Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.
Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.
Crit Rev Food Sci Nutr. 2022;62(24):6605-6645. doi: 10.1080/10408398.2021.1903384. Epub 2021 Mar 29.
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
基于人体感官的设备，如电子鼻、电子舌和电子眼，可用于分析多种食品基质中的不同化合物。这些传感器可以检测复杂食品样品中存在的一种或多种化合物，并且当应用不同的化学计量学工具时，获得的响应可以用于多个目标。在本系统评价中，我们使用了系统评价和荟萃分析的首选报告项目指南，以解决与食品质量控制（FQC）相关的电子感应与化学计量学方法的问题。从 PubMed、Scopus 和 Web of Science 中总共选择了 109 篇符合条件的文章。因此，我们预测电子感应与化学计量学工具之间的关联对于 FQC 至关重要。大多数研究都应用了初步方法，如探索性分析，而分类/回归方法的研究较少。值得一提的是，基于人工智能/机器学习的非线性方法在大多数情况下具有优于非线性方法的分类/回归性能，尽管它们的应用较少。另一种有前途的方法是电子感应设备之间的数据融合或与其他分析技术的结合。此外，还讨论了小型化设备和纳米传感器应用的一些未来趋势。
