Performance of fluorescence spectroscopy for beef meat authentication: Effect of excitation mode and discriminant algorithms

Aït‐Kaddour, Abderrahmane; Loudiyi, M.; Ferlay, Anne; Gruffat, Dominique

doi:10.1016/j.meatsci.2017.11.002

Cited by 25 publications

(9 citation statements)

References 44 publications

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“…In one of the scarce studies, FFFS combined with chemometric tools (PLS and PLS-DA) was successfully applied to classify three different beef muscles, namely the semitendinosus, rectus abdominis, and infraspinatus muscles [248]. These results were confirmed recently in a similar study [95]; in this study, FFFS achieved better accuracy in discrimination of beef muscles than synchronous fluorescence spectroscopy.…”

Section: Meat and Meat Productssupporting

confidence: 78%

“…Fluorescence in honey is ascribed to proteins, polyphenolic compounds, and Maillard reaction products [23,93,94]. The unique fluorescence patterns of food products have been successfully utilized in authentication studies of food of animal origin, including meat [95,96], fish [97,98], milk [16,17] dairy products [86,90], and honey [23,88,[99][100][101][102].…”

Section: Fluorescence Spectroscopymentioning

confidence: 99%

See 1 more Smart Citation

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Hassoun

Måge

Schmidt

et al. 2020

Foods

105

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Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

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Section: Meat and Meat Productssupporting

confidence: 78%

Section: Fluorescence Spectroscopymentioning

confidence: 99%

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Hassoun

Måge

Schmidt

et al. 2020

Foods

105

View full text Add to dashboard Cite

show abstract

“…Likewise, visible spectroscopy can be used as a non-invasive method for tissue characterization [13]. Furthermore, fluorescence spectroscopy can be employed for quality evaluation of meat and meat-based products [7,14,15,16,17]. Recently, near-infrared spectroscopy and hyperspectral imaging have also been investigated for non-destructive prediction of quality and compositional analysis of meat [18,19,20,21,22] and for the determination of adulterants in meat [23].…”

Section: Introductionmentioning

confidence: 99%

Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics

Sahar

Allen

Sweeney

et al. 2019

Foods

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The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R2C) and cross-validation (R2CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R2CV: 0.91 (quartering, 24 h) and R2CV: 0.96 (LTL muscle, 48 h)) and drip loss (R2CV: 0.82 (quartering, 24 h) and R2CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods.

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“…At the same time, there was some redundant information, such as collinear variables, which would cause severe difficulty to build the regression model. This problem could be solved by principal component analysis (PCA) which compressed spectral information by data reconstruction and dimensional reduction (Aït-Kaddour et al, 2018). After compression, several top principal components (PCs) were extracted from the original spectral data.…”

Section: Prediction Models Of K Value In Pork Meatmentioning

confidence: 99%

Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively

Zhao

et al. 2019

Food Sci. Technol

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Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. On this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simultaneously, their K values were determined by high performance liquid chromatography (HPLC). A back propagation artificial neural network (BP-ANN) prediction model of K value was established. The precision of BP-ANN was further improved after optimization by the algorithm of Adaptive boosting (AdaBoost), whose root mean square error of prediction (RMSEP) and correlation coefficient (R P) were 9.89% and 0.84 respectively in the prediction set, indicating that the non-linear models (BP-ANN and BP-AdaBoost) were superior to the linear principal component regression (PCR) model. The topological neural network architecture was much more suitable for analyzing complicated regression relationship between K value and THz spectra. Ot can be concluded that the THz spectral coupled with BP-AdaBoost algorithm is capable of predicting the pork K value.

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Performance of fluorescence spectroscopy for beef meat authentication: Effect of excitation mode and discriminant algorithms

Cited by 25 publications

References 44 publications

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics

Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively

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