基于近红外光谱的掺伪花生油鉴别模型优化
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摘要
为实现掺伪花生油的快速鉴别,基于近红外光谱技术,利用偏最小二乘法(PLS法)建立掺伪花生油鉴别模型,并采用不同预处理方法进行模型优化。研究结果表明:利用PLS法建立的模型,对于花生油中掺入大豆油样品的鉴别,在漫反射方式下、采用二阶导数谱、Norris平滑方法、附加散射矫正光程方式、因子数为6时最为理想,其预测集相关系数为0.967 9;对于花生油中掺入菜籽油样品的鉴别,在漫反射方式下、采用二阶导数谱、S-G平滑方法、光程不矫正、因子数为5时最为理想,其预测集相关系数为0.994 8。该分析模型可以为花生油品质监控和快速定量鉴别掺伪提供参考。
Near infrared spectroscopy(NIR) combined with partial least squares(PLS) was employed to identify adulterated peanut oil. Different pretreatment methods were used to optimize the model. The results indicated that for the identification of peanut oil adulterated with soybean oil, the best model established by PLS was under diffuse reflection mode, using secondorder derivative spectrum, Norris smoothing method and additional scattering correction light path method, and the factor was6, in which the correlation coefficient of prediction set(r_p) was 0.967 9. For the identification of peanut oil adulterated with rapeseed oil, the ideal model was under diffuse reflection condition, second-order derivative spectrum, S-G smoothing method and no correction of optical path, and the factor was 5, in which the r_p was 0.994 8. The model could be used in the rapid analysis adulteration in peanut oil.
Near infrared spectroscopy(NIR) combined with partial least squares(PLS) was employed to identify adulterated peanut oil. Different pretreatment methods were used to optimize the model. The results indicated that for the identification of peanut oil adulterated with soybean oil, the best model established by PLS was under diffuse reflection mode, using secondorder derivative spectrum, Norris smoothing method and additional scattering correction light path method, and the factor was6, in which the correlation coefficient of prediction set(r_p) was 0.967 9. For the identification of peanut oil adulterated with rapeseed oil, the ideal model was under diffuse reflection condition, second-order derivative spectrum, S-G smoothing method and no correction of optical path, and the factor was 5, in which the r_p was 0.994 8. The model could be used in the rapid analysis adulteration in peanut oil.
引文
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[12]彭丹,李晓晓,毕艳兰.最小二乘支持向量机和脂肪酸融合信息应用于花生油掺伪玉米油检测[J].食品科学,2017,38(16):234-238.
[13]张严.近红外光谱技术快速鉴别与检测食用油掺伪研究[D].郑州:河南工业大学,2015.
[14]STARK E,LUCHTER K.Near-Infrared Analysis(NIRA)-a technology for quantitalive and qualitative-analysis[J].APSRV,1986(22):335-399.
[2]陈金体,续颖,黄秋娜.基于近红外光谱技术在食用油检测中的应用分析[J].食品安全导刊,2016(9):89-90.
[3]马亭,邹世春,陈文锐,等.应用校正变换矩阵法识别掺伪食用油[J].分析测试学报,2000(2):21-24.
[4]支敏.气相色谱法鉴别食用油掺假[D].唐山:河北联合大学,2014.
[5]宋志强,张恒,郑晓,等.近红外光谱技术在食用植物油脂检测中的应用[J].武汉工业学院学报,2013,32(2):1-5.
[6]DAAM TAYLOR,BRIAN K.Potential of visible and near infrared spectroscopy to quantify phenol formaldehyde resin content in oriented strand board[J].European Journal of Wood and Wood Products,2009,67(1):3-5.
[7]YANG Z Y,TU Y Y,DONG F,et al.Liquid chromatographic-mass spectometric analysis of phenolics and evaluation of the antioxidant compounds of extracts from flowers of the tea plant(Camellia sinensis)[J].Journal of the Pharmaceutical Society of Japan,2007,127(4):10-12.
[8]YANG Z,JIE G L,HE P M,et al.Study on the antioxidant activity of tea flowers(Camellia sinensis)[J].Asia Pacific Journal of Clinical Nutrition,2007,16(1):148-152.
[9]HAN Q,LING Z J,HE P M,et al.Immunomodulatory and antitumor activity of polysacchanide isolated from tea plant flower[J].Progress in Biochemisty and Biophysics,2010,37(6):646-653.
[10]曾路路,涂斌,尹成,等.激光近红外结合SVM的花生油掺伪定性定量分析[J].中国粮油学报,2016,31(8):126-130,137.
[11]涂斌,陈志,彭博,等.基于多源光谱特征融合技术的花生油掺伪检测[J].食品与发酵工业,2016,42(4):169-173.
[12]彭丹,李晓晓,毕艳兰.最小二乘支持向量机和脂肪酸融合信息应用于花生油掺伪玉米油检测[J].食品科学,2017,38(16):234-238.
[13]张严.近红外光谱技术快速鉴别与检测食用油掺伪研究[D].郑州:河南工业大学,2015.
[14]STARK E,LUCHTER K.Near-Infrared Analysis(NIRA)-a technology for quantitalive and qualitative-analysis[J].APSRV,1986(22):335-399.
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