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首页> 外文期刊>Food analytical methods >Multiproduct, Multicomponent and Multivariate Calibration: a Case Study by Using Vis-NIR Spectroscopy
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Multiproduct, Multicomponent and Multivariate Calibration: a Case Study by Using Vis-NIR Spectroscopy

机译：多程序，多组分和多元校准：使用VIS-NIR光谱法进行案例研究

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摘要

Vitamin C and total acidity were determined in industrialized fruit nectar and soy juices through visible-near infrared (Vis-NIR) spectroscopy and multiproduct, multicomponent, and multivariate calibration, based on partial least squares (PLS2) regression. Since samples with different types, flavors, and sugar content (light or not) were together in the model construction, the samples present higher heterogeneity and it was necessary to optimize the calibration and validation sets by outlier elimination based on leverage and unmodelled residuals in spectral data. The model was developed and validated by the evaluation of the parameters of merit such as accuracy, analytical sensitivity, adjust, linearity, residual prediction deviation, limits of detection, and quantification. The results achieved indicates that the multiproduct, multicomponent, and multivariate calibration model developed from Vis-NIR spectroscopy and PLS2 regression can be used in the industrial routine analysis as an alternative to titration reference methods that are time- and reagent-consuming methods, making the methodology extremely attractive from the industrial point of view.
机译：通过可见近红外（Vis-NIR）光谱和多程序，多元组分，多元组分，基于部分最小二乘（PLS2）回归，在工业化果实花蜜和大豆汁中测定维生素C和总酸度。由于具有不同类型，口味和糖含量（光或不）的样品在模型结构中，样品具有更高的异质性，因此必须通过基于光谱的杠杆和未刻度的残差来优化校准和验证集。数据。该模型是通过评估和验证的优点参数，例如精度，分析灵敏度，调整，线性，剩余预测偏差，检测限，以及量化的参数进行开发和验证。所实现的结果表明，从VIR-NIR光谱和PLS2回归产生的多份制，多组分和多变量校准模型可以在工业常规分析中使用，作为滴定参考方法的替代方案，即耗时的方法，使得方法论极具吸引力，从工业角度极具吸引力。

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