Determining quality parameters of fish oils by means of 1H nuclear magnetic resonance, mid-infrared, and near-infrared spectroscopy in combination with multivariate statistics

Giese, Editha; Winkelmann, Ole; Rohn, Sascha; Fritsche, Jan

doi:10.1016/j.foodres.2017.12.041

Cited by 26 publications

(11 citation statements)

References 54 publications

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“…Some wavenumbers in the spectrum may obtain low signal‐to‐noise ratio but little correlation with the characteristic absorbtion of the assaying chemicals will have an effect on the ATR‐NIR and the ATR‐MIR models and therefore must be abscised from the spectral data. In view of polyphenols containing aromatic compounds linked with OH groups and aromatic hydrocarbons groups, the principal ATR‐NIR and ATR MIR signals of the DHS were assigned (Tables S2 and S3) according to the literature …”

Section: Resultsmentioning

confidence: 99%

“…containing aromatic compounds linked with OH groups and aromatic hydrocarbons groups, the principal ATR-NIR and ATR MIR signals of the DHS were assigned (Tables S2 and S3) according to the literature. [18][19][20][21][22][23] In the ATR-NIR spectra of DHS samples (Figure 1), according to the literature, the absorption at 4650-4060 21,22 and 8830-8630 cm -1 21-24 may be the sum frequency and the two-stage frequency doubling of -C-H about a benzene ring in the polyphenol, respectively; the absorption at 5100-4900 and 10100-9900 cm -1 might correspond to the sum frequency and the two-stage frequency doubling of phenolic hydroxyl group (-OH). 20,23 According to the assignments of NIR signals, 12 wavelength region selection modes (Table 3) including full ATR-NIR spectra, wavelength range suggested by OPUS 7.5 and ATR-NIR ranges relative to polyphenols were analysed to obtain the optimal wavelength ranges for a preferably predicting model for the TP contents in DHS.…”

Section: Wavelength Region Selection Modesmentioning

confidence: 99%

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Assessment of ATR‐NIR and ATR‐MIR spectroscopy as an analytical tool for the quantification of the total polyphenols in Dendrobium huoshanense

Hao

Chen

2020

Phytochemical Analysis

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In this study, the potentiality of applying attenuated total reflectance near‐infrared (ATR‐NIR) and attenuated total reflectance mid‐infrared (ATR‐MIR) techniques combined with a partial least squares (PLS) regression technology to quantify the total polyphenols (TPs) in Dendrobium huoshanense (DHS) was investigated and compared. The real TP contents in the DHS samples were analysed using methods of reference. The capability of the two IR spectroscopic techniques to quantify the TPs in DHS was assessed by the root‐mean‐square error of calibration (RMSEC) and determination coefficients (R2). The results showed that both NIR and MIR might be used as a fast and simple tool to replace traditional chemical assays for the determination of the TP contents in DHS, and the best NIR model showed slightly better prediction performance [root‐mean‐square error of prediction (RMSEP): 0.307, R2: 0.9122, ratio performance deviation (RPD): 4.43] than the best MIR model (RMSEP: 0.440, R2: 0.9069, RPD: 3.09). Results from this study indicated that both the NIR and MIR models could be used to quantify the TP in DHS, and ATR‐NIR appeared to be the more predominant and more robust technique for the quantification of the TP in DHS.

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Section: Resultsmentioning

confidence: 99%

Section: Wavelength Region Selection Modesmentioning

confidence: 99%

Assessment of ATR‐NIR and ATR‐MIR spectroscopy as an analytical tool for the quantification of the total polyphenols in Dendrobium huoshanense

Hao

Chen

2020

Phytochemical Analysis

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“…Vibrational spectroscopic techniques combined with multivariate calibration approaches have been used in numerous analytical applications for reliable, in-line, on-line, or at-line analysis. Several research and review manuscripts discuss the potential use of vibrational spectroscopy for the structural analysis of lipids [ 19 , 20 ], quantitative analysis of fatty acids in fish and FO supplements [ 21 , 22 ], authentication and quality parameters of FOs [ 23 ], and detection of adulterations in food-based biological samples [ 24 , 25 ]. A tabular representation of the related literature is also shown in the ESI in Table S9 .…”

Section: Introductionmentioning

confidence: 99%

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

et al. 2022

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This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.

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“…Its extension PLS-2 allows for access to various target quantities simultaneously in a single model . This method has not been exploited to its full potential in the field of fuels. , For further data analysis from individual techniques, the combination or fusion of data from different spectroscopic techniques has been published recently. − After suitable preprocessing, such as baseline correction, normalization, and selection of suitable ranges, concatenation of the subspectra yields a pseudo-spectrum with enhanced information content as a result of the characteristic features of the spectroscopic techniques combined, referred to as low-level data fusion. ,, The workflow of low-, mid-, and high-level data fusion is visualized in Figure . Mid-level data fusion exceeds the mathematical and decision process complexity through building a multivariate model, e.g., principal component analysis (PCA) or PLS, for each technique.…”

Section: Introductionmentioning

confidence: 99%

Using Compact Proton Nuclear Magnetic Resonance at 80 MHz and Vibrational Spectroscopies and Data Fusion for Research Octane Number and Gasoline Additive Determination

et al. 2019

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Commercial fuels are characterized by parameters, such as research octane number and contents of additives, such as ethanol, ethyl-t-butyl ether, ethyl-tert-methyl ether, olefins, etc. For fast and easy parameter determination without the need for sample preparation, we used compact and benchtop near-infrared (NIR), proton nuclear magnetic resonance ( 1 H NMR) at 80 MHz, and two Raman spectrometers to predict selected relevant fuel parameters of 179 samples known from CFR motor and norm-compliant analyses. Repeatability and reproducibility criteria according to ASTM and ISO norms served as goodness of prediction measures. The prediction relied on partial least squares regression type 1 yielding one target parameter and type 2 yielding simultaneously n target values. While PLS-1 provided more accurate results, PLS-2 might be further applicable to RON and oxygenated additive content determination. Among the methods applied, benchtop Raman and 1 H NMR performed best. Low-, mid-, and high-level data fusion were applied to transform pretreated subspectra from up to three individual techniques to result in pseudo-spectra, combined score matrices, or decision models, which further improved the accuracy of the RON prediction. Best results for RON were obtained with mid-level fusion of NIR, NMR, and Raman data yielding 63% of the predicted values within reproducibility of 0.2 and up to 97% within repeatability of 0.7 RON.

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Determining quality parameters of fish oils by means of 1H nuclear magnetic resonance, mid-infrared, and near-infrared spectroscopy in combination with multivariate statistics

Cited by 26 publications

References 54 publications

Assessment of ATR‐NIR and ATR‐MIR spectroscopy as an analytical tool for the quantification of the total polyphenols in Dendrobium huoshanense

Assessment of ATR‐NIR and ATR‐MIR spectroscopy as an analytical tool for the quantification of the total polyphenols in Dendrobium huoshanense

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

Using Compact Proton Nuclear Magnetic Resonance at 80 MHz and Vibrational Spectroscopies and Data Fusion for Research Octane Number and Gasoline Additive Determination

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