Research on the Rapid Detection Model of Tomato Sugar Based on Near-Infrared Reflectance Spectroscopy

Tomato is a kind of nutritious vegetable in the world. It is deeply loved by people and is widely grown worldwide, especially in China. Tomatoes not only play an important role in people' s lives but also play a pivotal role in our industrial production. The export of tomatoes in our country is also increasing. The sugar, acidity, vitamin C and solid soluble content of tomato are important evaluation indicators to reflect the internal quality of tomato, and the content of solid soluble content is the sum of these internal qualities, which can better characterize the internal quality of tomato, so the solid soluble content of tomato can be achieved. The rapid detection of solid content is of great help to our industrial production and daily life, and the traditional method will do an irreversible destructive analysis of tomato samples, which is time-consuming and labor-intensive. It is not easy to meet the needs of modern industrial production in my country. Therefore, the development of rapid non-destructive testing technology for internal tomato quality has become a problem to be solved. In recent years, near-infrared spectroscopy has been widely used in many fields with the advantages of being fast and non-destructive. In this paper, based on the near-infrared spectroscopy detection method, the correlation modeling and prediction of soluble solids content reflecting the sweetness of tomato were studied. A near-infrared spectroscopy detection platform was built in the experiment, and a total of 255 tomato samples of different maturity and varieties were selected, and spectral data and soluble solid value were collected for each sample. The research compares spectral data preprocessing methods such as SNV, MSC, NOR and SG and uses the K-S algorithm to divide the modeling calibration and validation sets. At the same time, to improve the detection reliability and modeling efficiency, the research and comparison of band selection algorithms such as CARS, RF, SPA and UVE are carried out for spectral data dimensionality reduction. In the experimental results, the preprocessing of SNV plus second-order 15-point SG smoothing combination combined with the selection of CARS bands, the model established by using the selected 54 bands has a better prediction effect, and the correlation coefficient R-2 of correction, verification and cross-validation is up to 0. 90, 0. 89 and 0. 91, the root mean square error RMSE was 0. 14, 0. 15 and 0. 14 degrees Brix, respectively. The results show that the self-built near-infrared spectroscopy detection platform can better realize the rapid detection of tomato sugar.