A hybrid optimization method for sample partitioning in near-infrared analysis.

The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fuses with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

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

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.

Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy.

Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management.

Rapid Detection of Pomelo Fruit Quality Using Near-Infrared Hyperspectral Imaging Combined With Chemometric Methods.

Pomelo is an important agricultural product in southern China. Near-infrared hyperspectral imaging (NIRHI) technology is applied to the rapid detection of pomelo fruit quality. Advanced chemometric methods have been investigated for the optimization of the NIRHI spectral calibration model. The partial least squares (PLS) method is improved for non-linear regression by combining it with the kernel Gaussian radial basis function (RBF). In this study, the core parameters of the PLS latent variables and the RBF kernel width were designed for grid search selection to observe the minimum prediction error and a relatively high correlation coefficient. A deep learning architecture was proposed for the parametric scaling optimization of the RBF-PLS modeling process for NIRHI data in the spectral dimension. The RBF-PLS models were established for the quantitative prediction of the sugar (SU), vitamin C (VC), and organic acid (OA) contents in pomelo samples. Experimental results showed that the proposed RBF-PLS method performed well in the parameter deep search progress for the prediction of the target contents. The predictive errors for model training were 1.076% for SU, 41.381 mg/kg for VC, and 1.136 g/kg for OA, which were under 15% of their reference chemical measurements. The corresponding model testing results were acceptably good. Therefore, the NIRHI technology combined with the study of chemometric methods is applicable for the rapid quantitative detection of pomelo fruit quality, and the proposed algorithmic framework may be promoted for the detection of other agricultural products.

FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn.

Protein and total fat are two ingredients to measure the quality of corn. The aim of this study is to evaluate the quality of corn by the dual-component join determination through Fourier transform near infrared (FT-NIR) spectroscopic analysis. The calibration models were established by the systematic study performed respectively in the four regions of the whole range, the second overtone, the first overtone, and the combination. Whittaker smoother was introduced as an attractive alternative data preprocessing method. With the optimized parameters, Whittaker smoother indicates its priority for improving modeling results in any of the four regions. The predictive abilities were compared between the joint analysis of protein and total fat and the separate analysis of each single component by partial least squares (PLS) modeling. The uncertainty in parameter was further estimated for the linear models. It is suggested that the joint analysis of dual-component always leads to better predictive results, and also provided good evaluation results for the independent validation samples. For the joint analysis, the optimal region for protein was the combination (5400-4000 cm(-1)), and the optimal region for total fat was the first overtone (7200-5400 cm(-1)). The optimal PLS models also provided appreciate predictive performance for both protein and total fat. And the parameter uncertainty determination provided an acceptable estimate of the measured uncertainty for the FT-NIR analysis of corn. In general, the joint analysis of dual-component is a better strategy for FT-NIR analysis of corn, and it is hoped to be tested for other objects.