Correction to: Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS-competitive adaptive reweighted sampling.

The authors would like to call the reader's attention to the fact that, unfortunately, there was a mistake regarding the affiliations of three of the authors in this manuscript; please find the correct information below.

Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS-competitive adaptive reweighted sampling.

A novel strategy of variable selection approach named dynamic backward interval partial least squares-competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS.

Spectral interval combination optimization (ICO) on rapid quality assessment of Solanaceae plant: a validation study.

A novel spectral variable selection method, named as interval combination optimization (ICO), was proposed in the previous study of us. In the present study, ICO coupled with near infrared (NIR) spectroscopy was applied to the rapid determination of four primary constituents including total sugar, reducing sugar, total nitrogen and nicotine in Nicotiana plant. Partial least squares regressions was performed after ICO algorithm. The full spectrum was divided into forty equal-width intervals, and the interval with lower root mean squared error of cross-validation was selected for further analysis. As a result, only 155 variables were retained from 1555 variables for each constituent. Particularly, as a variables selection method, ICO improved the prediction accuracy of calibration model and obtained a satisfactory result compared with full-spectrum data. Results revealed that NIR combined with ICO could be efficiently used for rapid analysis of quality associated constituents of Nicotiana plant. Moreover, this study provided a supplementary verification of the proposed variable selection method for the further applications.

Moving window smoothing on the ensemble of competitive adaptive reweighted sampling algorithm.

A novel chemometrical method, named as MWS-ECARS, which is based on using the moving window smoothing upon an ensemble of competitive adaptive reweighted sampling, is proposed as the spectral variable selection approach for multivariate calibration in this study. In terms of elimination of uninformative variables, an ensemble of CARS is carried out first and MWS is then performed to search for effective variables around the high frequency variables. The variable subset with the lowest standard error of cross-validation (SECV) is treated as the optimal threshold and the corresponding moving window width is regarded as the optimal window width. The method was applied to mid-infrared (MIR) spectra of active ingredient in pesticide, near-infrared (NIR) spectra of soil organic matter and NIR spectra of total nitrogen in Solanaceae plants for variable selection. Overall results show that MWS-ECARS is a promising selection method with an improved prediction performance over three variable selection methods of variable importance projection (VIP), uninformative variables elimination (UVE) and genetic algorithms (GA).

A modification of the bootstrapping soft shrinkage approach for spectral variable selection in the issue of over-fitting, model accuracy and variable selection credibility.

In this study, we proposed a new computational method stabilized bootstrapping soft shrinkage approach (SBOSS) for variable selection based on bootstrapping soft shrinkage approach (BOSS) which can enhance the analysis of chemical interest from the massive variables among the overlapped absorption bands. In SBOSS, variable is selected by the index of stability of regression coefficients instead of regression coefficients absolute value. In each loop, a weighted bootstrap sampling (WBS) is applied to generate sub-models, according to the weights update by conducting model population analysis (MPA) on the stability of regression coefficients (RC) of these sub-models. Finally, the subset with the lowest RMSECV is chosen to be the optimal variable set. The performance of the SBOSS was evaluated by one simulated dataset and three NIR datasets. The results show that SBOSS can select the fewer variables and supply the least RMSEP and latent variable number of the PLS model with the best stability comparing with methods of Monte Carlo uninformative variables elimination (MCUVE), genetic algorithm (GA), competitive reweighted sampling (CARS), stability of competitive adaptive reweighted sampling (SCARS) and BOSS.

Spectral interval optimization on rapid determination of prohibited addition in pesticide by ATR-FTIR.

BACKGROUND: Acetamiprid, as a low-toxicity pesticide, has already been extensively used to increase plant production and quality. Although fipronil has been prohibited, it is usually illicitly added to acetamiprid due to its particular insecticidal action and effect, so it is highly desirable to obtain a rapid and effective method to detect its concentration. Mid-infrared spectroscopy (MIR) combined with two variable selection methods, interval combination optimization (ICO) and interval partial least squares (iPLS), were used to determinate the prohibited addition of fipronil. RESULTS: The full spectra for both ICO and iPLS were divided into 40 equal-width intervals. Consequently, 45 and 135 characteristic variables were extracted from ICO and iPLS to establish the models. Compared with iPLS, the ICO model acquired a more suitable spectral region and as a result gained a higher prediction accuracy. Specifically, the ICO method selected the characteristic wavelengths ascribed to CF and CN (in five-membered heterocyclics), iPLS chose the intervals associated with CF and SO. CONCLUSION: Results revealed that MIR combined with ICO could be efficiently used for rapid identification of illegal addition and had great potential to provide on-site pesticide quality control. © 2018 Society of Chemical Industry.

Quantitative determination of additive Chlorantraniliprole in Abamectin preparation: Investigation of bootstrapping soft shrinkage approach by mid-infrared spectroscopy.

A novel method, mid-infrared (MIR) spectroscopy, which enables the determination of Chlorantraniliprole in Abamectin within minutes, is proposed. We further evaluate the prediction ability of four wavelength selection methods, including bootstrapping soft shrinkage approach (BOSS), Monte Carlo uninformative variable elimination (MCUVE), genetic algorithm partial least squares (GA-PLS) and competitive adaptive reweighted sampling (CARS) respectively. The results showed that BOSS method obtained the lowest root mean squared error of cross validation (RMSECV) (0.0245) and root mean squared error of prediction (RMSEP) (0.0271), as well as the highest coefficient of determination of cross-validation (Qcv2) (0.9998) and the coefficient of determination of test set (Q2test) (0.9989), which demonstrated that the mid infrared spectroscopy can be used to detect Chlorantraniliprole in Abamectin conveniently. Meanwhile, a suitable wavelength selection method (BOSS) is essential to conducting a component spectral analysis.

Rapid Detection of Atrazine at Workplace with Near-Infrared Spectroscopy.

As a wildly used herbicide, Atrazine is mainly produced in China. In order to strengthen the routine detection of Atrazine exposure concentration and protect the health of occupational contact workers, it's of great importance to develop on-site rapid detection method. A self-assembled near infrared spectrometer was used to record spectra of laboratory prepared atrazine solutions with concentration range from 10 to 1 000 mg·L-1. The influences of different pretreatment methods, such as multiplicative scatter correction, standard normal variate, first order derivative (D1), second order derivative and their combinations, different variable selection methods, such as competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA), different regression methods, such as partial least square (PLS) and support vector regression(nu-SVR), on the model prediction accuracy were investigated. Results show that D1 is the best pretreatment method; GA obtain better results than CARS on selecting highly related spectral variables; nu-SVR model perform better than PLS model. The nu-SVR model constructed with 16 spectral variables selected by GA obtained the best results, whose coefficient of determination for calibration, the coefficient of determination for validation, root mean square error of calibration, root mean square error of validation (RMSEV) and residual validation deviation (defined as SD/RMSEV where SD denotes standard deviation) are 1, 0.99, 17.54 mg·L-1, 25.42 mg·L-1 and 11.43, respectively. These results indicate near infrared spectroscopy combined with chemometrics has great potential to quantify Atrazine concentration at workplace. This research explores the feasibility of quantification Atrazine at workplace with near infrared spectroscopy for the first time, which has great reference value for similar work in the future.

A novel algorithm for spectral interval combination optimization.

In this study, a new wavelength interval selection algorithm named as interval combination optimization (ICO) was proposed under the framework of model population analysis (MPA). In this method, the full spectra are divided into a fixed number of equal-width intervals firstly. Then the optimal interval combination is searched iteratively under the guide of MPA in a soft shrinkage manner, among which weighted bootstrap sampling (WBS) is employed as random sampling method. Finally, local search is conducted to optimize the widths of selected intervals. Three NIR datasets were used to validate the performance of ICO algorithm. Results show that ICO can select fewer wavelengths with better prediction performance when compared with other four wavelength selection methods, including VISSA, VISSA-iPLS, iVISSA and GA-iPLS. In addition, the computational intensity of ICO is also economical, benefit from fewer tune parameters and faster convergence speed.

The application of directly suspended droplet microextraction for the evaluation of phthalic acid esters in cow's milk by gas chromatography mass spectrometry.

A simple and rapid method was developed for evaluating 16 phthalic acid esters (PAEs) at the µg/kg level in a complex milk matrix using directly suspended droplet microextraction-gas chromatography mass spectrometry (DSDME-GC-MS). The different parameters for extraction and for the DSDME experiment were optimized, including You are free to submit the revised manuscript at a later date as a new submission. 10 g/L trichloroacetic acid concentration, 100 µL cyclohexane micro-droplet organic solvent, 1100 rpm stirring speed, 10 min extraction time and no salt amount. Validation experiments showed good linearity (γ>0.9878, 0.002-0.4 µg/mL), satisfactory precision (RSD<11%), and good accuracy (relative recovery of 70.2-108%) when analyzing milk samples using the optimized method. The limits of detection (LODs) ranged between 0.001 and 0.2 µg/L, and the limits of quantification (LOQs) ranged between 0.003 and 0.7 µg/L for 15 PAEs. Dinonyl phthalate (DINP) had a low response and did not have good linearity. The proposed method was successfully applied for the analysis of PAEs in real milk samples.

A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm.

The competitive adaptive reweighted sampling-successive projections algorithm (CARS-SPA) method was proposed as a novel variable selection approach to process multivariate calibration. The CARS was first used to select informative variables, and then SPA to refine the variables with minimum redundant information. The proposed method was applied to near-infrared (NIR) reflectance data of nicotine in tobacco lamina and NIR transmission data of active ingredient in pesticide formulation. As a result, fewer but more informative variables were selected by CARS-SPA than by direct CARS. In the system of pesticide formulation, a multiple linear regression (MLR) model using variables selected by CARS-SPA provided a better prediction than the full-range partial least-squares (PLS) model, successive projections algorithm (SPA) model and uninformative variables elimination-successive projections algorithm (UVE-SPA) processed model. The variable subsets selected by CARS-SPA included the spectral ranges with sufficient chemical information, whereas the uninformative variables were hardly selected.

Comparison of several supervised pattern recognition techniques for detecting additive methamidophos in rotenone preparation by near-infrared spectroscopy.

In this paper, different supervised pattern recognition methods have been applied to detect the manually additive methamidophos in rotenone preparation. The aim of this paper was to examine the performances of different supervised pattern recognition techniques: soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), artificial neutral networks (ANN), and support vector machine (SVM). The results obtained show that SVM is the most effective techniques with 100.0% classification accuracy followed by ANN, PLS-DA and with the accuracy of 97.5% and 93.3% respectively while SIMCA yields the poorest result of 85.8%. We hope that the results obtained in this study will help both further chemometric investigations and investigations in the sphere of applied vibrational spectroscopy of sophisticated multicomponent systems. Furthermore, the use of portable instrument and satisfactory classification also indicated the possibility of detecting illicit-addition at scene by near-infrared (NIR) spectroscopy which makes a great sense in pesticide quality control.