Research on COE Program with Machine Learning Algorithms
Jiashi Li
Department of Statistics and Applied Probability, UC Santa Barbara, CA, U.S.A.
Keywords: COE Coffee, Machine Learning, One-Hot Coding, Hedonic Pricing Theory.
Abstract: The market price of COE coffee depends on its heterogeneity characteristics, and hedonic model theory is the
dominant research approach for coffee prices. In this paper, the correlation between coffee prices and the New
York C-futures index is first verified. Further, machine learning algorithms such as Support Vector Regression
(SVR), Multilayer Perceptron (MLP) models are used to study the factors affecting prices. A more general
coding of coffee types is designed and improved the one-hot coding of the regression model. The performance
shows that the improved model is better in terms of performance. The prediction accuracy of the model
improved by 24.65% after generalized coding of coffee categories. The study further explores the
implementation of the hedonic pricing theory.
The growing demands for specialty coffee have led to
a rapidly growing market for specialty coffee in many
countries. The percentage of adults who consume
specialty coffee has increased in recent years.
Specialty coffee quality is a key factor in stabilizing
market development (Traore, 2018, Wilson, 2018,
Fields, 2018).
Many Latin American countries participate in the
Cup of Excellence (COE) program. Every year an
auction of coffee is held. A jury tastes the coffee
based on samples of brown beans submitted by the
farms. Each cup is given a score from 0 to 100, those
scoring 84 or more quality points in the competition
are awarded the prestigious Cup of Excellence Award
(Bacon, 2004). The winning coffees are ranked
according to their scores, and the coffee with the
highest score in a given category is awarded first
place, followed by the highest quality.
Scholars have used extensively COE dataset to
predict the price of specialty coffee. They studied the
role of product differentiation and quality production
in the world coffee market (Teuber, 2010, Ferreira,
2016, Liska, 2016, Cirillo, 2016). There is a growing
literature on the relationship between coffee quality
and regional environmental characteristics,
especially for the so-called specialty coffees, but
consumer price analysis can provide useful
information on coffee quality differences. A
comprehensive analysis is possible if the datasets can
cover both objective and subjective quality attributes.
Donnet analyzes the importance of sensory and
reputational attributes in the origin markets (Donnet,
2007, Weatherspoon, 2007, Hoehn, 2007). They
found that country of origin effect is evident except
sensory quality and scores.
The hedonic price model has been used to study
the relationship between prices and attributes of
agriculture, food, and real estate. This model is
inspired by Waugh's publication "Quality Factors
Affecting Vegetable Prices article and the work of
Rosen. This approach is used to measure and analyze
the contribution of a product's attributes (Hu, 2019,
He, 2019, Han, 2019, Gu, 2011, Zhu, 2011, Jiang,
2011). The price of the product is usually modeled as
a parameter. Thus, the regression model is used to
predict the quality fraction and the price based on
various attributes.
However, achieving healthy, sustainable price is a
challenge. The framework presented in this paper
integrates machine learning and consumption
models. Aggregate multiple regressions on various
subsamples of the dataset to improve the prediction
accuracy. Improving prediction accuracy and thus
controlling overfitting. Tree regression algorithms
are considered as non-linear, non-parametric methods
with high generalization.
Through the analysis of historical data, the
original purpose of establishing COE coffee is
validated. It is to keep the futures price from affecting
Li, J.
Research on COE Program with Machine Learning Algorithms.
DOI: 10.5220/0011191800003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 563-568
ISBN: 978-989-758-593-7
๎€ 2022 by SCITEPRESS โ€“ Science and Technology Publications, Lda. All rights reserved
the price of coffee, so as not to affect the income of
the farms and to ensure the stability of the coffee
market. Further, the historical data is modeled and
analyzed using machine learning algorithms. The
factors that have a strong influence on the price are
explored to provide suggestions for a good operation
of COE coffee in the future.
This paper is structured as follows: Section 2
describes the boutique coffee market and the
fundamentals of the Hedonic method. Section 3
introduces the one-hot coding approach with a
generalization that improves the accuracy of the
regression model. Simulation experimental results
are analyzed and modeling implications of the
improved model and the classical and regression
models: support vector regression and multilayer
perceptron models are compared in Section 4.
Finally, the conclusion is made in Section 5.
2.1 The Hedonic Price Model
The hedonic price model is used to determine the
effect of the substance and its properties on the
quality fraction or price of specialty coffee. Through
hedonic price analysis, the intrinsic and symbolic
attributes of each buyer for various coffees can be
expressed through price (Niklas, 2020, Rinke, 2020,
Oladunni, 2017, Sharma, 2017).
Regression models are commonly used to predict
the quality score and price of coffee. It is used to
predict the quality fraction and price of coffee based
on various attributes.
The formula is as follows Eq. (1).
where ๐‘Ž
denotes the number of characteristic
attributes. Therefore, the price of ๐‘ is given as Eq.
The hedonic price of feature ๐‘– can be defined as
Eq. (3).
In the hedonic pricing model, some variables can
be measured in the theoretical model. The framework
of the hedonic model can be used to find the effect of
a certain characteristic on the price while keeping
certain variables. It is also a price model nowadays
mainly used to calculate special goods.
2.2 The Regression Model
Regression is the prediction of continuous type data.
Studying the relationship between the dependent and
independent variables, such as sales prediction or
manufacturing defect prediction, the goal of a
regression model is to get each sample in the training
set to fit as close as possible to a linear model. While
general regression models use mean square error
MSE as the loss function.
Figure 1: MLP model.
Multilayer perceptron (MLP) is an artificial
neural network with a three-layer model. In addition
to the input layer and output layer, the middle layer
can have multiple hidden layers. The simplest MLP
contains only one hidden layer, that is, a three-layer
structure, as shown in figure 1.
2.3 The Support Vector Regression
Support Vector Regression (SVR) implements
different models depending on the input data, and
does regression if the input labels are continuous
values. By seeking to minimize the structured risk
to improve the generalization ability of the learning
machine, the empirical risk and the confidence range
are minimized, so as to achieve the purpose of
obtaining good statistical laws even in the case of a
small statistical sample size.
The SVM is formulated as the following linear
estimation function Eq. (4):
where ๐‘ค denotes the algorithm weight vector, ๐‘
denotes a constant, and โˆ…
denotes a mapping in
the feature space function.
0, ๐‘œ๐‘กโ„Ž๐‘’๐‘Ÿ๐‘ค๐‘–๐‘ ๐‘’
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
The formula Eq. (5), where ๐œ€ is a precision
parameter representing the radius of the tube located
around the regression function ๐‘“(๐‘ฅ).
In the SVR, the coefficients ๐‘ค and ๐‘ can be
estimated by the minimization method.
Economic factors interact with coffee prices in a
complex and non-linear way. The machine learning
algorithms are used to analyze historical data or past
experiences to optimize the performance criteria of
the model. For the problem of predicting the value or
number of variables, typical machine learning
algorithms (MLAs), mainly supervised learning
methods, can be divided into the following
categories: kernel learning methods, tree-based
methods, distance-based methods, and neural
network methods. To compare the MLA performance
between different theories and architectures, three
commonly used machine learning algorithms are
selected. Multi-layer perceptron models, Support
vector regression and multiple regression models are
used to improve prediction accuracy and avoid over-
Higher quality coffee gets better bids and prices
in the market. Altitude is an easily available and
commonly used proxy indicator. A systematic
comparison of price and altitude reveals a statistically
insignificant correlation between altitude and price.
The correlation between altitude and price was not
significant. Certification has a greater impact on price
than altitude. These relationships between price,
quality, and certification deserve further study. The
market attempts to establish a healthy coffee price
to achieve coffee quality, farm profitability
sustainability and healthy development. Refined
monitoring of coffee prices should provide an
important influence for a fair market.
Table 1: One-hot coding.
The one-hot codes are shown in table 1. The
classical one-hot coding method for coffee categories
is improved, from one coding for each category to a
combined coding method for each category. The new
method reduces the number of categories and
reconstructs the original sparse category features to
make the features more dense. The generalized
category is 32 categories. The encoding after
generalization is shown in table 2.
Research on COE Program with Machine Learning Algorithms
Table 2: Improved coding method.
This study utilizes the data set of the 2004-2011 Cup
of Excellence sponsored by the Alliance for Coffee
Excellence (Wilson 2015, Wilson 2015). The dataset
includes information on the final auction price of
each coffee, the grade of the coffee, the quantity of
coffee, farm data, elevation of the coffee trees,
processing method and origin.
Coffee price distribution is shown in fig.3, which
conforms to the cumulative Gaussian distribution.
The Gaussian mixture model assumes that the data of
each sub-data is consistent with the Gaussian
distribution, and the current data presents a
distribution that is the result of superimposing the
Gaussian distributions of each cluster together.
The relevance between the price and the futures
index is verified by the Pearson coefficient.
Fig. 2 is a heat map of the relationship between
the fields of the dataset. It can be found that the
relationship between the parameters of the coffee, the
highest correlation between the score and the price, is
0.568. These data indicate that all other data of the
coffee have an impact on its price, such as the taste of
the coffee, flavor, etc., which are to be further
Figure 2: Parameter coefficient heat map.
Figure 3: Distribution of the coffee_price.
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
Figure 4: Improved Regression.
Figure 5: SVR model.
Figure 7: MLP model.
Fig.4, Fig. 5, and Fig. 6, show the prediction
results for the dataset after modeling the three
algorithms, and after statistics and calculations, the
accuracy data of the four algorithms are shown in
table 3. It can be found that the classical multiple
regression is the least effective, at 49.51%. The
improved algorithm of generalized coding is the best
with 74.61%. It is higher than the two models, SVR
and MLP. The statistics show that the classical
multiple regression parameters vary greatly with an
intercept of -100. while the other three tested models
have an intercept error within -1 and the coefficient
errors of the parameters are within a 0.1 range.
Table 3: Score of compared algorithm.
Algorithm SVR MLP
Score 62.74% 56.98%
Algorithm C_Regression I_Regression
Score 49.51% 74.16%
This paper investigates the COE coffee bidding
system and designs a generalized coding of the
categories that affect the price. The validation was
carried out by three unsupervised learning algorithms
of machine learning. Simulation results reveals that
the improved regression algorithm outperforms
support vector machine and multilayer perceptron
models. The regression model after generalizing the
coffee category ranked first in terms of prediction
accuracy, followed by the SVR model. The third
place is the MLP model, and the last is the classical
regression model. Numerically, the proposed
improved model has an accuracy 24.65 higher than
the traditional regression algorithm, 11.42 higher
than SVR, and 17.18% higher than MLPA.
Limited by the number of samples and the
insufficient collection of factors about coffee, such as
coffee taste, major buying countries, sales volume,
etc., the accuracy of the algorithm can be further
improved. In the future, as the number of samples
increases and the quality of the dataset improves, the
neural network algorithm will be used to do further
prediction and analysis. The next step is to apply the
model to other related fields.
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Research on COE Program with Machine Learning Algorithms
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