Search

The pupose of this review was to explore the application of machine learning in the realm of flavor molecule research, particularly in the analysis of the flavor of Jasmine tea. The study of flavor molecules is fundamental to understand and optimize the taste and quality of food, especially tea. The introduction of machine learning technology has opened new horizons for the identification and analysis of flavor molecules. This paper firstly outlined the basic concepts and research methods of flavor molecules, and a detailed discussion on the application of machine learning in deciphering the relationships between molecular structures and flavor characteristics, as well as in the analysis, prediction and optimization of the flavor and intelligent processing of Jasmine tea and other applications, put forward the research prospect. Thereby, this review could enhance the quality of Jasmine tea and provide technical support for the further development of the tea industry.

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A novel fingerprinting platform for multiplex detection of flavor molecules in Baijiu was developed by using a surface-enhanced Raman scattering (SERS) nanosensor array in combination with machine learning. The SERS sensors were constructed by core–shell Fe3O4@Ag nanoparticles modified with molecules carrying end-groups of hydroxyl, pyridyl, methyl, and amino, respectively, which interacted with flavors and led to changes in the sensors' spectra. All the Raman spectra acquired from the nanosensor array contacting with the sample were concatenated into a single SERS super-spectrum, representing the flavor fingerprint which was recognized through machine learning. Principal component analysis, support vector machine, and partial least squares were utilized to build classification and quantitation models for predictive analyses. The SERS nanosensor array was successfully used for fingerprinting ten typical flavors in Baijiu including four esters, three alcohols, and three acids, with an accuracy of 100%, linear detection ranges over two orders of magnitude, and limits of detection ranging from 3.45 × 10−3 mg/L of phenylethyl acetate to 1.21 × 10−2 mg/L of ethyl hexanoate. It was also demonstrated that satisfactory accuracies (recoveries) ranging from 96.2 to 104% and relative standard deviations ranging from 0.65 to 2.78% were obtained for the simultaneous quantification of 3-methylbutyl acetate and phenylethyl acetate in eighteen Baijiu samples of three flavor types including sauce flavor, strong flavor, and light flavor. Compared with the existing detection techniques, this chemical fingerprinting platform is easy to use, highly sensitive, and can perform multiplex detection, which has great potential for practical applications. [ABSTRACT FROM AUTHOR]

Flavor microencapsulation has been widely used in foods, medicines, cosmetics, and other fields. The use of amylose-based complexes is receiving increasing attention because they are biodegradable, non-toxic, and biocompatible, and these complexes provide a new way to develop and impart flavors. In this study, with the aim of investigating the properties of amylose--flavor complexes, linear aliphatic alcohols (C6-C9) and the aromatic alcohol naphthol were used to prepare inclusion complexes by the sealed heating method in yields ranging from 45 to 50%. In addition, scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) were utilized to investigate the structural characteristics of these inclusion complexes, and it was found that the amylose-nheptanol inclusion complex was most stable. Furthermore, nuclear magnetic resonance (NMR) spectroscopy revealed that V-type crystals existed in the amylose--flavor complexes. Moreover, TGA showed that all amylose--flavor inclusion complexes have excellent thermal stability. The encapsulation efficiency of the complexes increased with decreasing chain length, but the encapsulation efficiency of the complexes with the aromatic alcohol naphthol was lower than that of the complexes with some linear alcohols. This indicated that the stereoscopic confirmation of a-naphthol may influence the complexes being formed. [ABSTRACT FROM AUTHOR]