Artificial bionic taste sensors coupled with chemometrics for rapid detection of beef adulteration

Lu, Biao; Han, Fangkai; Aheto, Joshua Harrington; Rashed, Marwan M.A.; Pan, Zhenggao

doi:10.1002/fsn3.2494

Cited by 8 publications

(6 citation statements)

References 52 publications

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“…In addition, the potential of −1.0 V and 1.0 V was used as the minimal and maximal values, respectively. e current between the taste sensors and counter electrode was determined when the voltage between the working and reference electrodes with the amplitude of each pulse reached 0.2 V [30,31].…”

Section: E-tongue Determination Of Beersmentioning

confidence: 99%

Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer

Chen

Wei

Zhang

et al. 2022

Journal of Food Quality

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Ficus hirta Vahl. (FHV) is widely consumed because of its functional and aromatic compounds. The incorporation of adjuncts contributes to the functional and flavor properties of beers. This study aims to enrich FHV extractions to develop beers with satisfactory physicochemical, antioxidant, and sensory characteristics. As a result, beers with 0.1 g/mL (P1) and 0.067 g/mL (P3) FHV extraction showed the highest values of physicochemical properties including °Brix, antioxidant activity, foam, lightness, and color intensity. Electronic nose and tongue results show that the aroma of P1 and taste of P3 were quite different from those of other FHV beers, resulting in substantially high consumer preference. The liking drivers of FHV beers were color appearance, hop and malty odor, sweet and malty flavor, thickness, and carbonation mouthfeel. However, the astringency flavor attribute was the disliking factor for beers. The results of this study may provide some references and guidelines for the development of Ficus hirta Vahl. functional beer to control the physicochemical, antioxidative, and sensory properties of the beer.

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Section: E-tongue Determination Of Beersmentioning

confidence: 99%

Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer

Chen

Wei

Zhang

et al. 2022

Journal of Food Quality

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“…The reason is primarily due to the relationships among the FT-NIR data matrices of the animal blood food used were more complex than linear as a result of the essential characteristic of FT-NIR, which is spectral of several wavenumbers may contain information from the same organic chemicals, and spectral of each wavenumber may contain chemical information from several organic chemicals in the food materials. ELM has a significant advantage over linear discriminant analysis algorithms for processing non-linear problems due to its superior ability to self-learning and self-adjusting (31). The Kennard-Stone algorithm was also used to select onethird of samples from each adulteration level group as the prediction set and the remaining samples were utilized as the training set.…”

Section: Identification Of the Adulterated Duck Blood Tofumentioning

confidence: 99%

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Han

Li²,

Aheto³

et al. 2022

Front. Nutr.

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This work aims to investigate a feasible and practical technique for the authentication of edible animal blood food (EABF) using Fourier transform near-infrared (FT-NIR) coupled with fast chemometrics. A total of 540 samples were used, including raw duck blood tofu (DBT), cow blood-based gel (CBG), pig blood-based gel (PBG), and DBT binary and ternary adulterated with CBG and PBG. The protein, fat, total sugar, and 16 kinds of amino acids were measured to validate the difference in basic organic matters among EABFs according to species. Fisher linear discriminate analysis (Fisher LDA) and extreme learning machine (ELM) were implemented comparatively to identify the adulterated EABF. To predict adulteration levels, four extreme learning machine regression (ELMR) models were constructed and optimized. Results showed that, by analyzing 27 crucial spectral variables, the ELM model provides higher accuracy of 93.89% than Fisher LDA for the independent samples. All the correlation coefficients of the optimized ELMR models’ training and prediction sets were better than 0.94, the root mean square errors were all less than 3.5%, and the residual prediction deviation and the range error ratios were all higher than 4.0 and 12.0, respectively. In conclusion, the FT-NIR paired with ELM have great potential in authenticating the EABF. This work presents amino acids content in EABFs for the first time and built tracing models for rapid authentication of DBT, which can be used to manage the EABF market, thereby preventing illegal adulteration and unfair competition.

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“…The data analysis and modelling mainly depend on machine learning (ML) methods. Support vector machine (SVM), back propagation neural network (BP) and extreme learning machine (ELM) have been widely used in the detection of meat and adulteration [ 16 , 24 , 25 , 26 , 27 ]. The uses of new algorithms to optimise parameters and their combination with practical problems have become an important research direction for machine learning in recent years [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

Fan

Zhu

et al. 2022

Foods

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The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g−1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

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Artificial bionic taste sensors coupled with chemometrics for rapid detection of beef adulteration

Cited by 8 publications

References 52 publications

Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer

Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

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