Fast detection of minerals in rice leaves under chromium stress based on laser-induced breakdown spectroscopy

Minerals in rice leaves is a crucial indicator of plant health, and their concentrations can be used to guide plant man-agement. It is important to predict mineral content in contaminated rice rapidly. In this study, laser-induced break-down spectroscopy (LIBS) was applied to quantify minerals (Ca, Cu, Fe, K, Mg, Mn, and Na) in rice leaves under chromium (Cr) stress. Two feature extraction methods, including principal component analysis (PCA) and extreme gradient boosting (XGBoost), were compared to identify important variables that related to mineral concentrations. Results showed that partial least square regression (PLSR) achieved good performance in Ca, Fe Mg, K, Mn, and Na, with correlation coefficient of 0.9782, 0.8712, 0.8933, 0.9206, 0.9856, and 0.9865, root mean square error of 219.25, 14.78, 1192.47, 385.12, 9.56, and 124.32 mg/kg, respectively. In addition, the correlation between different spectral lines were further analyzed. Cr exhibited a positive correlation with Ca, Mg, and Na, and a negative correlation with Mn, Cu, and K. The proposed method provides a high-accuracy and fast approach for minerals prediction in rice leaves under Cr stress, which is important for environmental protection and food safety.

Automated summary

Minerals in rice leaves are important indicators of plant health and are used to guide plant management. This study used LIBS to predict mineral content in rice leaves under Cr stress. PLSR achieved good performance in predicting Ca, Fe, Mg, K, Mn, and Na concentrations. The correlation between different spectral lines was also analyzed. This method provides a fast and accurate approach for predicting minerals in rice leaves under Cr stress, which is crucial for environmental protection and food safety.