Abstract
Vegetables are one of the most important nitrate sources of human diary diet. Establishing of fast and accurate in situ nitrate monitoring approaches that could be used in the plant growth process and vegetable markets is essential. Incorporating the unique feature of N − O asymmetric stretch absorption in the mid-infrared region (1500–1200 cm−1), portable attenuated total reflectance–Fourier-transform infrared (ATR-FTIR) spectroscopic instrument, along with the Euclidean distance-modified extreme learning machine (ED-ELM) model, was firstly employed to evaluate the nitrate contents in leafy vegetables. A total of 1224 samples of four popular vegetables (Chinese cabbage, water spinach, celery, and lettuce) were analyzed. The results indicated that the coefficient of variation of nitrate contents between different vegetable samples was large (20–30%) and the value of mean values has highly exceeded the World Health Organization (WHO)–specified maximum tolerance limits. Chinese cabbage: 7550 ± 1664 mg kg−1; water spinach: 4219 ± 1029 mg kg−1; celery: 4164 ± 1214 mg kg−1; lettuce: 4322 ± 1024 mg kg−1). Moreover, The ED-ELM model showed a better performance with the RMSEP of 799.7 mg kg−1 (calibration range from 805 to 14,104 mg kg−1 and validation range from 2132 to 11,793 mg kg−1), R2 of 0.93, RPD of 2.22, the optimized calibration dataset number of 100, and the number of hidden neurons of 30. The results confirmed that ATR-FTIR, 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.
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Abbreviations
- ATR:
-
Attenuated total reflectance spectroscopy
- ED:
-
Euclidean distance
- ED-ELM:
-
Euclidean distance–modified extreme learning machine model
- ELM:
-
Extreme learning machine
- ELM :
-
Legates and McCabe index
- FTIR:
-
Fourier-transform infrared
- ATR-FTIR:
-
Attenuated total reflectance spectroscopy–Fourier-transform infrared
- PCA:
-
Principal component analysis
- PC1:
-
The first principal component
- PC2:
-
The second principal component
- PC3:
-
The third principal component
- PLS:
-
Partial least squares
- R2 :
-
Coefficients of determination
- RMSE:
-
Root-mean-square-error
- RMSEC :
-
The root-mean-square-error of the calibration dataset
- RMSEP :
-
Root-mean-square-error of the validation dataset
- RPD:
-
The ratio of performance to deviation
- WI:
-
Willmott’s index
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Funding
This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA23030107), The National Natural Science Foundation of China (42077019), and the National Key Research and Development Program of China (2017YFD0200107).
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Changwen Du designed and directed the experiment, and made revisions of the manuscript. Fei Ma conducted the experiment, processed the data analysis, and composed the manuscript. Shuailin Zheng collected the vegetables, prepared the samples, and recorded the spectra data. Yaxiao Du collected the vegetables, prepared the samples, and measured nitrate contents. All authors read and approved the final manuscript.
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Fei Ma declares that she has no conflict of interest. Changwen Du declares that he has no conflict of interest. Shuailin Zheng declares that she has no conflict of interest. Yaxiao Du declares that he has no conflict of interest.
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Ma, F., Du, C., Zheng, S. et al. In Situ Monitoring of Nitrate Content in Leafy Vegetables Using Attenuated Total Reflectance − Fourier-Transform Mid-infrared Spectroscopy Coupled with Machine Learning Algorithm. Food Anal. Methods 14, 2237–2248 (2021). https://doi.org/10.1007/s12161-021-02048-7
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DOI: https://doi.org/10.1007/s12161-021-02048-7