A Long Short-Term Memory Neural Network Based Simultaneous Quantitative Analysis of Multiple Tobacco Chemical Components by Near-Infrared Hyperspectroscopy Images

A multi-component concentration spectral modeling method with parallel drift resistance based on disorderly difference

Spectral detection based on spectrophotometry is an important multi-component concentration detection method. At present, commonly used machine learning methods in the field of spectral analysis can only be used for prediction and cannot analyze how the concentration of each component affects the spectrum. In addition, for common spectral parallel drift in spectrophotometry, traditional derivative preprocessing methods are susceptible to noise and cannot reverse restore the original spectrum. These issues are not conducive to obtaining accurate interpretable spectral models and limits further improvement in detection accuracy. To solve the above problems, we applied mathematical methods to establish a basic model of absorbance spectra for multi-component mixed solutions, and analyzed the influence of spectral parallel drift on it. Then, we proposed an anti-drift modeling method based on the adjacent difference method. This method not only eliminates drift in the spectrum, but also achieves reverse optimization estimation of the original spectral model. In addition, in response to the phenomenon that the adjacent difference method amplifies spectral noise, we analyzed its reasons and proposed a disorderly difference method that can perfectly balance the advantages of adjacent difference method and span difference method. This method has better performance when applied to anti-drift modeling. Finally, the effectiveness of the method and improvement measures was validated by applying them on cobalt ion spectral dataset with different concentrations of iron and copper ions in zinc hydrometallurgy.

Regression prediction of tobacco chemical components during curing based on color quantification and machine learning

Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical components. In order to clarify the relationship between the changes of tobacco color and chemical components, here we established several prediction models of chemical components with the color values of tobacco based on machine learning algorithms. The results of correlation analysis showed that tobacco moisture content was highly significantly correlated with the parameters such as a*, H* and H°, the reducing sugar and total sugar content of tobacco was significantly correlated with the color values, and the starch content was highly significantly correlated with the color values except for b* and C*. The random forest models performed best in predicting tobacco moisture, reducing sugar, total sugar and starch constructed with the R2 of the model validation set was higher than 0.90, and the RPD value was greater than 2.0. The consistent between the predictions and measurements verified the availability and feasibility using color values to predict some chemical components of the tobacco leaves with high accuracy, and which has distinct advantages and potential application to realize the real-time monitoring of some chemical components in the tobacco curing process.

Quantitative modelling of Plato and total flavonoids in Qingke wort at mashing and boiling stages based on FT-IR combined with deep learning and chemometrics

Craft beer brewers need to learn process control strategies from traditional industrial production to ensure the consistent quality of the finished product. In this study, FT-IR combined with deep learning was used for the first time to model and analyze the Plato degree and total flavonoid content of Qingke beer during the mashing and boiling stages and to compare the effectiveness with traditional chemometrics methods. Two deep learning neural networks were designed, the effect of variable input methods on the effectiveness of the models was discussed. The experimental results showed that the CARS-LSTM model had the best predictive performance, not only as the best quantitative model for Plato in the mashing (R2p = 0.9368) and boiling (R2p = 0.9398) phases but also as the best model for TFC in the boiling phase (R2p = 0.9154). This study demonstrates the great potential of deep learning and provides a new approach to quality control analysis in beer brewing.

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

Series fusion of scatter correction techniques coupled with deep convolution neural network as a promising approach for NIR modeling

Deep convolution neural network (CNN) with one-dimensional (1D) convolution structure is a potential and effective nonlinear method for near infrared (NIR) spectroscopy analysis. However, it is also a challenge to build a reliable CNN calibration model since industrial NIR data present serious scattering effect which will seriously interfere with important information. Thus, this paper proposed a promising approach, namely series fusion of scatter correction technologies (SCSF), where CNN built on the series splicing data of normalized raw spectra, standard normal variable (SNV) spectra and first derivative (1d) spectra. Two real NIR cases (one is the identification of alcohols/diesel blends and the other is the prediction of methanol and ethanol content in alcohols/diesel blends) were introduced to explore the feasibility and effectiveness of the presented model. Through the comparative analysis with CNN based on raw spectra, SNV spectra and 1d spectra, as well as common support vector machine (SVM) and BP neural network, the proposed SCSF coupled with CNN cannot only achieve 97.73 % recognition rate for three types of diesel, but also significantly improve the prediction accuracy of methanol and ethanol. Satisfactory results show that SCSF approach can be regarded as series boosting of multiple scatter correction technologies to improve overall performance without mastering data prior information and professional knowledge. Further, the proposed SCSF applied to CNN deep learning is simple and efficient, and can be recommended for actual implementation in industrial NIR applications.

Quantitative Analysis of Routine Chemical Constituents of Tobacco Based on Thermogravimetric Analysis

As the most basic indexes to evaluate the quality of tobacco, the contents of routine chemical constituents in tobacco are mainly detected by continuous-flow analysis at present. However, this method suffers from complex operation, time consumption, and environmental pollution. Thus, it is necessary to establish a rapid accurate detection method. Herein, different from the ongoing research studies that mainly chose near-infrared spectroscopy as the information source for quantitative analysis of chemical components in tobacco, we proposed for the first time to use the thermogravimetric (TG) curve to characterize the chemical composition of tobacco. The quantitative analysis models of six routine chemical constituents in tobacco, including total sugar, reducing sugar, total nitrogen, total alkaloids, chlorine, and potassium, were established by the combination of TG curve and partial least squares algorithm. The accuracy of the model was confirmed by the value of root mean square error for prediction. The models can be used for the rapid accurate analysis of compound contents. Moreover, we performed an in-depth analysis of the chemical mechanism revealed by the result of the quantitative model, namely, the regression coefficient, which reflected the correlation degree between the six chemicals and different stages of the tobacco thermal decomposition process.