A online NIR sensor for the pilot-scale extraction process in Fructus aurantii coupled with single and ensemble methods

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy

Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R²) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.

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).

Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics

Traditional methods for identification of Panax notoginseng (PN) such as high performance liquid chromatography (HPLC) and gas chromatography (GC) are time-consuming, laborious and difficult to realize rapid and online analysis. In this research, the feasibility of identification and quantification of PN with rhizoma curcumae (RC), Curcuma longa (CL) and rhizoma alpiniae offcinarum (RAO) are investigated by using near infrared (NIR) spectroscopy combined with chemometrics. Five chemical pattern recognition methods including hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN), support vector machine (SVM) and extreme learning machine (ELM) are used to build identification model of the dataset with 109 samples of PN and its three adulterants. Then seven datasets of binary, ternary and quaternary adulterations of PN are designed, respectively. Five multivariate calibration methods, i.e., principal component regression (PCR), support vector regression (SVR), partial least squares regression (PLSR), ANN and ELM are used to build quantitative model and compared for each dataset, separately. Finally, in order to further improve the prediction accuracy, SG smoothing, 1st derivative, 2nd derivative, continuous wavelet transform (CWT), standard normal variate (SNV), multiple scatter correction (MSC) and their combinations are investigated. Results show that PLS-DA and SVM can achieve 100% classification accuracy for identification of 109 PN with its three adulterants. PLSR is an optimal calibration method by comprehensive consideration of prediction accuracy, over-fitting and efficiency for the quantitative analysis of seven adulterated datasets. Furthermore, the predictive ability of the PLSR model for PN contents can be improved obvious by pretreating the spectra by the optimal preprocessing method, with correlation coefficients of which all higher than 0.99.

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping

Our work proved that processing trajectory could effectively obtain a more reliable and robust quantitative model compared with the step-by-step optimization method. The use of systematic tracking was investigated as a tool to optimize modeling parameters including calibration method, spectral pretreatment and variable selection latent factors. The variable was selected by interval partial least-squares (iPLS), backward interval partial least-square (BiPLS) and synergy interval partial least-squares (SiPLS). The models were established by Partial least squares (PLS) and Bagging-PLS. The model performance was assessed by using the root mean square errors of validation (RMSEP) and the ratio of standard error of prediction to standard deviation (RPD). The proposed procedure was used to develop the models for near infrared (NIR) datasets of active pharmaceutical ingredients in tablets and chlorogenic acid of Lonicera japonica solution in ethanol precipitation process. The results demonstrated the processing trajectory has great advantages and feasibility in the development and optimization of multivariate calibration models as well as the effectiveness of bagging model and variable selection to improve prediction accuracy and robustness.

New sensor technologies in quality evaluation of Chinese materia medica: 2010-2015

New sensor technologies play an important role in quality evaluation of Chinese materia medica (CMM) and include near-infrared spectroscopy, chemical imaging, electronic nose and electronic tongue. This review on quality evaluation of CMM and the application of the new sensors in this assessment is based on studies from 2010 to 2015, with prospects and opportunities for future research.