Quantitative Determination of Cr in Ink by Laser-induced Breakdown Spectroscopy(LIBS)Using ZnO as Adsorbent

Laser-induced breakdown spectroscopy(LIBS) technique was applied to detecting chromium in ink with ZnO as adsorbent, and the LIBS spectra were preprocessed by wavelet denoising. The laser energy and delay time were optimized depending on the signal-to-noise ratio(SNR) and intensity of three analysis atomic lines(Cr 425.43 nm, Cr 427.48 nm and Cr 428.97 nm). Compared with other analysis lines, atomic line of Cr 427.48 nm was selected as the analysis line for the quantitative analysis of Cr in ink as the calibration curve of it showed a better linear relationship (correlation coefficient R²=0.9778), and the relative error of Cr in the measured ink was 52.96%. Since the single spectral line used for calibration curve method is often influenced by matrix effect and other factors, partial least squares regression(PLS) as multivariate calibration method has been applied to predicting the concentration of Cr in ink, and the relative error of Cr in the measured ink was 10.48%. The result obtained from the PLS method was better than that from the calibration curve when comparing the relative error, demonstrating that, based on adsorbent, LIBS combined with PLS provides an effective, practical and convenient technique for the determination of trace element in aqueous solution.     

Rapid Simultaneous Determination of Andrographolides in Andrographis paniculata by Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for the quality control of traditional Chinese medicine has been evaluated. Seven quantitative parameters, andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, and alcohol-soluble extract of Andrographis paniculata, were evaluated by NIRS. The reference values of andrographolides were determined by high-performance liquid chromatography, and the others were obtained using the standard methods of the 2015 Chinese Pharmacopoeia. The predicted values were determined by a quantitative model using NIRS based on partial least square regression. Different spectral preprocessing methods, spectral ranges, and optimum number of factors were selected to optimize the models. All models were estimated by the combination of various parameters, including the correlation coefficient of calibration for andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, alcohol-soluble extract (values of 0.980, 0.984, 0.989, 0.983, 0.987, 0.988, 0.979, respectively), root mean square error of calibration (values of 0.156, 0.038, 0.050, 0.029, 0.604, 0.431, 0.135, respectively), root mean square error of prediction (values of 0.169, 0.041, 0.050, 0.033, 0.280, 0.493, 0.140, respectively), root mean square error of cross-validation (values of 0.626, 0.114, 0.158, 0.046, 1.145, 0.774, 0.508, respectively), and ratio of standard deviation to standard error of prediction (values of 4.583, 4.690, 4.796, 4.899, 4.899, 4.690, 5.099, respectively). The results show that the calibration models by NIRS are reliable and can be applied for the quantification for seven parameters from A. paniculata for quality control in traditional Chinese medicine production and processing.     

Determination of Chlorpyrifos Residue by Near-Infrared Spectroscopy in White Radish Based on Interval Partial Least Square (iPLS) Model

Abstract

This article presents a multivariate method of rapidly determining chlopyrifos residue in white radish, based on near-infrared spectroscopy and partial least squares (PLS) regression. Interval PLS (iPLS) was utilized to select the optimum wave number range. The number of PLS components and the number of intervals were optimized according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The result showed that the iPLS model was more reliable than the full model and that near-infrared spectroscopy with iPLS algorithm could be used successfully to analyze chlorpyrifos residue in white radish.     

Compositional Analysis of Cement Raw Meal by Near-Infrared (NIR) Spectroscopy

Abstract

A rapid and accurate method is presented to determine CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃ in cement raw meal using near-infrared (NIR) spectroscopy. Multiplicative scatter correction (MSC) was employed to eliminate the scattering signal and partial least squares (PLS) regression was used to build the analysis model. The results demonstrated good performance by this approach for the determination of CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃. NIR spectroscopy exhibits the feasibility to characterize the quality of cement raw meal. Compared with prompt gamma neutron activation analysis (PGNAA) and X-ray fluorescence (XRF), this method is more efficient and safer.     

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).     

The Use of Laser Induced Breakdown Spectroscopy (LIBS) to Study Catalyst Deactivation of V2O5/γ‐Al2O3 as Compared to Inductively Coupled Plasma Optical Emission Spectrometry

Laser Induced Breakdown Spectroscopy (LIBS) method is introduced as a novel approach in this work to study catalyst deactivation of V₂O₅/γ‐‐Al₂O₃ for gas‐phase dehydration of glycerol and producing acrolein. The LIBS results of V₂O₅/γ‐Al₂O₃ samples are compared with those data that are obtained by Inductively Coupled Plasma Optical Emission Spectrometry (ICP‐OES). Experimental data of LIBS data specify that line intensities of vanadium are decreased by deactivation of V₂O₅/γ‐Al₂O₃ catalyst. A comparison between the results of LIBS test as well as ICP‐OES analysis shows that the amount of vanadium is decreased in the catalyst. Moreover, coke formation changes the surface of the catalyst. The results of deactivation of V₂O₅/γ‐Al₂O₃ are also compared with Pd/C catalyst deactivation.     

Rapid Determination of Phenolics,Flavonoids, and Antioxidant Properties of Physalis ixocarpa Brot. ex Hornem. and Physalis angulata L. by Infrared Spectroscopy and Partial Least Squares

Physalis ixocarpa Brot. ex Hornem. and Physalis angulata L. are two edible species of the family Solanaceae, which have an important variety of antioxidant compounds present in their roots, stems, leaves, calyces, and fruits. This work reports the development of multivariate models based on the use of partial least square (PLS) analysis and Fourier transform infrared (FTIR) spectroscopy for the quantitative determination of total phenolics, total flavonoids, free radical scavenging activity, total antioxidant capacity, and reducing power in the extracts of roots, stems, and leaves of both P. ixocarpa and P. angulata. Standard chromatographic and colorimetric techniques were used to determine the quantitative actual values (references) in the extracts, which served as input data to develop the multivariate PLS models. Optimized FTIR-PLS models were realized by cross-validation procedures, obtaining the determination coefficients for prediction between 0.792 and 0.905 for P. ixocarpa, and between 0.756 and 0.893 for P. angulata. In this form, FTIR spectroscopy with multivariate analysis could represent a versatile tool to evaluate quantitatively concentrations of bioactive compounds and antioxidant properties in the extracts of both species, requiring a very short time at low cost.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.