Detection of nitrate content in lettuce based on chlorophyll fluorescence spectrum

Abstract: With the development of spectral technology, many researchers have done a lot of research on the nondestructive testing methods of nitrate content in leaves and have achieved some results. However, most of the studies have used visible and near infrared spectroscopy or mid infrared spectroscopy technology, and the chlorophyll fluorescence spectrum technology is still not used. Therefore, this paper used chlorophyll fluorescence spectrum analysis technology to study the relationship between nitrate content and fluorescence spectrum. With aerosol cultured lettuce as the research object, the chlorophyll fluorescence spectrum characteristic parameters were extracted to establish the function relationship between the characteristic parameters of chlorophyll fluorescence spectrum and the nitrate content of lettuce leaves, which laid a theoretical foundation for the rapid and nondestructive detection of nitrate content in lettuce leaves. The peak values of fluorescence intensity of 500-550, 650-715 and 715-800 nm were the characteristic parameters of chlorophyll fluorescence spectrum. It could be seen that the characteristic parameter of 500-550 nm was relatively weak, and it showed irregular variation with the change of nitrate content in lettuce leaves. Therefore, this band was not the desired band of this paper. The characteristic parameters of 650-715 and 715-800 nm showed clear and regular variation with the change of nitrate content in lettuce leaves, so they could be used for further study. In this paper, non dimensional treatment and standardized treatment of nitrate content in lettuce leaves and the characteristic parameters of 650-715 and 715-800 nm were in progress, and the linear fitting method was adopted. The results showed that the characteristic parameter of 650-715 nm and the nitrate content of lettuce leaves presented a significant linear relationship. The determination coefficient of regression model between them was 0.816 and the standard error was 0.147. Adding another variable in the regression model could improve the accuracy of the model a little; and the determination coefficient of regression model was 0.820 and the standard error was 0.146. And as the sample capacity was certain, the increase of the independent variable would cause the decrease of the degree of freedom, which then would affect the goodness of the fit of the regression model. Therefore, this paper chose the characteristic parameter of 650-715 nm to establish the regression model, which could well reflect the relationship between nitrate content of lettuce leaves and characteristic parameters of chlorophyll fluorescence spectrum. Thirty samples from the same batch were used as the calibration set of the regression model, and the regression model already established was used to predict the nitrate content of lettuce leaves. After verification, the determination coefficient between the predicted value and the measured value was 0.752, and the standard error was 0.172. The results show that the regression model has a good predictive effect on nitrate content of lettuce leaves and it can be used as the basis for rapid and nondestructive detection of nitrate content in lettuce leaves.