Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis‐NIR spectroscopy

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 preprocessing of spectral data with MSC (RPD CV = 1.767; SE CV = 545.745; bias CV = −3.107; slope CV = 0.698) and SNV (RPD CV = 1.768; SE CV = 545.337; bias CV = −3.201; slope CV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPD CV = 1.679; SE CV = 572.669; bias CV = −7.046; slope CV = 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 (RPD CV = 1.482; SE CV = 648.672; bias CV = −3.805; slope CV = 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.

Non‐invasive and rapid quality assessment of thermal processed and canned tender jackfruit: NIR spectroscopy and chemometric approach

“…The model was considered as excellent when the SE P was less than two times the standard error of laboratory (SE L ). The SE P values of the AA (0.311 mg/100 g F.W), TPC (0.028 mg GAE/g D.W.), and TFC (1.537 mg RE/g D.W.) were almost 1.5–2 times the SE L (AA = 0.18 mg/100 g F.W; TFC = 0.97 mg RE/g D.W; TPC = 0.02 mg GAE/g D.W.) which indicates the excellent performance of the developed PLSR models (Mahanti et al ., 2020). The statistical results obtained in the present study for AA ( R 2 = 0.762; RPD = 2.059) estimation were better than the results reported in the previous studies for spinach ( R 2 = 0.33; RPD = 1.21) (Pérez‐Marín et al ., 2019), acerola ( R 2 = 0.65) (Malegori et al ., 2017), and citrus fruits ( R 2 = 0.77–0.87) (Santos et al ., 2021).…”

A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

“…The model constructed using the full spectrum indicates that SNV is superior to MSC, SG, SG-SNV, and SG-MSC for identifying the starch content of potato slices quickly. Although suitable values of R c (0.9020), RMSEC (2.06), R p (0.9069), RMSEP (2.06), and RPD (2.33) were obtained, the full spectrum model is unsuitable for practical application due to the time-consuming and laborious modeling process; 42 therefore, the characteristic wavelength model was established. We found that under the same pretreatment conditions, the model based on the characteristic wavelength exhibited superior performance to the model based on the full spectrum, which indicates that developing a model based on the characteristic wavelength is ideal.…”

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

“…To eliminate the noise and highlight the useful information in the spectral data, different preprocessings of the raw spectrum has been studied, such as SG smoothing, multivariate scattering correction (MSC), first derivative, etc. ( Mahanti et al., 2020 ; Zhang et al., 2023 ). In this study, the raw spectra were preprocessed with continuous wavelet transform (CWT), which has great potential for extracting spectral information of CFPGE parameters.…”