Near-infrared spectroscopy based on transfer learning, detection of tobacco chemical composition

The use of near-infrared spectroscopy enables rapid and efficient detection of tobacco leaf chemical components. However, changes in environmental and climatic factors can affect the accuracy of the originally established prediction model. To reduce the workload involved in re-sampling and modeling, a more stable prediction model was established using transfer learning, transferring valuable information from fragmented leaf spectra to enhance the stability of the whole-leaf spectral measurement model. New prediction models were developed for total sugars, reducing sugars, total alkaloids, potassium, and chlorine contents. Experimental results show that the mathematical model established through transfer learning provides highly consistent predictions with results obtained through continuous flow methods. It is more stable than existing models and can be directly applied for model transfer without the need for re-sampling. It can be used for the analysis of tobacco leaf chemical components in the tobacco production process, meeting the requirements for rapid detection of routine chemical components in tobacco leaves. The model exhibits high accuracy in predicting new samples and can meet the demands of tobacco production companies.