| 近红外光谱法预测面条中马铃薯泥含量模型的建立和应用 |
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| 引用本文： | 吕都,董楠,王梅,王辉,刘永翔,李飞,刘嘉.近红外光谱法预测面条中马铃薯泥含量模型的建立和应用[J].食品与机械,2019(8):55-58,95. |
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| 作者姓名： | 吕都 董楠 王梅 王辉 刘永翔 李飞 刘嘉 |
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| 作者单位： | 贵州省农业科学院生物技术研究所;贵州省农业科学院食品加工研究所;贵州省农业科学院马铃薯研究所 |
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| 基金项目： | 国家重点研发计划(编号:2016YFNC010104);贵州省科技计划项目(编号:黔科合基础[2017]1179,黔科合支撑[2019]2828号,黔科合重大专项字[2014]6016);贵州省农业科学院课题(编号:黔农科院科技创新[2017]07号),黔农科院青年基金[2019]10号) |
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| 摘 要： | 制作了已知含量的马铃薯泥面条170份,采集马铃薯泥面条的近红外吸收光谱,采用偏最小二乘法(PLS)建立并优化马铃薯泥含量预测模型。结果表明:吸收谱图扫描范围为9 403.6~5 446.2cm-1时,选择矢量归一化法预处理光谱,获得的含量预测模型预测能力好,模型的外部验证决定系数为0.956 4、预测均方根误差为3.73%,斜率为1.029,模型的相对分析误差为2.04。采用近红外光谱法建立的预测模型可以较好的预测未知面条样品中马铃薯泥的含量。
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| 关 键 词： | 近红外光谱 马铃薯 面条 含量 |
Establishment and application of prediction model for content determination of mashed potato in noodles by NIR spectroscopy |
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| LV Du,DONG Nan,WANG Mei,WANG Hui,LIU Yong-xiang,LI Fei,LIU Jia.Establishment and application of prediction model for content determination of mashed potato in noodles by NIR spectroscopy[J].Food and Machinery,2019(8):55-58,95. |
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| Authors: | LV Du DONG Nan WANG Mei WANG Hui LIU Yong-xiang LI Fei LIU Jia |
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| Affiliation: | (Biotechnology Institute of Guizhou Province, Guiyang, Guizhou 550006, China;Food Processing Institute of Guizhou Province, Guiyang, Guizhou 550006, China;Potato Institute of Guizhou Province, Guiyang, Guizhou 550006, China) |
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| Abstract: | A fast determination method was established for content determination of mashed potato in noodles.Studied 170 potato noodles by near infrared spectroscopy and stoichiometry to establish prediction model by PLS.The result indicated that the prediction model based on wave number of 9 403.6~5 446.2 cm-1,and spectral pretreatment by the standard normal variate transformation(SNV)showed an excellent prediction accuracy.The external validation determinants was 0.956 4,the root mean square error of prediction was 3.73%,the slope was 1.029,and relative prediction deviation was 2.04.The results indicate that near infrared spectroscopy can be used as an efficient way to detect the mashed potatoes content of potato noodles. |
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| Keywords: | NIR potato noodles quantification |
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