Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques

Qin, Jianwei; Vasefi, Fartash; Hellberg, Rosalee S.; Akhbardeh, Alireza; Isaacs, Rachel B.; Yilmaz, Ayse Gamze; Hwang, Chansong; Baek, Insuck; Schmidt, Walter F.; Kim, Moon S.

doi:10.1016/j.foodcont.2020.107234

Cited by 66 publications

(52 citation statements)

References 34 publications

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“…Eggs from poultry fed omega-3 fatty acids contain an intentional adulterant that can be detected in the spectral signature of the eggs [75]. Work involving fish fillet authentication using vibrational spectroscopy has also been published [21,76].…”

Section: Vibrational Spectroscopymentioning

confidence: 99%

“…Again, few or no studies have been found in the literature regarding the application of NMR or fluorescence spectroscopy. A recent study investigated the use of HSI in 4 different spectroscopic modes, including reflectance in the VIS/NIR region, fluorescence, reflectance in the short-wave infrared region, and Raman spectroscopy for discriminating between 6 fish species and differentiating between fresh and frozen-thawed fish [21]. By testing several machine learning classifiers, the authors obtained the best results when using the VIS/NIR and the short-wave infrared techniques for the identification of fish species and detection of thawed fish, respectively.…”

Section: Fish and Seafood Productsmentioning

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

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111

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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: Vibrational Spectroscopymentioning

confidence: 99%

Section: Fish and Seafood Productsmentioning

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

Self Cite

111

View full text Add to dashboard Cite

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“…Even without building classification models, by means of PCA, it was possible to distinguish samples according to freshness and to attribute the main source of variation (between fresh samples and the twice frozen–thawed ones) to the alteration of the lipidic structure. Classification models were instead built by Qin and collaborators in order to establish the freshness of red snapper fillets that, for this purpose, were subjected to two sequential freezing–thawing cycles [ 101 ]. No sample pre-treatment was required and line-scan hyperspectral images were collected signals in four different modes: visible and near-infrared (VNIR) region, short-wave infrared (SWIR) region, fluorescence mode, and Raman mode at 785 nm laser light source.…”

Section: Detection Of Frozen–thawed Seafoods By Spectroscopic Techmentioning

confidence: 99%

Emerging Techniques for Differentiation of Fresh and Frozen–Thawed Seafoods: Highlighting the Potential of Spectroscopic Techniques

et al. 2020

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Fish and other seafood products have a limited shelf life due to favorable conditions for microbial growth and enzymatic alterations. Various preservation and/or processing methods have been developed for shelf-life extension and for maintaining the quality of such highly perishable products. Freezing and frozen storage are among the most commonly applied techniques for this purpose. However, frozen–thawed fish or meat are less preferred by consumers; thus, labeling thawed products as fresh is considered a fraudulent practice. To detect this kind of fraud, several techniques and approaches (e.g., enzymatic, histological) have been commonly employed. While these methods have proven successful, they are not without limitations. In recent years, different emerging methods have been investigated to be used in place of other traditional detection methods of thawed products. In this context, spectroscopic techniques have received considerable attention due to their potential as being rapid and non-destructive analytical tools. This review paper aims to summarize studies that investigated the potential of emerging techniques, particularly those based on spectroscopy in combination with chemometric tools, to detect frozen–thawed muscle foods.

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“…This makes NIR hyperspectral imaging a powerful tool which is able to simultaneously obtain spatial and spectral information to do qualitative or quantitative analysis. Especially, NIR hyperspectral imaging technology has been used for fast non-destructive testing in various applications, including seed identification, plant and food quality detection ( Gutierrez et al, 2018 ; Femenias et al, 2020 ; Qin et al, 2020 ). NIR spectroscopy combined with chemometric analysis to separate CRISPR/Cas9-induced rice mutants from normal rice and found that SVM with a successive projections algorithm (SPA) achieved the best classification performance ( Feng et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…And also deoxynivalenol (DON) contaminated wheat kernels were successfully non-destructively classified and quantified with the help of NIR imaging technology using PLSR and LDA ( Femenias et al, 2020 ). Apart from that, fish filet substitution and mislabeling were detected by multiple hyperspectral technologies and the combination of classifiers and spectral dataset had made a great help to choose the best model ( Qin et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Zhang

Yang²,

Feng

et al. 2020

Front. Plant Sci.

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Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.

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Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques

Cited by 66 publications

References 34 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

Emerging Techniques for Differentiation of Fresh and Frozen–Thawed Seafoods: Highlighting the Potential of Spectroscopic Techniques

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

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