ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

The application of trace amounts of sugar solution additives to tobacco leaves is a critical step in the tobacco processing and cutting technology, significantly impacting the physical and chemical properties of the leaves and enhancing cigarette quality. However, current precision detection methods for sugar solution additives primarily focus on dosage monitoring, lacking an evaluation of the post-application effects.The hyperspectral imaging technology and deep learning methods were utilized to perform non-destructive detection and visualization analysis of trace additives in tobacco leaves after sugar solution application. A prediction system based on a deep learning convolutional neural network (CNN) model was developed, incorporating multiple spectral preprocessing methods and feature band selection techniques to optimize model performance and improve the detection accuracy of additive content in tobacco leaves. Spectral data from tobacco samples with varying proportions of propylene glycol were collected by using a hyperspectral imaging system. The data were preprocessed by using three methods: standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay filtering, which were employed for data preprocessing respectively. Feature bands were selected through competitive adaptive reweighted sampling (CARS), principal component analysis (PCA), and identification of spectral trough points, resulting in six common consistent key wavelengths at 1146nm, 1614nm,2511nm, 2517nm, 2522nm, and 1941nm. CNN, random forest (RF), and partial least squares regression (PLSR) models were constructed to predict the additive content, with hyperspectral data visualization conducted by using the CNN method. The results showed that the SNV-PCA-CNN model achieved the best predictive performance for both the training set (R2C was 0.9880, RMSE was 0.0020kg/kg) and the test set (R2P was 0.9896, RMSE was 0.0021kg/kg), and the cumulative contribution rate was close to 99% by taking the first-four principal components, demonstrating excellent fitting and generalization capabilities. The predictive ability of the deep learning CNN model significantly outperformed the performance of traditional machine learning methods RF and PLSR, reflecting the CNN model sufficient generalization capabilities for hyperspectral data of tobacco samples with sugar solution additives. The combination of hyperspectral imaging and the CNN model showed great potential for detecting trace additives in tobacco leaves, providing technical support for non-destructive testing and precise control in the tobacco processing industry.