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首页> 外文期刊>Food research international >Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality
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Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality

机译：应用于光谱的多元数据分析：果汁和水果品质的潜在应用

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摘要

The goal of building a multivariate calibration model is to predict a chemical or physical property from a set of predictor variables, for example the analysis of sugar concentration in fruits using near infrared (NIR) spectroscopy. Effective multivariate calibration models combined with a rapid analytical method should be able to replace laborious and costly reference methods. The quality of a calibration model primarily depends on its predictive ability. In order to build, interpret and apply NIR calibrations not only the quality of spectral data but also other properties such as effect of reference method, sample selection and interpretation of the model coefficients are also important. The objective of this short review is to highlight the different steps, methods and issues to consider when calibrations based on NIR spectra are developed for the measurement of chemical parameters in both fruits and fruit juices. The same principles described in this paper can be applied to other rapid methods like electronic noses, electronic tongues, and fluorescence spectroscopy.
机译：建立多元校准模型的目的是根据一组预测变量来预测化学或物理性质，例如，使用近红外（NIR）光谱分析水果中的糖浓度。有效的多元校准模型与快速分析方法相结合，应该能够取代费力且昂贵的参考方法。校准模型的质量主要取决于其预测能力。为了建立，解释和应用NIR校准，不仅光谱数据的质量，而且其他属性（例如参考方法的效果，样本选择和模型系数的解释）也很重要。这篇简短的评论的目的是强调开发基于NIR光谱的校准品以测量水果和果汁中化学参数时要考虑的不同步骤，方法和问题。本文中描述的相同原理可以应用于其他快速方法，例如电子鼻，电子舌和荧光光谱法。

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