Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy

Robert, Chima; Fraser‐Miller, Sara J.; Jessep, William; Bain, W. E.; Hicks, Talia; Ward, J.F.; Craigie, C.R.; Loeffen, M.P.F.; Gordon, Keith C.

doi:10.1016/j.foodchem.2020.128441

Cited by 49 publications

(19 citation statements)

References 44 publications

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“…The most often used method of multivariate analysis for this technique is partial least-squares regression (PLSR) analysis [62]. Several authors additionally use chemometrics to extract representative information from Raman spectra of meat and analyze the relation between the molecular structure and different radical groups to determine and assess meat quality (Figure 6) [63,64].…”

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

“…The optimal results were obtained with the prediction model "random frog-partial least squares (projection into latent structure)" (RF-PLS) for both Figure 6. Scheme of Raman spectroscopy and analysis of the obtained results [64] deoxy myoglobin (Rp = 0.8936; RMSEP = 2.91) and oxymyoglobin (Rp = 0.9762; RMSEP = 1.23).…”

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

“…The potential of Raman spectroscopy combined with three chemometric methods for differentiation of red meat samples (beef, lamb and venison; n = 90) is shown in [64] (Figure 7). PLSDA (partial least squares discriminant analysis) and SVM (support vector machines) classifications were used for creation of classification models, while PCA was used for the exploratory research (Figure 8).…”

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

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Raman spectroscopic techniques for meat analysis: A review

Пчелкина

Чернуха

Fedulova

et al. 2022

Teor. prakt. pererab. mâsa (Print)

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Raman spectroscopy (vibrational spectroscopy) proved to be an effective analytical approach in the field of geology, semiconductors, materials and polymers. Over the past decade, Raman spectroscopy has attracted the attention of researchers as a non-destructive, highly sensitive, fast and eco-friendly method and has demonstrated the unique capabilities of food analysis. The use of Raman spectroscopic methods (RSMs) to assess the quality of meat and finished products is rapidly expanding. From the analysis of one sample, you can get a large amount of information about the structure of proteins, the composition of fatty acids, organoleptic parameters, autolysis and spoilage indicators, authentication of raw materials, technological properties. An important advantage of the method is the comparability of the results obtained with the data of traditional analytical methods. Traditional methods of determining the quality of meat are often time-consuming, expensive and lead to irreversible damage to a sample. It is difficult to use them in production conditions directly on the meat processing lines. Technological advances have made it possible to develop portable Raman spectroscopes to use directly in production. The article presents the basic principles of Raman spectroscopy, system atizes the results of the use of RSMs for the analysis of meat quality from different types of slaughter animals and provides tools for analyzing the data of the obtained spectra. Raman spectra have many dependent variables, so chemometric assays are used to work with them. Literature analysis has shown that currently there is no unified database of meat spectra in the world, standardized protocols for conducting research and processing the obtained results. In Russia, the use of RSMs is a new,

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Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

See 1 more Smart Citation

Raman spectroscopic techniques for meat analysis: A review

Пчелкина

Чернуха

Fedulova

et al. 2022

Teor. prakt. pererab. mâsa (Print)

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“…2.3 | Instrumentation: 1064 -nm dispersive Raman system (InGaAs) Suspended samples of L. intermedia and four other frozen samples (D. geminata, Nostoc, Spirogyra and Zygnema) were measured using a WP 1064 -nm Raman spectrometer described previously. [14] Spectral measurements were acquired with Dash Spectrometer Control Software v2.1 over the 313 to 1895 cm À1 spectral window with resolution of 6 cm À1 . Each measurement was carried out at 460 mW power, 15 s integration time Â 10 co-added scans.…”

Section: Sample Preparationmentioning

confidence: 99%

Lake snow caused by the invasive diatom Lindavia intermedia can be discriminated from different sites and from other algae using vibrational spectroscopy

Ahmmed

Fraser‐Miller

Arachchige

et al. 2021

J Raman Spectroscopy

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Lake snow refers to mucilaginous suspended material in lakes comprising of extracellular polysaccharides (EPS) and other components. The present study employed Raman and infrared (IR) spectroscopy to discriminate lake snow produced by the diatom Lindavia intermedia from different lakes (W anaka, Wakatipu and H awea) in New Zealand and from mucilaginous samples associated with other algae. It is possible to distinguish algal material including extracellular polymeric substances produced by L. intermedia and the genera Didymosphenia, Zygnema, Spirogyra and Nostoc. Furthermore, the study also explored the use of Raman spectroscopy for quantitative detection of lake snow suspended in a water column using partial least squares regression (PLSR). Thirty-three (33) samples (lake snow, 21; Didymosphenia, 3; Zygnema, 3; Spirogyra, 3; Nostoc, 3) were analysed using Raman and IR spectroscopy. The data analysis was carried out through support vector machine (SVM) and principal component analysis-linear discriminate analysis (PCA-LDA)-based classification methods. The SVM classification model provided better accuracy (100%) in species discrimination for both the calibration and full cross-validation sets compared to the accuracy (92%) obtained by the PCA-LDA model. The PCA analysis separated lake snow based on both sampling location and sampling depth. A partial least squares regression (PLSR) model was constructed using different dilutions (0.0001 to 0.0284 mg/ml) of lake snow suspension with two different spectral preprocessing methods (PP1, smoothing + SNV transformation; PP2, smoothing + RBC + SNV transformation) to investigate the ability of 1064 nm Raman in the quantification of suspended algal loading in the Abbreviations: PCA, principal component analysis; SVM, support vector machine; LDA, linear discriminate analysis; PLSR, partial least square regression analysis; EPS, extracellular polymeric substance; TEPs, transparent exopolymer particles; RMSE C , root-mean-square of error of calibration; RMSE V , root-mean-square of error of cross-validation.

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“…[ 80,81 ] Combining the PLS‐DA and SVM classification model with Raman spectroscopy was found to be an effective red meat detection technique. [ 82 ] In the analysis of the Raman spectra of the meat mixtures, the integral intensity ratios of the 937/1003, 879/1003, 856/1003, 829/1003, and 480/1003 cm −1 pairs of bands were determined, the intensities of which were related to the reference intensity of the band at 1003 cm −1 . [ 83 ] Some researchers use Raman spectroscopy to analyze edible oil produced by food manufacturers.…”

Section: Applicationmentioning

confidence: 99%

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

Pan

Zhang

Daengngam

et al. 2021

J Raman Spectroscopy

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In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques has paved the way for achieving efficient analytical tools capable of identifying mixture components, thus leading to an apparent breakthrough in the identification of mixtures beyond traditional chemical analysis methods. This review summarizes the work of Raman spectroscopy in identifying the composition of substances; reviews the preprocessing process of Raman spectroscopy, artificial intelligence analysis methods, and analysis procedures; and examines the application of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail.

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Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy

Cited by 49 publications

References 44 publications

Raman spectroscopic techniques for meat analysis: A review

Raman spectroscopic techniques for meat analysis: A review

Lake snow caused by the invasive diatom Lindavia intermedia can be discriminated from different sites and from other algae using vibrational spectroscopy

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

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