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.

[Multivariate Calibration Combined with Mass Spectrometry for Rapid Analysis].

A mixture of four substances of benzaldehyde, iso-octane, butyl acetate, acetophenone were quantitatively analyzed by mass spectrometry combined with chemometrics. The mass chromatogram data of mixture were proceeded with two methods for quantitative analysis. One is feature selection--Multiple Linear Regression (MLR) and the other is full spectrum--Partial Least Squares (PLS). The results show that the RMSEP of benzaldehyde were 0.062 and 0.091 after selecting m/z spectrum and full spectrum respectively; RMSEP of isooctane were 0.048 and 0.057 after selecting spectrum and full spectrum respectively; which of butyl acetate were 0.021 and 0.020 and of acetophenone were 0.010 and 0.032. The feature selection results of the mixture were better than that of the full spectrum modeling results expect butyl acetate which got similar results by the two methods.

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.

[Determination of calcium and magnesium in tobacco by near-infrared spectroscopy and least squares-support vector machine].

The purpose of the present paper is to determine calcium and magnesium in tobacco using NIR combined with least squares-support vector machine (LS-SVM). Five hundred ground and dried tobacco samples from Qujing city, Yunnan province, China, were surveyed by a MATRIX-I spectrometer (Bruker Optics, Bremen, Germany). At the beginning of data processing, outliers of samples were eliminated for stability of the model. The rest 487 samples were divided into several calibration sets and validation sets according to a hybrid modeling strategy. Monte-Carlo cross validation was used to choose the best spectral preprocess method from multiplicative scatter correction (MSC), standard normal variate transformation (SNV), S-G smoothing, 1st derivative, etc., and their combinations. To optimize parameters of LS-SVM model, the multilayer grid search and 10-fold cross validation were applied. The final LS-SVM models with the optimizing parameters were trained by the calibration set and accessed by 287 validation samples picked by Kennard-Stone method. For the quantitative model of calcium in tobacco, Savitzky-Golay FIR smoothing with frame size 21 showed the best performance. The regularization parameter λ of LS-SVM was e16.11, while the bandwidth of the RBF kernel σ2 was e8.42. The determination coefficient for prediction (Rc(2)) was 0.9755 and the determination coefficient for prediction (Rp(2)) was 0.9422, better than the performance of PLS model (Rc(2)=0.9593, Rp(2)=0.9344). For the quantitative analysis of magnesium, SNV made the regression model more precise than other preprocess. The optimized λ was e15.25 and σ2 was e6.32. Rc(2) and Rp(2) were 0.9961 and 0.9301, respectively, better than PLS model (Rc(2)=0.9716, Rp(2)=0.8924). After modeling, the whole progress of NIR scan and data analysis for one sample was within tens of seconds. The overall results show that NIR spectroscopy combined with LS-SVM can be efficiently utilized for rapid and accurate analysis of calcium and magnesium in tobacco.

Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization.

PROBLEMS: Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model. AIMS: Using Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information. METHODS: Optimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks. RESULTS: Compared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions. CONCLUSION: Raman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.

Rapid analysis the type of customs paper using Micro-NIR spectrometers and machine learning algorithms.

Quick identification of paper types for customs is extremely crucial. Although there are a variety of researches focus on the discrimination of paper, these techniques either require complex preprocessing or large-scale instruments, which are not suitable for customs environments. In this study, we predicted the type of customs paper by using a Micro-NIR spectrometer, and compared the results with Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR). Four different classification algorithms, including linear and non-linear classifiers: K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares-support vector machine (LS-SVM) were employed to classify the type of paper. 20 groups of datasets were selected by Monte Carlo sampling. For Micro-NIR data, the performances of KNN and LS-SVM were outstanding than SIMCA and PLS-DA, with the average accuracies 96.06% and 98.91%, respectively. The outcome of SIMCA and PLS-DA were similar, with the average accuracies 93.00% and 93.97%. Based on the standard derivation, the best stability of models was LS-SVM (1.06%), followed by PLS-DA (1.12%), KNN (1.22%) and SIMCA (3.07%). Compared with ATR-FTIR, the effects of Micro-NIR were better, which were embodies in the better KNN and SIMCA models, and the comparable LS-SVM model. The result demonstrated that the Micro-NIR combined with machine learning algorithms was an effective method to classify the type of customs paper efficiently and quickly, even better than ATR-FTIR.

[Simultaneous determination of instant coffee, plant fat and sugar content in liquid coffee beverage by diffuse reflectance near-infrared spectroscopy].

The diffuse reflectance near-infrared spectra of 20 liquid coffee beverage samples were collected by FT-NIR spectrometer combined with integral sphere in this thesis. The quantitative calibration models of instant coffee, plant fat and sugar were developed respectively. The result indicated that for the calibration models of instant coffee, plant fat and sugar, the dimensions of the calibration models are 4, 5 and 4 respectively; the determination coefficients (R2) are 98.97%, 99.94% and 99.18% respectively; the root mean square errors of calibration (RMSEC) are 1.62, 0.42 and 1.58 respectively; the root mean square errors of cross validation (RMSECV) are 2.12, 0.72 and 2.01 respectively. The result of F-test showed that a very remarkable correlation exists between the estimated and specified values for each calibration model. This research indicated that NIR spectroscopy can be applied in the rapid, accurate and simultaneous determination of the three main ingredients in liquid coffee beverage. This research can provide some references for the quality control of liquid coffee beverage and the determination of the substance with chemical-fixation composition in liquid formula food.

[UV spectroscopy coupled with partial least squares to determine the enantiomeric composition in chiral drugs].

In the present study, sucrose was used as a chiral selector to detect the molar fraction of R-metalaxyl and S-ibuprofen due to the UV spectral difference caused by the interaction of the R- and S-isomer with sucrose. The quantitative model of the molar fraction of R-metalaxyl was established by partial least squares (PLS) regression and the robustness of the models was evaluated by 6 independent validation samples. The determination coefficient R2 and the standard error of calibration set (SEC) was 99.98% and 0.003 respectively. The correlation coefficient of estimated value and specified value, the standard error and the relative standard deviation (RSD) of the independent validation samples was 0.999 8, 0.000 4 and 0.054% respectively. The quantitative models of the molar fraction of S-ibuprofen were established by PLS and the robustness of models was evaluated. The determination coefficient R2 and the standard error of calibration set (SEC) was 99.82% and 0.007 respectively. The correlation coefficient of estimated value and specified value of the independent validation samples was 0.998 1. The standard error of prediction (SEP) was 0.002 and the relative standard deviation (RSD) was 0.2%. The result demonstrates that sucrose is an ideal chiral selector for building a stable regression model to determine the enantiomeric composition.

Feature selection of infrared spectra analysis with convolutional neural network.

Data-driven deep learning analysis, especially for convolution neural network (CNN), has been developed and successfully applied in many domains. CNN is regarded as a black box, and the main drawback is the lack of interpretation. In this study, an interpretable CNN model was presented for infrared data analysis. An ascending stepwise linear regression (ASLR)-based approach was leveraged to extract the informative neurons in the flatten layer from the trained model. The characteristic of CNN network was employed to visualize the active variables according to the extracted neurons. Partial least squares (PLS) model was presented for comparison on the performance of extracted features and model interpretation. The CNN models yielded accuracies with extracted features of 93.27%, 97.50% and 96.65% for Tablet, meat, and juice datasets on the test set, while the PLS-DA models obtained accuracies with latent variables (LVs) of 95.19%, 95.50% and 98.17%. Both the CNN and PLS models demonstrated the stable patterns on active variables. The repeatability of CNN model and proposed strategies were verified by conducting the Monte-Carlo cross-validation.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types.

Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. In this research, the capability of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with pattern recognition methods was investigated for the discrimination of animal fur in six types. This work was to explore the non-destructive application of ATR-FTIR technique in discriminant analysis of animal fur. All spectra were collected by ATR-FTIR of the wavenumber ranging from 4000 to 650 cm-1. Data pretreatments included moving average smoothing and multiplicative scatter correction (MSC). Four supervised classification algorithms were chosen to categorize the types of fur: soft independent modeling of class analogy (SIMCA), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM). PLS-DA and LS-SVM were both effective approaches, with a 100% classification accuracy rate. The accuracy of PCA-LDA and SIMCA was 98.33% and 99.44%, respectively. Furthermore, LS-SVM model obtained using Monte-Carlo sampling method also obtained 100% prediction accuracy, while all other methods produced misclassification. LS-SVM corrected the non-linearities for the animal fur FTIR data but also remarkably improved the prediction performance level. The results of this study revealed that the combination of ATR-FTIR and chemometrics has a huge potential for animal fur discrimination.

[Near-infrared spectroscopy quantitative analysis model based on inverse regression].

In the present paper, an inverse regression method is used in near infrared (NIR) spectroscopy analysis to reduce dimension of predictor at first, then estimate linear regression function using the new derived low dimensional data. A real data set of 103 corn samples was used for analysis with this new inverse regression method. Taking 103 corn samples as experiment materials, seventy samples were chosen randomly to establish predicting model, the remaining thirty-three corn samples were viewed as prediction set. The new derived model is used to the prediction set. The coefficient is 0.986 and the average relative error is 2.1% between the model predication results and Kjeldahl's value for the protein content, and the resulis of using partial least square regression are 0.978 and 2.5%, respectively. The results demonstrate that the inverse regression method is feasible and has good property in near-infrared spectroscopy quantitative analysis, and also provides a new idea for chemometrics quantitative analysis.

A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine.

Spectral data fusion strategies combined with the extreme learning machine (ELM) algorithm was applied to determine the active ingredient in deltamethrin formulation. Ultraviolet-visible spectroscopy (UV-vis) is a rapid and sensitive detection method for specific components that are sensitive to ultraviolet irradiation. Alternatively, near-infrared spectroscopy (NIR) technology can be applied over a broader range. To determine a feasible method with a higher sensitivity and broader application range, the active ingredient of deltamethrin formulation was comprehensively investigated by combining the spectral data fusion strategy with ELM by employing UV-vis, NIR and fusion strategies, individually. Consequently, the results demonstrated that the low-level fusion strategy exhibited better predictive ability (lower RMSEP of 0.0645% and higher R2 of 0.9978) than mid-level fusion and individual methods. ELM combined with data fusion is proved to be an efficient method for the rapid analysis of deltamethrin formulations. Furthermore, this study provides a potential approach for pesticide quality control as well as on-site monitoring.

Non-destructive analysis the dating of paper based on convolutional neural network.

A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.74%, 95.31% and 98.77%, respectively. Based on the characteristics of CNN model and with the help of network localization, active variables could be recognized in the whole spectrum. Although the localization of active variables showed a discriminative pattern, the selected spectral regions were similar. Most important variables focused on the 1700-1400 cm-1, were corresponding to cellulose crystallinity, which was consisted with the ageing processing. The present work gave the potential of FT-IR combined with chemometric techniques could estimate the dating of unknown paper. Meanwhile, the analysis of active variables obtained further indicated the worthy of CNN model for document dating.

[Rapid detection of dichlorvos in chlorpyrifos by mid-infrared and near-infrared spectroscopy].

In the present study, the content of dichlorvos in chlorpyrifos was rapidly determined by mid-infrared and near-infrared spectroscopy. The quantitative models were established by partial least squares (PLS) method and optimized. The independent validation sets and 7 test samples were used to evaluate the model accuracy. The results showed that mid-infrared and near-infrared spectroscopy can accurately determine the content of dichlorvos in chlorpyrifos. The RMSEC (the root mean square error of calibration) of the mid-infrared model and near-infrared model was 0.013 and 0.020, respectively. R2 (determination coefficient) both were 1.000. For 7 test samples, RMSEP before model recalibration is 0.22 (MIRS) and 0.09 (NIRS). The adaptability of the near-infrared model was much better and model updating was unnecessary. To sum up, MIR and NIR are both rapid and easy-operation method with simple pretreatment.

[Study on rapid determination of active ingredient of agrochemicals by near-infrared spectroscopy].

The main problem of disqualification of the agrochemicals is the insufficiency and abuse of its active ingredient, but lacking of the rapid and on the site analysis method. In the present thesis, the content of haloxyfop-r-methyl in the emulsifiable concentration was analyzed quantitatively by the FT-NIR spectroscopy combined with partial least square (PLS) method. The calibration models of haloxyfop-r-methyl were developed, the determination coefficients (R2) of the calibration models were no less than 0.999 9, the SEC were less than 0.019, and the SEP were less than 0.030. Meanwhile, the factors affecting the calibration model were studied and the validation was done by the actual sample. The result indicated that the method of near-infrared spectroscopy can predict the content of the active ingredient in emulsifiable concentration accurately; while the resolution of the instrument and the content of addition agent will not affect the prediction precision of the calibration model remarkably. Therefore, it is a feasible, convenient and quick method to analyze the active ingredient in the commodity agrochemicals by near-infrared spectroscopy, which has an important significance in the on-line determination, analysis on site in the enterprise and the rapid quantitative analysis of agrichemicals in the department of quality monitoring.

[Quantitative analysis of the content of deltamethrin in agrochemicals by near-infrared, attenuated total reflectance infrared and raman spectroscopy].

The method of near-infrared, attenuated total reflectance infrared and Raman spectroscopy was used for the rapid determination of the content of deltamethrin in agrochemicals. The quantitative models were established by PLS (partial least squares) method and optimized. The independent validation sets were used to evaluate the model accuracy. The determination coefficient R2 and RMSECV of the near-infrared model and mid-infrared model were 0.9999, 0.022 and 0.9996 and 0.056, respectively. The accuracy of both was similar. The determination coefficient R2 and RMSECV of Raman were 0. 996 7 and 0.172, which exhibits the lower accuracy. The result indicated that near-infrared, mid-infrared and Raman spectroscopy can be applied to the rapid determination of the content of the active ingredients precisely, which has an important significance in the on-line determination, analysis on site in the enterprise and the rapid quantitative analysis of agrichemicals in the department of quality monitoring.

[Near-infrared spectrum quantitative analysis model based on principal components selected by elastic net].

Elastic net is an improvement of the least-squares method by introducing in L1 and L2 penalties, and it has the advantages of the variable selection. The quantitative analysis model build by Elastic net can improve the prediction accuracy. Using 89 wheat samples as the experiment material, the spectrum principal components of the samples were selected by Elastic net. The analysis model was established for the near-infrared spectrum and the wheat's protein content, and the feasibility of using Elastic net to establish the quantitative analysis model was confirmed. In experiment, the 89 wheat samples were randomly divided into two groups, with 60 samples being the model set and 29 samples being the prediction set. The 60 samples were used to build analysis model to predict the protein contents of the 29 samples, and correlation coefficient (R) of the predicted value and chemistry observed value was 0. 984 9, with the mean relative error being 2.48%. To further investigate the feasibility and stability of the model, the 89 samples were randomly selected five times, with 60 samples to be model set and 29 samples to be prediction set. The five groups of principal components which were selected by Elastic net for building model were basically consistent, and compared with the PCR and PLS method, the model prediction accuracies were all better than PCR and similar with PLS. In view of the fact that Elastic net can realize the variable selection and the model has good prediction, it was shown that Elastic net is suitable method for building chemometrics quantitative analysis model.

[Fast determination of the proportion of metalaxyl-M enantiomers by UV spectroscopy].

In the present study, beta-cyclodextrin(betaCD) was used as chiral selector to detect the proportion of chiral isomers of metalaxyl. The proportion of metalaxyl enantiomers can be detected by ultraviolet (UV)spectroscopy since the interaction between the R, S isomer of metalaxyl with beta-CD is different. The quantitative models were established by partial least squares regression (PLS) and the robust of models was evaluated by independent validation samples. The determination coefficient R2 of calibration set in the quantitative model was 0.999 0. The standard error of calibration set (SEC) and the relative standard deviation (RSD) of the model was respectively 0.006 7 and 0.89%; The correlation coefficient r of estimated value and specified value of the 6 independent validation samples was 0.998 5. The standard error of prediction (SEP) and RSD was respectively 0.008 9 and 1.17%. This method is rapid and easy to operate in practical applications.

[Spectrofluorimetric study of supramolecular inclusion between rodenticide brodifacoum and beta-cyclodextrin and application].

The supramolecular interaction between beta-cyclodextrin and brodifacoum, an anticoagulant rodenticide of the second generation, was studied by spectroscopy. The results showed that brodifacoum and beta-cyclodextrin could form an inclusion complex with an association constant of 1.048 x 10(4) L x mol(-1) and a 1 : 1 stoichiometry based on Benesi-Hildebrand equation. The inclusion mechanism was proposed to explain the inclusion mode. It was indicated that the hydrophobic group of brodifacoum molecule, biphenyl, entered into the cavity of beta-cyclodextrin. At the same time, it was also observed the significant enhancement of fluorescence of brodifacoum after forming inclusion complex. According to the fluorescence enhancement phenomenon, a spectrofluorimetric method of detecting brodifacoum in aqueous media was established with the linear range of 8.0 x 10(-8)-4.0 x 10(-6) mol x L(-1) and the correlation coefficient of 0.999 4. The detection limit of the method was 8.8 x 10(-9) mol x L(-1). The proposed method was successfully applied to determine the trace amount of brodifacoum in environment water and the recovery was in the range of 87.3% to 103.9%.

High-level Fusion Coupled with Mahalanobis Distance Weighted (MDW) Method for Multivariate Calibration.

Near infrared spectra (NIR) technology is a widespread detection method with high signal to noise ratio (SNR) while has poor modeling interpretation due to the overlapped features. Alternatively, mid-infrared spectra (MIR) technology demonstrates more chemical features and gives a better explanation of the model. Yet, it has the defects of low SNR. With the purpose of developing a model with plenty of characteristics as well as with higher SNR, NIR and MIR technologies are combined to perform high-level fusion strategy for quantitative analysis. A novel chemometrical method named as Mahalanobis distance weighted (MDW) is proposed to integrate NIR and MIR techniques comprehensively. Mahalanobis distance (MD) based on the principle of spectral similarity is obtained to calculate the weight of each sample. Specifically, the weight is assigned to the inverse ratio of the corresponding MD. Besides, the proposed MDW method is applied to NIR and MIR spectra of active ingredients in deltamethrin and emamectin benzoate formulations for quantitative analysis. As a consequence, the overall results show that the MDW method is promising with noticeable improvement of predictive performance than individual methods when executing high-level fusion for quantitative analysis.