In manufacturing industry, process monitoring is a key to observe the product quality, operational health, safety, and also for achieving good/satisfactory process control performance. In polymer processing, the level of control of the process operational quality is vastly dependent upon the performance of the process monitoring techniques as it is quite difficult to observe the inside of an extruder barrel (or processing chamber) during the process operation. Currently, a number of physical sensing devices/techniques are widely available for industrial applications for monitoring of parameters, such as melt temperature, melt pressure, screw/processing speed, product dimensions, and so on, in real-time. However, there may be some limitations in using physical sensors in practical process measurements (such as melt viscosity and melt temperature profile) due to several constraints such as their access requirements, disruptive effects to the melt flow, fragility, complexity, and so on. Therefore, soft sensing techniques should be highly useful for improved process monitoring and modeling, and hence for advanced process control. In this paper, a general description is presented on the state of the art of soft sensor design. Then, a comprehensive review is made on the use of soft sensing techniques in polymer processing applications while identifying their capabilities and limitations. Eventually, the importance of developing of such soft sensing techniques (together with some sort of built-on intelligence) for the advancement of process monitoring is discussed while indicating some of the possible directions for future industry.

/pdf/design-and-applications-of-soft-sensors-in-polymer-5f5l88ogf3.pdf

Design and Applications of Soft Sensors in Polymer Processing: A Review

Lignin is one of the most studied and analyzed materials due to its importance in cell structure and in lignocellulosic biomass. Because lignin exhibits autofluorescence, methods have been developed that allow it to be analyzed and characterized directly in plant tissue and in samples of lignocellulose fibers. Compared to destructive and costly analytical techniques, fluorescence microscopy presents suitable alternatives for the analysis of lignin autofluorescence. Therefore, this review article analyzes the different methods that exist and that have focused specifically on the study of lignin because with the revised methods, lignin is characterized efficiently and in a short time. The existing qualitative methods are Epifluorescence and Confocal Laser Scanning Microscopy; however, other semi-qualitative methods have been developed that allow fluorescence measurements and to quantify the differences in the structural composition of lignin. The methods are fluorescence lifetime spectroscopy, two-photon microscopy, Föster resonance energy transfer, fluorescence recovery after photobleaching, total internal reflection fluorescence, and stimulated emission depletion. With these methods, it is possible to analyze the transport and polymerization of lignin monomers, distribution of lignin of the syringyl or guaiacyl type in the tissues of various plant species, and changes in the degradation of wood by pulping and biopulping treatments as well as identify the purity of cellulose nanofibers though lignocellulosic biomass.

/pdf/fluorescence-microscopy-methods-for-the-analysis-and-1zvjwygd.pdf

Fluorescence Microscopy Methods for the Analysis and Characterization of Lignin

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.

/pdf/rapid-determination-of-cellulose-and-hemicellulose-contents-lz6lkjyy.pdf

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

Excessive illegal addition of talc in flour has always been a serious food safety issue. To achieve rapid detection of the talc content in flour (TCF) by near-infrared spectroscopy (NIRS), this study used a Fourier transform near-infrared spectrometer technique. The identification of efficient spectral feature wavelength selection (FWS), such as backward interval partial-least-square (BiPLS), competitive adaptive reweighted sampling (CARS), hybrid genetic algorithm (HGA), and BiPLS combined with CARS; BiPLS combined with HGA; and CARS combined with HGA, was also discussed in this paper, and the corresponding partial-least-square regression models were established. Comparing with whole spectrum modeling, the accuracy and efficiency of regressive models were effectively improved using feature wavelengths of TCF selected by the above algorithms. The BiPLS, combined with HGA, had the best modeling performance; the determination coefficient, root-mean-squared error (RMSE), and residual predictive deviation of the validation set were 0.929, 1.097, and 3.795, respectively. BiPLS combined with CARS had the best dimensionality reduction effect. Through the FWS by BiPLS combined with CARS, the number of modeling wavelengths decreased to 72 from 1845, and the RMSE of the validation set was reduced by 11.6% compared with the whole spectra model. The results showed that the FWS method proposed in this paper could effectively improve detection accuracy and reduce modeling wavelength variables of quantitative analysis of TCF by NIRS. This provides theoretical support for TCF rapid detection research and development in real-time. 

Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection

Quality control methods of current traditional Chinese medicine (TCM) preparation is time-consuming and difficult to assess in terms of overall efficiency of the drug. A non-destructive rapid near-infrared spectroscopy detection system for key chemical components and biological activity of Lanqin oral solution (LOS), one of the best-selling TCM formulations, was established for comprehensive quality evaluation. Near infrared spectral scanning was carried out on 101 batches of commercial LOS under the penetrated vial state and traditional state. RAW 264.7 cells were cultured to detect the anti-inflammatory ability of LOS, and the reference concentrations of epigoitrin, geniposide, and baicalin were obtained by HPLC. The quantitative models were optimized by three kinds of variable selection methods. The correlation coefficients of prediction value of the models were greater than 0.94. The system also passed the external validation. The performance of the non-invasive models was similar to the traditional models. The established non-destructive system can be applied to the rapid quality inspection of LOS to avoid unqualified drugs from entering the market and ensure drug effectiveness. The biological activity index of LOS was introduced and predicted by NIRs for the first time, which provides a new idea about the quality control of TCM formulations.

/pdf/non-invasive-detection-of-anti-inflammatory-bioactivity-and-2dcnsv0y.pdf

Non-Invasive Detection of Anti-Inflammatory Bioactivity and Key Chemical Indicators of the Commercial Lanqin Oral Solution by Near Infrared Spectroscopy

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

/pdf/rapid-measurement-of-cellulose-hemicellulose-and-lignin-icp5qmqi.pdf

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

The modeling approach of stacked neural networks based on absolute error criteria is proposed, and applied to inferential estimation of polypropylene melt index. Single neural network model generalization capability can be significantly improved by using stacked neural network model. Proper determination of the stacking weights is essential for good stacked neural networks model performance, so determination of appropriate weights for combining individual neural networks based on absolute error criteria is proposed. For the purpose of comparison, single neural network models and stacked neural network models based on absolute error criteria are developed and evaluated. The application of the proposed modeling method to the development of melt index soft sensor in an industrial polypropylene polymerization plant demonstrates its effectiveness.

Inferential estimation of polypropylene melt index using stacked neural networks based on absolute error criteria

Raman spectroscopy has numerous advantages as a means of analyzing materials and is widely used in petrochemical, material, food, biological, medical, and other fields. Its analysis process is fast, nondestructive, and requires no prepreparation. Meanwhile, the research on applying machine learning methods in Raman spectral recognition is becoming increasingly popular. In this study, an end‐to‐end deep learning method called deep residual shrinkage‐VGG (DRS‐VGG) is proposed, which is able to match Raman spectral features with model structure and reduces the reliance on feature engineering. The addition of identity shortcut and soft thresholding in the model eliminates redundant signals to achieve end‐to‐end spectral identification. The effectiveness of the proposed model is verified in three subsets of the RRUFF Raman database and bacterial Raman dataset from different perspectives without data augmentation, and the recognition accuracy is 97.84%, 92.81%, and 95.08%, respectively. Compared with other methods, the proposed DRS‐VGG model achieved a significant improvement in speed or accuracy. The model's understanding of the spectra is visualized by the gradient‐weighted class activation mapping (Grad‐CAM), which explains the excellent classification performance. Additionally, the weight pruning technique is used to achieve model compression and improve recognition accuracy by shrinking the weights and fine‐tuning the biases.

An end‐to‐end deep learning approach for Raman spectroscopy classification

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Papers

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Inferential estimation of polypropylene melt index using stacked neural networks based on absolute error criteria

An end‐to‐end deep learning approach for Raman spectroscopy classification