Raman spectroscopic quantification of calcium carbonate in spiked milk powder samples

Smith, Geoffrey; Gordon, Keith C.; Holroyd, Stephen E.

doi:10.1016/j.vibspec.2013.04.005

Cited by 20 publications

(9 citation statements)

References 18 publications

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“…These spectral peaks were regarded as the most important variables for milk powder sample discrimination. Other chemical components, such as calcium carbonate, starch, and whey in milk powder, could also be successfully determined by Raman spectroscopy using chemometric methods (such as PCA, PLSR, and PLSDA) (Almeida and others ; de Almeida and others ; Smith and others ).…”

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

132

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Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

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Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

132

View full text Add to dashboard Cite

show abstract

“…No baseline correction, normalization, smoothing, or filtration of Raman spectra was performed. This point deserves special attention, because most of the reported papers on detecting milk adulteration with whey (Almeida et al, 2012), calcium carbonate (Smith et al, 2013), melamine, urea, ammonium sulfate, and dicyandiamide (Cheng et al, 2010;Qin et al, 2013) have performed baseline correction, smoothing, or filtration.…”

Section: Resultsmentioning

confidence: 99%

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network

Rocha

Paiva

Anjos

et al. 2015

Journal of Dairy Science

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In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring.

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“…The main component of the LS514 tribofilm, CaCO 3 , is prominently represented by the strong, sharp peak at 1085 cm À 1 [17]. The clarity of this peak proves to be the exception, as the amorphous character of the tribofilms results in otherwise wider peaks of relatively low intensity.…”

Section: Raman Spectroscopymentioning

confidence: 98%

Friction and wear reductions under slip-rolling contact through chemically reactive tribofilm generation during pre-conditioning of steel alloys

Burbank

Woydt

2015

Wear

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Raman spectroscopic quantification of calcium carbonate in spiked milk powder samples

Cited by 20 publications

References 18 publications

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network

Friction and wear reductions under slip-rolling contact through chemically reactive tribofilm generation during pre-conditioning of steel alloys

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