Competitive Adaptive Reweighted Sampling Research Articles

Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

Non-destructive prediction of anthocyanin concentration in whole eggplant peel using hyperspectral imaging

Accurately detecting the anthocyanin content in eggplant peel is essential for effective eggplant breeding. The present study aims to present a method that combines hyperspectral imaging with advanced computational analysis to rapidly, non-destructively, and precisely measure anthocyanin content in eggplant fruit. For this purpose, hyperspectral images of the fruits of 20 varieties with diverse colors were collected, and the content of the anthocyanin were detected using high performance liquid chromatography (HPLC) methods. In order to minimize background noise in the hyperspectral images, five preprocessing algorithms were utilized on average reflectance spectra: standard normalized variate (SNV), autoscales (AUT), normalization (NOR), Savitzky–Golay convolutional smoothing (SG), and mean centering (MC). Additionally, the competitive adaptive reweighted sampling (CARS) method was employed to reduce the dimensionality of the high-dimensional hyperspectral data. In order to predict the cyanidin, petunidin, delphinidin, and total anthocyanin content of eggplant fruit, two models were constructed: partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). The HPLC results showed that eggplant peel primarily contains three types of anthocyanins. Furthermore, there were significant differences in the average reflectance rates between 400–750 nm wavelength ranges for different colors of eggplant peel. The prediction model results indicated that the model based on NOR CARS LS-SVM achieved the best performance, with a squared coefficient of determination (R2) greater than 0.98, RMSEP and RMSEC less than 0.03 for cyanidin, petunidin, delphinidin, and total anthocyanin predication. These results suggest that hyperspectral imaging is a rapid and non-destructive technique for assessing the anthocyanin content of eggplant peel. This approach holds promise for facilitating the more effective eggplant breeding.

Visualisation of moisture content distribution maps and classification of freshness level of loquats

Moisture content (MC) and freshness undergo evident changes during the storage of loquat fruits and thus are important indices of loquat fruit quality. Conventional methods for assessing MC and freshness are typically destructive and time-consuming, and cannot provide distribution maps of MC in the whole loquats. In this study, the use of hyperspectral imaging (HSI) was investigated for rapid and non-destructive prediction of MC in loquats, and for the visualisation of their MC distribution maps. Furthermore, freshness level discrimination models for loquats at different storage times were established using partial least squares discriminant analysis (PLS-DA), multinomial logistic regression (MLogR), and back-propagation neural network (BPNN) classification models. MC was well-predicted, with the best residual predictive deviation (RPD) of 2.40. Using the best prediction model, the distribution maps of MC in loquats were visualised. Additionally, the competitive adaptive reweighted sampling (CARS)-BPNN model obtained superior discrimination performance, with 95.56 % and 93.33 % accuracy for the calibration and prediction sets. Our results show that HSI is a promising fast and non-destructive method for determining MC and freshness of loquats.

Quantitative prediction and visualization of matcha color physicochemical indicators using hyperspectral microscope imaging technology

Color physicochemical indicators of matcha are related to sensory quality, and the distributions of color physicochemical indicators can reflect the uniformity of matcha powder. This study aimed to explore the feasibility of using hyperspectral microscope imaging (HMI) technology to predict and visualize matcha color physicochemical indicators. The average spectra (400–998 nm) were extracted from the regions of interest (ROI). Subsequently, competitive adaptive reweighted sampling (CARS), interval random frog (iRF), successive projections algorithm (SPA), and the combination of iRF and SPA (iRF-SPA) were adopted to screen the characteristic spectra variables that were associated with specific color physicochemical indicators. Partial least squares (PLS) regression was applied to develop the calibration models for predicting the color physicochemical indicators. The results showed that the iRF-SPA-PLS models obtained the optimal prediction results, with predictive correlation coefficients (Rp) of 0.9262 for L*, 0.8826 for a*, 0.8583 for b*, 0.8243 for chlorophyll a, 0.7518 for chlorophyll b, and 0.8093 for chlorophyll total. Finally, the distribution maps of matcha color physicochemical indicators were visualized using these optimized models. The results suggested that the HMI technology coupled with chemometrics has enormous potential to predict and visualize the color physicochemical indicators of matcha, which could reflect the uniformity of matcha powder to ensure the stability of matcha quality. The approach presented in this study can also be applied to quality control of other powdery foods.

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics.

Dendrobium officinale has drawn increasing attention as a dual-use plant with herbal medicine and food applications. The efficient quality evaluation of D. officinale is essential to ensuring its nutritional and pharmaceutical value. Given that traditional analytical methods are generally time-consuming, expensive, and laborious, this study developed a rapid and efficient approach to assess the quality of D. officinale from different geographical origins by near-infrared (NIR) spectroscopy and chemometrics. Total saponins, mannitol, and naringenin were utilized as quality indicators. Two wavelength selection methods, namely, uninformative variable elimination and competitive adaptive reweighted sampling (CARS), were utilized to enhance the prediction accuracy of the quantification model. Moreover, multiple spectral pretreatment methods were applied for model optimization. Results indicated that the partial least squares (PLS) model constructed based on the wavelengths selected by CARS exhibited superior performance in predicting the contents of the quality indicators. The coefficient of determination (RP2) and root mean square error (RMSEP) in the independent test sets were 0.8949 and 0.1250 g kg-1 for total saponins, 0.9664 and 0.2192 g kg-1 for mannitol, and 0.8570 and 0.003159 g kg-1 for naringenin, respectively. This study revealed that NIR spectroscopy and the CARS-PLS model could be used as a rapid and accurate technique to evaluate the quality of D. officinale.

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

Hyperspectral imaging combined with GA-SVM for maize variety identification.

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

Influences of season on cattle slurry compositions predicted by micro-NIR spectrometry

In this study, we used a low-cost micro-near-infrared (NIR) spectrometer to analyze the composition of cattle slurries and examined the influence of the season (spring vs. winter) and characteristic wavelength on the performance of the corresponding quantitative models. One hundred and twenty-seven cattle slurry samples collected from five farms in spring and winter (Heilongjiang, China) were characterized in terms of total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), available potassium (AK), organic matter (OM), and pH using reference methods. The partial least squares (PLS) method was used to establish seasonal models that involved winter and a mixed model to quantify cattle slurry composition. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The results showed that the winter model successfully predicted the TP content using the full band. The coefficient of determination for prediction (R2p) and ratio of prediction to deviation (RPD) were 0.846 and 3.077, respectively. This model was useful for predicting TK and AK contents and pH (R2p = 0.551–0.773, RPD = 2.251–2.727) and moderately useful for predicting TN and OM contents (R2p = 0.517 and 0.498, respectively; RPD = 1.892 and 2.094, respectively). For all compositions, the prediction performance was better for the winter models than for the mixed models. Although the use of characteristic wavelengths improved the efficiency of model construction, it had no positive effect on the performance of the mixed models. This study enables the setup of a rapid testing method for safely returning slurry to the field.

Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction, first derivative, second derivative, and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination, competitive adaptive reweighted sampling, and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

Non-Destructive Classification of Maize Seeds Based on RGB and Hyperspectral Data with Improved Grey Wolf Optimization Algorithms

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm’s search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future.

Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique.

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG-CARS-RF and SG-CARS-SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.

Improve the accuracy of FT-NIR for determination of zearalenone content in wheat by using the characteristic wavelength optimization algorithm

The research contributes a unique method to achieve high-precision quantification of zearalenone (ZEN) in wheat, significantly improving accuracy in the analysis. Fourier transform near infrared spectroscopy (FT-NIR) was employed to capture the spectral information of wheat with different mildew degrees. Three feature selection models, competitive adaptive reweighted sampling (CARS), support vector machine-recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble-least absolute shrinkage and selection operator (MFE-LASSO) were introduced to processed pre-processed near-infrared spectral data and established partial least squares (PLS) regression according to the selected features. The outcomes indicated that the optimal generalization performance was achieved by the PLS model optimized through the MFE-LASSO model. The root mean square error of prediction (RMSEP) was 18.6442 μg·kg-1, coefficient of predictive determination (RP2) was 0.9545, and relative percent deviation (RPD) was 4.3198. According to the results, it is feasible to construct a stoichiometric model for the quantitative determination of ZEN in wheat by using FT-NIR combined with feature selection algorithm, and this method can also be extended to the detection of various molds in other cereals in the future.

Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.

Soluble solid content (SSC), firmness, and color (L*, a*, and b*) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, L*, a*, and b*, respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, L*, a*, and b* were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality.

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm-1. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Quantitative analysis of reducing sugars in solid‐state fermentation based on NIR combined with GA‐AdaBoost

SummaryThis study focuses primarily on the real‐time monitoring of reducing sugar levels throughout solid‐state ethanol fermentation. A predictive model for accurate quantification of reducing sugar levels will be formulated through a comprehensive analysis of near‐infrared spectra across different fermentation stages. Building a predictive model using Adaptive Boosting (AdaBoost), The spectral data was pre‐processed using the Standard Normal Variate (SNV) method, followed by feature band selection using the Genetic Algorithm (GA), resulting in the identification of 181 characteristic wavelengths. The resulting GA‐AdaBoost model has excellent performance metrics: of 0.989, RMSEC of 0.393, of 0.978, RMSEP of 0.640, RPD of 6.651 and RER of 0.142. Comparative evaluations against Competitive Adaptive Reweighted Sampling (CARS), Successive Projection Algorithm (SPA), Uninformative Variable Elimination (UVE) and Principal Component Analysis (PCA) consistently underscore the superior efficacy of the GA‐AdaBoost model. These results robustly confirm its reliability in accurately quantifying reducing sugars in solid‐state fermentation processes.

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible-Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm.

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400-1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R2 and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

Quantitative detection of crude protein in brown rice by near-infrared spectroscopy based on hybrid feature selection

It is important for rice breeding and quality evaluation to predict the protein content of brown rice rapidly and easily. In this study, near-infrared spectroscopy (NIRS) was utilized to establish a model for detecting crude protein content in brown rice based on 349 samples prepared from three kinds of brown rice, and the performance of the model was evaluated. Improved interval partial least squares (iPLS) was used to divide and screen different feature intervals after spectral preprocessing in this research. On this basis, competitive adaptive reweighted sampling (CARS) optimized the selected feature intervals, and finally 14 effective spectral features concentrated in 1160 nm–1338 nm were selected from 1050 features. The above hybrid feature selection has more advantages than the single selection. Support vector regression (SVR) calibration model was established, and partial least squares regression (PLSR) model commonly used in similar studies was selected as a comparison. The optimal spectral preprocessing method was selected according to the model prediction effect. The coefficient of determination (R2), R2 of cross-validation set (Rcv2), root mean square error of prediction (RMSEP), and relative percent difference (RPD) evaluated for the prediction model reached 0.9185, 0.8876, 0.2040% and 3.5194, respectively. The results showed that the designed method can be used for the rapid determination of the crude protein content of brown rice, providing a convenient, efficient and non-destructive method for related detection.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.