Abstract

The food authenticity assessment is an increasingly important issue in food quality and safety. The application of an electronic nose based on ultra-fast gas chromatography technique enables rapid analysis of the volatile compounds from food samples. Due to the fact that this technique provides chemical profiling of natural products, it can be a powerful tool for authentication in combination with chemometrics. In this article, a methodology for classification of Not From Concentrate (NFC) juices was presented. During research samples of 100% orange juice, 100% apple juice, as well as mixtures of these juices with known percentage of base juices were tested. Classification of juice samples was carried out using unsupervised and supervised statistical methods. As chemometric methods, Hierarchical Cluster Analysis, Classification Tree, Naïve Bayes, Neural Network, and Random Forest classifiers were used. The ultra-fast GC technique coupled with supervised statistical methods allowed to distinguish juice samples containing only 1.0% of impurities. The developed methodology is a promising analytical tool to ensure the authenticity and good quality of juices.

Graphical abstract

中文翻译：

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基于超快速气相色谱的橙汁掺假检测新分析方法

 抽象的

食品真实性评估是食品质量和安全中日益重要的问题。基于超快速气相色谱技术的电子鼻的应用能够快速分析食品样品中的挥发性化合物。由于该技术提供了天然产物的化学分析，因此它可以成为与化学计量学相结合的强大认证工具。本文介绍了非浓缩果汁 (NFC) 的分类方法。在研究过程中，对 100% 橙汁、100% 苹果汁以及这些果汁与已知比例的基础果汁的混合物进行了测试。使用无监督和监督统计方法对果汁样品进行分类。作为化学计量学方法，使用了层次聚类分析、分类树、朴素贝叶斯、神经网络和随机森林分类器。超快速 GC 技术与监督统计方法相结合，可以区分仅含有 1.0% 杂质的果汁样品。所开发的方法是一种很有前景的分析工具，可确保果汁的真实性和良好品质。

 图形概要