An efficient variable selection method based on random frog for the multivariate calibration of NIR spectra

Determination of seawater COD spectra using double-loop contraction and sorted frog optimization

This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet-visible (UV-Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV-Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra.

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion

Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress

The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (R_p=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R_p=0.9161) for SP and CARS-ELM (R_p=0.9021) for MDA, all with R_p higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.

Application of the combination method based on RF and LE in near infrared spectral modeling

The dimensionality of near-infrared (NIR) spectral data is often extremely large. Dimensionality reduction of spectral data can effectively reduce the redundant information and correlation between spectral variables and simplify the model, which is crucial to increasing the model's performance. As a nonlinear feature extraction method, Laplacian Eigenmaps (LE) may preserve the local neighborhood information of the dataset, has high robustness, and is simple to compute. However, when the LE algorithm maps the data from high-dimensional space to low-dimensional space, it is often disturbed by irrelevant information and multicollinearity in the spectral data, which lowers the model's prediction performance. Random Frog (RF) algorithm can eliminate noise and collinearity in the spectrum. Therefore, before using the LE algorithm, we use the RF algorithm to eliminate irrelevant information in the spectrum and reduce the correlation between the spectra variables to increase the efficiency of the LE algorithm. We used the RF + LE algorithm to reduce the dimensionality of two public NIRS datasets (soil datasets and pharmaceutical tablets datasets) and compared it with RF and LE algorithms alone. We utilized Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) to establish regression models. The experimental findings demonstrate that compared with the RF algorithm and LE algorithm, the RF + LE combination method can reduce the dimension of spectral variables and model complexity, and improve regression models' prediction accuracy and stability. It is an effective dimensionality reduction method for the near-infrared spectrum.

Non-destructive assessment of equivalent umami concentrations in salmon using hyperspectral imaging technology combined with multivariate algorithms

This study utilized equivalent umami concentrations (EUC) to characterize umami intensity in salmon with different freeze-thaw times. A rapid and non-destructive method was established to determine EUC values in salmon which is based on hyperspectral imaging (HSI) system combined with multiple characteristic variable screening methods. The established CARS-PLS model showed greater advantages in correlating the reference values of spectral data with EUC in salmon with Rc of 0.9012, Rp of 0.9009, RMSECV of 0.82, and RMSEP of 0.88. The model was employed pixel-wise to visualize the distribution of EUC with different freeze-thaw times, which demonstrated the reduction of EUC value with the increasing of freeze-thaw times. Therefore, this reseearch showed hyperspectral imaging (HSI) system combined with chemometrics possesses a substantial capability to predict and visualize the EUC of salmon, which would provide an intuitive understanding of salmon quality prediction and detection.

Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares

Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.