Wavelength Selection Method Research Articles

Efficient prediction of SOC and aggregate OC components by continuous wavelet transform spectra under different feature selection methods

Soil Organic Carbon (SOC) fixation plays a vital role in reducing greenhouse gas emissions and the formation of soil aggregates is recognized as a crucial process in SOC fixation. In the current study, we characterized spectral information on SOC and aggregate Organic carbon (OC) components by using a rapid and robust method. Soil samples were collected from different layers to obtain the SOC and aggregate OC components to record corresponding spectra. The Continuous Wavelet Transform (CWT) spectra were then combined with various characteristic wavelength selection methods to build Multivariate Linear Regression (MLR), BP neural network (BP), and Random Forest (RF) models. Finally, the optimal combination of characteristic wavelengths based on the best-performing model for each index was determined. The results demonstrated that the model's accuracy, which was built using the set of characteristic wavelengths of the best model for all metrics, was higher than that of the best model for a single metric. This was due to the correlation between SOC and aggregate OC components. The best set of characteristic wavelengths were those selected by the Sequential Backward Selection method with wavelet decomposition scales of 23, 26, and 27, and those selected by the Minimum Redundancy Maximum Relevance method with wavelet decomposition scale 27. The RF model for Free silt and clay particles organic carbon (FSP-OC) showed the highest accuracy among the four SOC prediction models. In addition, it was discovered that as the wavelet decomposition scales become more similar, the extracted characteristic wavelengths also become more similar, resulting in similar model predictions. In summary, the accuracy of the predictive model for SOC and its constituents can be demonstrably improved by reasonable combination of characteristic wavelengths selection method and wavelet decomposition scale.

Rapid multi-indicator quality evaluation of Eucommia ulmoides by near-infrared spectroscopy combined with chemometrics

ObjectiveUsing near-infrared spectroscopy and chemometrics, to establish a method for determination of multiple indicators in Eucommia ulmoides (EU) to achieve its rapid quality evaluation. Method83 batches of EU decoction slices were collected, and the content of pinoresinol diglucoside, chlorogenic acid and geniposidic acid in them was determined by high-performance liquid chromatography, the content of total flavonoids was determined by UV–Vis spectrophotometry, while the antioxidant activity of EU was measured by methods of DPPH, ABTS and FRAP, respectively. The near-infrared spectra of EU were acquired in diffuse reflectance mode, which were subjected to multivariate statistical analysis in relation with the above measured values. In order to achieve good model performance, various methods for spectra pretreatment and wavelength selection were explored. Especially, with the help of four wavelength selection methods based on swarm intelligence optimization, such as genetic algorithm (GA), gray wolf algorithm (GWO), improved global flower pollination algorithm (mgFPA), and improved sine and cosine optimization algorithm (ISCA), the partial least squares regression models were improved prominently. The model quality was evaluated by calculating the coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) in the sets of calibration and prediction. ResultsRegardless of which indicator in EU, the model performance was fairly good, especially after the wavelength selection by GA and other three methods, where R2 > 0.9, RPD > 3 in both calibration and prediction, indicating that the models had good prediction accuracy. Based on the overall algorithmic efficiency and model results, we have the following ranking: GA > GWO > mgFPA > ISCA. ConclusionThe present models provide a novel method for rapid and comprehensive quality evaluation of EU, which may be of great significance for quality evaluation and quality control of EU.

Explainable AI-driven wavelength selection for hyperspectral imaging of honey products

Hyperspectral imaging (HSI) offers a rapid and non-destructive method for inspecting various food and agricultural products. Though HSI delivers rich data with its continuous spectral bands, it simultaneously presents challenges due to its high dimensionality, potential redundancy, and noise interference. To tackle these challenges, dimension reduction becomes essential in HSI data processing. Our study integrated 11 explainable artificial intelligence (XAI) algorithms with convolutional neural networks (CNNs) to craft an advanced wavelength selection method tailored for honey product analysis. We developed two CNN models and applied five normalization techniques to differentiate honey derived from 23 distinct botanical sources. Through the XAI algorithms, we determined the significance of each spectral wavelength, pinpointing the most characteristic bands. We then used the top eight spectral bands—selected from the averaged significance scores across the XAI algorithms—to optimize the combination of CNN classifiers and normalization techniques. This approach not only streamlines HSI data analysis but also validates the robustness of our XAI-based feature selection methodology. Experimental results demonstrated that the CNN achieved a macro average F1 score of ≥0.99 using just 8 spectral bands selected by averaging the feature importance scores from different XAI algorithms. The generalization ability of our proposed band selection method was further demonstrated by experiments on three HSI remote sensing databases. Overall, our research highlights the potential of XAI algorithms in feature selection, significantly reducing the dimensionality of HSI data without compromising performance. This approach establishes a methodological foundation for designing a cost-effective multispectral imaging system capable of distinguishing the botanical origin of honey.

Improving transferability and service life of the calibration model of total plant alkaloids in tobacco leaves on seven NIR spectroscopy devices by multi-step wavelength selection methods

The rapid determination of total plant alkaloids (TPA) content in tobacco products by means of near infrared (NIR) spectroscopy is becoming conventional detection method in tobacco industry. An established NIR model of TPA is anticipated to be shared by other devices as long as possible not needing to update the model. Present study developed a three-step wavelength selection method SISCW (selecting important and stable characteristic wavelengths）to build a robust NIR calibration model of TPA to improve its transferability and to prolong its use period at many devices. For this, 292 flue-cured tobacco leaf samples from more than 10 regions of China, which were harvested in 2011–2013 and tested on the primary NIR device, were used to build the model. 77 samples harvested from 2011 to 2013 were applied to verify the transferability of the model on six secondary NIR devices. 180 samples harvested from 2014 to 2020 that were divided into 7 groups according to their growing years, were used to examine the long-term application capacity of the model on the seven NIR devices. The SISCW method integrates an image processing method of scale invariant feature transformation (SIFT) with analyzing standard deviation of the sample spectra (SDSS) and water absorption coefficients to select important and stable characteristic wavelengths (SISCW). The wavelengths selected by the SISCW were recorded as Uisc. The results indicated that the partial least square (PLS) calibration model of TPA based on Uisc (SISCW-PLS) performed well on both primary and the six secondary devices when it was employed to predict TPA of the 77 samples of 2011–2013. The model has run 7 years on the primary and 3 of the secondary devices from 2014 to 2020. This emphasizes the SISCW method has great potential for improving transferability and service-life of TPA calibration model.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220–1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.

PROSPECT-GPR: Exploring spectral associations among vegetation traits in wavelength selection for leaf mass per area and water contents

Leaf mass per area (LMA) and equivalent water thickness (EWT) are key indicators providing information on plant growth status and agricultural management, and their retrieval is commonly done through radiative transfer models (RTMs) such as the PROSPECT model. However, the PROSPECT model is frequently hampered by the ill-posed problem as a consequence of measurement and model uncertainties. Here, we propose a wavelength selection method to improve the inversion of EWT and LMA by integrating PROSPECT with a machine learning algorithm (Gaussian process regression (GPR); PROSPECT-GPR for short). The GPR model conducted sorting of wavelengths and the PROSPECT-D was used to determine the optimal number of characteristic wavelengths. The results demonstrated that the estimation of EWT (R2 = 0.80; RMSE = 0.0021) and LMA (R2 = 0.71; RMSE = 0.0021) using the proposed wavelengths and PROSPECT inversion all exhibited superior accuracy in comparison with those from previous studies. The efficacy of PROSPECT-GPR in exploring the spectral linkage among vegetation traits was demonstrated by selecting wavelengths associated with leaf structure parameter N and EWT (1368 nm) that turn out to contribute to the estimation of LMA. The findings lay a strong foundation for understanding the spectral linkage among vegetation traits, and the proposed wavelength selection method provides valuable insights for selecting informative spectral wavelengths for RTMs inversion and designing future remote sensors.

Interval interaction moving window partial least squares for wavelength interval selection in near infrared spectroscopy

Variable selection is a highly effective approach to improve model performance when analyzing and modeling data from near-infrared spectroscopy. Moving window partial least squares (MWPLS), a well-known and classical wavelength interval selection algorithm, was widely used in the field of near-infrared spectroscopy. However, it has gradually faded from the spotlight due to its limitations. In this study, a modified MWPLS method called interval interaction moving window partial least squares (iMWPLS), was proposed to improve some limitations of MWPLS, including the lack of consideration for the combination effects of variables, the tendency to select redundant wavelength intervals, and the need for artificial intervention. By utilizing the framework of MWPLS, it could iteratively search, test, and add the intervals with the local minimum errors to the set of basic intervals. The method has high sensitivity to the combination effects of variables and enables accurate selection of intervals. Three NIR datasets, including corn starch, pharmaceutical tablets, and soil dataset were tested to evaluate the performance of iMWPLS. The results show that iMWPLS has better performance than other seven wavelength selection methods, including MWPLS, SEPA-MWPLS, fiPLS, biPLS, siPLS, iVISSA, and iRF.

Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures

Process analytical technology (PAT) is an essential tool within pharmaceutical manufacturing to ensure consistent quality and maintain process control. Near-infrared (NIR) spectroscopy is one of the most popular PAT techniques, particularly for monitoring active pharmaceutical ingredient (API) concentrations. To interpret the spectral outputs of NIR spectroscopy, advanced multivariate models are required. Calibration-free models such as iterative optimization technology (IOT) algorithms are increasingly of interest, due primarily to their reduced material and time burdens. Variable/wavelength selection is a common method to improve prediction performance and robustness for IOT by focusing on spectral regions with the most relevant information. However, currently proposed wavelength selection approaches rely on training sets for optimization, therefore reducing or removing the advantages of IOT over empirical calibration-dependent models. In this work, a true calibration-free wavelength selection method is proposed based on measuring the difference between individual wavelengths of a mixture spectra and the net analyte signals via a wavelength angle mapper (WAM). An extension of the WAM utilizing a spectral window of wavelength instead of individual wavelengths, called SWAM, was also developed. However, the SWAM method does require a small training set to optimize wavelength selection parameters. The WAM and SWAM methods showed similar prediction performance for API in pharmaceutical powder blends when compared against other calibration-dependent models and the base IOT algorithm.

Transferring near infrared spectral calibration models without standards via multistep wavelength selection

Two case studies were conducted to verify calibration model transfer methods without standards by multi-step wavelength selection, using 3–7 near infrared spectrometers to predict ingredients in corn and total plant alkaloids (TPA) in tobacco leaves. Based on the characteristic wavelengths of Uc, which are selected using the scale-invariant feature transform (SIFT), this study advances two multistep wavelength selection methods by selecting wavelengths with high independence and a high standard deviation of the sample spectra (SDSS). The first method, SIFT-SDSS-CORX, selects important characteristic wavelengths Uc-i from Uc whose SDSS is greater than a threshold SDSSacrit. Subsequently, rx, the correlation coefficient matrix between spectral signals of Uc-i, is calculated, and only one wavelength is retained from those whose correlation coefficients exceed a threshold, rxacrit. The wavelength set Uc-i-rx, which is finally screened, is important and independent. In the second method, SIFT-CORX-SDSS, Uc-rx is first selected from Uc by retaining only one wavelength from those whose correlation coefficients between spectral signals of Uc exceed a threshold, rxbcrit. Subsequently, the wavelengths Uc-rx-i with SDSS exceeding a threshold SDSSbcrit are selected from Uc-rx. Near infrared spectroscopy calibration models for predicting protein and oil in corn and TPA in tobacco leaves were built using partial least squares regression (PLS) based on different wavelength sets of Uc, Uc-i, Uc-i-rx, Uc-rx, and Uc-rx-i, respectively. The latent variables used in the PLS models were determined by an accumulative contribution ratio over 99.9%. The results indicate that the PLS models built on Uc-i-rx and Uc-rx-i are effective on both primary and secondary units for corn and tobacco samples. This study utilises a three-step wavelength selection method to select highly independent, important, and characteristic spectral variables, thereby enhancing the robustness, simplicity, and interpretability of NIR) calibration models and facilitating their transfer to secondary units without standards.

Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm

Wavelength selection is crucial to the success of near-infrared (NIR) spectroscopy analysis as it considerably improves the generalization of the multivariate model and reduces model complexity. This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. The proposed iFPA consists of three phases. First, the flower pollination algorithm is applied to search for informative spectral variables, followed by variable elimination. Subsequently, the iFPA performs a local search to determine the best continuous interval spectral variables. The interpretability of the selected variables is assessed on three public NIR datasets (corn, diesel and soil datasets). Performance comparison with other competing wavelength selection methods shows that the iFPA used in conjunction with the PLSR model gives better prediction performance, with the root mean square error of prediction values of 0.0096–0.0727, 0.0015–3.9717 and 1.3388–29.1144 are obtained for various responses in corn, diesel and soil datasets, respectively.

A wavelength selection method based on optical properties for slight bruise detection on Fuji apples in the early stage

In this study, a new method was proposed to select effective wavelengths in visible and near infrared waveband from 400 nm to 1050 nm to detect bruises of Fuji apples. Different from traditional methods which selected wavelengths based on the spectral data, this method selected wavelengths by comprehensively considering the tissues’ optical properties and apples’ diffuse reflectance. The measurement results of normal and bruised apples showed that in the detection band, more obvious tissues’ optical property differences led to larger diffuse reflectance difference between normal and bruised apples. 545 nm was chosen to be the characteristic wavelength based on the optical property difference between normal and bruised apple tissues. This result was close to 552 nm which was selected based on diffuse reflectance difference. Each wavelength was assumed to be an independent classifier for bruised/normal classification. Receiver operating characteristic (ROC) analysis was used to evaluate all the classifiers. The evaluation result shows that the waveband around 545 nm is the best classifier. In addition, if the threshold of the classifier is set properly, 720 nm is also a good choice in detecting bruise for its high reflectance. This method not only selected effective wavelengths but also provided theoretical guidance of wavelength selection.

New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models

The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380–1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.

Spectral separation degree method for Vis-NIR spectroscopic discriminant analysis of milk powder adulteration

Adulteration detection of adding ordinary milk powder to high-end dedicated milk powder is challenging due to the high similarity. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the discriminant analysis models of pure brand milk powder and its adulterated milk powder (including unary and binary adulteration) were established. Standard normal variate transformation and Norris derivative filter (D = 2, S = 11, G = 5) were jointly used for spectral preprocessing. The separation degree and separation degree spectrum between two spectral populations were proposed and used to describe the differences between the two spectral populations, based on which, a novel wavelength selection method, named separation degree priority combination-kNN (SDPC-kNN), was proposed for wavelength optimization. SDPC-wavelength step-by-step phase-out-kNN (SDPC-WSP-kNN) models were established to further eliminate interference wavelengths and improve the model effect. The nineteen wavelengths in long-NIR region (1100–2498 nm) with a separation degree greater than 0 were used to establish single-wavelength kNN models, the total recognition-accuracy rates in prediction (RARP) all reached 100%, and the total recognition-accuracy rate in validation (RARV) of the optimal model (1174 nm) reached 97.4%. In the visible (400–780 nm) and short-NIR (780–1100 nm) regions with the separation degree all less than 0, the SDPC-WSP-kNN models were established. The two optimal models (N = 7, 22) were determined, the RARP values reached 100% and 97.4% respectively, and the RARV values reached 96.1% and 94.3% respectively. The results indicated that Vis-NIR spectroscopy combined with few-wavelength kNN has feasibility of high-precision milk powder adulteration discriminant. The few-wavelength schemes provided a valuable reference for designing dedicated miniaturized spectrometer of different spectral regions. The separation degree spectrum and SDPC can be used to improve the performance of spectral discriminant analysis. The SDPC method based on the separation degree priority proposed is a novel and effective wavelength selection method. It only needs to calculate the distance between two types of spectral sets at each wavelength with low computational complexity and good performance. In addition to combining with kNN, SDPC can also be combined with other classifier algorithms (e.g. PLS-DA, PCA-LDA) to expand the application scope of the method.

Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS.

In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV=0.0336, R2 c=0.9951 in the calibration set; RMSEP=0.0688, R2 p=0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.

Trampoline-related knee injuries and differences between children and adults. A cross-sectional study

Compared with the complexity of chemical methods, near-infrared spectroscopy (NIRS) is widely used in the detection of protein content because of its advantages of being fast and non-destructive. Aiming to tackle the problem that the raw near-infrared spectroscopy contains many redundant wavelengths, which affects the accuracy of quantitative prediction and requires expertise to process, we propose an end-to-end network: Band Reweighted Network (BR-Net) that automates wavelength reweighted and quantitative prediction of protein content in rapeseed. Unlike extracting part of wavelengths by the traditional wavelength selection methods, BR-Net retains all spectral wavelengths and assigns different weights to the wavelengths to express the correlation with the corresponding concentration, which enables wavelength selection without ignoring the information contained in the less relevant wavelengths. We compare BR-Net with traditional selection methods such as SPA, LARS, CARS, and UVE to verify its efficiency and robustness, finding that the R2 of the training set and test set are 0.9797 and 0.9215, the RMSEC and RMSEP are 0.4053 and 0.8501, respectively, and the RPD is 3.5686, which prove BR-Net outperforms all the traditional methods. The network described here is universally applicable to a variety of NIR quantitative analyses.

Peroneusläsion beim Fußball: Die Steigbügelplastik sorgt für ein funktionell gutes Ergebnis – schützt allerdings nicht vor einer Re-Ruptur des vorderen Kreuzbandes – ein Case Report

Compared with the complexity of chemical methods, near-infrared spectroscopy (NIRS) is widely used in the detection of protein content because of its advantages of being fast and non-destructive. Aiming to tackle the problem that the raw near-infrared spectroscopy contains many redundant wavelengths, which affects the accuracy of quantitative prediction and requires expertise to process, we propose an end-to-end network: Band Reweighted Network (BR-Net) that automates wavelength reweighted and quantitative prediction of protein content in rapeseed. Unlike extracting part of wavelengths by the traditional wavelength selection methods, BR-Net retains all spectral wavelengths and assigns different weights to the wavelengths to express the correlation with the corresponding concentration, which enables wavelength selection without ignoring the information contained in the less relevant wavelengths. We compare BR-Net with traditional selection methods such as SPA, LARS, CARS, and UVE to verify its efficiency and robustness, finding that the R2 of the training set and test set are 0.9797 and 0.9215, the RMSEC and RMSEP are 0.4053 and 0.8501, respectively, and the RPD is 3.5686, which prove BR-Net outperforms all the traditional methods. The network described here is universally applicable to a variety of NIR quantitative analyses.

BR-Net: Band reweighted network for quantitative analysis of rapeseed protein spectroscopy

Compared with the complexity of chemical methods, near-infrared spectroscopy (NIRS) is widely used in the detection of protein content because of its advantages of being fast and non-destructive. Aiming to tackle the problem that the raw near-infrared spectroscopy contains many redundant wavelengths, which affects the accuracy of quantitative prediction and requires expertise to process, we propose an end-to-end network: Band Reweighted Network (BR-Net) that automates wavelength reweighted and quantitative prediction of protein content in rapeseed. Unlike extracting part of wavelengths by the traditional wavelength selection methods, BR-Net retains all spectral wavelengths and assigns different weights to the wavelengths to express the correlation with the corresponding concentration, which enables wavelength selection without ignoring the information contained in the less relevant wavelengths. We compare BR-Net with traditional selection methods such as SPA, LARS, CARS, and UVE to verify its efficiency and robustness, finding that the R2 of the training set and test set are 0.9797 and 0.9215, the RMSEC and RMSEP are 0.4053 and 0.8501, respectively, and the RPD is 3.5686, which prove BR-Net outperforms all the traditional methods. The network described here is universally applicable to a variety of NIR quantitative analyses.

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

The growth quality of Pinus massoniana (Lamb.) seedlings is closely related to the survival rate of afforestation. Moisture content detection is an important indicator in the cultivation of forest seedlings because it can directly reflect the adaptability and growth potential of the seedlings to the soil environment. To improve the accuracy of quantitative analysis of moisture content in P. massoniana seedlings using near-infrared spectroscopy, a total of 100 P. massoniana seedlings were collected, and their near-infrared diffuse reflectance spectra were measured in the range of 2500 to 800 nm (12,000 to 4000 cm−1). An integrated learning framework was introduced, and a quantitative detection model for moisture content in P. massoniana seedlings was established by combining preprocessing and feature wavelength selection methods in chemometrics. Our results showed that the information carried by the spectra after multiple scattering correction (MSC) preprocessing had a good response to the target attribute. The stacking learning model based on the full-band spectrum had a prediction coefficient of determination R2 of 0.8819, and the prediction accuracy of moisture content in P. massoniana seedlings could be significantly improved compared to the single model. After variable selection, the spectrum processed by MSC and feature selection with uninformative variable elimination (UVE) showed good prediction effects in all models. Additionally, the prediction coefficient of determination R2 of the support vector regression (SVR)—adaptive boosting (AdaBoost)—partial least squares regression (PLSR) + AdaBoost model reached 0.9430. This indicates that the quantitative analysis model of moisture content in P. massoniana seedlings established through preprocessing, feature selection, and stacking learning models can achieve high accuracy in predicting moisture content in P. massoniana seedlings. This model can provide a feasible technical reference for the precision cultivation of P. massoniana seedlings.

Prediction performance of NIR calibration models developed with different chemometric techniques to predict oil content in a single kernel of maize

Determining the biochemical content of intact seeds without damaging them provides significant advantages in plant breeding programs. Determination of oil content is one of the most tedious analyses at single kernel level among biochemical analyses. Near infrared reflectance (NIR) spectroscopy is one of the methods that can be an alternative to biochemical analyses in order to determine the oil content at the single seed level without damaging the sample. The aim of this study was to develop calibration models that will enable the determination of oil content in a single maize kernel by means of NIR spectroscopy and to compare the predictive power of the models developed using different chemometric techniques. A total of 500 seeds from 10 different genotypes that differ from each other in terms of oil content (from 1.11% to 10.9%) were used as experimental material. Spectral data were collected between 8333 and 4166 cm−1 on a desktop NIR device. Prediction models were constructed using partial least squares regression (PLSR) and support vector machines (SVM) methods. The model development process was carried out in the SelectWave (https://bafr.shinyapps.io/SelectWave/) application and models (n = 360) were created to determine oil content at single seed level by using 5 different pretreatments, 4 different derivative options, and 9 different wavelength selection methods. Model robustness was evaluated for the calibration samples (n = 341), external validation samples (n = 98), and test samples (n = 50). The most successful prediction result was obtained from the SVM model with the pretreatment combination of None+SVM+None (RMSECal=0.46, R2Cal=95.11, RPDCal=4.53, RMSEVal=0.78, R2Val=84.50, RPDVal = 2.55, RMSETest=0.83, R2Test=82.59, RPDTest = 2.42). Results showed that oil content in single kernel of maize could be correctly predicted by NIR calibration models based on SVM method coupling with the pretreatment of None+SVM+None combination.

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics.

Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in Fritillaria thunbergii. A total of three machine learning algorithms were utilized, including a gradient boosting machine (GBM), partial least squares regression (PLSR) and support vector regression (SVR). Three promising wavelength selection methods were evaluated for comparison, namely, a competitive adaptive reweighted sampling method (CARS), a random frog method (RF), and an uninformative variable elimination method (UVE). Compared to full wavelengths, the selected wavelengths produced excellent results. Overall, RC2, RV2, RP2, RSMEC, RSMEV and RSMEP for the selected variables are as follows: 0.9967, 0.8899, 0.9403, 1.9853 mg kg-1, 11.3934 mg kg-1, 8.5354 mg kg-1; 0.9933, 0.9316, 0.9665, 5.9332 mg kg-1, 18.3779 mg kg-1, 11.9356 mg kg-1; 0.9992, 0.9736, 0.9686, 1.6707 mg kg-1, 10.2323 mg kg-1, 10.1224 mg kg-1 were obtained for Cd Cu and Pb, respectively. Experimental results showed that all three methods could perform variable selection effectively, with GBM-UVE for Cd, SVR-RF for Pb, and GBM-CARS for Cu providing the best results. The results of the study suggest that LIBS coupled with wavelength selection can be used to detect heavy metals rapidly and accurately in Fritillaria by extracting only a few variables that contain useful information and eliminating non-informative variables.