A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models

Abstract

<jats:p>The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable’s population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.</jats:p>

Geographical Traceability of &lt;i&gt;Clinacanthus nutans&lt;/i&gt; with Near-Infrared Pectroscopy and Chemometrics

“…GS is a traversal algorithm that tries all (c, g) parameter pairs and then finds the (c, g) parameter pair with the highest accuracy, namely the optimal parameters, through cross-validation [25]. GA is a computational model that simulates natural selection and genetic mechanisms of Darwin's theory of evolution and is a method of searching for an optimal solution [26]. PSO is a stochastic optimization method based on populations.…”

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

“…In general, a three-axis accelerometer acts as the head installation of the vibration test system and is used to gather the raw vibration signal synchronously from three orthogonal axes (Xiao et al, 2017;Feng et al, 2020aFeng et al, , 2020bLi et al, 2020).…”

“…The genetic algorithm (GA)-deep neural network non-linear regression prediction model has been proposed to achieve a robot's high-precision positioning performance under any external payload (Chen et al, 2020a(Chen et al, , 2020b. Feng et al (2020aFeng et al ( , 2020b proposed a novel optimisation framework of grid search moving window-linear predictive coding-GA for near-infrared (NIR) calibration, and experiments have shown that this algorithm provides an effective method for improving the performance of the NIR calibration models. Gao et al (2018) proposed a novel backpropagation neural network (BPNN)-PSO hybrid algorithm for the kinematic parameter identification of industrial robots with an enhanced convergence response; their results proved that the proposed algorithm has fewer iterations and a faster convergence speed.…”