Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

BACKGROUND

Wheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non-destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL).

RESULTS

This study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full-band spectral data and the characteristic wavelengths. The findings indicate that the MSC-competitive adaptive reweighted sampling-SEL model demonstrated the highest prediction accuracy, with Rp 2 (test set-determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg-1, respectively.

CONCLUSION

The predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non-destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.

A New inversion method of the initial state of Annulus in killing

Blowout is a major safety concern for well control in oil and gas well engineering and well killing is an important technical means to avoid blowout. Mastering the initial state of the wellbore is the basis to ensure well killing. The traditional gas column model cannot effectively explain the development law of wellbore pressure. Therefore, this paper is based on the actual killing conditions and the principle of gas-liquid two-phase flow. This paper establishes the physical model and mathematical model of the initial state of the wellbore annulus under overflow conditions. By means of the finite difference method and cyclic iteration calculation, the initial state of the wellbore annulus can be accurately inverted, and the calculation results are verified by an example. The results show that, when the wellhead pressure is determined, the wellbore pressure distribution and annular air phase distribution are more reasonable and the initial length of the overflow section is larger, the calculation results of the inversion method are closer to the actual engineering situation. The findings of this study can help to better understand wellbore state before killing and provide a theoretical basis for optimizing killing parameters, which is of great significance for avoiding well control safety risks during killing.