Kennard-Stone Algorithm Research Articles

Multi-criteria screening of acidic soils by energy-dispersive X-ray fluorescence and random forest-based pattern recognition

Soil acidity is an issue often found in humid tropical areas, characterized by pedogenetically evolved soils with low pH, base saturation percentage (BSP), and cation exchange capacity (CEC), impacting its productivity. Assessing these soil parameters (pH, CEC, and BSP) across large areas is expensive and time-consuming. Energy-dispersive X-ray Fluorescence (EDXRF) together with chemometrics show promise for acidity analysis but requires expertise to interpret numeric results. This study proposes a screening method for analysis of acidic soils using EDXRF data and Random Forest-based pattern recognition. Distinct horizons from 15 soil groups from humid and semi-arid tropical environments in Pernambuco state, Northeast Brazil, were analyzed using a benchtop EDXRF instrument to acquire the elemental information. Chemometric analyses were performed employing R software and autoscaled elemental emission line peak intensities. Classification models were built in Weighted Random Forest (WRF) to classify samples based on pH (<5.0 or ≥5.0), CEC (<15 cmolc.kg−1 or ≥15 cmolc.kg−1), and BSP (<50 % or ≥50 %) thresholds. Kennard-Stone algorithm split the samples into 70 % training and 30 % test samples. Cross-validation (k-fold = 5, five repetitions) ensured model optimization. WRF with simulated annealing (SA) produced better results than those with genetic algorithms (GA-WRF) and using all variables (ALL-WRF). SA-WRF demonstrated slight improvements, with accuracy higher than 94 % and other classification parameters higher than 89.0 %. These promising results suggest the effectiveness of the WRF method combined with classification-relevant emission lines, selected by the SA algorithm, for rapid and accurate screening of acidic soils in humid and semi-arid tropical environments.

Variation Analysis of Starch Properties in Tartary Buckwheat and Construction of Near-Infrared Models for Rapid Non-Destructive Detection.

Due to the requirements for quality testing and breeding Tartary buckwheat (Fagopyrum tartaricum Gaerth), it is necessary to find a method for the rapid detection of starch content in Tartary buckwheat. To obtain samples with a continuously distributed chemical value, stable Tartary buckwheat recombinant inbred lines were used. After scanning the near-infrared spectra of whole grains, we employed conventional methods to analyze the contents of Tartary buckwheat. The results showed that the contents of total starch, amylose, amylopectin, and resistant starch were 532.1-741.5 mg/g, 176.8-280.2 mg/g, 318.8-497.0 mg/g, and 45.1-105.2 mg/g, respectively. The prediction model for the different starch contents in Tartary buckwheat was established using near-infrared spectroscopy (NIRS) in combination with chemometrics. The Kennard-Stone algorithm was used to split the training set and the test set. Six different methods were used to preprocess the spectra in the wavenumber range of 4000-12,000 cm-1. The Competitive Adaptive Reweighted Sampling algorithm was then used to extract the characteristic spectra, and the prediction model was built using the partial least squares method. Through a comprehensive analysis of each parameter of the model, the best model for the prediction of each nutrient was determined. The correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp) of the best models for total starch and amylose were greater than 0.95, and the Rc and Rp of the best models for amylopectin and resistant starch were also greater than 0.93. The results showed that the NIRS-based prediction model fulfilled the requirement for the rapid determination of Tartary buckwheat starch, thus providing an effective technical approach for the rapid and non-destructive testing of starch content in the food science and agricultural industry.

Quantitative prediction of soil chromium content using laboratory-based visible and near-infrared spectroscopy with different ensemble learning models

Soil heavy metal chromium pollution poses significant threats to human health and ecosystems, necessitating accurate quantitative prediction methods for effective monitoring and management. This study aims to develop robust predictive models for soil chromium content in farmland soils of Mojiang Hani Autonomous County, Pu’er City, Yunnan Province, China. These models utilize ensemble learning techniques based on visible and near-infrared spectroscopy. Operations before model building involved partitioning datasets with the Kennard-Stone algorithm to ensure representative training and testing sets. Visible and near-infrared spectroscopy-data preprocessing was performed using Savitzky-Golay smoothing and first-order derivative transformations to enhance signal quality. Bands selection was achieved through the Successive Projections Algorithm (SPA), effectively reducing data dimensionality and collinearity. Six ensemble learning models were constructed and assessed for their predictive performance: Bagging-DTR, Random Forest (RF), Adaboost-DTR, XGBoost-DTR, Stacking-1, and Stacking-2. These models utilized Decision Trees (DTR) and Linear Regression (LR) as base learners. Results demonstrated that ensemble models significantly outperformed individual base learners. Notably, the Stacking-2 model achieved the highest accuracy with an R^2 of 0.954, RMSE of 125.967 mg/kg, and RPD of 4.667. To validate the model’s practical applicability, spatial interpolation of soil Cr content was conducted using the Kriging method based on Stacking-2 model predictions. The spatial distribution maps of measured and predicted values exhibited high congruence, underscoring the model’s effectiveness in accurately mapping Cr distribution across the study area. This study underscores the efficacy of integrating ensemble learning with visible and near-infrared spectroscopy-data preprocessing and SPA for precise soil heavy metal prediction. The findings offer valuable insights and a scientific basis for enhanced soil quality monitoring, environmental risk assessment, and informed agricultural land management and pollution control.

Comparative analysis of the performance of different approaches for the adaptation of a calibration model for diesel analysis

This work presents a comparative study of three model adaptations from an existing PLS model between two infrared spectrophotometers used in diesel analysis. Domain invariant partial least squares (di-PLS), dynamic orthogonal projection (DOP), and model updating (MU) were compared. The density of diesel was used as a case study. A set of 1156 diesel fuel samples was randomly split into a training set (766 samples) and a validation set (390 samples) of the PLS model. Also, a new set of 105 diesel samples was divided with the Kennard-Stone algorithm into a transferring set (15 samples) used to adapt the calibration model and a test set (90 samples) used to perform the external validation. The comparison was carried out considering the root mean square error of prediction (RMSEP) after model adaptation and the percentage of samples whose prediction error falls within the tolerance limits admitted by the reference method according to the ASTM-E-1655 standard.The results showed reductions of 80%, 87%, and 92% in RMSEP after applying di-PLS, DOP, and MU, respectively. However, di-PLS and DOP failed the test of agreement with the reference method since more than 5% of the validation samples had prediction errors beyond the admitted tolerance limits. In the case study, the results point to MU as the only valid model adaptation to be used in diesel analysis. These results are expected to be of value to the audience interested in the implementation of infrared spectroscopy in industrial applications.

A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.

Contribution to Characterizing the Meat Quality of Protected Designation of Origin Serrana and Preta de Montesinho Kids Using the Near-Infrared Reflectance Methodology.

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the "Serrana" and "Preta de Montesinho" breeds certified as "Cabrito Transmontano" and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the "Serrana" breed proved to be more tender than that of the "Preta de Montesinho". However, the meat from the "Preta de Montesinho" breed showed higher intramuscular fat content and was lighter than that from the "Serrana" breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard-Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R2 > 0.99.

基于可见/近红外光谱的鲜枣可溶性固形物在线检测

Soluble solid content (SSC) is one of the important evaluation indexes of the internal quality and taste of fresh jujube. In order to realize the online nondestructive detection of SSC of fresh jujube, this paper took Huping jujube as the research object, adopted self-constructed nondestructive online testing system to collect the spectral information of jujubes (350~2500 nm), and studied the influence of the rotational speed of 4 r/min on the online prediction model of SSC of jujube. Kennard-Stone (KS) algorithm was used to divide the sample into correction set and prediction set. Six commonly used preprocessing methods such as SG smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), orthogonal signal correction (OSC), first derivative (FD), and second derivative (SD) were applied to the spectral data, and the regression coefficient (RC) algorithm and the successive projections algorithm (SPA) were utilized to select informative wavelengths, and a quantitative prediction model for the SSC of Huping jujube was established using partial least squares regression (PLSR). The results indicate that the PLSR prediction model established by preprocessing the original spectrum with OSC and combining it with RC algorithm to select characteristic wavelengths was optimal. Therefore, when predicting the SSC of Huping jujube, the optimal model was OSC-RC-PLSR, and the correlation coefficients of the correction set and prediction set were 0.846 and 0.782, respectively, and the corrected root mean square error (RMSEC) and predicted root mean square error (RMSEP) were 1.962 and 2.247, respectively. The results show that non-destructive detection of soluble solid content of jujube can be achieved by combining visible-near-infrared spectroscopy and appropriate regression model, which provides an innovative way for online sorting and identifying fresh jujube.

Investigating the common pharmacophoric points of PDK1 inhibitors as anti-cancer agents using an alignment independent 3D-QSAR, molecular docking and molecular dynamic simulation

The glycolytic enzyme 3-Phosphoinositide-dependent protein kinase-1 (PDK1), a member of the serine/threonine kinase family, is a promising target for developing novel anticancer drugs due to its vital role in growth and survival of cancer cells and tumor angiogenesis. A MIF base alignment independent, Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) study was conducted to highlight the correlation between PDK1 inhibitors and their structural characteristics, thus indicating the most effective molecular properties of the inhibitors as anticancer agents. Using the Kennard-Stone algorithm to separate calibration and validation subsets, Partial Least-Square (PLS) method application shows excellent agreement between experimental and predicted values (R2=0.85, Q2LOO=0.77 and R2Perd=0.93), making it a reliable model for future drug design approaches. In addition, the active site of the PDK1 receptor protein was examined via Molecular Docking with both passive and active compounds. In order to study the effect of inhibitors on PDK1 structure, 10 ns Molecular Dynamics (MD) simulation of free protein and protein-ligand complex was performed and compared. Comparing the 3D-QSAR, docking and molecular simulation outcomes, the prevailing pharmacophoric use of PDK1 inhibitors as anti-cancer agents have been highlighted. The comprehensive information of this approach reveals important structural variables and their relationship with PDK1 inhibitory activity and PDK1 cavity residues which could be valuable for designing anticancer candidates.

Increasing the efficiency of li-ion battery cycle life testing with a partial-machine learning based end of life prediction

Extensive cycle life studies are conducted in the laboratory to understand the aging behavior of a li-ion battery under various environmental and operational conditions. However, designing an optimal test plan is not straightforward and usually leads to test plans with test points providing little information gain. This paper proposes a machine learning-enhanced testing procedure, which stops the expensive cycling of a subset of tested batteries and replaces their physical test result with a machine learning-based prediction. The core component of the proposed testing procedure is a test subset selection algorithm, which selects a subset of batteries during the cycling whose aging can be predicted with low errors. This is done by extracting aging features during the initial cycling period and feeding them into the Kennard-Stone algorithm to select batteries that have a similar distribution of their aging features as batteries that are physically cycled. Based on three different aging studies and four different machine learning-based predictors, it is shown that the proposed selection approach helps to reliably find subsets that can be predicted with significantly lower prediction errors than random selections. A feature set with capacity-, internal resistance-, and incremental capacity-based features is proposed, which helps to select battery subsets with low prediction errors across all three tested aging studies. Furthermore, it is shown that a higher dissimilarity between a battery and the batteries within the training set also results in a higher prediction error for the respective battery. Therefore, a dissimilarity-based threshold is defined to effectively select the most batteries for the prediction. For the three investigated aging studies, the proposed ML-enhanced testing approach allows to stop the cycling of 30% to 75% of all batteries while ensuring EOL predictions with < 10% MAPE.

QSPR models for solvation enthalpy based on quantum chemical descriptors

After generation of quantum chemical descriptors calculated with the GEDIIS/GDIIS optimizer in Gaussian 09, five quantum chemical descriptors related to molecular polarizability, atomic charges, charge product between solvent and solute, were selected as the optimal descriptor subset for developing quantitative structure–property relationship (QSPR) models of 7215 enthalpies of solvation and vaporization. The random forest (RF) algorithm was used to develop the RF Model Ⅰ whose dataset division for training and testing was mainly based on the solvent types. The RF Model Ⅰ has the number (n) of enthalpies of solvation and vaporization being 3633, coefficient of determination R2 being 0.986, root mean square (rms) error being 2.598 kJ/mol for the training set and n = 3582, R2 = 0.933, rms = 5.501 kJ/mol for the test set. The RF Model Ⅵ based on Kennard-Stone algorithm for dataset selection possesses n = 4810, R2 = 0.987, rms = 2.550 kJ/mol (training set), n = 2405, R2 = 0.940, rms = 4.659 kJ/mol (test set). These statistical results are very accurate, compared with other QSPR models on enthalpies of solvation reported in the literature. Furthermore, the RF Model Ⅰ and Ⅵ based on large data sets can be used for predicting both of the solvation enthalpies and vaporization enthalpies.

Classification of Lubricating Oil Types Using Mid-Infrared Spectroscopy Combined with Linear Discriminant Analysis–Support Vector Machine Algorithm

To realize the classification of lubricating oil types using mid-infrared (MIR) spectroscopy, linear discriminant analysis (LDA) was used for the dimensionality reduction of spectrum data, and the classification model was established based on the support vector machine (SVM). The spectra of the samples were pre-processed by interval selection, Savitzky–Golay smoothing, multiple scattering correction, and normalization. The Kennard–Stone algorithm (K/S) was used to construct the calibration and validation sets. The percentage of correct classification (%CC) was used to evaluate the model. This study compared the results obtained with several chemometric methods: PLS-DA, LDA, principal component analysis (PCA)-SVM, and LDA-SVM in MIR spectroscopy applications. In both calibration and verification sets, the LDA-SVM model achieved 100% favorable results. The PLS-DA analysis performed poorly. The cyclic resistance ratio (CRR) of the calibration set was classified via the LDA and PCA-SVM analysis as 100%, but the CRR of the verification set was not as good. The LDA-SVM model was superior to the other three models; it exhibited good robustness and strong generalization ability, providing a new method for the classification of lubricating oil types by MIR spectroscopy.

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Detection of tomato water stress based on terahertz spectroscopy.

China's tomato cultivation area is nearly 15 thousand km2, and its annual tomato output is about 55 million tons, accounting for 7% of its total vegetable production. Because of the high drought sensitivity of tomatoes, water stress inhibits their nutrient uptake, leading to a decrease in tomato quality and yield. Therefore, the rapid, accurate and non-destructive detection of water status is important for scientifically and effectively managing tomato water and fertilizer, improving the efficiency of water resource utilization, and safeguarding tomato yield and quality. Because of the extreme sensitivity of terahertz spectroscopy to water, we proposed a tomato leaf moisture detection method based on terahertz spectroscopy and made a preliminary exploration of the relationship between tomato water stress and terahertz spectral data. Tomato plants were grown at four levels of water stress. Fresh tomato leaves were sampled at fruit set, moisture content was calculated, and spectral data were collected through a terahertz time-domain spectroscope. The raw spectral data were smoothed using the Savitzky-Golay algorithm to reduce interference and noise. Then the data were divided by the Kennard-Stone algorithm and the sample set was partitioned based on the joint X-Y distance (SPXY) algorithm into a calibration set and a prediction set at a ratio of 3:1. SPXY was found to be the better approach for sample division. On this basis, the stability competitive adaptive re-weighted sampling algorithm was used to extract the feature frequency bands of moisture content, and a multiple linear regression model of leaf moisture content was established under the single dimensions of power, absorbance and transmittance. The absorbance model was the best, with a prediction set correlation coefficient of 0.9145 and a root mean square error of 0.1199. To further improve the modeling accuracy, we used a support vector machine (SVM) to establish a tomato moisture fusion prediction model based on the fusion of three-dimensional terahertz feature frequency bands. As water stress intensified, the power and absorbance spectral values both declined, and both were significantly and negatively correlated with leaf moisture content. The transmittance spectral value increased gradually with the intensification of water stress, showing a significant positive correlation. The SVM-based three-dimensional fusion prediction model showed a prediction set correlation coefficient of 0.9792 and a root mean square error of 0.0531, indicating that it outperformed the three single-dimensional models. Hence, terahertz spectroscopy can be applied to the detection of tomato leaf moisture content and provides a reference for tomato moisture detection.

Research on a nondestructive model for the detection of the nitrogen content of tomato.

The timely detection of information on crop nutrition is of great significance for improving the production efficiency of facility crops. In this study, the terahertz (THz) spectral information of tomato plant leaves with different nitrogen levels was obtained. The noise reduction of the THz spectral data was then carried out by using the Savitzky-Golay (S-G) smoothing algorithm. The sample sets were then analyzed by using Kennard-Stone (KS) and random sampling (RS) methods, respectively. The KS algorithm was optimized to divide the sample sets. The stability competitive adaptive reweighted sampling (SCARS), uninformative variable elimination (UVE), and interval partial least-squares (iPLS) algorithms were then used to screen the pre-processed THz spectral data. Based on the selected characteristic frequency bands, a model for the detection of the nitrogen content of tomato based on the THz spectrum was established by the radial basis function neural network (RBFNN) and backpropagation neural network (BPNN) algorithms, respectively. The results show that the root-mean-square error of correction (RMSEC) and root-mean-square error of prediction (RMSEP) of the BPNN model were respectively 0.1722% and 0.1843%, and the determination coefficients of the correction set (Rc 2) and prediction set (Rp 2) were respectively 0.8447 and 0.8375. The RMSEC and RMSEP values of the RBFNN model were respectively 0.1322% and 0.1855%, and the Rc 2 and Rp 2 values were respectively 0.8714 and 0.8463. Thus, the accuracy of the model established by the RBFNN algorithm was slightly higher. Therefore, the nitrogen content of tomato leaves can be detected by THz spectroscopy. The results of this study can provide a theoretical basis for the research and development of equipment for the detection of the nitrogen content of tomato leaves.

The artificial neural network-based QSPR and DFT prediction of lipophilicity for thioguanine

By the importance of exploring anti-cancer properties of thioguanine (TG), the relationships between quantum chemical indices and lipophilicity of TG tautomers were investigated using the quantitative structure-property relationship (QSPR) approach in two isolated and chitosan-encapsulated states. Accordingly, twenty numbers of different tautomeric forms of TG were selected to predict the logP using the QSPR models. Density functional theory (DFT) calculations along with Dragon package were applied to estimate the required quantum chemical descriptors. The Pearson correlation coefficient statistical test and Kennard-Stone algorithm were used to measure the statistical relationship and data splitting into training and testing set, respectively. Furthermore, the multiple linear regression (MLR) and artificial neural network (ANN) methods were employed for generating the models. In this regard, the analysis of variance (ANOVA) was used to form a basis criterion for testing the significance of MLR and ANN results. Moreover, the leave one out (LOO) method was used for examining the prediction efficiency of select models. The obtained result indicated benefits of proposed models for predicting reliable results of logP.

Different variable selection and model updating strategies about sex classification of silkworm pupae

When the model was applied to discriminate the sex of silkworm pupae of new species, the accuracy was not satisfied. Rebuilding the model need abundant samples and was time consuming. Updating the original model with few samples was usually used, which could also gave high performance. Hence, different model updating strategies were explored via visible (VIS)/near-infrared (NIR) hyperspectral imaging (HSI) spectroscopy in this paper. The calibration model including 150 samples was established on two species (221B-403, 223B-404) and the prediction set had 50 samples. Different variable selection methods including uninformative variable elimination (UVE) and synergy interval-partial least-squares (SiPLS) were adopted. Then partial least squares-discriminant analysis (PLS-DA) and extreme learning machine (ELM) were used to build the model. The best performance were given by UVE-PLSDA and SiPLS-PLSDA models with the accuracy of 98%. When SiPLS-PLSDA model was explored to discriminate the sex of new species “9312-shanheB”, the accuracy was just 70.83%. Different sample selecting methods, containing Kennard Stone (KS) algorithm, Manhattan distance, Euclidean distance and Chebyshev distance, were used to update the original calibration model. The updated SiPLS-PLSDA model based on KS gave the highest performance with 100% when used to discriminate the sex of silkworm pupa with different species in the same year. In addition, the updated model reached the accuracy of 94.44% when used to discriminate the sex of silkworm pupa with different species in different year. It indicated that the proposed updating strategy could be further effectively explored to differentiate the sex of other new species.

Rapid Determination of Polysaccharides in Cistanche Tubulosa Using Near-Infrared Spectroscopy Combined with Machine Learning.

Cistanche tubulosa, as a homology of medicine and food, not only has a unique medicinal value but also is widely used in healthcare products. Polysaccharide is one of its important quality indicators. In this study, an analytical model based on near-infrared (NIR) spectroscopy combined with machine learning was established to predict the polysaccharide content of C. tubulosa. The polysaccharide content in the samples determined by the phenol-sulfuric acid method was used as a reference value, and machine learning was applied to relate the spectral information to the reference value. Dividing the samples into a calibration set and a prediction set using the Kennard-Stone algorithm. The model was optimized by various preprocessing methods, including Savitzky-Golay (SG), standard normal variate (SNV), multiple scattering correction (MSC), first-order derivative (FD), second-order derivative (SD), and combinations of them. Variable selection was performed through the successive projections algorithm (SPA) and stability competitive adaptive reweighted sampling (sCARS). Four machine learning models were used to build quantitative models, including the random forest (RF), partial least-squares (PLS), principal component regression (PCR), and support vector machine (SVM). The evaluation indexes of the model were the coefficient of determination (R2), root-mean-square error (RMSE), and residual prediction deviation (RPD). RF performs best among the four machine learning models. R2c （calibration set coefficient of determination） and RMSEC （root mean square error of the calibration set）, %, were 0.9763. and 0.3527 for calibration, respectively. R2p （prediction set coefficient of determination）, RMSEP （root mean square error of the prediction set）, %, and RPD were 0.9230, 0.5130, and 3.33 for prediction, respectively. The results indicate that NIR combined with the RF is an effective method applied to the quality evaluation of the polysaccharides of C. tubulosa. Four quantitative models were developed to predict the polysaccharide content in C. tubulosa, and good results were obtained. The characteristic variables were basically determined by the sCARS algorithm, and the corresponding characteristicgroups were analyzed.

Soil salinization mapping across different sandy land-cover types in the Shiyang River Basin: A remote sensing and multiple linear regression approach

Soil salinization has a critical impact on land in arid and semi-arid regions. Consequently, mapping and monitoring saline soils have been the subject of growing attention for many scientists due to the irregular spectral reflectivity related to geographic locations and landscape characteristics, which are challenging for policy-makers to improve and sustain the ecological balance. This study applied geoinformation techniques combined with multiple linear regression modeling to map soil salinity over different land-cover types in the Shiyang River Basin. Based on field knowledge and area accessibility, eighty samples were collected, then Kennard-Stone (K–S) algorithm was used for sample partition, 70% for training, and 30% for validation. Variance Inflation Factor (VIF) was applied to identify multicollinearity and adjust the model covariates to ensure proper specification and functioning. Mobile sand, water channels, built-up, and mountainous areas were masked. The results revealed the model's high performance with a coefficient of determination R2 of 0.898, a probability level of 95%, a Root Mean Square Error (RMSE) of 1.653, a Ratio of the Performance to the Interquartile range (RPIQ) of 4.182, and standard error of Electric conductivity (EC) laboratory measurements to the standard error of the predicted EC (SE Lab/SE Pred) of 1.048. Overall, an area of 13834.59 km2, accounting for 33.54% of the entire study region, is under salinization threat. The basin's lower reach is the most affected, with 9576.25 km2, accounting for 57.64% of the eco-region, and 4258.34 km2, around 32.12% of land in the middle reach, suffering from soil salinization. 80.46% of farmland in the lower reach and 35.99% in the middle reach are saline soils. Around 98.9% of Gobi in the lower reach and 91.55% in the middle reach suffer from soil salinization, 96.62% of rangeland in the lower reach and 44.62% in the middle are under salinization risk. Generally, the highest salinity values were recorded in the intermontane basins, followed by the Gobi regions, rangeland, a significant part of cropland in the lower reach, and a small part of cropland in the middle reach. This research establishes a scientific foundation for soil salinization scenarios monitoring under arid climates; hence the outcome results are crucial for complementary landscape planning, ecological restoration projects, and water resources allocation in the Shiyang River Basin.

Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900–1425 cm−1, 1475–1710 cm−1, 2800–3000 cm−1, and 3090–3700 cm−1, to be modeled and detected respectively. The final results showed that the ranges 900–1425 cm−1 and 1475–1710 cm−1 had superior classification accuracies. The region 900–1425 cm−1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475–1710 cm−1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.

Establishment of a Nomogram-Based Prognostic Model (LASSO-COX Regression) for Predicting Progression-Free Survival of Primary Non-Small Cell Lung Cancer Patients Treated with Adjuvant Chinese Herbal Medicines Therapy: A Retrospective Study of Case Series

Nowadays, Jin-Fu-Kang oral liquid (JFK), one of Chinese herbal medicines (CHMs) preparations, has been widely used as an adjuvant therapy for primary non-small cell lung cancer (PNSCLC) patients with the syndrome of deficiency of both Qi and Yin (Qi–Yin deficiency pattern) based on Traditional Chinese Medicine (TCM) theory. However, we found insufficient evidence of how long-term CHM treatment influence PNSCLC patients’ progression-free survival (PFS). Thus, using electronic medical records, we established a nomograph-based prognostic model for predicting PNSCLC patients’ PFS involved with JFK supplementary formulas (JFK-SFs) over 6 months, in order to preliminarily investigate potential predictors highly related to adjuvant CHMs therapies in theoretical epidemiology. In our retrospective study, a series of 197 PNSCLC cases from Long Hua Hospital were enrolled by non-probability sampling and divided into 2 datasets at the ratio of 5:4 by Kennard–Stone algorithm, as a result of 109 in training dataset and 88 in validation dataset. Besides, TNM stage, operation history, sIL-2R, and CA724 were considered as 4 highly correlated predictors for modeling based on LASSO-Cox regression. Additionally, we respectively used training dataset and validation dataset for establishment including internal validation and external validation, and the prediction performance of model was measured by concordance index (C-index), integrated discrimination improvement, and net reclassification indices (NRI). Moreover, we found that the model containing clinical characteristics and bio-features presented the best performance by pairwise comparison. Next, the result of sensitivity analysis proved its stability. Then, for preliminarily examination of its discriminative power, all eligible cases were divided into high-risk or low-risk progression by the cut-off value of 57, in the light of predicted nomogram scores. Ultimately, a completed TRIPOD checklist was used for self-assessment of normativity and integrity in modeling. In conclusion, our model might offer crude probability of uncertainly individualized PFS with long-term CHMs therapy in the real-world setting, which could discern the individuals implicated with worse prognosis from the better ones. Nevertheless, our findings were prone to unmeasured bias caused by confounding factors, owing to retrospective cases series.