Advancing Stable Isotope Analysis for Alcoholic Beverages’ Authenticity: Novel Approaches in Fraud Detection and Traceability
Abstract
:1. Introduction
2. Origin Traceability
2.1. Wine
2.2. Chinese Baijiu
2.3. Beer
2.4. Other Alcoholic Beverages
2.5. General Principles for the Authenticity Assessment of Alcoholic Beverages
- (1)
- Ensure sample authenticity: Representative and authentic samples are essential for training a robust authenticity model.
- (2)
- Select appropriate stable isotope indicators: Carbon isotopes of ethanol are commonly used to detect ethanol adulteration and ingredient composition. Oxygen isotopes of water can identify origin and water adulteration. Carbon isotopes of ethanol and glycine can differentiate vintage.
- (3)
- Establish a comprehensive database: The database should include information on geographic location, climatic conditions, production date, raw ingredient sources, and brewing processes. Integration with heavy isotopes (e.g., Sr, Pb) and elemental fingerprinting enhance traceability accuracy.
- (4)
- Develop discriminant models: Combine chemometric methods with mass spectrometry data to identify authenticity markers and statistically evaluate traceability models.
3. Adulteration Identification
3.1. Adulteration with Exogenous Alcohol
3.1.1. Wine
3.1.2. Chinese Baijiu
3.1.3. Other Alcoholic Beverages
3.2. Exogenous Water
3.2.1. Wine
3.2.2. Chinese Baijiu and Beer
3.2.3. Other Alcoholic Beverages
3.3. Chaptalization
3.3.1. Wine
3.3.2. Beer
3.3.3. Other Alcoholic Beverages
3.4. Trace Compounds
3.4.1. Wine
3.4.2. Chinese Baijiu
3.4.3. Other Alcoholic Beverages
4. Vintage
4.1. Vintage Wine
4.2. Aged Chinese Baijiu
4.3. Other Alcoholic Beverages
5. Conclusions and Perspective
- (1)
- Constructing a comprehensive database: Establish a worldwide stable isotope database of a wide range of alcoholic beverages and regions to facilitate rapid comparative analyses.
- (2)
- Developing novel markers: Identify new indicator molecules that integrate stable isotope data with other chemical and sensory analyses for more accurate authentication.
- (3)
- Advancing analytical technologies: Incorporate emerging technologies such as LC-IRMS, AI-driven isotopic analysis, and IoT-enabled real-time authentication tools.
- (4)
- Promoting interdisciplinary collaboration: Leverage expertise from fields such as ecology, food science, and information technology to drive innovation and application of stable isotope technology.
- (5)
- Addressing practical challenges: Explore ways to reduce the costs of isotopic analysis and improve accessibility for small-scale producers.
- (6)
- Considering climate change impacts: Climate change is emerging as a significant factor that could influence the authenticity and traceability of alcoholic beverages, particularly wine. Rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events are already affecting grape-growing regions worldwide [28]. These changes can lead to shifts in the isotopic signatures of grapes and wines, as stable isotopes are influenced by climatic conditions during plant growth [28]. As climate change progresses, it will be essential to develop new research lines that account for these shifts. This includes updating isotopic databases to reflect changing climatic conditions, exploring novel biomarkers that are less sensitive to climate variability, and integrating isotopic analysis with other tools such as geographic information systems (GIS) and machine learning to enhance traceability and authenticity verification [101].
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model of Origin Traceability | Geographical Origins | Amount | Stable Isotope and Methods | Machine Learning | Accuracy Rate (%) | References |
---|---|---|---|---|---|---|
Country | France, Spain, Italy, Australia, the United States, South Africa, Chile, and China | 600 | δ13C of glycerol and ethanol: GC-C-IRMS, δ18O of wine water: Gasbench-IRMS | DA | 83.9 | [47] |
RF | 55.8 | |||||
ANN | 93.1 | |||||
Argentina and Austria | 62 | δ18O of wine water: Gasbench-IRMS, δ13C of ethanol: EA-IRMS | LDA | 98.4 | [50] | |
America, Asia, Europe, and Oceania | 80 | δ18O of beer water: Gasbench-IRMS, δ13C of beer: EA-IRMS, 87Sr/86Sr of beer: MC-ICP-MS | DA | 99 | [4] | |
State/Province | Changji, Mile, and Changli regions in China | 188 | δ18O of wine water: GC-P-IRMS | PLS-DA | 95 | [51] |
SVM | 97 | |||||
Hebei Province, Helanshan, Diqing, and Yantai in China | 62 | δ13C of glycerol and ethanol: GC-C-IRMS, δ18O of wine water: Gasbench-IRMS | DA | 100 | [47] | |
Helan Mountain, Xinjiang, Yunchuanzang, Yanhuai Valley, and Hexi Corridor in China | 142 | δ13C of glycerol and ethanol: LC-IRMS, δ18O of wine water: Gasbench-IRMS | LDA | 90.8 | [33] | |
Helan Mountain, Xinjiang, Southwest Mountain, and Loess Plateau regions in China | 104 | δ18O of wine water: Gasbench-IRMS | ANN | 90.9 | [38] | |
LDA | 88.5 | |||||
Zone B (Croatian Uplands), Zone CI (Slavonia and Croatian Danube), and Zone CII (Croatian Istria and Kvarner) in Croatia | 190 | δ18O of wine water: Gasbench-IRMS, δ13C of wine: EA-IRMS | GDA | 86.3 | [34] | |
Bordeaux, Burgundy, Languedoc-Roussillon and Rhone regions in French | 240 | δ13C of glycerol and ethanol: GC-C-IRMS, δ18O of wine water: Gasbench-IRMS | ANN | 98.2 | [32] | |
PLS-DA | 92.5 | |||||
Shanghai, Shaoxing, Xiaogan Beijing in China and Korea | 44 | δ2H and δ18O: Gasbench-IRMS | PLS-DA | 97.73 | [52] | |
Village/Town | Three towns (Foshan, Yunling, and Benzilan) Deqin County, plateau of Diqing, Yunnan, China | 36 | δ13C of wine: EA-IRMS, δ18O of wine: EQ-IRMS | DA | 80.6 | [49] |
ANN | 87.7 | |||||
Six counties (YL, BZL, YM, DW, YuL, FS) in Diqing, Yunnan, China | / | δ13C of wine: EA-IRMS, δ18O of wine water: Gasbench-IRMS, 87Sr/86Sr: MC-ICP-MS | DA | 100 | [44] |
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Ma, Y.; Li, Y.; Shao, F.; Lu, Y.; Meng, W.; Rogers, K.M.; Sun, D.; Wu, H.; Peng, X. Advancing Stable Isotope Analysis for Alcoholic Beverages’ Authenticity: Novel Approaches in Fraud Detection and Traceability. Foods 2025, 14, 943. https://doi.org/10.3390/foods14060943
Ma Y, Li Y, Shao F, Lu Y, Meng W, Rogers KM, Sun D, Wu H, Peng X. Advancing Stable Isotope Analysis for Alcoholic Beverages’ Authenticity: Novel Approaches in Fraud Detection and Traceability. Foods. 2025; 14(6):943. https://doi.org/10.3390/foods14060943
Chicago/Turabian StyleMa, Yiqian, Yalan Li, Feilong Shao, Yuanyu Lu, Wangni Meng, Karyne M. Rogers, Di Sun, Hao Wu, and Xiaodong Peng. 2025. "Advancing Stable Isotope Analysis for Alcoholic Beverages’ Authenticity: Novel Approaches in Fraud Detection and Traceability" Foods 14, no. 6: 943. https://doi.org/10.3390/foods14060943
APA StyleMa, Y., Li, Y., Shao, F., Lu, Y., Meng, W., Rogers, K. M., Sun, D., Wu, H., & Peng, X. (2025). Advancing Stable Isotope Analysis for Alcoholic Beverages’ Authenticity: Novel Approaches in Fraud Detection and Traceability. Foods, 14(6), 943. https://doi.org/10.3390/foods14060943