Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way?
Abstract
:1. Introduction
1.1. Perception of Quality in Pinot Noir Wines
1.2. Modelling Human Responses to Sensory Stimuli
1.3. Chemical and Physiochemical Correlates of Perceived Quality
1.4. Computational Modelling of Perceived Quality
1.5. Industry Relevance
2. Methods
2.1. Sensory Methods
2.1.1. Participants
2.1.2. Wines
2.1.3. Experimental Design
2.1.4. Procedure
2.2. Mathematical Methods
2.2.1. Identifying Essential Modulators
2.2.2. Buckingham’s Pi Theorem for Predicting Wine Quality Indices
- Selecting important variables through DA before conducting further analysis saves time. It shows a clear direction in selecting relevant variables that ultimately improve the accuracy of the model in predicting wine quality.
2.3. Machine Learning Analysis
2.4. Data Analysis
3. Results
3.1. Sensory Results
- Visual influence (i.e., the glass-colour manipulation) was not a major driver of judgments of perceived quality, despite anecdotal evidence suggesting that it would be.
- Perceived quality differed significantly across the 18 wines, but wine region was not a significant factor (the within-region variability was too great to find the between-region significant variation with such a small # of wines in the sample set).
- The perception of the quality in the wines was highly and positively associated with perceived varietal typicality, a concept that refers to whether a Pinot noir wine exemplifies a taster’s concept of what a Pinot noir wine should be like. (i.e., is the wine true to grape type?)
- The perception of quality was closely and positively associated with perceived complexity, and to a lesser degree, with the perceived familiarity of a wine, these two concepts have been shown in food science research to be important influencers of consumer preferences and behaviour.
- Key, specific drivers of Pinot noir quality were wine attributes of attractive fruit aromatics, expressiveness, overall structure, harmony and balance.
- The 18 wines differed significantly on most of the in-mouth attributes assessed, including the perceived overall quality.
- Again, quality and varietal typicality appeared virtually synonymous concepts for the tasters.
- The major sensory dimension separating the wines was a tactile aspect, with wines judged as soft, gentle, smooth, silky, velvety and supple; as opposed to wines judged to be sour, bitter, coarse, rough, astringent and with harsh tannins (PCA output).
- A second important dimension was related to wine overall body, with attributes of weight, heaviness, density, fullness, roundness and volume opposing the descriptor ‘thin/watery’.
- Perceived overall quality was positively associated with the tactile attributes relating to the soft/smooth aspects, and negatively correlated with bitterness, astringency and harsh tannins.
- Overall, mouthfeel attributes important to quality were multi-dimensional, involving tactile (e.g., harsh/soft), body/weight and oiliness/viscosity dimensions.
3.2. Mathematical Results for Quality Indices (Selection of Optimal Essential Modulators and Evaluating Pi-Terms)
3.3. Mathematical Results for Case Study 1 and Case Study 2
3.4. Validation/Significance of ML Models
3.5. Limitations
4. Summary of Findings and Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Wine Identity | NZ Region | Vintage | Price Point | Vine Yield | Production Philosophy | Closure | RRP NZD |
---|---|---|---|---|---|---|---|
WAR16 | Wairarapa | 2016 | Premium | Low | BioGro cert | SC | 82 |
WPP16 | Wairarapa | 2016 | Commercial | Mod | conventional | SC | 26 |
WPP13 | Wairarapa | 2013 | Commercial | Mod | conventional | SC | 26 |
WE16 | Wairarapa | 2016 | Premium | Low | Organic in transition | SC | 52 |
WCR16 | Wairarapa | 2016 | Premium | Low | conventional | SC | 140 |
MCH16 | Marlb | 2016 | Premium | Low | BioGro cert | Cork | 44 |
MPR16 | Marlb | 2016 | Commercial | High | conventional | SC | 15 |
MG16 | Marlb | 2016 | Premium | Low | BioGro cert | SC | 63 |
MG13 | Marlb | 2013 | Premium | Low | BioGro cert | SC | 63 |
OMD16 | Central Otago | 2016 | Commercial | High | conventional | SC | 28 |
OFRCP16 | Central Otago | 2016 | Premium | Low | Demeter cert | SC | 76 |
OFRB316 | Central Otago | 2016 | Premium | Low | Demeter cert | SC | 102 |
OQR16 | Central Otago | 2016 | Premium | Low | BioGro and Demeter cert | SC | 79 |
OQR13 | Central Otago | 2013 | Premium | Low | BioGro and Demeter cert | SC | 79 |
NN16 | Nelson | 2016 | Premium | Low | BioGro cert | SC | 67 |
NS16 | Nelson | 2016 | Commercial | High | conventional | SC | 13 |
CPB16 | Nth Canterbury | 2016 | Commercial | Mod | conventional | SC | 22 |
CG16 | Nth Canterbury | 2016 | Premium | Low | organic in transition | SC | 43 |
Descriptors Experiment 1 | Anchors | Descriptors Experiment 2 | Anchors |
---|---|---|---|
Attractive fruit aromatics | Low–Intense | Overall quality | Poor–Good |
Attractive floral aromatics | Low–Intense | Softness/gentleness/suppleness | Low–Intense |
Earthy/mushroom notes | Low–Intense | Smoothness/silky/velvety | Low–Intense |
Reductive notes | Low–Intense | Weight/heaviness/density | Low–Intense |
Bitterness | Low–Intense | Volume/fullness/roundness | Low–Intense |
Astringency | Low–Intense | Viscosity/mouth-coating/oiliness | Low–Intense |
Sweetness | Low–Intense | Bitterness | Low–Intense |
Harshness of tannins | Low–Intense | Dry/puckering | Low–Intense |
Green/herbaceous notes | Low–Intense | Dusty | Low–Intense |
Overall quality | Poor–Good | Coarse/grainy/rough | Low–Intense |
Balanced acidity | Poor–Good | Harsh tannins/aggressive | Low–Intense |
Elegance/precision | Poor–Good | Astringency | Low–Intense |
Softness/silkiness | Poor–Good | Thin/watery | Low–Intense |
Freshness | Poor–Good | Burning sensation/hot | Low–Intense |
Expressiveness | Poor–Good | Sourness/acidity | Low–Intense |
Fruit ripeness | Poor–Good | Overall body | Poor–Good |
Oak influence | Poor–Good | Pinot noir varietal typicality | Poor–Good |
Concentration in mouth | Poor–Good | ||
Overall structure | Poor–Good | ||
Pinot noir varietal typicality | Poor–Good |
Essential Modulators | Chemical Compounds |
---|---|
Fruity (π1) | Ethyl octanoate |
Ethyl butanoate | |
Ethyl hexanoate | |
Herbal (π2) | Hexan-1-ol |
(E)-Hex-3-en-1-ol | |
Heptan-1-ol | |
Floral (π3) | (E)-1-(2,6,6-Trimethylcyclohexa-1,3-dien-1-yl) but-2-en-1-one (Beta) |
2-Phenylethan-1-ol | |
3,7-Dimethylocta-1,6-dien-3-ol (linalool) | |
Oak and Woody (π4) | 4-Ethyl-2-methoxyphenol (eugenol) |
Benzaldehyde | |
2-Methoxyphenol (guaiacol) | |
Others (π5) | Phenol |
4-Ethyl-2-methoxyphenol (eugenol) |
List of Chemical Compounds | |
---|---|
Ethyl acetate | (E)-Hex-3-en-1-ol |
3-Methylbutyl acetate | Ethyl heptanoate |
Ethyl pentanoate | (E)-Hex-2-en-1-ol |
Ethyl 2-hydroxypropanoate | Octan-1-ol |
(Z)-Hex-3-en-1-ol | (E)-1-(2,6,6-Trimethylcyclohexa-1,3-dien-1-yl) but-2-en-1-one |
Ethyl octanoate | 2-Methoxyphenol |
Benzaldehyde | (E)-4-(2,6,6-Trimethylcyclohexen-1-yl) but-3-en-2-one |
Ethyl decanoate | 2-Methylpropanoic acid |
(2E)-3,7-Dimethylocta-2,6-dien-1-ol | Butanoic acid |
(E)-4-(2,6,6-Trimethylcyclohex-2-en-1-yl) but-3-en-2-one | Ethyl 2-methylpropanoate |
Acetic acid | Hexyl Acetate |
2-Methylbutanoic acid | Heptan-1-ol |
Ethyl butanoate | 2-Methyl butyl acetate |
Ethyl 3-methylbutanoate | 3,7-Dimethylocta-1,6-dien-3-ol |
3-Methylbutan-1-ol | 3,7-Dimethyloct-6-en-1-ol |
2-Phenylethan-1-ol | methyl-2-aminobenzoate |
2-Methylpropyl acetate | 2-Methoxy-4-prop-2-enylphenol |
Ethyl 2-methylbutanoate | Methyl-2-aminobenzoate |
(2Z)-3,7-Dimethylocta-2,6-dien-1-ol | 3-Methylbutanoic acid |
2-Phenethyl acetate | Hexanoic acid |
Phenol | Octanoic acid |
4-Ethyl-2-methoxyphenol | 2-Methylpropan-1-ol |
Ethyl (E)-3-phenylprop-2-enoate | Ethyl hexanoate |
Hexan-1-ol |
Parameters | Data1 | Data2 | Data3 |
---|---|---|---|
Input dimension | 14 | 6 | 6 |
Layers | 5 | 5 | 5 |
Node (layer1)/Activation | 64/RELU | 64/RELU | 128/RELU |
Node (layer2)/Activation | 64/ELU | 64/RELU | 128/ELU |
Node (layer3)/Activation | 32/ELU | 64/RELU | 64/ELU |
Node (layer4)/Activation | 16/ELU | 8/RELU | 64/ELU |
Node (layer 5) | 1 | 1 | 1 |
Optimiser | RMSprop | Adam | Adam |
Epoch with early stopping | 2000 | 2000 | 2000 |
Loss metric | MAE | MAE | MAE |
Wine ID Set A | Perceived Wine Quality | Wine Quality Proxy Indices (Mathematical Model) | ||||
---|---|---|---|---|---|---|
α = 0.8 n = 1.0 | α = 0.7 n = 1.0 | α = 0.8 n = 0.8 | α = 0.75 n = 1.0 | α = 0.75 n = 0.9 | ||
WAR16 | 5.02 | 5.848 | 4.521 | 4.678 | 5.154 | 4.639 |
MCH16 | 5.7 | 3.688 | 3.333 | 2.950 | 3.520 | 3.168 |
WCR16 | 6.55 | 6.849 | 5.418 | 5.479 | 6.101 | 5.491 |
WE16 | 6.09 | 6.271 | 5.051 | 5.017 | 5.636 | 5.072 |
OFRB316 | 6.35 | 6.428 | 4.997 | 5.142 | 5.678 | 5.110 |
OFRCP16 | 6.52 | 6.576 | 5.077 | 5.261 | 5.790 | 5.211 |
MG16 | 5.52 | 6.203 | 5.039 | 4.962 | 5.601 | 5.041 |
MG13 | 5.98 | 5.813 | 4.748 | 4.651 | 5.263 | 4.736 |
CG16 | 5.89 | 6.768 | 5.346 | 5.415 | 6.025 | 5.423 |
OMD16 | 6.07 | 6.920 | 5.471 | 5.536 | 6.163 | 5.546 |
NN16 | 6.02 | 6.768 | 5.360 | 5.415 | 6.032 | 5.429 |
WPP16 | 6.23 | 5.327 | 4.278 | 4.262 | 4.782 | 4.304 |
WPP13 | 5.43 | 4.863 | 4.071 | 3.890 | 4.459 | 4.013 |
CPB16 | 6.34 | 3.554 | 3.267 | 2.843 | 3.421 | 3.079 |
MPR16 | 4.72 | 5.257 | 4.353 | 4.205 | 4.792 | 4.313 |
OQR16 | 5.72 | 6.790 | 5.398 | 5.432 | 6.065 | 5.458 |
OQR13 | 5.3 | 6.730 | 5.322 | 5.384 | 5.995 | 5.396 |
NS16 | 4.82 | 6.742 | 5.242 | 5.394 | 5.954 | 5.359 |
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Tiwari, P.; Bhardwaj, P.; Somin, S.; Parr, W.V.; Harrison, R.; Kulasiri, D. Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way? Foods 2022, 11, 3072. https://doi.org/10.3390/foods11193072
Tiwari P, Bhardwaj P, Somin S, Parr WV, Harrison R, Kulasiri D. Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way? Foods. 2022; 11(19):3072. https://doi.org/10.3390/foods11193072
Chicago/Turabian StyleTiwari, Parul, Piyush Bhardwaj, Sarawoot Somin, Wendy V. Parr, Roland Harrison, and Don Kulasiri. 2022. "Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way?" Foods 11, no. 19: 3072. https://doi.org/10.3390/foods11193072
APA StyleTiwari, P., Bhardwaj, P., Somin, S., Parr, W. V., Harrison, R., & Kulasiri, D. (2022). Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way? Foods, 11(19), 3072. https://doi.org/10.3390/foods11193072