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Ability of near infrared spectroscopy and chemometrics to measure the phytic acid content in maize flour
Spectroscopy Letters ( IF 1.7 ) Pub Date : 2021-07-18 , DOI: 10.1080/00387010.2021.1950189
Mehmet Şerment 1 , Fatih Kahrıman 1
Affiliation  

Abstract

Phytic acid is one of the important biochemical components in maize as in many plant species. Near infrared spectroscopy has a potential for determination of the phytic acid content in the maize grain. However, there are a limited number of studies on the determination of phytic acid in maize. Also, the effect of chemometric methods on the success of near infrared spectroscopy calibration models for phytic acid content has not been investigated sufficiently yet. To fill these gaps, we create a total of 360 different prediction models and evaluate the effect of chemometric methods on prediction robustness. To develop calibration models, 4 derivatives, 5 pretreatments, 9 wavelength selection methods were used, and partial least squares regression and support vector machines regression methods were applied. Model reliability was evaluated by external validation. Results revealed that spectral pretreatment and wavelength selection methods improve model prediction results. In general, support vector machines yielded more successful results than partial least squares models in detecting phytic acid. The best model was the combination of first derivative + standard normal variate + interval partial least squares combined with support vector regression. While creating calibration models for phytic acid detection, it was concluded that the use of appropriate chemometric methods increases the success of the model.



中文翻译:

近红外光谱和化学计量学测量玉米粉中植酸含量的能力

摘要

与许多植物物种一样,植酸是玉米中重要的生化成分之一。近红外光谱具有测定玉米籽粒中植酸含量的潜力。然而,关于测定玉米中植酸的研究数量有限。此外,化学计量学方法对植酸含量的近红外光谱校准模型成功的影响尚未得到充分研究。为了填补这些空白,我们共创建了 360 个不同的预测模型,并评估了化学计量学方法对预测稳健性的影响。为了开发校准模型,使用了 4 个导数、5 个预处理、9 个波长选择方法,并应用了偏最小二乘回归和支持向量机回归方法。通过外部验证评估模型可靠性。结果表明,光谱预处理和波长选择方法改善了模型预测结果。一般来说,支持向量机在检测植酸方面比偏最小二乘模型产生了更成功的结果。最好的模型是一阶导数+标准正态变量+区间偏最小二乘结合支持向量回归的组合。在为植酸检测创建校准模型时,得出的结论是,使用适当的化学计量学方法可以提高模型的成功率。在检测植酸方面,支持向量机比偏最小二乘模型产生了更成功的结果。最好的模型是一阶导数+标准正态变量+区间偏最小二乘结合支持向量回归的组合。在为植酸检测创建校准模型时,得出的结论是,使用适当的化学计量学方法可以提高模型的成功率。在检测植酸方面,支持向量机比偏最小二乘模型产生了更成功的结果。最好的模型是一阶导数+标准正态变量+区间偏最小二乘结合支持向量回归的组合。在为植酸检测创建校准模型时,得出的结论是,使用适当的化学计量学方法可以提高模型的成功率。

更新日期:2021-08-30
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