基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

引用本文: 莫欣欣, 孙通, 刘木华, 叶振南. 基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究[J]. 分析化学, 2017, 45(11): 1694-1702. doi: 10.11895/j.issn.0253-3820.170329 shu

Citation: MO Xin-Xin, SUN Tong, LIU Mu-Hua, YE Zhen-Nan. Rapid Quantitative Detection and Model Optimization of Trans Fatty Acids in Edible Vegetable Oils by Near Infrared Spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2017, 45(11): 1694-1702. doi: 10.11895/j.issn.0253-3820.170329 shu

基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

1.
江西农业大学工学院, 江西省高校生物光电技术及应用重点实验室, 南昌 330045;
2.
江西出入境检验检疫局, 综合技术中心, 南昌 330038
基金项目:
本文系国家自然科学基金（No.31401278）、江西省自然科学基金（No.20151BAB204025）和江西省教育厅科学研究基金（No.GJJ13254）项目资助

摘要: 利用近红外光谱技术对食用植物油中反式脂肪酸（Trans fatty acids，TFA）含量进行快速定量检测，并通过波段选择、预处理方法、变量筛选及建模方法对TFA含量预测模型进行优化。采用Antaris Ⅱ傅里叶变换近红外光谱仪在4000~10000 cm^-1光谱范围采集98个食用植物油样本的近红外透射光谱，然后采用气相色谱法测定TFA的真实含量。首先，对样本原始光谱进行波段、预处理方法优选；在此基础上，采用竞争自适应重加权法（Competitive adaptive reweighted sampling，CARS）筛选TFA相关的重要变量，最后应用主成分回归、偏最小二乘和最小二乘支持向量机方法分别建立食用植物油中TFA含量的预测模型。研究结果表明，近红外光谱技术检测食用植物油中的TFA含量是可行的，优化后的最佳预测模型的校正集和预测集R²分别为0.992和0.989，RMSEC和RMSEP分别为0.071%和0.075%。最佳预测模型所用的变量仅26个，占全波段变量的0.854%。此外，与全波段偏最小二乘预测模型相比，其预测集R²由0.904上升为0.989，RMSEP由0.230%下降为0.075%。由此表明，模型优化非常必要，CARS能有效筛选TFA相关的重要变量，极大减少建模变量数，从而简化预测模型，并较大提高预测模型的精度和稳定性。

关键词:

English

Rapid Quantitative Detection and Model Optimization of Trans Fatty Acids in Edible Vegetable Oils by Near Infrared Spectroscopy

1.
Key Laboratory of Jiangxi University for Optics-Electronics Application of Biomaterials, College of engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2.
Comprehensive Technology Center, Jiangxi Entry-Exit Inspection and Quarantine Bureau, Nanchang 330038, China
Received Date: 25 May 2017
Revised Date: 15 August 2017
Fund Project:
This work was supported by the National Natural Science Foundation of China (No.31401278) and the Natural Science Foundation of Jiangxi Province, China (No.20151BAB204025)

Abstract: Near infrared spectroscopy (NIR) was used to detect trans fatty acids (TFA) in edible vegetable oils quantitatively. And prediction model of TFA was optimized through band selection, pretreatment method, variable selection and modeling method. NIR spectra of 98 edible vegetable oil samples were collected in spectral range of 4000-10000 cm^-1 using an Antaris Ⅱ Fourier transform near infrared spectrometer, and the true content of TFA was measured by gas chromatography. First, optimization of waveband and pretreatment method was conducted on original spectra. On this basis, competitive adaptive reweighted sampling (CARS) was used to select important variables that related to TFA. Finally, the prediction models of TFA content in edible vegetable oils were established using principal component regression (PCR), partial least square (PLS) and least square support vector machine (LS-SVM). The results indicated that NIR spectroscopy was feasible for detecting TFA content in edible vegetable oils, R² of the best prediction model after optimized in calibration and prediction sets were 0.992 and 0.989, and root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.071% and 0.075%, respectively. Only 26 variables were used in the best prediction model, accounting for 0.854% of the whole waveband variables. In addition, compared with the full waveband PLS prediction model, the R² in prediction set increased from 0.904 to 0.989, and RMSEP decreased from 0.230% to 0.075%. It shows that model optimization is very necessary, CARS method can select important variables related to TFA effectively and immensely reduce the number of modeling variables, so it can simplify the prediction model, and greatly improve the accuracy and stability of prediction model.

Key words:

Edible vegetable oils
/ Near infrared spectroscopy
/ Model optimization
/ Competitive adaptive reweighted sampling variable selection
/ Quantitative detection

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沈阳化工大学材料科学与工程学院沈阳 110142

基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

English

Rapid Quantitative Detection and Model Optimization of Trans Fatty Acids in Edible Vegetable Oils by Near Infrared Spectroscopy

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

English

Rapid Quantitative Detection and Model Optimization of Trans Fatty Acids in Edible Vegetable Oils by Near Infrared Spectroscopy

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

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CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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通讯作者: 陈斌, bchen63@163.com

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