In Situ Monitoring of Nitrate Content in Leafy Vegetables Using Mid-Infrared Attenuated Total Reflectance Spectroscopy coupled with Intelligent Algorithm

Abstract

Abstract 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.

Plant phenotyping relevance

生葉野菜の硝酸含量という植物形質を、携帯型FTIR-ATR分光と知的アルゴリズムで非破壊推定する方法を開発・評価しており、測定・抽出手法が研究の中心である。

Code and data availability

The paper describes FTIR-ATR spectra of 1224 vegetable samples and ED-ELM/PLS/ELM modeling in MATLAB, but provides no public repository, dataset deposit, or author code URL. The data availability statement only says datasets are included within the article (additional files), which does not qualify as a public paper-­‐

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