E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

• Published in: Critical reviews in food science and nutrition Vol. 62; no. 24; pp. 6605 - 6645
• Main Authors: Galvan, Diego, Aquino, Adriano, Effting, Luciane, Mantovani, Ana Carolina Gomes, Bona, Evandro, Conte-Junior, Carlos Adam
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 29.08.2022; Taylor & Francis Ltd
• Subjects: Algorithms; Artificial intelligence; Chemometrics; Chemometrics tools; Classification; Data integration; Data processing; Electronic devices; electronic eye; electronic nose; Electronic noses; electronic tongue; Electronic tongues; Food; food control; Food quality; Literature reviews; Machine learning; Meta-analysis; Multisensor fusion; multivariate analysis; nutrition; Quality control; Sensors; Systematic review; Chemometrics tools; electronic eye; electronic nose; food control; electronic tongue; multivariate analysis
• ISSN: 1040-8398; 1549-7852; 1549-7852
• DOI: 10.1080/10408398.2021.1903384

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.