Sensomics - From conventional to functional NIR spectroscopy - Shining light over the aroma and taste of foods. (September 2019)

• Record Type: Journal Article
• Authors: Chapman, J.; Elbourne, A.; Truong, Vi Khanh; Newman, L.; Gangadoo, S.; Rajapaksha Pathirannahalage, P.; Cheeseman, S.; Cozzolino, D.
• Is Part Of: Trends in food science & technology. Volume 91(2019)
• Page Start: 274
• Page End: 281
• Publication Date: 2019-09
• Subjects: Taste -- Aroma -- Near infrared -- Chemometrics -- Conventional NIR -- Functional spectroscopy; Food industry and trade -- Periodicals; Food -- Biotechnology -- Periodicals; 664.005
• DOI: 10.1016/j.tifs.2019.07.013
• Languages: English
• ISSNs: 0924-2244

Abstract

Abstract: The increasing desire to control quality in foods in real-time (e.g. on-line, at line, market) demands the development of innovative and easy to use analytical systems [e.g. a combination of sensors with multivariate data analysis (MVA)]. It has been long established that the ability to analyse complex chemical samples such as foods is now essential to achieve the consistent quality demanded by consumers, to ensure uniformity and consistency within a brand and even to avoid fraud, which can have direct implications when it comes to food integrity. It is well known that a broad range of factors (e.g. chemical, physical properties) contribute to the sensory characteristics of foods (e.g. smell, taste and colour) including the origin of raw materials and processing steps. In such complex matrices the use of sensor systems combined with MVA (chemometrics) is especially promising as tools for an encompassing analysis and understanding of these contributing factors. This article reviews recent applications of near infrared sensors (conventional and functional spectroscopy) for the measurement, monitoring and prediction of aroma and taste in several food matrices. Highlights: Sensory properties of foods are very important to determine food quality. Rapid analytical methods based in spectroscopy are currently used by the food industry. The measurement and prediction of sensory parameters using NIR is discussed.