Predicting the composition of aroma components in Baijiu using hyperspectral imaging combined with a replication allocation strategy-enhanced stacked ensemble learning model.
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| Názov: | Predicting the composition of aroma components in Baijiu using hyperspectral imaging combined with a replication allocation strategy-enhanced stacked ensemble learning model. |
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| Autori: | Huang Y; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China., Tian J; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China. Electronic address: tjp893@126.com., Hu X; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China; Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin 644000, China., Yang H; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China., Xie L; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China., Zhou Y; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China., Xia Y; School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China., Huang D; Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin 644000, China., He K; Guizhou Xijiu Co., Ltd, China. |
| Zdroj: | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2025 Nov 15; Vol. 341, pp. 126398. Date of Electronic Publication: 2025 May 14. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE |
| Imprint Name(s): | Publication: Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994- |
| Výrazy zo slovníka MeSH: | Odorants*/analysis , Hyperspectral Imaging*/methods , Soy Foods*/analysis , Machine Learning*, Esters/analysis ; Acids/analysis ; Ensemble Learning |
| Abstrakt: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ester and acid aroma compounds are crucial components affecting the fragrance of Baijiu, and their composition can endow the Baijiu with a fruity, acidic, floral, or roasted aroma. This study aims to quantitatively detect the ester and acid content in Soy Sauce-Aroma Type Baijiu (SSAB) using hyperspectral imaging (HSI) technology and a stacked ensemble learning (SEL) model. To mitigate the impact of data imbalance, an improved oversampling technique known as the replication allocation strategy (RAS) was utilized. After comparing the study results, it was found that the established RF-RAS-SEL model yielded the best performance, with an Rp2 of 0.9803 and RMSEP of 0.3314 mg/L for predicting ester content and an Rp2 of 0.9914 and an RMSEP of 0.4565 mg/L for predicting acid content. These findings demonstrate that HSI can achieve the non-destructive and accurate detection of esters and acids in SSAB, providing a novel method for analyzing Baijiu aroma. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Hyperspectral imaging; Integrated learning model; Organic acids; Organic esters; Oversampling; Soy Sauce Aroma-Type Baijiu |
| Substance Nomenclature: | 0 (Esters) 0 (Acids) |
| Entry Date(s): | Date Created: 20250518 Date Completed: 20250606 Latest Revision: 20250609 |
| Update Code: | 20250609 |
| DOI: | 10.1016/j.saa.2025.126398 |
| PMID: | 40382953 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Ester and acid aroma compounds are crucial components affecting the fragrance of Baijiu, and their composition can endow the Baijiu with a fruity, acidic, floral, or roasted aroma. This study aims to quantitatively detect the ester and acid content in Soy Sauce-Aroma Type Baijiu (SSAB) using hyperspectral imaging (HSI) technology and a stacked ensemble learning (SEL) model. To mitigate the impact of data imbalance, an improved oversampling technique known as the replication allocation strategy (RAS) was utilized. After comparing the study results, it was found that the established RF-RAS-SEL model yielded the best performance, with an Rp2 of 0.9803 and RMSEP of 0.3314 mg/L for predicting ester content and an Rp2 of 0.9914 and an RMSEP of 0.4565 mg/L for predicting acid content. These findings demonstrate that HSI can achieve the non-destructive and accurate detection of esters and acids in SSAB, providing a novel method for analyzing Baijiu aroma.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.) |
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| ISSN: | 1873-3557 |
| DOI: | 10.1016/j.saa.2025.126398 |