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Discrimination of yerba mate (Ilex paraguayensis St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis

机译:通过近红外光谱和多变量分析根据样品的地理来源区分马黛茶(巴拉圭冬青)

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Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens.
机译:近红外反射(NIR)光谱与多变量数据分析相结合,用于区分马黛茶(Ilex paraguayensis St. Hil。)样品的地理起源。样品是从当地市场购买的,并在单色仪中以反射率在NIR区域(1100-2500 nm)中进行扫描。主成分分析(PCA),偏最小二乘判别分析(PLS-DA)和线性判别分析(LDA)用于根据样品的地理来源根据近红外光谱对样品进行分类。开发分类模型时,将全交叉验证用作验证方法。使用PLS-DA和LDA获得的总分类率分别为76%和100%。结果表明,近红外光谱结合多变量数据分析的实用性是一种客观,快速的方法,可根据其地理来源对马黛茶样品进行分类。尽管如此,近红外光谱仪可能会在食品链中提供初步筛选,并使昂贵的方法可以更有效地用于可疑标本。

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