Sensory properties of rehydrated Toona sinensis shoots after dehydrated by different drying methods and association with gamma-glutamyl transferase reaction.

Sulfur-containing compounds are responsible for the aroma of Toona sinensis shoot (TS). In this study, vacuum-freeze-drying (VFD), microwave-drying (MD), and hot-air-drying at 100 and 40 °C (HAD100 and HAD40, respectively), were applied to dehydrate perishable TS for preservation. VFD-TS retained most aroma of fresh/raw TS after rehydration. The content of sulfur-containing compounds reached to 118.00 µg/g with leading by methyl thiirane, (E,E)/(E,Z)/(Z,Z)-bis-(1-propenyl) disulfides, and (Z)/(E)-2-mercapto-3,4-dimethyl-2,3-dihydrothiophenes accounting for 86.33 %. They were undetected in the rehydrated MD-TS and HAD100-TS, as the indigenous enzymes in TS were deactivated under their dehydration conditions. Interestingly, the sulfur-containing compounds was restored by 77.47 % after the TS was treated by gamma-glutamyl transferase (GGT). Thus, the release of sulfur-containing compounds from TS could depend on GGT reaction. It was different from alliaceous vegetables relying on alliinase reaction. The results revealed the aroma formation in TS and provided an approach to enhance the aroma of TS dried by different methods.

Characterization of moisture migration and diffusion in two types of tobacco biomass during the dehydration process by the TG-NMR analysis.

The tobacco waste generated from the tobacco agriculture and industry, including the discarded stem and leaf, often needs dehydration pretreatment before thermal conversion utilization. In order to study the water activity and migration of tobacco waste during the pretreatment process, TG-NMR (Thermogravimetric Nuclear Magnetic Resonance) was used to obtain the drying curves and LF-NMR (Low Field Nuclear Magnetic Resonance) T2 inversion spectrum at each stage of tobacco drying. Meanwhile, the variation pattern of pore distribution during the dehydration process of two types of tobacco waste has been obtained. Combined with the pore distribution changes, a possible spatial migration mode of water was proposed. The change of adsorption energy of water during tobacco drying was calculated, and verified the above hypothesis. This study results provide reference for the optimization of dehydration pretreatment process for different tobacco waste in order to reduce energy consumption during recycling of tobacco biomass.

Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm.

Introduction: The classification of the four tobacco shred varieties, tobacco silk, cut stem, expanded tobacco silk, and reconstituted tobacco shred, and the subsequent determination of tobacco shred components, are the primary tasks involved in calculating the tobacco shred blending ratio. The identification accuracy and subsequent component area calculation error directly affect the composition determination and quality of the tobacco shred. However, tiny tobacco shreds have complex physical and morphological characteristics; in particular, there is substantial similarity between the expanded tobacco silk and tobacco silk varieties, and this complicates their classification. There must be a certain amount of overlap and stacking in the distribution of tobacco shreds on the actual tobacco quality inspection line. There are 24 types of overlap alone, not to mention the stacking phenomenon. Self-winding does not make it easier to distinguish such varieties from the overlapped types, posing significant difficulties for machine vision-based tobacco shred classification and component area calculation tasks. Methods: This study focuses on two significant challenges associated with identifying various types of overlapping tobacco shreds and acquiring overlapping regions to calculate overlapping areas. It develops a new segmentation model for tobacco shred images based on an improved Mask region-based convolutional neural network (RCNN). Mask RCNN is used as the segmentation network's mainframe. Convolutional network and feature pyramid network (FPN) in the backbone are replaced with Densenet121 and U-FPN, respectively. The size and aspect ratios of anchors parameters in region proposal network (RPN) are optimized. An algorithm for the area calculation of the overlapped tobacco shred region (COT) is also proposed, which is applied to overlapped tobacco shred mask images to obtain overlapped regions and calculate the overlapped area. Results: The experimental results showed that the final segmentation accuracy and recall rates are 89.1% and 73.2%, respectively. The average area detection rate of 24 overlapped tobacco shred samples increases from 81.2% to 90%, achieving high segmentation accuracy and overlapped area calculation accuracy. Discussion: This study provides a new implementation method for the type identification and component area calculation of overlapped tobacco shreds and a new approach for other similar overlapped image segmentation tasks.

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.

The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1-4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model's classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products.

Comparative investigation on thermal decomposition of powdered and pelletized biomasses: Thermal conversion characteristics and apparent kinetics.

In this work, the thermal degradation behaviors of two kinds of biomasses (pinewood and rice husk) with powder and pellet under three oxygen concentrations were investigated by a self-designed macro-thermogravimetric analyzer. An obvious hysteresis of thermal degradation of biomass pellets was observed under three conditions. The maximum activation energy of biomass pellets was significantly greater than that of biomass powders, while their average activation energies were almost equal based on distributed activation energy model. For the oxygen-rich combustion, the comprehensive combustion character index of powdered and pelletized biomasses ranged from 3.92 × 10-7 to 5.16 × 10-7%2·min-2·°C-3 and from 1.82 × 10-7 to 1.91 × 10-7%2·min-2·°C-3, respectively. Furthermore, the derived biochar of powdered biomass has a higher caloricity than that of pelletized biomass during combustion by TG-DSC analysis. The performances of thermal degradation observed by macro-thermogravimetric analyzer could factually reveal the influence of mass and heat transfer on the thermochemical conversion of powdered and pelletized biomasses.