Authentication of Australian Red Wines Using Fluorescence Spectroscopy and Machine Learning Classification
Date
2021
Authors
Ranaweera, Ranaweera Kaluarachchige Ruchira
Editors
Advisors
Jeffery, David
Bastian, Susan
Capone, Dimitra
Bastian, Susan
Capone, Dimitra
Journal Title
Journal ISSN
Volume Title
Type:
Thesis
Citation
Statement of Responsibility
Conference Name
Abstract
Verification of geographical origin, grape variety, and year of
production of wine is essential in validating quality, identifying fraud, and
improving the economic value of wine according to those important extrinsic
factors. The identity of a wine is influenced mainly by its origin, as reflected in a
wine’s composition. Therefore, analytical methods that identify authentication
markers to discriminate wine according to the origin (or other variables) are
required.
Over the years, numerous methods for wine authentication have been
identified, from traditional analytical methods to rapid advanced instrumental
techniques. However, there is a lack of a robust but simple technique that gives
rapid results and is sensitive enough to discriminate wines accurately. This forms
the topic of the thesis, which begins with a published book chapter that covers
current aspects of wine authenticity and traceability in terms of technological
and consumer perspectives (Chapter 1). Different spectroscopic approaches
and chemometric methods used in wine authentication in the past decades
have been evaluated for their characteristics in the next chapter, published as a
review paper (Chapter 2).
As a rapid, straightforward, selective, and sensitive method that yields a
molecular fingerprint of wine, fluorescence spectroscopy was identified upon
reviewing the literature as a promising method to investigate wine
authentication. Several original research studies were subsequently performed
with the aim of understanding the potential of applying fluorescence
spectroscopy in combination with multivariate data analysis for wine
authentication (Chapters 3 to 6). Finally, the conclusions and future directions
of the study are included in the final chapter (Chapter 7).
In the initial research publication using spectrofluorometric analysis
(Chapter 3), a method based on absorbance-transmission and fluorescence
excitation-emission matrix (known as the A-TEEM technique) was investigated as a tool for regional authentication of commercial Australian
Cabernet Sauvignon wines from three different Geographical Indications (GIs) in
comparison to wines from Bordeaux, France as an international benchmark. The
potential of A-TEEM spectroscopy for wine authentication was assessed in
comparison to elemental profiling using inductively coupled plasma-mass
spectrometry (ICP-MS) as a reference method for geographical authentication.
Among other multivariate algorithms used for classification of the wines, a
novel machine learning technique known as extreme gradient boosting
discriminant analysis (XGBDA) yielded 100 % correct classification for all tested
regions using the fluorescence data, and overall 97.7 % for ICP-MS. This result
emphasised the possibility of applying A-TEEM and XGBDA for accurate
authentication of wines.
With these encouraging GI authentication results, a further study was
undertaken to verify the origin of wine according to both geographical and
varietal variations. A wide range of commercially-produced but unreleased
wines from ten different Australian GIs and three varieties (Shiraz, Cabernet
Sauvignon, and Merlot) were studied in the second research publication
(Chapter 4). This study identified the effectiveness of combining absorbance and
fluorescence data from A- TEEM as a multi-block data set to maximise the
model’s robustness. Excellent results were obtained in relation to crossvalidation
for each class (100 % for variety and 99.7 % for region of origin), again
highlighting the effectiveness of A- TEEM data with XGBDA. In addition, ATEEM
data was interrogated using partial least squares regression (PLSR) models to
rapidly quantify 24 phenolic compounds of relevance to red wine (i.e.,
anthocyanins, flavonols, flavan-3-ols, hydroxycinnamates). Principal component
analysis of the phenolic compound concentrations revealed differences among
the varieties and regions, helping to understand the chemical markers that were
important in classification. These findings further strengthen the potential of
using the A-TEEM technique for differentiation of wine, not only from GIs at state level
but also those from adjacent regions such as Clare, Barossa, and Eden Valleys within a
state. Further testing the A-TEEM technique for its ability to discriminate wine at a
sub-regional level, research-scale and commercial unreleased Shiraz wines from five
different areas within the Barossa Valley GI along with Eden Valley GI were
analysed to explore their intra-regional variations. The samples were from three
consecutive years, which allowed for authentication testing according to the
vintage, as reported in the third original research study submitted for
publication (Chapter 5). The sensitivity of the A-TEEM technique allied with
XGBDA facilitated 100 % accuracy in classifying Shiraz wines according to the
sub-region of origin and year of production. Additionally, A-TEEM data were
modelled with PLSR in comparison to reference method data to predict basic
chemical parameters of the samples (i.e., pH, alcohol %v/v, titratable acidity), which
enhances the utility of the A-TEEM technique as a rapid method for deployment
in the wine industry.
In wine authentication, it is important to understand the impact of
winemaking processes on chemical markers at different stages of production.
Hence, variations in molecular fingerprint of wines throughout the process such as after
primary fermentation, after malolactic fermentation, and before blending were
determined with the A-TEEM technique. XGBDA discriminated wines
according to their origin (variety and region) with 100 % accuracy, eliminating
the influence of stage of processing on spectral signature. Also, blending
different grape varieties or wine from different GIs (as permitted by relevant
regulations) is crucial in winemaking. However, it is important to determine
whether blending a small proportion (up to 15 % of other varietal or
regional wine as per Wine Australia regulations) can be detected for
authentication purposes. Unreleased commercially-produced monovarietal
wines were prepared with a series of blends containing Shiraz with Cabernet
Sauvignon and Shiraz with Grenache and analysed with regression. XGB
regression precisely predicted the percentage in the blend, achieving R2 CV of
1.00 and RMSECV of 0.00028 in comparison to PLSR, which did not perform as
well. The results of this final study of the thesis were submitted for publication
as a short communication (Chapter 6). In summary, this PhD thesis has been devoted to the development
of a rapid analytical method to accurately authenticate wine according to
geographical origin, variety, and vintage. The use of absorbance and/or
fluorescence spectroscopy in conjunction with machine learning classification
proved to be highly promising for this purpose. The outcomes of this thesis not
only contribute to enriching scientific research but also offer opportunities for
potential commercial application in the wine industry as a powerful tool for
wine analysis, and in particular, validation of origin and composition.
School/Discipline
School of Agriculture, Food and Wine
Dissertation Note
Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2021
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