| 基于近红外光谱技术的茶油原产地快速鉴别 |
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| 引用本文： | 文韬,郑立章,龚中良,李立君,谢洁飞,马强.基于近红外光谱技术的茶油原产地快速鉴别[J].农业工程学报,2016,32(16):293-299. |
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| 作者姓名： | 文韬 郑立章 龚中良 李立君 谢洁飞 马强 |
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| 作者单位： | 1. 中南林业科技大学机电工程学院,长沙,410004;2. 中南林业科技大学理学院,长沙,410004 |
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| 基金项目： | 国家自然科学基金（31401281）；湖南省自然科学基金（14JJ3115）；湖南省高校科技创新团队支持计划（2014207） |
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| 摘 要： | 为研究茶油原产地溯源问题，维护其市场秩序，促进公平竞争。该文利用近红外光谱技术采集湖南、江西、安徽和浙江4个不同产地茶油的光谱数据，并运用 Savitzky-Golay 平滑（savitzky-golay, SG）、多元散射校正（multiplicative scatter correction, MSC）、一阶导数（first derivation, FD）和矢量归一化（vector normalization, VN）等4种方法对其进行预处理。采用偏最小二乘法（partial least squares, PLS）提取最佳主成分，构建 PLS 回归模型；同时，采用主成分分析（principal component analysis, PCA）和 PLS 算法提取最佳主成分，作为 BP 人工神经网络（BP artificial neural network, BPANN）输入变量，构建 PCA-BPANN 和 PLS-BPANN 模型。以验证集相关系数 RP 和验证集均方根误差 RMSEP 为模型的评价指标，分别优选最佳 PLS 和 BPANN 模型。试验结果表明，SG-PLS-DA 和 SG-PLS-BPANN-DA 模型对未知样本的整体分类准确率均大于90%。其中，SG-PLS-BPANN-DA 的鉴别效果优于前者，其建模集相关系数 RC、均方根误差 RMSEC 分别为0.974、0.170，验证集相关系数 RP、均方根误差 RMSEP 分别为0.972、0.172，对上述两类样本集的总体分类准确率分别为98.15%、95.83%，该模型能较准确鉴别茶油原产地。研究结果可为快速辨别茶油原产地提供参考。
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| 关 键 词： | 近红外光谱 模型 油脂 预处理 茶油 原产地 鉴别 |
| 收稿时间： | 2016/4/15 0:00:00 |
| 修稿时间： | 2016/6/17 0:00:00 |
Rapid identification of geographical origin of camellia oil based on near infrared spectroscopy technology |
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| Wen Tao,Zheng Lizhang,Gong Zhongliang,Li Lijun,Xie Jiefei and Ma Qiang.Rapid identification of geographical origin of camellia oil based on near infrared spectroscopy technology[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(16):293-299. |
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| Authors: | Wen Tao Zheng Lizhang Gong Zhongliang Li Lijun Xie Jiefei and Ma Qiang |
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| Institution: | 1. School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China,1. School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China,1. School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China,1. School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China,1. School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China and 2. School of Science, Central South University of Forestry and Technology, Changsha 410004, China |
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| Abstract: | Abstract: The identification of geographical origin of camellia oil is very significant in food market to maintain the market order and promote fair competition. The objective of this research was to evaluate the feasibility of a high efficient and nondestructive detection of the geographical origin of camellia oil by using near infrared spectroscopy combined with chemometrics methods. In this paper, 4 kinds of camellia oil samples obtained from Hunan, Jiangxi, Anhui and Zhejiang Province were tested with 39 samples for each kind. The samples were randomly divided into 2 groups, i.e. calibration set (30, 27, 27 and 24 were respectively for Hunan, Jiangxi, Anhui and Zhejiang) and validation set (9, 12, 12 and 15 were respectively for Hunan, Jiangxi, Anhui and Zhejiang). Use the FTIR (Fourier transform infrared spectroscopy) spectrometer to gather the samples'' near infrared spectrum information. The raw spectral data contain not only chemical information of camellia oil samples, but also some interferential information produced by environmental factors, so reasonable preprocessing method was adopted to eliminate the influence of these factors, which helped to improve the accuracy and reliability of modeling and prediction. Four spectral preprocessing methods were proposed, including the Savitzky-Golay (SG), the multiplicative scattering correction (MSC), the first derivation (FD) and the vector normalization (VN). These methods were conducted by using the Unscrambler 10.3 software. The partial least squares (PLS) was proposed to establish the identification model. Meanwhile, the principal component analysis (PCA) and PLS were used to extract the best principal components (PCs), which were used as input variables for the back propagation artificial neural network (BPANN) to establish the PCA-BPANN and PLS-BPANN identification models. Then, the results of prediction were compared according to the correlation coefficient of validation (RP) and the root mean square error of validation (RMSEP). The results of experiment showed that the identification of the geographical origin of camellia oil was ideal using the SG-PLS-DA and SG-PLS-BPANN-DA model, and the correct recognition rates of calibration set and validation set were more than 90%. In calibration set, one sample of Hunan was mistakenly identified as the sample of Jiangxi, one sample of Anhui was mistakenly identified as the sample of Jiangxi, 3 samples of Jiangxi were mistakenly identified as the samples of Hunan, and one sample of Jiangxi was identified as the sample of Anhui using the SG-PLS-DA method; and the correct recognition rate was 94.44%. And in validation set, 2 samples of Jiangxi were mistakenly identified as the samples of Hunan, and 2 samples of Anhui were mistakenly identified as the samples of Jiangxi; the correct recognition rate was 91.67%. The correlation coefficient of calibration (RC) was 0.942 and the root mean square error of calibration (RMSEC) was 0.189, and the RP and the RMSEP were 0.932 and 0.192, respectively. There was one sample of Hunan mistakenly identified as the sample of Jiangxi in calibration set, and the validation set was the same. There was one sample of Jiangxi mistakenly identified as the sample of Anhui in calibration set, and there was one sample of Anhui mistakenly identified as the sample of Jiangxi in validation set using the SG-PLS-BPANN-DA model; the correct recognition rate of validation set was 95.83%. Therefore, the identification model established by the SG-PLS-BPANN-DA method was better. The RC and the RMSEC were respectively 0.974 and 0.170, and the RP and the RMSEP were respectively 0.972 and 0.172. Consequently, the SG-PLS-BPANN-DA model can more accurately identify the origin of camellia oil, and provide technical support for quickly, non-destructively and accurately identifying the geographical origin of camellia oil. |
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| Keywords: | near infrared spectroscopy models oils and fats preprocessing camellia oil geographical origin identification |
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