Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

Zhu, X.; Li, Gaoyang; Shan, Yang

doi:10.1111/ijfs.12756

Cited by 15 publications

(8 citation statements)

References 37 publications

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“…The goodness of a model fit was evaluated using the root mean square error of cross validation (RMSECV) and the coefficient of determination ( R 2 ). The RMSECV and R 2 were calculated using eqns and (Zhu et al ., ).

RMSECV = \sqrt{\sum_{i = 1}^{n} {(m_{i} - p_{i})}^{2} / n}

R^{2} = 1 - \sum_{i = 1}^{n} {(m_{i} - p_{i})}^{2} /\sum_{i = 1}^{n} {false(m_{i} - \bar{m} false)}^{2}

…”

Section: Methodsmentioning

confidence: 97%

Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Ahmad

Nache

Hinrichs

et al. 2016

Int J of Food Sci Tech

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The purpose of this study is to evaluate the potential of fluorescence spectroscopy to predict the nutritional parameters of twenty-six commercially available wheat flours from different vendors. Principal component analysis (PCA) was used to clearly identify the correlations among different types of flours. A partial least square regression (PLSR) model gives a good prediction for moisture, fat and carbohydrates using cross-validation, with a R 2 of 0.86, 0.88 and 0.89 respectively. However, the protein, sucrose and salt contents showed little correlation in PLSR. Locally weighted regression (LWR) provides a significant improvement in the prediction of all of the nutritional parameters. The error decreases with an increasing R 2 to 0.96, 0.93, 0.99, 0.98, 0.99, 0.88, 0.95 and 0.99 for the energetic value, protein, fat, moisture, carbohydrate, sucrose, salt and saturated fatty acid contents respectively, for different wheat flours. Hence, fluorescence, which is a non-invasive and rapid method, can be used to evaluate the nutritional parameters of different types of wheat flours. Estimation of nutritional parameters M. H. Ahmad et al. † Flour type 405 contains less than 0.5% mineral contents. ‡ Flour type 550 contains 0.5-0.63% mineral contents. § Flour type 1050 contains 0.91-1.2% mineral contents. ¶ Whole wheat flour contains 1.2-1.8% mineral contents. Estimation of nutritional parameters M. H. Ahmad et al. † Root mean square error of cross-validation. ‡ Coefficient of determination for cross-validation. Estimation of nutritional parameters M. H. Ahmad et al. † Root mean square error of cross-validation. ‡ Coefficient of determination for cross-validation. Estimation of nutritional parameters M. H. Ahmad et al.

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RMSECV = \sqrt{\sum_{i = 1}^{n} {(m_{i} - p_{i})}^{2} / n}

R^{2} = 1 - \sum_{i = 1}^{n} {(m_{i} - p_{i})}^{2} /\sum_{i = 1}^{n} {false(m_{i} - \bar{m} false)}^{2}

…”

Section: Methodsmentioning

confidence: 97%

Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Ahmad

Nache

Hinrichs

et al. 2016

Int J of Food Sci Tech

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“…; Zhu et al . ). These techniques have been extensively developed in food industry and agriculture in the last 40 years, with a boom starting in the late 80's, measuring various compositions of grains, meat, fruit and vegetables, etc.…”

Section: Introductionmentioning

confidence: 97%

Evaluation of Near-Infrared and Mid-Infrared Spectroscopy for the Determination of Routine Parameters in Chinese Rice Wine

Shen

Wei

et al. 2016

Journal of Food Processing and Preservation

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The goals of this study were to apply near-infrared (NIR) and mid-infrared (MIR) spectroscopy for the determination of routine quality parameters in Chinese rice wine, and compare the performance of the two spectroscopic methods. A total of 80 rice wine samples were measured by NIR and MIR spectroscopy. Calibration modes were established by three chemometric methods with full crossvalidation. The result indicated that the performance of partial least squares regression models was the best. MIR method was found to be better than NIR for alcohol degree (R 2 v 50.942, RPD54.3), total sugar (R 2 v 50.974, RPD56.0), nonsugar solid (R 2 v 50.910, RPD53.2) and total acid (R 2 v 50.854, RPD52.6) content determination. On the other hand, NIR was a little superior for pH value (R 2 v 50.797, RPD52.3) determination. The results provided useful information to understand the difference between the two spectroscopic methods and to determine the most suitable method for Chinese rice wine quality supervision. PRACTICAL APPLICATIONSBoth near-infrared (NIR) and mid-infrared (MIR) spectroscopy are very attractive techniques for nondestructive detection of quality of food products. The present study was to carry out a comparative study to evaluate NIR and MIR spectroscopy combined with chemometrics for the determination of routine parameters in rice wine, namely alcohol degree, total sugar, total acid, nonsugar solid and pH value, and to evaluate their efficiency and predictive power for unknown samples. This study could provide useful information to understand the difference between the two methods and to determine the most suitable method for the brewery industry.

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“…However, inorganic elements could chelate with organic compounds, so it indirectly reflected in near-infrared spectra (Kumagai et al 2013;Chen et al 2010). Recently, NIRS has been applied to analyze inorganic elements concentration in different plant species, including cadmium and arsenic in rice (Kumagai et al 2013;Font et al 2005;Zhu et al 2015), arsenic and lead in red paprika (Moros et al 2008). In addition, inorganic elements concentration in sediment (Xia et al 2007), soil (Moros et al 2009), and water samples (Ning et al 2012;Kleinebecker et al 2013) were also determined by NIRS.…”

Section: Introductionmentioning

confidence: 99%

Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration

Zhao

Cai

et al. 2019

J Cotton Res

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Background: Manganese (Mn) is an essential microelement in cottonseeds, which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures. Therefore a rapid, low-cost, and reagent-free analytical way is demanded to substitute the traditional analytical method. Results: The Mn content in cottonseed meal was investigated by near-infrared spectroscopy (NIRS) and chemometrics techniques. Standard normal variate (SNV) combined with first derivatives (FD) was the optimal spectra pre-treatment method. Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm method (SPA) were employed to extract the informative variables from the full NIR spectra. The linear and nonlinear calibration models for cottonseed Mn content were developed. Finally, the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM, with root mean squares error of prediction (RMSEP) of 1.994 6, coefficient of determination (R 2 ) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively. Conclusions: The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal, which can be used as an alternative way to substitute for traditional analytical method.

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Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

Cited by 15 publications

References 37 publications

Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

Evaluation of Near-Infrared and Mid-Infrared Spectroscopy for the Determination of Routine Parameters in Chinese Rice Wine

Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration

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