Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

Hydrothermal carbonization of tobacco stalk for fuel application.

Tobacco stalks are an abundant biomass resource which are otherwise treated as waste. In this work, the effect of hydrothermal carbonization temperature and time on the structures, chemical compositions and combustion characteristics of hydrochars obtained from tobacco stalks were evaluated. The carbon content, higher heating value, and energy yield increased with accompanying decrease in hydrogen and oxygen contents with the increase of treatment temperature and time. The evolution of the H/C and O/C atomic ratios indicated dehydration and devolatilization processes occurred during hydrothermal carbonization. The weight loss, combustion range and characteristic temperatures of tobacco stalks were significantly modified after hydrothermal carbonization, resulting in higher ignition temperatures and higher energy density. The kinetics model, Coats-Redfern method revealed the activation energy of hydrochars in zone 2 and 3 were among 43.7-74.8kJ/mol and 46.7-85.8kJ/mol, respectively. Our results show that hydrothermal carbonization reaction can facilitate transforming tobacco stalks into energy-rich solid fuel.