Background: Vegetables are one of the most important nitrate sources of human diary diet. Establishing fast and accurate in situ nitrate monitoring approaches that could be used in the plant growth process and vegetable markets is essential.
Results: Incorporating the unique feature of N-O asymmetric stretch absorption in the mid-infrared region (1500-1200 cm-1), portable Fourier-transform infrared attenuated total reflectance (FTIR-ATR) spectroscopic instruments, along with the Euclidean distance-modified intelligent algorithm extreme learning machine (ED-ELM) model, were employed to evaluate the nitrate contents in leafy vegetables. A total of 1224 samples of four popular vegetables (Chinese cabbage, swamp cabbage, celery, and lettuce) were analyzed. The results indicated that the nitrate contents (mean values: Chinese cabbage: 7550 mg/kg; swamp cabbage: 4219 mg/kg; celery: 4164 mg/kg; lettuce: 4322 mg/kg) highly exceeded the World Health Organization (WHO))-specified maximum tolerance limits. The ED-ELM model showed a better performance with the root-mean-square-error of 799.7 mg/kg, the determination coefficients of 0.93, the ratio of performance to deviation of 2.22, the optimized calibration dataset number of 100, and the number of hidden neurons of 30.
Conclusion: The results confirmed that FTIR-ATR, along with the suitable model algorithms, could be used as a potential rapid and accurate method to monitor the nitrate contents in the fields of agriculture and food safety.