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首页> 外文期刊>LWT-Food Science & Technology >Determination of total phenolic compounds in yerba mate (Ilex paraguariensis) combining near infrared spectroscopy (NIR) and multivariate analysis
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Determination of total phenolic compounds in yerba mate (Ilex paraguariensis) combining near infrared spectroscopy (NIR) and multivariate analysis

机译:结合近红外光谱(NIR)和多变量分析法测定大麦草(Ilex paraguariensis)中的总酚类化合物

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摘要

A quick method to determine the content of phenolic compounds (TPC) in yerba mate based on near infrared spectroscopy (NIR) was studied aiming to provide reductions of cost and analysis time for the mate industry without generating laboratory effluents. A total of 114 samples of yerba mate from different regions of the State of Parana, Brazil (Southeast, South Central and Metropolitan Area of Curitiba) was analyzed by the Folin-Ciocalteu method. The average contents of TPC found were 84.82, 133.31 and 37.00 mg g(-1), respectively. The samples were separated into three groups (regarding the different growing regions) using the principal component analysis (PCA). Models were developed using partial least squares (PLS) and some preprocessing strategies of the spectral data were evaluated. The best result was obtained by Multiplicative Scatter Correction (MSC) and first derivative with six latent variables. The best model presented a correlation coefficient of 0.81 with a prediction error of 12%. The results showed that NIR can be applied as an efficient method for the assessment of TPC, which allows classifying samples in relation to its origin of planting, processing and agronomic practices applied, aiding the decision of referral and use of yerba mate in different final products. (C) 2014 Elsevier Ltd. All rights reserved.
机译:研究了一种基于近红外光谱法(NIR)的快速测定yerba mate中酚类化合物(TPC)含量的方法,旨在在不产生实验室流出物的情况下为伴侣行业降低成本和缩短分析时间。通过Folin-Ciocalteu方法分析了来自巴西巴拉那州不同地区(库里提巴的东南部,中南部和大都市区)的114份大叶象伴侣。发现的TPC的平均含量分别为84.82、133.31和37.00 mg g(-1)。使用主成分分析(PCA)将样品分为三组(关于不同的生长区域)。使用偏最小二乘(PLS)开发了模型,并对光谱数据的一些预处理策略进行了评估。最好的结果是通过乘法散射校正(MSC)和具有六个潜在变量的一阶导数获得的。最佳模型的相关系数为0.81,预测误差为12%。结果表明,NIR可以用作评估TPC的有效方法,它可以根据种植,加工和所应用的农艺方法对样品进行分类,从而有助于在不同的最终产品中推荐和推荐使用yerba mate。 。 (C)2014 Elsevier Ltd.保留所有权利。

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