Quantification of beef, pork, and chicken in ground meat using a portable NIR spectrometer

Silva, Lorena C.R.; Folli, Gabrielly S.; Santos, Layla P.; Barros, Iago H.A.S.; Oliveira, Bruno G.; Borghi, Flávia T.; Santos, Francine D.; Filgueiras, Paulo R.; Romão, Wanderson

doi:10.1016/j.vibspec.2020.103158

Cited by 43 publications

(13 citation statements)

References 36 publications

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“…When mixing veal sausages with pork or pork fat, a 100% correct discrimination rate above a 10% ( w / w ) adulteration level was established by Schmutzler et al, 2015 [ 74 ]. Similar discrimination results were obtained in ground meat blends, e.g., in binary blends of chicken/beef (R 2 p 0.99, RMSEP 3.5% ( w / w )) and ternary blends of beef/chicken/pork (R 2 p 0.93, RMSEP 4.7% ( w / w )) by Silva et al, 2020 [ 75 ].…”

Section: Current Applications Of Handheld Devices For Food Authenticationsupporting

confidence: 80%

Handheld Devices for Food Authentication and Their Applications: A Review

Müller-Maatsch

Ruth

2021

Foods

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This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

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Section: Current Applications Of Handheld Devices For Food Authenticationsupporting

confidence: 80%

Handheld Devices for Food Authentication and Their Applications: A Review

Müller-Maatsch

Ruth

2021

Foods

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“…The use of NIR spectroscopy to analyze samples is fast, non-destructive, efficient, and accurate, with no consumption of chemical reagents and no environmental pollution [ 12 , 13 , 14 , 15 ]. In the past, NIR spectroscopy was applied to identify a range of substances, including the detection of cement raw material content [ 16 ] and zearalenone and deoxynivalenol in maize [ 17 ], prediction of the Ca concentration in apples [ 18 ], content of 20 minerals in beef [ 19 ], residue amount of cyhalothrin in Brassica rapa [ 20 ], authentication and traceability of lemon [ 21 ], and so forth [ 22 , 23 ]. The success of these applications is based on the fact that NIR spectroscopy can simultaneously report a large number of analytes using the stretching vibrations of hydrogen groups ( X - H , X is: C , O , N , S , etc.)…”

Section: Introductionmentioning

confidence: 99%

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

Jun

et al. 2022

Foods

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The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.

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“…Fourier transform near-infrared spectroscopy is a promising green analytical technique presented with the following characteristics: rapid, convenient, non-destructive, and reliable ( 9 ). It has been widely used to develop methods for the authentication of animal source foods ( 10 ), with a particular emphasis on meat adulteration, for example, detection of pork adulteration in veal products ( 11 ), analysis of the minced lamb and beef fraud ( 12 ), chicken meat authenticity ( 13 ), quantitative detection of binary and ternary adulteration of minced beef with pork and duck meat ( 14 ), quantification of pork meat in other meats ( 15 ), and quantification of beef, pork, and chicken in ground meat ( 16 ), to name but a few. To our knowledge, however, the utilization of FT-NIR to test the authenticity of animal blood food has not been reported.…”

Section: Introductionmentioning

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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Quantification of beef, pork, and chicken in ground meat using a portable NIR spectrometer

Cited by 43 publications

References 36 publications

Handheld Devices for Food Authentication and Their Applications: A Review

Handheld Devices for Food Authentication and Their Applications: A Review

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

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

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