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Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy.
J Food Sci. 2020 Oct; 85(10):3653-3662.JF

Abstract

The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications.

Authors+Show Affiliations

Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India.Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India.Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India.Department of Soil Chemistry and Fertility, ICAR-Indian Institute of Soil Science, Bhopal, India.

Pub Type(s)

Evaluation Study
Journal Article

Language

eng

PubMed ID

32888324

Citation

Mahanti, Naveen Kumar, et al. "Chemometric Strategies for Nondestructive and Rapid Assessment of Nitrate Content in Harvested Spinach Using Vis-NIR Spectroscopy." Journal of Food Science, vol. 85, no. 10, 2020, pp. 3653-3662.
Mahanti NK, Chakraborty SK, Kotwaliwale N, et al. Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy. J Food Sci. 2020;85(10):3653-3662.
Mahanti, N. K., Chakraborty, S. K., Kotwaliwale, N., & Vishwakarma, A. K. (2020). Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy. Journal of Food Science, 85(10), 3653-3662. https://doi.org/10.1111/1750-3841.15420
Mahanti NK, et al. Chemometric Strategies for Nondestructive and Rapid Assessment of Nitrate Content in Harvested Spinach Using Vis-NIR Spectroscopy. J Food Sci. 2020;85(10):3653-3662. PubMed PMID: 32888324.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy. AU - Mahanti,Naveen Kumar, AU - Chakraborty,Subir Kumar, AU - Kotwaliwale,Nachiket, AU - Vishwakarma,Anand Kumar, Y1 - 2020/09/05/ PY - 2020/06/01/received PY - 2020/06/24/revised PY - 2020/07/22/accepted PY - 2020/9/6/pubmed PY - 2020/12/15/medline PY - 2020/9/5/entrez KW - PLS KW - nitrate KW - spectral pre-processing KW - spectroscopy KW - spinach KW - vegetable SP - 3653 EP - 3662 JF - Journal of food science JO - J Food Sci VL - 85 IS - 10 N2 - The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications. SN - 1750-3841 UR - https://www.unboundmedicine.com/medline/citation/32888324/Chemometric_strategies_for_nondestructive_and_rapid_assessment_of_nitrate_content_in_harvested_spinach_using_Vis_NIR_spectroscopy_ DB - PRIME DP - Unbound Medicine ER -