Root Mean Square Error Of Cross-validation Research Articles

Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression

Numerous statistical models have established the relationship between ambient fine particulate matter (PM2.5, with an aerodynamic diameter of less than 2.5 μm) and satellite aerosol optical depth (AOD) along with other meteorological/land-related covariates. However, all the models assumed that all covariates affect the PM2.5 concentration at the same scale, and none could provide a posterior uncertainty analysis at each regression point. Therefore, a multiscale geographically and temporally weighted regression (MGTWR) model was proposed by specifying a unique bandwidth for each covariate. However, the lack of a method for predicting values at unsampled points in the MGTWR model greatly restricts its corresponding application. Thus, this study developed a method for inferring unsampled points and used the posterior uncertainty assessment value to improve the model accuracy. With the aid of the high-resolution satellite multi-angle implementation of atmospheric correction (MAIAC) AOD product, daily PM2.5 concentrations with a 1 km × 1 km resolution were generated over the Beijing-Tianjin-Hebei region between 2013 and 2019. The coefficient of determination (R2) and root mean square error (RMSE) of the fitted MGTWR results vary from 0.90 to 0.94 and from 10.66 to 25.11 μg/m3, respectively. The sample-based and site-based cross-validation R2 and RMSE vary from 0.81 to 0.89 and from 14.40 to 34.43 μg/m3 respectively, demonstrating the effectiveness of the proposed inference method at unsampled points. With the uncertainty constraint, the sample-based and site-based validated MGTWR R2 results for all years are further improved by approximately 0.02–0.04, demonstrating the effectiveness of the posterior uncertainty assessment constraint method. These results suggest that the inference method proposed in this study is promising to overcome the defects of the MGTWR model in inferring the prediction values at unsampled points and could consequently enhance the wide applications of MGTWR modeling.

AUTHENTICITY ANALYSIS OF BEEF MEATBALL ADULTERATION WITH WILD BOAR USING FTIR SPECTROSCOPY COMBINED WITH CHEMOMETRICS

The aim of this research to detect and analyze the wild boar in beef meatball using FTIR combined with chemometric. Then, the specific of FTIR region for detecting wild boar in beef meatball in the commercial product. The wild boar meatball was made with different a concentration range between 0-100%. The wild boar meatball was analyzed using FTIR. The FTIR spectrum was processed using chemometrics method like Part Least Square (PLS) and Principal Component Analysis (PCA). The PLS look for the highest of determination coefficient and smaller of root mean square error of calibration (RMSEC) or Root Mean Square Error of Cross-Validation (RMSECV). PCA analysis will distinguish the wild boar meatball and beef meatball product. The result of this research showed that the FTIR spectrum of wild boar and beef in meatball have different characteristics. The result from FTIR spectrum processing showed that specific spectrum of wild boar at wavenumber 999 - 1481 cm-1 combined with 1650 - 1793 cm-1 which is processed at 1st derivative. The PLS resulted from the calibration method has the determination coefficient (R2) value is 0.9991 and RMSEC of 1.028%. While the validation methods resulted are R2 and RMSECV which are 0.9999 and 0.300% respectively. This research showed the meatball from wild boar can be distinguished from beef and the commercial meatballs are not made from them.

Competitive Adaptive Reweighted Sampling Method for Fault Detection

According to Darwin’s theory of evolution, a variable combination with the strongest correlation between selection process variables and quality indicators is developed. This is called competitive adaptive reweighted sampling (CARS). In this article, the absolute value of the partial least squares regression coefficient is used to assess the importance of each variable. Next, we select variables based on the regression coefficients of variables and quality indicators, including forced variable selection based on exponential decreasing function (EDF) and competitive variable selection based on adaptive reweighted sampling (ARS). Finally, we use cross-validation (CV) to select the subset with the lowest root mean square error of CV (RMSECV). The Tennessee-Eastman (TE) process is used to evaluate the performance of the proposed fault detection method. The results show that CARS can find the best combination of certain key variables, improving the fault detection capability of quality indicators.

Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC

Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

Optimizing spatial data reduction in hyperspectral imaging for the prediction of quality parameters in intact oranges

This study evaluated hyperspectral imaging (900–1700 nm) and the optimal binning strategy for the reduction of spatial data to obtain quantitative maps of some quality attributes in intact oranges. Artificial neural network (ANN) was used to develop prediction models using 198 oranges. Different levels of pixel binning were tested for predicting the samples of an external test set (N = 66). The best models obtained achieved root mean square error of cross validation (RMSECV) values of 0.87 %, 0.23 g L−1, 2.78 and 1.11 for SSC, TA, MI and BrimA, respectively. Models were then applied to different spatial resolution sample images. The coefficients of determination (R2) and the root mean square error of prediction (RMSEP) values for the test set were then compared. A chemical image was developed to display the distribution of SSC, TA, MI and BrimA in the binned images of the orange fruit, demonstrating the potential of using a 10 × 10 spatial binning (corresponding to a 99 % reduction in the original dataset) to develop prediction models for quantifying taste attributes in intact oranges.

In situ monitoring of grape seed protein hydrolysis by Raman spectroscopy.

Raman spectroscopy was used to monitor the enzymatic hydrolysis process of grape seed protein. The degree of hydrolysis (DH), IC50 of the ACE inhibitory activity, and peptide content of the digestive products of grape seed protein were analyzed offline. The partial least squares (PLS), interval partial least squares (IPLS), and joint interval partial least squares (Si-PLS) models of DH, IC50 , and peptide content were established and the optimal pretreatment method was selected. In the optimal model, the corrected model r of the grape seed protein hydrolysis degree is 0.997, the Root Mean Square Error of Cross Validation (RMSECV) is 0.507%. The predicted model r value is 0.9932, the Root Mean Square Error of Prediction (RMSEP) is 1.15%. The corrected model r value of the IC50 is 0.9965, the RMSECV is 11.9%. The r value and RMSEP of predicted model are 0.9978 and 9.64%. The corrected model r value of the peptide content is 0.9955, the RMSECV is 12.7%, the predicted model r value is 0.9953, and the RMSEP is 15.4%. These results showed that in situ real-time monitoring of grape seed protein hydrolysis process can be achieved by Raman spectroscopy. PRACTICAL APPLICATIONS: This study uses Raman spectroscopy method to establish the quantification of proteolysis, IC50, and peptide content of the simulated digestive products during grape seed proteolysis. Analyze the model to monitor and evaluate the target parameters during the entire grape seed proteolysis process. In situ real-time monitoring of grape seed proteolysis is of great significance to the entire grape seed active peptide industry.

Quantitative analysis of coal quality by mutual information-particle swarm optimization (MI-PSO) hybrid variable selection method coupled with spectral fusion strategy of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectroscopy (FTIR)

Coal quality analysis can provide basic work for the design and operation of related equipment and technological processes, which mainly including ash content and volatile matter. Variable selection plays an important role in the model and properly variable selection methods which can promote the predictive performance of the model and reduce the modeling time. In this article, a hybrid variable selection method mutual information-particle swarm optimization (MI-PSO) was used to realize precise screening of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectrometer (FTIR) spectral characteristic variables. Firstly, to obtain the fusion spectrum matrix, the pre-processed LIBS and FTIR spectrum matrix were directly connected end to end. Then the MI was used to eliminate redundant variables in the spectral data, and the PSO was used to further filter the retained variables to find a set of variables with higher prediction accuracy. Finally, a mutual information-particle swarm optimization-kernel extreme learning machine (MI-PSO-KELM) model is constructed based on the selected variables that the ash content and the volatile matter variables are 6499, 5093 respectively. The performance of MI-PSO-KELM is better than the KELM model based on LIBS and FTIR and the KELM model based on LIBS and FTIR primary spectral fusion, and can more accurately predict the ash content and volatile matter of coal quality analysis. The results for the determination of ash content, the optimal cross-validation (root mean square error of cross validation (RMSECV) =1.6170, determination coefficient of cross validation (RCV2) =0.9684) and prediction set the root mean square error of prediction (RMSEP) =0.9687, determination coefficient of prediction (RP2 = 0.9821) results are obtained by the MI-PSO based on primary spectral fusion model. For the determination of the volatile matter, the best results of cross-validation (RMSECV = 1.2886, RCV2=0.9868) and prediction set (RMSEP = 1.3218, RP2=0.9789) are obtained by the MI-PSO based on primary spectral fusion model. It shows that the spectral fusion technology can help improve the performance of LIBS spectral quantitative analysis, and a spectrum fusion method based on LIBS and FTIR is established to realize the rapid determination of ash content and volatile matter of coal.

Rapid Qualitative and Quantitative Analysis of Caffeine and Sodium Benzoate in Annaca by Infrared Spectroscopy.

Objective To establish an infrared spectroscopic method for the rapid qualitative and quantitative analysis of caffeine and sodium benzoate in Annaka samples. Methods Qualitative and quantitative modeling samples were prepared by mixing high-purity caffeine and sodium benzoate. The characteristic absorption peaks of caffeine and sodium benzoate in Annaka samples were determined by analyzing the infrared spectra of the mixed samples. The quantitative model of infrared spectra was established by partial least squares （PLS）. Results By analyzing the infrared spectra of 17 mixed samples of caffeine and sodium benzoate （the purity of caffeine ranges from 10% to 80%）, the characteristic absorption peaks for caffeine were determined to be 1 698, 1 650, 1 237, 972, 743, and 609 cm-1. The characteristic absorption peaks for sodium benzoate were 1 596, 1 548, 1 406, 845, 708 and 679 cm-1. When the detection of all characteristic absorption peaks was the positive identification criteria, the positive detection rate of caffeine and sodium benzoate in 48 seized Annaka samples was 100%. The linear range of PLS quantitative model for caffeine was 10%-80%, the coefficient of determination （ R2） was 99.9%, the root mean square error of cross validation （RMSECV） was 0.68%, and the root mean square error of prediction （RMSEP） was 0.91%; the linear range of PLS quantitative model for sodium benzoate was 20%-90%, the R2 was 99.9%, the RMSECV was 0.91% and the RMSEP was 1.11%. The results of paired sample t test showed that the differences between the results of high performance liquid chromatography method and infrared spectroscopy method had no statistical significance. The established infrared quantitative method was used to analyze 48 seized Annaka samples, the purity of caffeine was 27.6%-63.1%, and that of sodium benzoate was 36.9%-72.3%. Conclusion The rapid qualitative and quantitative analysis of caffeine and sodium benzoate in Annaka samples by infrared spectroscopy method could improve identification efficiency and reduce determination cost.

Determination of ethyl octanoate in Chinese liquor using FT-NIR spectroscopy

To quantitatively detect ethyl octanoate in Chinese liquor, Fourier-transform near-infrared (FT-NIR) spectroscopy was performed in the present work, with 162 Chinese liquor samples selected from Luoyang Dukang Distillery. The chemical values of ethyl octanoate were determined by gas chromatography (GC), and spectral data from 12,000 to 4000 cm-1 were collected. The calibration model was established with partial least squares (PLS) regression, and then validated using internal cross-validation. The predictability of the model was further confirmed by the validation set as external validation. After comparing the effects of the models set up with sample data under different pre-processing methods, the model was built within the spectral region of 6101.7 - 5449.8 cm-1; based on the SNV pre-processing method which was selected as the optimal model. The coefficient of determination (R2) for cross-validation of the model was 0.9507, and the corresponding root mean square errors of cross-validation (RMSECV) was 3.91 mg L-1. The R2 for external validation was 0.9537, and the root mean square errors of prediction (RMSEP) was 3.62 mg L-1. The results demonstrated that using NIR spectroscopy to determine ethyl octanoate in Chinese liquor is feasible and can achieve satisfactory results.

Development of near‐infrared reflectance spectroscopy calibration for sugar content in ground soybean seed using Perten DA7250 analyzer

AbstractThere is growing interest in developing calibrations for soybean [Glycine max (L.) Merr.] seed sugars on near‐infrared reflectance spectroscopy (NIRS) instruments to increase the efficiency of sugar profiling. In this study, a set of 253 samples from Virginia Tech's soybean germplasm with a wide range of sugar content were used to create prediction models for sucrose, raffinose, and stachyose in ground soybean seed on the Perten DA7250 NIRS instrument. Following acquisition of spectral data, seed sugars were extracted from ground samples and analyzed using high‐performance liquid chromatography (HPLC) to obtain reference data. Spectral and HPLC data were modeled using partial least squares regression (PLSR) on CAMO Unscrambler X software and internally cross‐validated using the same software. Resulting calibrations showed high quantitative accuracy with the coefficient of determination of calibration (R2C) = .901, the coefficient of determination of cross‐validation (R2CV) = .869, root mean squared error of calibration (RMSEC) = .516, and root mean squared error of cross‐validation (RMSECV) of .596 for sucrose and R2C = .911, R2CV = .891, RMSEC = .361, and RMSECV of .405 for stachyose. These calibrations appear suitable for use in breeding operations. Meanwhile, performance of the raffinose calibration remained poor with R2C = .568, R2CV = .476, RMSEC = .129, and RMSECV = .143. Alternative methods for more accurate and rapid quantification of raffinose concentration in soybean seed should be investigated.

Potential of On-the-Go Gamma-Ray Spectrometry for Estimation and Management of Soil Potassium Site Specifically

High resolution data on plant available potassium (Ka) is crucial to optimize variable rate potassium fertilizer recommendations, and subsequently improve crop growth and yield. A gamma-ray passive spectrometry sensor was evaluated for on-the-go mapping and management of the spatial distribution of Ka over a 8.4 ha field at Huldenberg, Belgium. During the on-the-go measurement, a 5 s sampling interval was used while driving at 3 km/h speed along 10 m parallel transects. Two calibration models to predict Ka across the field were developed and compared: (1) a simple third order polynomial function (3DPF) was established between the sensor reading of the naturally occurring radioactive isotope of potassium (K-40) and laboratory measured Ka and (2) a partial least squares regression (PLSR) model linking gamma-ray spectra and laboratory measured Ka. Although a relatively small number of samples (45 samples) were used for the development of the PLSR calibration model, the cross-validation analysis resulted in a very good performance with a coefficient of determination (R2) of 0.85, a residual prediction deviation (RPD) of 2.67, a root mean square error of cross-validation (RMSECV) of 2.29 (mg/100 g) and a ratio of performance to interquartile distance (RPIQ) of 2.61. This was a much better result that that obtained with the 3DPF model (R2 = 0.69). The spatial distribution of Ka developed based on 3DPF and PLSR methods showed great similarity with the corresponding map developed using the data from the laboratory analysis. The calculated variable rate fertilizer recommendation based on gamma-ray data showed marginal differences in the amount of K2O fertilizer applied, compared to the uniform rate fertilization based on the conventional laboratory chemical soil analyses. The on-the-go measurement of Ka using gamma-ray spectrometry shows high potential, although the technology needs to be evaluated in a larger number of fields.

Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy

With the trend of portable and miniaturization, Raman spectrometer requires more advanced analytical methods providing more rapid and accurate analysis performance for in-situ analysis. In this work, a hybrid variable selection method based on V-WSP and variable importance measurement (VIM) coupled with random forest (RF) was used to improve the quantitative analysis performance of portable laser Raman instruments for quantitative analysis of methanol content in methanol gasoline. First, five preprocessing methods were applied to reduce the infection information in the raw spectra, respectively. Based on the spectra data processed by multivariate scattering correction (MSC), V-WSP was employed to filter the infection or redundant information in Raman spectroscopy, and 579 variables were obtained when the correlation threshold is 0.9600. Then, the variables were further eliminated by VIM. Finally, 43 variables were obtained by the V-WSP-VIM method. In data processing, out of bag (OOB) error estimation and 10-flod cross validation (CV) were applied to optimize the parameters of preprocessing methods, V-WSP, VIM and RF model. The results fully demonstrated that compared with the RF model based on raw spectra, the RF model based on V-WSP-VIM method can achieve a better prediction performance for the quantitative analysis of methanol content in methanol-gasoline, with the coefficients of determination of cross-validation (R2CV) improving from 0.9100 to 0.9662, the root mean square error of cross-validation (RMSECV) reducing from 0.0572 to 0.0365%, the coefficients of determination of prediction set (R2P) improving from 0.9214 to 0.9407, the root mean square error of prediction set (RMSEP) reducing from 0.0420 to 0.0382%, the variables reducing from 1044 to 43 and the modeling time reducing from 72.94 to 6.41 s. The results indicates that V-WSP-VIM coupled with RF is an effective method to improve the performance of portable laser Raman spectrometer for quantitative analysis of methanol content in methanol gasoline.

PhotoMetrix UVC: A New Smartphone-Based Device for Digital Image Colorimetric Analysis Using PLS Regression

A novel free PhotoMetrix UVC is proposed for both the operation of a universal serial bus video camera (UVC) and multivariate image analysis, allowing a full solution for point-of-use analysis. A UVC was placed in an open-source 3D-printed chamber illuminated by a white light-emitting diode (LED) with controlled intensity of light. The digital images captured were converted into red, green, and blue (RGB) histograms, and regression models were used within the app. As a proof-of-concept, four adulterants in raw milk samples were determined. The coefficient of determination (R2Cal) for all models was higher than 0.99, and no significant differences (p < 0.05) between the measured and predicted values were identified. The root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) were satisfactory, with values less than 0.1 and 0.7 g L-1, respectively. The recoveries ranged from 90 to 120% in spiked milk samples, and partial least square (PLS) models showed root mean square error of prediction (RMSEP) of 0.28, 0.33, 0.48 and 0.39 g L-1 for chloride, hypochlorite, hydrogen peroxide and starch, respectively. The PhotoMetrix UVC app was feasible for the colorimetric chemical analysis using a smartphone improving the applicability, mobility, and usability.

Application of spectra pre-treatments on firmness assessment of intact sapodilla using vis-nir spectroscopy

This study aimed to obtain the best calibration model from various spectra pre-treatment methods to assess sapodilla fruit firmness using vis-nir spectroscopy. Before the spectra data measurement, samples were treated with storage of 0, 5 and 10 days at room temperature. Spectra data measurement was carried out using the NirVana AG410 visible and near infrared spectrometer from 312 to 1050 nm with interval of 3 nm. RAW spectra were pre-treated using the multiplicative scatter correction (MSC), standard normal variate (SNV), and Savitzky-Golay first derivative (dg1) with 9 points of smoothing. The calibration model was developed using PLS (partial least squares) method. Validation was done by K fold cross validation method. The results showed the MSC and SNV spectra were able to eliminate noises of RAW spectra, whereas in the dg1 spectra, noises were still visible. The best model was acquired by SNV spectra with R2 (coefficient of determination) of calibration and validation of 0.882 and 0.870, root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) values of 2.92 and 3.08, and the ratio of performance to deviation (RPD) of 2.76. The result indicated the spectra pre-treatments were able to improve the accuracy of calibration model on assessment of sapodilla fruit firmness.

A green and high throughput method for salt determination in crude oil using digital image-based colorimetry in a portable device

A digital image-based (DIB) method using colorimetry via a portable device is proposed for salt determination in crude oil. Chloride was extracted to an aqueous phase, followed by colorimetric quantification using digital images. A USB camera was attached to a homemade 3D-printed chamber with controlled light intensity. The images were converted into red, green, and blue (RGB) histograms, and a partial least squares regression (PLS) model was constructed using the free PhotoMetrix UVC app. The experimental parameters were optimized, as the ratio between silver nitrate and the potassium chromate, the lighting, the volume of solution in the measurement chamber, the focal distance, and the pH effect. A miniaturized extraction method was developed for sample preparation, and the main parameters were evaluated: sample mass, time, temperature, solvent, or the use of a demulsifier and its amount. The PLS regression model was evaluated in terms of the coefficient of determination (R2), the root mean squared error of calibration (RMSEC), the root mean squared error of cross validation (RMSECV) and the root mean squared error of prediction (RMSEP). The detection limit was 69 mg L−1 (0.034 mg g−1 using 10 g of sample). No differences were observed (p < 0.05) when comparing the results from the DIB method with the reference values. The extraction method’s simplicity combined with the digital image-based colorimetric analysis allowed an easy-to-use and environmentally friendly method. Other important advantages include low cost, the possibility to be portable, and high throughput in comparison to standard methods.

Comparison of Spectroscopic Techniques Combined with Chemometrics for Cocaine Powder Analysis.

Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC-MS) and gas chromatography with flame-ionization detection (GC-FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

Fast assay of ethyl acetate content of double-base oblate spherical propellant after balling process by near-infrared spectroscope

Ethyl acetate (EAC) is commonly used as a solvent in the balling process of double-base oblate spherical propellant (DOSP) because of its excellent performance in swelling. However, the residual content of EAC in the DOSP has an adverse effect on combustion performance of DOSP. Therefore, it is necessary to fast assay the EAC content of DOSP in the production process for avoiding economic loss caused by unqualified products. This work is to investigate the feasibility of using a Fourier transform near-infrared (NIR) spectroscope coupled with partial least squares (PLS) algorithm to construct a quantitative model, which can rapidly monitor the EAC content of DOSP after balling process. In the NIR spectra, two characteristic peaks were observed in the range of 6096–5464 cm−1 and 4824–4524 cm−1, which were employed as modeling intervals for analyzing the content of EAC in DOSP intermediate samples. According to the minimum value of the root mean square error of cross-validation (RMSECV), first derivative and standard normal variate (SNV) were chosen as original spectral preprocessing method, and the optimum number of PLS factors for EAC calibration model was 6. The quantitative EAC model was developed based on PLS algorithm. The determination coefficients of the calibration (RC2) and prediction (RP2) for the developed model were equal to 0.9786 and 0.9883, respectively. The root means square error of calibration and prediction (RMSEC and RMSEP) were 0.0536 and 0.0412, respectively. The residual predictive deviation (RPD) values of calibration and prediction were 6.83 and 10.7, respectively. Using independent samples as prediction set to test robustness of the developed model, and the average deviation between traditional measured values and NIR predicted values was 0.017%. Therefore, the experimental results indicated that the NIR spectroscope can give a fast and accurate analysis the EAC content of DOSP after the balling process.

Assessment of milk fat based on signal-to-ground voltage

This study proposes an electromagnetic method for assessing fat content by measuring the signal-to-ground voltage in milk. The milk-filled spiral glass tube, acting as the secondary coil of a transformer, under different magnetic fluxes at the same frequency. The measurement system included two connected sample coils in a close-loop configuration and an open-loop configuration. The voltage at the four terminals of two coils was detected. The results indicated that the voltage at these four terminals had differential response characteristics, but their sum was consistent regardless of the configuration. A linear correlation (R2 = 0.845 and R2 = 0.941) was observed between milk fat and signal-to-ground voltage. The root mean square error (RMSE) of calibration (RMSEC) was 0.368 and 0.230 g/100 g fat content, respectively, whereas the RMSE of cross validation (RMSECV) was 0.232 and 0.599 g/100 g (fat content) for the 700 Hz, respectively.

Raman Spectroscopy as Molybdenum and Tungsten Content Analysis Tool for Mesoporous Silica and Beta Zeolite Catalysts.

Raman spectroscopy was used for the quantitative determination of Mo and W in Mo- and W-supported mesoporous silica (Mo/SBA-15 and W/SBA-15, respectively) and Mo-supported beta zeolite (Mo-BEA). Three Raman quantitative models were developed and optimized for the metal contents of Mo/SBA-15, W/SBA-15, and Mo/BEA. Subsequently, the models were characterized using the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficient, and predicted residual error sum of squares (PRESS) diagnostic function. The calibration range of the models were in the range of approximately 2–40 wt% for the SBA-15 support and 1–21 wt% for the BEA support because the BEA support presented lower Mo absorption than the SBA-15 support. The RMSEC, RMSECV, and RMSEP values were below 1.80% for all developed models. The highest and lowest correlation coefficients corresponded to the W/SBA-15 (0.9984) and Mo/BEA (0.9777) models, respectively. The change in catalyst support affected the mentioned chemometric parameters (Mo/SBA-15 vs. Mo/BEA). Subsequently, Raman spectroscopy combined with the temperature control stage was used to study the calcination of Mo/BEA, Mo/SBA-15, and W/SBA-15 using three-dimensional diagrams, in which the changes in catalyst structure were analyzed as functions of the temperature and time. Raman spectroscopy was determined to be a suitable analytical tool for the quantitative analysis of the metal contents of the catalyst and optimization of the calcination process. Therefore, Raman spectroscopy can be used during catalyst manufacturing.