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

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Abstract

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.

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Funding

This study was supported by grants from the National Natural Science Foundation of China (31770113, 22074009) and the Chongqing Chongqing Natural Science Foundation (CSTB2022NSCQ--MSX1373).

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Authors and Affiliations

  1. College of Environment and Resources, Chongqing Technology and Business University, Chongqing, 400067, China

    Xiao-Lan Wei, Lan Jiang & Qin-Ling Shi

  2. College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 400067, China

    Zhi-Hong Mo

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  1. Xiao-Lan Wei
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  2. Lan Jiang
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Contributions

XLW: conceptualization, funding acquisition, and writing; LJ: methodology, investigation, and writing; QLS: investigation and software; ZHM: conceptualization, funding acquisition, supervision, and validation.

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Correspondence to Xiao-Lan Wei or Zhi-Hong Mo.

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Wei, XL., Jiang, L., Shi, QL. et al. Machine-learning-assisted SERS nanosensor platform toward chemical fingerprinting of Baijiu flavors. Microchim Acta 190, 207 (2023). https://doi.org/10.1007/s00604-023-05794-z

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