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首页> 外文期刊>Food analytical methods >Industrial Hydrogenation Process Monitoring Using Ultra-compact Near-Infrared Spectrometer and Chemometrics
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Industrial Hydrogenation Process Monitoring Using Ultra-compact Near-Infrared Spectrometer and Chemometrics

机译：使用超紧凑近红外光谱仪和化学测量学的工业氢化过程监测

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摘要

The hydrogenation process of soybean oil is monitored through time-consuming methodologies that demand sample preparation and produce chemical residues. Thus, it is necessary to develop faster low-cost waste-free instrumentation methodologies. The aim of this work was to evaluate an ultra-compact near-infrared spectrometer in tandem with the partial least squares regression (PLSR) or support vector regression (SVR) in the control of the hydrogenation process. Models were used to predict the saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), trans fatty acids (TFA), polyunsaturated fatty acids (PUFA), and the iodine value (IV). The values predicted by the PLSR and SVR models were compared to the experimental values obtained by gas chromatography. A methodology for feature selection was also assessed, which was able to reduce by up to 85% the variables used in the models without loss of performance. The values obtained for root mean square error of cross validation, root mean square error of calibration, root mean square error of prediction, and r (2) remained very close for both PLSR and SVR. Regarding RSD, all values were above 5% for the PLSR models, whereas for the SVR, the RSD presented values lower than 5% for IV and UFA. It is worth noting that the spectrometer used has low cost, effortless assembly, and easy handling, which allows its use in any environment. Through the results obtained, it was demonstrated that the ultra-compact NIRS spectrometer in tandem with PLSR or SVR represent an alternative to monitor the industrial hydrogenation process of soybean oil.
机译：通过耗时的方法来监测大豆油的氢化过程，要求样品制备和生产化学残留物。因此，有必要开发更快的低成本废物仪器方法。这项工作的目的是评估串联的超紧凑近红外光谱仪，其在氢化过程中的控制中的部分最小二乘回归（PLSR）或支持载体回归（SVR）。模型用于预测饱和脂肪酸（SFA），不饱和脂肪酸（UFA），单不饱和脂肪酸（MUFA），反式脂肪酸（TFA），多不饱和脂肪酸（PUFA）和碘值（IV）。将PLSR和SVR模型预测的值与通过气相色谱获得的实验值进行比较。还评估了特征选择的方法，该方法能够减少多达85％的模型中使用的变量而不会损失性能。为横向验证的均方根误差，校准的根均方误差，预测的根均方误差和R（2）的校准的根均方误差和R（2）获得的值非常接近。关于RSD，PLSR模型的所有值高于5％，而对于SVR，RSD呈现的值低于IV和UFA的值低于5％。值得注意的是，使用的光谱仪具有低成本，轻松的装配和易于处理，允许其在任何环境中使用。通过获得的结果，证明了用PLSR或SVR串联的超紧凑型NIRS光谱仪代表监测大豆油的工业氢化过程的替代方案。

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  • 来源
    《Food analytical methods》 |2018年第1期|共13页
  • 作者

    Garcia Pereira Juliana Mendes; Sanchez Jorge Leonardo; de Lima Patricia Casarin; Possebon Gabriela; Tanamati Augusto; Coelho Tanamati Ailey Aparecida; Bona Evandro;

  • 作者单位

    Fed Univ Technol Parana UTFPR Postgraduat Program Food Technol PPGTA Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

    Fed Univ Technol Parana UTFPR Postgraduat Program Food Technol PPGTA Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

    Fed Univ Technol Parana UTFPR Food Dept Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

    Fed Univ Technol Parana UTFPR Food Dept Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

    Fed Univ Technol Parana UTFPR Food Dept Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

    Fed Univ Technol Parana UTFPR Postgraduat Program Food Technol PPGTA Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

    Fed Univ Technol Parana UTFPR Postgraduat Program Food Technol PPGTA Via Rosalina Maria dos Santos 1233 POB 271 BR-87301899 Campo Mourao Parana Brazil;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 食品工业;
  • 关键词

    Fatty acids; Iodine value; PLSR; SVR; Feature selection;

    机译：脂肪酸;碘值;PLSR;SVR;特征选择;

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