{"title":"Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review","authors":"T. Basile, D. Mallardi, M. Cardone","doi":"10.3390/chemosensors11120579","DOIUrl":null,"url":null,"abstract":"In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.","PeriodicalId":10057,"journal":{"name":"Chemosensors","volume":"271 ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemosensors","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/chemosensors11120579","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.
期刊介绍:
Chemosensors (ISSN 2227-9040; CODEN: CHEMO9) is an international, scientific, open access journal on the science and technology of chemical sensors published quarterly online by MDPI.The journal is indexed in Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Engineering Village and other databases.