Micro near infrared spectroscopy (MicroNIRS) based on on-line enrichment: Determination of trace copper in water using glycidyl methacrylate-based monolithic material

Chen, Guiping; Mei, Yuan; Wei, Tao; Zhang, Cheng; Tang, Huirong; Iqbal, Jibran; Du, Yukou

doi:10.1016/j.aca.2010.04.064

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…Theoretically, there were no absorption bands for inorganic elements in the NIR region. 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).…”

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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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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“…Theoretically, there are no absorption bands for inorganic complexes in the near‐infrared region. However, it is considered that organic complexes and chelates with inorganic components are reflected in near‐infrared spectra changes (Kumagai et al ., ; Chen et al ., ). NIRS has been applied to the analysis of inorganic elements concentration in sediment (Xia et al ., ; Moros et al ., ), the characterisation of soil (Kooistra et al ., ; Cozzolino & Morón, ; Siebielec et al ., ; Chodak et al ., ; Moros et al ., ; Cave et al ., ), plant tissues (Font et al ., ; Huang et al ., ; Cozzolino et al ., ), water samples (González‐Martín et al ., ; Sheng et al ., ; Chen et al ., ; Li et al ., ; Ning et al ., ) and food (Font et al ., , ; Moros et al ., ; Wu et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…However, it is considered that organic complexes and chelates with inorganic components are reflected in near-infrared spectra changes (Kumagai et al, 2003;Chen et al, 2010). NIRS has been applied to the analysis of inorganic elements concentration in sediment (Xia et al, 2007;Moros et al, 2008a), the characterisation of soil (Kooistra et al, 2001;Cozzolino & Mor on, 2003;Siebielec et al, 2004;Chodak et al, 2007;Moros et al, 2009;Cave et al, 2013), plant tissues (Font et al, 2004a;Huang et al, 2009;Cozzolino et al, 2011), water samples (Gonz alez-Mart ın et al, 2002Sheng et al, 2009;Chen et al, 2010;Li et al, 2011;Ning et al, 2012) and food (Font et al, 2004b(Font et al, , 2005Moros et al, 2008b;Wu et al, 2009). Although Kumagai et al (2003) in Japan used NIRS and canonical discriminant analysis (CDA) to discriminate the Cd content level (0.4 mg kg À1 ) of rice, the overall correct classification rate was 85.2% for 106 samples.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Shan

2015

Int J of Food Sci Tech

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Summary The feasibility of prediction of cadmium (Cd) content in brown rice was investigated by near‐infrared spectroscopy (NIRS) and chemometrics techniques. Spectral pretreatment methods were discussed in detail. Synergy interval partial least squares (siPLS) algorithm was used to select the efficient combinations of spectral subintervals and wavenumbers during constructing the quantitative calibration model. The performance of the final model was evaluated by the use of root mean square error of cross‐validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficients for calibration set and prediction set (Rc and Rp), respectively. The results showed that the optimum siPLS model was achieved when two spectral subinterval and fifty‐two variables were selected. The predicted result of the best model obtained was as follows: RMSECV = 0.232, Rc = 0.930, RMSEP = 0.250 and Rp = 0.915. Compared with PLS and interval PLS models, siPLS model was slightly better than those methods. These results indicate that it is feasible to predict and screen Cd content in brown rice using NIRS.

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“…Near-infrared spectroscopy (NIRS) allows for fast, nondestructive, cost-effective materials characterization with minimal sample preparation (Chen et al 2010;Foley et al 1998). For these reasons, NIRS is a favored analytical technique within many industries including agriculture (Morra et al 1991), food (Cen and He 2007), textiles (Barton II et al 2002) and biomedicine (Maki et al 1995;Durduran et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Time-resolved laser spectroscopy for the in situ characterization of methacrylate monomer flow within spruce

et al. 2016

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Time-resolved diffuse optical spectroscopy (TRS) was investigated as a nondestructive method to characterize the post-impregnation distribution of methacrylate monomers within spruce (Picea abies). TRS was also used to monitor the flow of methacrylate monomers in situ, within spruce, during impregnation with both spatial and temporal resolution. The data were compared to fluid flow models developed by Darcy and Bramhall demonstrating that neither of these models were able to accurately describe the experimental results, highlighting the need for development of new models. Nondestructive characterization by TRS did not require staining of the monomer treatment solution, multivariate analysis or complex sample pre-treatment, thus highlighting the facile applicability of this technique.

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Micro near infrared spectroscopy (MicroNIRS) based on on-line enrichment: Determination of trace copper in water using glycidyl methacrylate-based monolithic material

Cited by 10 publications

References 27 publications

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

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

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

Time-resolved laser spectroscopy for the in situ characterization of methacrylate monomer flow within spruce

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