjiang Jiangsu, China;

    School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013 Zhenjiang Jiangsu, China;

    School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013 Zhenjiang Jiangsu, China;

    The Research Center of China Hemp Materials, Beijing, China;

    The Research Center of China Hemp Materials, Beijing, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    near infrared spectroscopy; least-squares support vector machine; vinegar; total acid content; principle component analysis; back propagation artificial neural network; partial least-square;

    机译:近红外光谱最小二乘支持向量机尖酸刻薄;总酸含量;主成分分析;反向传播人工神经网络偏最小二乘;

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