| 王泽富，吴雪辉.基于红外光谱快速鉴别压榨油茶籽油与浸出油茶籽油的研究[J].中国油脂,2018,43(11):.[WANG Zefu, WU Xuehui.Rapid identification of pressed and extracted oil-tea camellia seed oils based on infrared spectroscopy[J].China Oils and Fats,2018,43(11):.] |
| 基于红外光谱快速鉴别压榨油茶籽油与浸出油茶籽油的研究 |
| Rapid identification of pressed and extracted oil-tea camellia seed oils based on infrared spectroscopy |
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| DOI： |
| 中文关键词: 红外光谱 油茶籽油 压榨 浸出 鉴别 偏最小二乘法 支持向量机 BP人工神经网络 |
| 英文关键词:infrared spectroscopy oil-tea camellia seed oil press extraction identification partial least squares support vector machine BP artificial neural network |
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| 摘要点击次数: 1277 |
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| 中文摘要: |
| 为规范油茶籽油市场、维护消费者权益，建立了快速、准确鉴别压榨油茶籽油和浸出油茶籽油的方法。通过傅里叶变换红外光谱仪对大量压榨油茶籽油和浸出油茶籽油样品进行扫描，提取特征波段数据，运用Savitzky-Golay平滑（SG）、多元散射校正（MSC）、标准正态变量变换（SNV）、一阶导数（FD）和二阶导数（SD）方法进行预处理，然后结合偏最小二乘法（PLS）、支持向量机（SVM）和BP人工神经网络（BPANN）建立鉴别模型。结果表明，偏最小二乘法和BP人工神经网络建模时，SG平滑预处理方法最好，得到的SG-PLS和SG-BPANN两模型的验证集相关系数、验证集均方根误差、鉴别准确率分别为0.767 9和0.921 2、0.322 6和0.205 9、88.46%和100%；支持向量机建模宜采用SNV预处理，建立的SNV-SVM模型验证集相关系数、验证集均方根误差和鉴别准确率分别为0.761 4、0.882 1、88.46%。因此，红外光谱技术用于鉴别压榨油茶籽油和浸出油茶籽油是可行的。 |
| 英文摘要: |
| In order to standardize the market of oil-tea camellia seed oils and safeguard the rights of consumers, a rapid and accurate method for identification of pressed and extracted oil-tea camellia seed oil was established. A large number of pressed and extracted oil-tea camellia seed oil samples were scanned by Fourier transform infrared spectroscopy to extract the characteristic band data. Savitzky-Golay smoothing (SG), multivariate scatter correction (MSC), standard normal transformation (SNV), first derivative (FD) and second derivative (SD) methods were used to preprocess, then combined with partial least squares (PLS), support vector machine (SVM) and BP artificial neural network (BPANN) to establish identification model. The results showed that when BPANN and PLS were used to establish the identification models, the results of SG were the best, and the correlation coefficient of validation (RP), the root mean square error of validation (RMSEP) and the identification accuracy of the SG-PLS model and SG-BPANN model were 0.767 9 and 0.921 2, 0.322 6 and 0.205 9, 88.46% and 100% respectively. The SNV was the optimal preprocessing method for SVM modeling, and the RP, RMSEP and the identification accuracy of the SNV-SVM model were 0.761 4, 0.882 1 and 88.46% respectively. Therefore, infrared spectroscopy could be applied to the identification of pressed and extracted oil-tea camellia seed oils. |
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