Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{YANG:2023:foodcont,
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author = "Yu Yang and Shangpeng Sun and Leiqing Pan and
Min Huang and Qibing Zhu",
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title = "Predictions of multiple food quality parameters using
near-infrared spectroscopy with a novel multi-task
genetic programming approach",
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journal = "Food Control",
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volume = "144",
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pages = "109389",
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year = "2023",
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ISSN = "0956-7135",
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DOI = "doi:10.1016/j.foodcont.2022.109389",
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URL = "https://www.sciencedirect.com/science/article/pii/S0956713522005825",
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keywords = "genetic algorithms, genetic programming, Prediction
model, Near-infrared technology, Multiple food quality
parameters, Evolutionary multi-task optimization,
Shared features, Private features",
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abstract = "In order to meet the increasing demand for food safety
and quality, new methods for simultaneous and rapid
determination of multiple food quality parameters
(FQPs) are urgently needed in the food industry.
Incorporating near-infrared (NIR) spectroscopy and
spectral prediction model for rapid, repeatable,
non-destructive, and low running costs quantitative
analysis of FQPs is enjoying increasing popularity in
the food industry. However, most existing
spectrum-based prediction models are trained under a
single-task learning framework, that is, a prediction
model for each quality parameter and spectrum is
constructed separately. This paradigm ignores possible
connections among prediction tasks of different FPQs,
which may result in the performance degradation of a
single FPQ prediction model. This study proposes a
novel multi-task genetic programming-based approach
named EM4GPO for building multiple FQPs predictions
simultaneously. In EM4GPO, the multi-dimensional trees
are used to encode the raw NIR spectrum to shared
features of multiple FQPs; for each FQP, a least square
support vector regression (LS-SVR) modeling is
performed on the shared features to obtain private
features and prediction model; during the optimization
process, a new algorithm is developed to optimize the
previously obtained shared and private features, and
LS-SVR prediction models through population evolution
by combining the multidimensional multiclass genetic
programming with multidimensional populations
optimization method with nondominated sorting method.
The proposed EM4GPO model is evaluated and compared
with nine popular NIR prediction models using 10 NIR
spectral datasets. The experimental results showed that
EM4GPO outperformed other commonly used methods in all
datasets which indicates that EM4GPO is competitive and
effective in solving the problem of multiple FQPs
predictions using the NIR spectrum",
- }
Genetic Programming entries for
Yu Yang
Shangpeng Sun
Leiqing Pan
Min Huang
Qibing Zhu
Citations