Machine-learning-assisted SERS nanosensor platform toward chemical fingerprinting of Baijiu flavors

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 x 10(-3) mg/L of phenylethyl acetate to 1.21 x 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.

Automated summary

A novel fingerprinting platform for multiplex detection of flavor molecules in Baijiu was developed using a SERS nanosensor array combined with machine learning. The SERS sensors, made of core-shell Fe3O4@Ag nanoparticles modified with different molecules, interacted with flavors, resulting in changes in their spectra. The Raman spectra of the nanosensor array in contact with the sample were concatenated into a single SERS super-spectrum, which represented the flavor fingerprint and was recognized by machine learning. This chemical fingerprinting platform showed high accuracy, a wide linear detection range, and low limits of detection for ten typical flavors in Baijiu. It also achieved satisfactory accuracy and precision for the quantification of specific flavors in different types of Baijiu samples. Compared to existing techniques, this platform is easy to use, highly sensitive, and capable of multiplex detection.