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Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg -1 , 1.457 g kg -1 , 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R 2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg -1 and 1.967 g kg -1 , and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The pandemic caused by Covid-19 is still present around the world. Despite advances in combating the disease, such as vaccine development, identifying infected individuals is still essential to optimize the control of human-to-human transmission of the virus. The main technique for detecting the virus is the RT-PCR method, which, despite its high relative cost, has a high accuracy in detecting the coronavirus. Given this, a method capable of performing the identification quickly, accurately, and inexpensively is necessary. Thus, this work aimed to analyze the feasibility of a new technique for identifying SARS-CoV-2 through the use of optical spectroscopy in the visible and near-infrared range (Vis-NIR) combined with machine learning algorithms. Spectral signals were obtained from nasopharyngeal swab samples previously analyzed using the RT-PCR method. The specimens were provided by the Molecular Diagnosis Laboratory of Covid-19 at Univasf. A total of 314 samples were analyzed, comprising 42 testing positive and 272 testing negative for Covid-19. Digital signal processing techniques, such as Savitzky-Golay filters and statistical methods were used to eliminate spurious elements from the original data and extract relevant features. Supervised machine learning algorithms such as SVM, Random Forest, and Naive Bayes classifiers were used to perform automatic sample identification. To evaluate the performance of the models, a 5-fold cross-validation technique was applied. With the proposed methodology, it was possible to achieve an accuracy of 75%, a sensitivity of 80%, and a specificity of 70%, in addition to an area under the ROC curve of 0.81, in the identification of nasopharyngeal swab samples from previously diagnosed individuals. From these results, it was possible to conclude that Vis-NIR spectroscopy is a promising, fast and relatively low cost technique to identify the SARS-CoV-2., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Variable selection is widely accepted as an important step in the quantitative analysis of visible and near-infrared (Vis-NIR) spectroscopy, as it tends to improve the model's robustness and predictive ability. In this study, a total of 140 lime concretion black soil samples were collected from two towns in Guoyang County, China. The Vis-NIR spectra measured in the laboratory were used to estimate soil pH by an extreme learning machine (ELM). First, the soil spectra were treated by the optimized continuous wavelet transform (CWT), and then four spectral feature selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; Monte Carlo uninformative variable elimination, MCUVE; genetic algorithm, GA) were applied with ELM in the CWT domain to determine the techniques with most predictions. For comparison, The PLS and SVM models were also developed. The coefficient of determination (R 2 ), root mean square error (RMSE), and residual prediction deviation (RPD) were used to evaluate the model performance. Based on the validation dataset, the performance of the ELM models was superior to that of the PLS and SVM models expect SPA and MCUVE. In the ELM models, the order of the prediction accuracy was GA-ELM (R 2 p  = 0.86; RMSE p  = 0.1484; RPD = 2.64), CARS-ELM (R 2 p  = 0.84; RMSE p  = 0.1565; RPD = 2.50), ELM (R 2 p  = 0.84; RMSE p  = 0.1572; RPD = 2.49), SPA-ELM (R 2  = 0.1599; RPD = 2.45). The proposed method of CARS-ELM had a relatively strong ability for spectral variable selection while retaining excellent prediction accuracy and short computing time (0.39 s). In addition, the variables selected by the four methods (CARS, SPA, MCUVE and GA) indicated the prediction mechanism for pH in lime concretion black soil may be the relation between pH and iron oxides and organic matter. In conclusion, CARS-ELM has great potential to accurately determine the pH in lime concretion black soil using Vis-NIR spectroscopy.p  = 0.84; RMSE p  = 0.1589; RPD = 2.47) and MCUVE-ELM (R 2 p  = 0.83; RMSE p  = 0.1599; RPD = 2.45). The proposed method of CARS-ELM had a relatively strong ability for spectral variable selection while retaining excellent prediction accuracy and short computing time (0.39 s). In addition, the variables selected by the four methods (CARS, SPA, MCUVE and GA) indicated the prediction mechanism for pH in lime concretion black soil may be the relation between pH and iron oxides and organic matter. In conclusion, CARS-ELM has great potential to accurately determine the pH in lime concretion black soil using Vis-NIR spectroscopy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Soil organic matter (SOM) is a key index for evaluating soil fertility and plays a vital role in the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy is an effective method for determining soil properties and is often used to predict SOM content. However, the key prerequisite for effective prediction of SOM content by Vis-NIR spectroscopy lies in the selection of appropriate preprocessing methods and effective data mining techniques. Therefore, in this study, six commonly used spectral preprocessing methods and effective characteristic band selection methods were selected to process the spectrum to predict SOM content. This study aims to determine a stable spectral preprocessing method and explore the predictive performance of different characteristic band selection methods. The results showed that: (i) The first derivative (FD) is the most stable spectral preprocessing method that can effectively improve the spectral characteristic information and the prediction effect of the model. (ii) The prediction effect of SOM content based on characteristic band selection methods is generally better than the full-spectra data. (iii) The precision of FD preprocessing spectrum combined with successive projections algorithm (SPA) in the partial least square regression prediction model of SOM content is the best. (iv) Although the prediction effect of the model based on the optimal band combination algorithm is slightly lower than that of SPA, it shows stable prediction performance, which provides a feasible method for SOM content prediction. In summary, the characteristic band selection method combined with FD can significantly improve the prediction accuracy of SOM content., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Using Vis-NIR spectroscopy to distinguish gestational sac from other abdominal tissues is the key to diagnosing female rabbits' pregnancy by optical means. This study aims to demonstrate the gestational sac and other abdominal tissues (hair, skin, breast, muscle, cecum, small intestine) of rabbits can be identified using Vis-NIR spectroscopy in vitro. These tissues' raw NIR spectra were recorded in the Vis-NIR range (490-940 nm) with interactive mode. The raw spectra of tissues were analyzed by the principal component analysis (PCA), and were pre-processed using five spectral pre-processing techniques (moving average filter (MF), De-trending (DT), first-order derivative (D1), Multivariate scattering correction (MSC), and standard normal variate (SNV)) to reduce signal noises. The raw and pre-processed spectra were classified using partial least squares discrimination analysis (PLS-DA). Two-way and multi-way PLS-DA model was conducted to understand the classification of each tissue from the gestational sac and to understand the classification of all tissues from the gestational sac, respectively. SNV-PLS-DA model had the best performance, and its multi-way accuracy (Ac), determination coefficients (R 2 ), and Q 2 were 0.89, 0.91, 0.77, respectively. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to select characteristic wavelengths (CWs). The SNV-SPA-PLS-DA model with eighteen CWs was better than the SNV-CARS-PLS-DA model. The results showed that Vis-NIR spectroscopy technology combined with PLS-DA could discriminate the gestational sac from the abdominal tissues. This study may help develop an optical diagnosis system for pregnant rabbits., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Soil organic matter (SOM) is an important index used to evaluate soil fertility and nutrient availability, and it is also an important component of precision agriculture. In this study, in order to quickly and efficiently estimate the SOM content of farmland soil, we took 190 farmland soil samples in Jingbian County and measureed the SOM content of the samples in the lab and collected the corresponding Vis-NIR spectroscopy data. Based on the six pretreatment methods, a competitive adaptive weighting algorithm (CARS) is used for characteristic wavelength selection. Random forest (RF) regression is used to establish the predictive SOM model. The results indicate that after the CARS algorithm screens the different spectral variables, the optimal variable sets of the seven spectral variables are 15, 40, 30, 23, 20, 26, and 23, respectively. The accuracy of the model is improved after the CARS algorithm screens the different spectral variables. A total of 15 characteristic variables from the 2151 spectral wavelengths were used as the optimal spectral variable subset; RF shortened the training time required during the SOM modeling process and dramatically improved the model's accuracy and predictive ability, and the R 2 of the validation set increased from 0.21 to 0.96, and the RPD increased from 0.46 to 3.02. The RPIQ increased from 1.25 to 4.41. Among the tested models, the CR-RF model produced the best results. The R 2 and RMSE values of the calibration set are 0.91 and 0.49, and the R 2 , RMSE, RPD, and RPIQ values of the validation set are 0.96, 0.51, 3.02, and 4.41, respectively. Accurate prediction of the SOM of the cultivated layer in the study area was realized., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR) has been recognized as a fast method to evaluate the content of soil organic matter (SOM) in various types of soil. The accuracy of Vis-NIR is comparable to conventional laboratory methods for estimating SOM. However, very few studies have applied Vis-NIR to estimate SOM in saline-alkali soil. This study aimed to investigate the efficiency of spectral data for evaluating SOM in saline-alkali soil. Soil samples (n = 291) were collected from the five major saline-alkali soil regions in Shaanxi. SOM was measured using standard methods and the samples were scanned using ASD Fieldspec4 at wavelength of 350-2500 nm to obtain spectral data. Twenty-six pre-processing methods were tested and partial least squares regression (PLSR) was used to estimate SOM. The best preprocessing was R + SG + SNV + FD. The calibration results were Pc = 15, Rc 2  = 0.92, RMSEC = 1.11, SEC = 1.12, Slope = 0.92, Offset = 0.45; the validation results were Rv 2  = 0.97, RPD = 5.21, RMSEP = 0.38, SEP = 0.38, Slope = 0.97, Offset = 0.17. Therefore, this main objective of the study was to propose an effective approach based on Vis-NIR spectroscopy and Chemometrics for predicting saline-alkali SOM contents in the center of Shaanxi, China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Soil visible and near infrared (Vis-NIR) has become an applicable and interesting technique to predict soil properties because it is a fast, cost-effective, and non-destruction technique. This study presents an application of diffuse reflectance spectroscopy (DRS) and chemometric techniques for evaluating concentrations of heavy metals in earth-cumulic-orthic-anthrosols soils. 44 soil samples of 0-30 cm were collected from three representative agriculture areas (Fufeng, Yangling, and Wugong transects with 16, 10, and 18 samples, respectively) and analyzed for Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb by Vis-NIR spectroscopy (350-2500 nm). Average levels of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb were 17.95, 274, 12.77, 7.29, 15.81, 7.51, 0.40, 12.58, and 21.05 mg kg-1, respectively. Twenty-four preprocessing methods were extracted sensitive bands. Partial least squares regression (PLSR) used to obtain effective bands and predict soil heavy metals concentrations. The accuracy of the predictive models were assessed in terms of coefficient of determination (R 2 ), the root mean squared error (RMSE), standard error (SE) and the ratio of performance to deviation (RPD). The results revealed that excellent predictions for Hg(Rv 2  = 0.99, RPD = 8.59, RMSEP = 0.12, SEP = 0.13), Cr (Rv 2  = 0.97, RPD = 5.96, RMSEP = 0.10, SEP = 0.10), Ni (Rv 2  = 0.93, RPD = 3.74, RMSEP = 0.13, SEP = 0.13), Pb (Rv 2  = 0.97, RPD = 5.57, RMSEP = 0.10, SEP = 0.01), and Cu (Rv 2  = 0.92, RPD = 3.38, RMSEP = 0.08, SEP = 0.08). Models for As (Rv 2  = 0.87, RPD = 2.58), Mn (Rv 2  = 0.80, RPD = 2.09), and Cd (RPD = 2.77) had Rv 2  < 0.9 and RPD<3.0, not excellent predictions. For the element of Zn, although Rv2 = 0.91, RPD = 3.13, the offset had too much deviation, and it cannot be considered an excellent model. Therefore, a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Cancer is increasing in incidence and the leading cause of death worldwide. Controlling and reducing cancer requires early detection and technique to accurately detect and quantify predictive biomarkers. Optical spectroscopy has shown promising non-destructive ability to display distinctive spectral characteristics between cancerous and normal tissues from different part of human organ. Nonetheless, not many information is available on spectroscopic properties of cancer cell lines. In this research, the visible-near infrared (VIS-NIR) absorbance spectroscopy measurement of cultured cervical cancer (HeLa) and prostate cancer cells (DU145) lines has been performed to develop spectral signature of cancer cells and to generate algorithm to quantify cancer cells. Spectroscopic measurement on mouse skin fibroblast (L929) was also taken for comparative purposes. In visible region, the raw cells' spectra do not produce any noticeable peak absorbance that provides information on color because the medium used for cells is colorless and transparent. NIR wavelength between 950 and 975 nm exhibit significant peak due to water absorbance by the medium. Development of spectral signature for the cells through the application of regression technique significantly enhances the diverse characteristics between L929, HeLa and DU145. The application of multiple linear regression allows high measurement accuracy of the cells with coefficient of determination above 0.94., (Copyright © 2019. Published by Elsevier B.V.)

Second-generation biodiesel manufactured from waste cooking oils (WCO) and inedible animal fats (AF) are one of the alternatives to the first generation (1G) vegetable oil-based biodiesel. In this study, a quality control method is proposed to evaluate methanol content in waste fat methyl esters and is based on near infrared spectroscopy (NIR) combined with multivariate analysis. More specifically, calibration models are constructed using partial least squares regression (PLS) for the prediction of methanol content in rapeseed oil methyl ester (ROME), waste cooking oil methyl ester (WCOME), chicken fat methyl ester (CFME) and pork fat methyl ester (PFME) by Vis-NIR spectrometer. The calibration models are based on the absorbance spectra and computed data from five wavelength regions of 400-2170 nm, 780-2170 nm, 1400-2170 nm, 1400-1600 nm and 1970-2170 nm. For the cases with the highest prediction ability obtained in this study, the coefficient of determination of the model's goodness-of-fit for methanol concentrations range 0-5% (v/v) was R 2  > 0.990, and for concentrations 0-1% (v/v) was R 2  > 0.994, indicating the spectroscopic approach effectiveness in methanol content detection relevant to the biofuel quality assessment. A pseudo-univariate limits of detection (LOD pu ) and quantification (LOQ pu ) as well as ratio of performance to deviation (RPD) were used to confirm the validity and to evaluate the practical applicability of developed models. In addition, the obtained results indicate the possibility of developing a transmission sensor for online monitoring of the production process and the quality of biofuel., (Copyright © 2018 Elsevier B.V. All rights reserved.)