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Xiang X, Lu J, Tao M, Xu X, Wu Y, Sun Y, Zhang S, Niu H, Ding Y, Shang Y. High-throughput identification of meat ingredients in adulterated foods based on centrifugal integrated purification-CRISPR array. Food Chem 2024; 443:138507. [PMID: 38277932 DOI: 10.1016/j.foodchem.2024.138507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/04/2024] [Accepted: 01/17/2024] [Indexed: 01/28/2024]
Abstract
Rapid, accurate, and sensitive analytical methods for the detection of food fraud are now an urgent requirement in the global food industry to ensure food quality. In response to this demand, a centrifugal integrated purification-CRISPR array for meat adulteration (CIPAM) was established. In detail, CIPAM system combines microneedles for DNA extraction and RAA-CRISPR/Cas12a integrated into a centrifugal microfluidic chip for the detection of meat adulteration. The RAA-CRISPR/Cas12a reaction reagents were pre-embedded into the different reaction chambers on the microfluidic chip to achieve the streamline of operations, markedly simplifying the detection process. The whole reaction was completed within 30 min with a detection limit of 0.1 % (w/w) in pig, chicken, duck, and lamb products. Referring to the results of the standard method, CIPAM system achieved 100 % accuracy. The automatic multiplex detection process implemented in the developed CIPAM system met the needs of food regulatory authorities.
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Affiliation(s)
- Xinran Xiang
- Fujian Key Laboratory of Aptamers Technology, Fuzhou General Clinical Medical School (the 900th Hospital), Fujian Medical University, Fuzhou 350001, China; Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Food Safety & Nutrition Function Evaluation, School of Life Science, Huaiyin Normal University, Huai'an 223300, China
| | - Jiaran Lu
- Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Food Safety & Nutrition Function Evaluation, School of Life Science, Huaiyin Normal University, Huai'an 223300, China
| | - Mengying Tao
- Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Food Safety & Nutrition Function Evaluation, School of Life Science, Huaiyin Normal University, Huai'an 223300, China
| | - Xiaowei Xu
- Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Food Safety & Nutrition Function Evaluation, School of Life Science, Huaiyin Normal University, Huai'an 223300, China
| | - Yaoyao Wu
- Fujian Key Laboratory of Aptamers Technology, Fuzhou General Clinical Medical School (the 900th Hospital), Fujian Medical University, Fuzhou 350001, China; Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Food Safety & Nutrition Function Evaluation, School of Life Science, Huaiyin Normal University, Huai'an 223300, China
| | - Yuqing Sun
- Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Food Safety & Nutrition Function Evaluation, School of Life Science, Huaiyin Normal University, Huai'an 223300, China
| | - Shenghang Zhang
- Fujian Key Laboratory of Aptamers Technology, Fuzhou General Clinical Medical School (the 900th Hospital), Fujian Medical University, Fuzhou 350001, China
| | - Huimin Niu
- Fujian Key Laboratory of Aptamers Technology, Fuzhou General Clinical Medical School (the 900th Hospital), Fujian Medical University, Fuzhou 350001, China
| | - Yu Ding
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China; National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Guangdong Provincial Key Laboratory of Microbial Safety and Health, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
| | - Yuting Shang
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China.
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Cozzolino D, Zhang S, Khole A, Yang Z, Ingle P, Beya M, van Jaarsveld PF, Bureš D, Hoffman LC. Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2024; 61:950-957. [PMID: 38487278 PMCID: PMC10933230 DOI: 10.1007/s13197-023-05890-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 09/13/2023] [Accepted: 10/30/2023] [Indexed: 03/17/2024]
Abstract
Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.
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Affiliation(s)
- D. Cozzolino
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - S. Zhang
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - A. Khole
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - Z. Yang
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - P. Ingle
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - M. Beya
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - P. F. van Jaarsveld
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - D. Bureš
- Institute of Animal Science, 104 00 Přátelství 815, 104 00 Prague, Czech Republic
- Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
| | - L. C. Hoffman
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
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Hoffman L, Ingle P, Hemant Khole A, Zhang S, Yang Z, Beya M, Bureš D, Cozzolino D. Discrimination of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) binary mixtures using a portable near infrared instrument combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 294:122506. [PMID: 36868023 DOI: 10.1016/j.saa.2023.122506] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Consumers demand safe and nutritious foods at accessible prices; where issues associated with adulteration, fraud, and provenance have become important aspects to be considered by the modern food industry. There are many analytical techniques and methods available to determine food composition and quality, including food security. Among them, vibrational spectroscopy techniques are at the first line of defence (near and mid infrared spectroscopy, and Raman spectroscopy). In this study, a portable near infrared (NIR) instrument was evaluated to identify different levels of adulteration between binary mixtures of exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) sourced from a commercial abattoir were used to make different binary mixtures (95 % %w/w, 90 % %w/w, 50 % %w/w, 10 % %w/w and 5 % %w/w) and analysed using a portable NIR instrument. The NIR spectra of the meat mixtures was analysed using principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Two isosbestic points corresponding to absorbances at 1028 nm and 1224 nm were found to be consistent across all the binary mixtures analysed. The coefficient of determination in cross validation (R2) obtained for the determination of the per cent of species in a binary mixture was above 90 % with a standard error in cross validation (SECV) ranging between 12.6 and 15 %w/w. Overall, the results of this study indicate that NIR spectroscopy can determine the level or ratio of adulteration in the binary mixtures of minced meat.
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Affiliation(s)
- L Hoffman
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - P Ingle
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - A Hemant Khole
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - S Zhang
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - Z Yang
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - M Beya
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - D Bureš
- Institute of Animal Science, 104 00 Přátelství 815, 104 00 Prague, Czech Republic; Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague, 165 00 Prague, Czech Republic
| | - D Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia.
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Hyperspectral imaging combined with convolutional neural network for accurately detecting adulteration in Atlantic salmon. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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5
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Cataltas O, Tutuncu K. Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy. PeerJ Comput Sci 2023; 9:e1266. [PMID: 37346694 PMCID: PMC10280583 DOI: 10.7717/peerj-cs.1266] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/08/2023] [Indexed: 06/23/2023]
Abstract
Background Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area. Methods This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirty-two latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models. Results R2, RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.
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Affiliation(s)
| | - Kemal Tutuncu
- Faculty of Technology, Selcuk University, Konya, Turkey
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Park S, Yang M, Yim DG, Jo C, Kim G. VIS/NIR hyperspectral imaging with artificial neural networks to evaluate the content of thiobarbituric acid reactive substances in beef muscle. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2023.111500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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7
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Application of NIR spectroscopy coupled with DD-SIMCA class modelling for the authentication of pork meat. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Wu X, Shin S, Gondhalekar C, Patsekin V, Bae E, Robinson JP, Rajwa B. Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System. Foods 2023; 12:foods12020402. [PMID: 36673494 PMCID: PMC9857504 DOI: 10.3390/foods12020402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/13/2022] [Accepted: 01/05/2023] [Indexed: 01/18/2023] Open
Abstract
Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.
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Affiliation(s)
- Xi Wu
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Sungho Shin
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Carmen Gondhalekar
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Valery Patsekin
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Euiwon Bae
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - J. Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
- Correspondence: ; Tel.: +1-765-496-1153
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LI M, AHETO JH, RASHED MMA, HAN F. Tracing models for checking beef adulterated with pig blood by Fourier transform near-infrared paired with linear and nonlinear chemometrics. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.104622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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10
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Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity. Foods 2023; 12:foods12010182. [PMID: 36613404 PMCID: PMC9818338 DOI: 10.3390/foods12010182] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 01/03/2023] Open
Abstract
A hand-held near infrared (NIR) spectrophotometer combined with a similarity index (SI) method was evaluated to identify meat samples sourced from exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius), and beef (Bos taurus) sourced from a commercial abattoir were used and analyzed using a hand-held NIR spectrophotometer. The NIR spectra of the commercial and exotic meat samples were analyzed using principal component analysis (PCA), linear discriminant analysis (LDA), and a similarity index (SI). The overall accuracy of the LDA models was 87.8%. Generally, the results of this study indicated that SI combined with NIR spectroscopy can distinguish meat samples sourced from different animal species. In future, we can expect that methods such as SI will improve the implementation of NIR spectroscopy in the meat and food industries as this method can be rapid, handy, affordable, and easy to understand for users and customers.
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11
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Detection of sodium hydrosulfite adulteration in wheat flour by FT-MIR spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01763-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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12
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Han F, Ming L, Aheto JH, Rashed MMA, Zhang X, Huang X. 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. Front Nutr 2022; 9:935099. [PMID: 36386895 PMCID: PMC9643882 DOI: 10.3389/fnut.2022.935099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/20/2022] [Indexed: 09/14/2023] Open
Abstract
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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Affiliation(s)
- Fangkai Han
- School of Biological and Food Engineering, Suzhou University, Suzhou, Anhui, China
| | - Li Ming
- School of Biological and Food Engineering, Suzhou University, Suzhou, Anhui, China
| | - Joshua H. Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Marwan M. A. Rashed
- School of Biological and Food Engineering, Suzhou University, Suzhou, Anhui, China
| | - Xiaorui Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
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Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network. Foods 2022; 11:foods11192977. [PMID: 36230054 PMCID: PMC9563429 DOI: 10.3390/foods11192977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022] Open
Abstract
Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.
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Adulteration discrimination and analysis of fresh and frozen-thawed minced adulterated mutton using hyperspectral images combined with recurrence plot and convolutional neural network. Meat Sci 2022; 192:108900. [DOI: 10.1016/j.meatsci.2022.108900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/17/2022]
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15
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Rapid identification and quantification of intramuscular fat adulteration in lamb meat with VIS–NIR spectroscopy and chemometrics methods. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01352-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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16
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Li J, Liang Y, Li W, Xu N, Zhu B, Wu Z, Wang X, Fan S, Wang M, Zhu J. Protecting ice from melting under sunlight via radiative cooling. SCIENCE ADVANCES 2022; 8:eabj9756. [PMID: 35148187 PMCID: PMC8836806 DOI: 10.1126/sciadv.abj9756] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 12/20/2021] [Indexed: 05/19/2023]
Abstract
As ice plays a critical role in various aspects of life, from food preservation to ice sports and ecosystem, it is desirable to protect ice from melting, especially under sunlight. The fundamental reason for ice melt under sunlight is related to the imbalanced energy flows of the incoming sunlight and outgoing thermal radiation. Therefore, radiative cooling, which can balance the energy flows without energy consumption, offers a sustainable approach for ice protection. Here, we demonstrate that a hierarchically designed radiative cooling film based on abundant and eco-friendly cellulose acetate molecules versatilely provides effective and passive protection to various forms/scales of ice under sunlight. This work provides inspiration for developing an effective, scalable, and sustainable route for preserving ice and other critical elements of ecosystems.
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Affiliation(s)
- Jinlei Li
- National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, P. R. China
| | - Yuan Liang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, P. R. China
| | - Wei Li
- GPL Photonics Lab, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P. R. China
| | - Ning Xu
- National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, P. R. China
| | - Bin Zhu
- National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, P. R. China
| | - Zhen Wu
- National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, P. R. China
| | - Xueyang Wang
- National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, P. R. China
| | - Shanhui Fan
- Department of Electrical Engineering, Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Minghuai Wang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, P. R. China
- Corresponding author. (J.Z.); (M.W.)
| | - Jia Zhu
- National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, P. R. China
- Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing 210023, P. R. China
- Corresponding author. (J.Z.); (M.W.)
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Han F, Huang X, Aheto JH, Zhang X, Rashed MMA. Fusion of a low-cost electronic nose and Fourier transform near-infrared spectroscopy for qualitative and quantitative detection of beef adulterated with duck. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:417-426. [PMID: 35014996 DOI: 10.1039/d1ay01949j] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A low-cost electronic nose (E-nose) based on colorimetric sensors fused with Fourier transform-near-infrared (FT-NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total volatile basic nitrogen, protein, fat, total sugar and ash contents were measured to investigate the differences of basic properties between raw beef and duck; GC-MS was employed to analyze the difference of the volatile organic compounds emitted from these two types of meat. For variable selection and spectra denoising, the simple T-test (p < 0.05) separately intergraded with first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction were performed and the results compared. Extreme learning machine models were built to identify the adulterated beef and predict the adulteration levels. Results showed that for recognizing the independent samples of raw beef, beef-duck mixtures, and raw duck, FT-NIR offered a 100% identification rate, which was superior to the E-nose (83.33%) created herein. In terms of predicting adulteration levels, the root means square error (RMSE) and the correlation coefficient (r) for independent meat samples using FT-NIR were 0.511% and 0.913, respectively. At the same time, for E-nose, these two indicators were 1.28% and 0.841, respectively. When the E-nose and FT-NIR data were fused, the RMSE decreased to 0.166%, and the r improved to 0.972. All the results indicated that fusion of the low-cost E-nose and FT-NIR could be employed for rapid and convenient testing of beef adulterated with duck.
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Affiliation(s)
- Fangkai Han
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, Anhui, P. R. China.
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
| | - Xiaorui Zhang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
| | - Marwan M A Rashed
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, Anhui, P. R. China.
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18
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Dashti A, Müller-Maatsch J, Weesepoel Y, Parastar H, Kobarfard F, Daraei B, AliAbadi MHS, Yazdanpanah H. The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification. Foods 2021; 11:71. [PMID: 35010197 PMCID: PMC8750306 DOI: 10.3390/foods11010071] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022] Open
Abstract
Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400-1000 nm) and a handheld NIR (900-1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95-100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.
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Affiliation(s)
- Abolfazl Dashti
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran
| | - Judith Müller-Maatsch
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands; (J.M.-M.); (Y.W.)
| | - Yannick Weesepoel
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands; (J.M.-M.); (Y.W.)
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran P.O. Box 11155-9516, Iran;
| | - Farzad Kobarfard
- Department of Medicinal Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran;
| | - Bahram Daraei
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
| | | | - Hassan Yazdanpanah
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran
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19
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Wang Q, Liu H, Bai Y, Zhao Y, Guo J, Chen A, Yang S, Zhao S, Tan L. Research progress on mutton origin tracing and authenticity. Food Chem 2021; 373:131387. [PMID: 34742042 DOI: 10.1016/j.foodchem.2021.131387] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/06/2021] [Accepted: 10/10/2021] [Indexed: 11/04/2022]
Abstract
With the globalization of the food market and the convenience of food transportation between countries, consumers are increasingly worried about the source and safety of the food they eat. Traceability has been identified as an important tool for ensuring food safety and quality. This review mainly introduces the principles of five food traceability technologies, summarizes the progress in mutton application, comprehensively compares and analyzes the five traceability technologies, and discusses their application prospects, advantages and disadvantages. It is aimed at promoting research and application of traceability technology in mutton safety, promoting establishment and improvement of food traceability system.
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Affiliation(s)
- Qian Wang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Haijin Liu
- Tibet Autonomous Region Agricultural and Livestock Product Quality and Safety Inspection Testing Center, Lhasa 850211, China
| | - Yang Bai
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jun Guo
- College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Ailiang Chen
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shuming Yang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Liqin Tan
- Changgao Agricultural Technology Extension Station, Beipiao 122109, China
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20
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Feng C, Xu D, Liu Z, Hu W, Yang J, Li C. A quantitative method for detecting meat contamination based on specific polypeptides. Anim Biosci 2021; 34:1532-1543. [PMID: 33254363 PMCID: PMC8495334 DOI: 10.5713/ajas.20.0616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/01/2020] [Accepted: 11/14/2020] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE This study was aimed to establish a quantitative detection method for meat contamination based on specific polypeptides. METHODS Thermally stable peptides with good responses were screened by high resolution liquid chromatography tandem mass spectrometry. Standard curves of specific polypeptide were established by triple quadrupole mass spectrometry. Finally, the adulteration of commercial samples was detected according to the standard curve. RESULTS Fifteen thermally stable peptides with good responses were screened. The selected specific peptides can be detected stably in raw meat and deep processed meat with the detection limit up to 1% and have a good linear relationship with the corresponding muscle composition. CONCLUSION This method can be effectively used for quantitative analysis of commercial samples.
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Affiliation(s)
- Chaoyan Feng
- Key Laboratory of Meat Processing and Quality Control, MOE; Key Laboratory of Meat Processing, MARA; Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control; College of Food Science and Technology, Nanjing Agricultural University, 210095, Nanjing,
China
| | - Daokun Xu
- Nanjing institute for Food and Drug Supervision and Inspection, 210095,
China
| | - Zhen Liu
- Nanjing institute for Food and Drug Supervision and Inspection, 210095,
China
| | - Wenyan Hu
- Nanjing institute for Food and Drug Supervision and Inspection, 210095,
China
| | - Jun Yang
- Nanjing institute for Food and Drug Supervision and Inspection, 210095,
China
| | - Chunbao Li
- Key Laboratory of Meat Processing and Quality Control, MOE; Key Laboratory of Meat Processing, MARA; Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control; College of Food Science and Technology, Nanjing Agricultural University, 210095, Nanjing,
China
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21
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Zheng M, Zhang Y, Gu J, Bai Z, Zhu R. Classification and quantification of minced mutton adulteration with pork using thermal imaging and convolutional neural network. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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22
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23
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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24
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Feng L, Wu B, Zhu S, He Y, Zhang C. Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins. Front Nutr 2021; 8:680357. [PMID: 34222304 PMCID: PMC8247466 DOI: 10.3389/fnut.2021.680357] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/25/2021] [Indexed: 01/25/2023] Open
Abstract
Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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25
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Bai Y, Liu H, Zhang B, Zhang J, Wu H, Zhao S, Qie M, Guo J, Wang Q, Zhao Y. Research Progress on Traceability and Authenticity of Beef. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1936000] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Yang Bai
- Laboratory of quality and safety of animal products, Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Haijin Liu
- Tibet Autonomous Region Agricultural and Livestock Product Quality and Safety Inspection Testing Center, Lhasa China
| | - Bin Zhang
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China
| | - Jiukai Zhang
- Agro-Product Safety Research Center Chinese Academy of Inspection and Quarantine, Beijing, China
| | - Hao Wu
- Food Inspection and Quarantine Center, Shenzhen Customs, Shenzhen, China
| | - Shanshan Zhao
- Laboratory of quality and safety of animal products, Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Laboratory of quality and safety of animal products, Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jun Guo
- College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Qian Wang
- Laboratory of quality and safety of animal products, Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yan Zhao
- Laboratory of quality and safety of animal products, Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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26
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Ghidini S, Chiesa LM, Panseri S, Varrà MO, Ianieri A, Pessina D, Zanardi E. Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy. Foods 2021; 10:foods10040885. [PMID: 33919551 PMCID: PMC8074186 DOI: 10.3390/foods10040885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/12/2021] [Accepted: 04/15/2021] [Indexed: 11/30/2022] Open
Abstract
The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.
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Affiliation(s)
- Sergio Ghidini
- Department of Food and Drug, University of Parma, Strada del Taglio 10, 43126 Parma, Italy; (S.G.); (M.O.V.); (A.I.); (E.Z.)
| | - Luca Maria Chiesa
- Department of Health, Animal Science and Food Safety, University of Milan, 20133 Milan, Italy;
| | - Sara Panseri
- Department of Health, Animal Science and Food Safety, University of Milan, 20133 Milan, Italy;
- Correspondence:
| | - Maria Olga Varrà
- Department of Food and Drug, University of Parma, Strada del Taglio 10, 43126 Parma, Italy; (S.G.); (M.O.V.); (A.I.); (E.Z.)
| | - Adriana Ianieri
- Department of Food and Drug, University of Parma, Strada del Taglio 10, 43126 Parma, Italy; (S.G.); (M.O.V.); (A.I.); (E.Z.)
| | - Davide Pessina
- Quality Department, Italian Retail Il Gigante SpA, 20133 Milan, Italy;
| | - Emanuela Zanardi
- Department of Food and Drug, University of Parma, Strada del Taglio 10, 43126 Parma, Italy; (S.G.); (M.O.V.); (A.I.); (E.Z.)
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27
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Yu H, Guo L, Kharbach M, Han W. Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications. Foods 2021; 10:802. [PMID: 33917964 PMCID: PMC8068357 DOI: 10.3390/foods10040802] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/02/2021] [Accepted: 04/06/2021] [Indexed: 11/17/2022] Open
Abstract
Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the representative multi-way models which were used for analyzing NIRS data, as well as the advances, advantages and limitations of different multi-way models. The applications of multi-way analysis in NIRS for the food industry in terms of food process control, quality evaluation and fraud, identification and classification, prediction and quantification, and image analysis are also reviewed. It is evident from this report that multi-way analysis is presently an attractive tool for modeling complex NIRS data in the food industry while its full potential is far from reached. The combination of multi-way analysis with NIRS will be a promising practice for turning food data information into operational knowledge, conducting reliable food analyses and improving our understanding about food systems and food processes. To the best of our knowledge, this is the first paper that systematically reports the advances on models and applications of multi-way analysis in NIRS for the food industry.
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Affiliation(s)
- Huiwen Yu
- Chemometric and Analytical Technology, Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, DK-1958 Frederiksberg C, Denmark;
| | - Lili Guo
- Department of Plant and Environmental Science, Faculty of Science, University of Copenhagen, Højbakkegaard Alle 13, DK-2630 Taastrup, Denmark
- College of Water Resources and Architectural Engineering, Northwest A&F University, Weihui Road 23, Yangling 712100, China
| | - Mourad Kharbach
- Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland;
| | - Wenjie Han
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China;
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28
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Sarmiento-García A, Palacios C, González-Martín I, Revilla I. Evaluation of the Production Performance and the Meat Quality of Chickens Reared in Organic System. As Affected by the Inclusion of Calliphora sp. in the Diet. Animals (Basel) 2021; 11:ani11020324. [PMID: 33525467 PMCID: PMC7912308 DOI: 10.3390/ani11020324] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/23/2021] [Accepted: 01/25/2021] [Indexed: 01/10/2023] Open
Abstract
The use of insects can be a possible source of protein. This study uses Calliphora sp. larvae (CLM) as a protein source in 320 one-day-old medium-growing male chicks (RedBro) during their first month of life. Chickens were randomly assigned to four dietary treatments. Each group consisted of 10 animals, and a total of 8 replicas. Control group was fed with a certified organic feed. The experimental treatments were supplemented with 5% (T2), 10% (T3), or 15% (T4) of CLM, reducing in each case the corresponding percentage of feed quantity. Productive development and meat quality were analyzed, and near infrared spectroscopy (NIRS) was used as a tool for classifying the samples. Chickens of T4 showed greater final body weight and total average daily gain, but they reduced consumption and feed conversion ratio (FCR). The chicken breast meat of T4 had lower cooking losses and higher palmitoleic acid content (p < 0.01). NIRS classified correct 92.4% of samples according to the food received. CLM is presented as a potential ingredient for the diet of medium-slow growing chickens raised in organic systems.
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Affiliation(s)
| | - Carlos Palacios
- Area of Animal Production, University of Salamanca, 37007 Salamanca, Spain;
| | - Inmaculada González-Martín
- Department of Analytical Chemistry, Nutrition and Bromathology, University of Salamanca, 37008 Salamanca, Spain;
| | - Isabel Revilla
- Area of Food Technology, Polytechnical High School of Zamora, University of Salamanca, 49022 Zamora, Spain;
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29
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Bwambok DK, Siraj N, Macchi S, Larm NE, Baker GA, Pérez RL, Ayala CE, Walgama C, Pollard D, Rodriguez JD, Banerjee S, Elzey B, Warner IM, Fakayode SO. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6982. [PMID: 33297345 PMCID: PMC7730680 DOI: 10.3390/s20236982] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022]
Abstract
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
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Affiliation(s)
- David K. Bwambok
- Chemistry and Biochemistry, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA;
| | - Noureen Siraj
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Samantha Macchi
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Nathaniel E. Larm
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Gary A. Baker
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Rocío L. Pérez
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Caitlan E. Ayala
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Charuksha Walgama
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - David Pollard
- Department of Chemistry, Winston-Salem State University, 601 S. Martin Luther King Jr Dr, Winston-Salem, NC 27013, USA;
| | - Jason D. Rodriguez
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave., St. Louis, MO 63110, USA;
| | - Souvik Banerjee
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - Brianda Elzey
- Science, Engineering, and Technology Department, Howard Community College, 10901 Little Patuxent Pkwy, Columbia, MD 21044, USA;
| | - Isiah M. Warner
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Sayo O. Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
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30
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Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217783] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Minced meat substitution is one of the most common forms of food fraud in the meat industry. Recently, Hyperspectral Imaging (HSI) has been used for the classification and identification of minced meat types. However, conventional methods are based only on spectral information and ignore the spatial variability of the data. Moreover, these methods first tend to reduce the size of the data, which to some extent ignores the abstract level information and does not preserve the spatial information. Therefore, this work proposes a novel Isos-bestic wavelength reduction method for the different minced meat types, by retaining only Myoglobin pigments (Mb) in the meat spectra. A total of 60 HSI cubes are acquired using Fx 10 Hyperspectral sensor. For each HSI cube, a set of preprocessing schemes is applied to extract the Region of Interest (ROI) and spectral preprocessing, i.e., Golay filtering. Later, these preprocessed HSI cubes are fed into a 3D-Convolutional Neural Network (3D-CNN) model for nonlinear feature extraction and classification. The proposed pipeline outperformed several state-of-the-art methods, with an overall accuracy of 94.0%.
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31
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Zhang W, Ma J, Sun DW. Raman spectroscopic techniques for detecting structure and quality of frozen foods: principles and applications. Crit Rev Food Sci Nutr 2020; 61:2623-2639. [DOI: 10.1080/10408398.2020.1828814] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Wenyang Zhang
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
- State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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32
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Sajali N, Wong SC, Abu Bakar S, Khairil Mokhtar NF, Manaf YN, Yuswan MH, Mohd Desa MN. Analytical approaches of meat authentication in food. Int J Food Sci Technol 2020. [DOI: 10.1111/ijfs.14797] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Nurhayatie Sajali
- School of Engineering and Technology University College of Technology Sarawak Sibu Sarawak Malaysia
- Halal Products Research Institute Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
| | - Sie Chuong Wong
- Department of Basic Science and Engineering Faculty of Agriculture and Food Sciences UPM Bintulu Sarawak Campus Bintulu Sarawak Malaysia
| | - Suhaili Abu Bakar
- Department of Biomedical Science Faculty of Medicine and Health Sciences Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
| | - Nur Fadhilah Khairil Mokhtar
- Halal Products Research Institute Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
- Konsortium Institut Halal IPT Malaysia (KIHIM), Ministry of Higher Education Malaysia, Federal Government Administrative Centre Putrajaya Malaysia
| | - Yanty Noorzianna Manaf
- Halal Products Research Institute Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
- Konsortium Institut Halal IPT Malaysia (KIHIM), Ministry of Higher Education Malaysia, Federal Government Administrative Centre Putrajaya Malaysia
| | - Mohd Hafis Yuswan
- Halal Products Research Institute Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
- Konsortium Institut Halal IPT Malaysia (KIHIM), Ministry of Higher Education Malaysia, Federal Government Administrative Centre Putrajaya Malaysia
| | - Mohd Nasir Mohd Desa
- Halal Products Research Institute Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
- Department of Biomedical Science Faculty of Medicine and Health Sciences Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia
- Konsortium Institut Halal IPT Malaysia (KIHIM), Ministry of Higher Education Malaysia, Federal Government Administrative Centre Putrajaya Malaysia
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Hassoun A, Måge I, Schmidt WF, Temiz HT, Li L, Kim HY, Nilsen H, Biancolillo A, Aït-Kaddour A, Sikorski M, Sikorska E, Grassi S, Cozzolino D. Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years. Foods 2020; 9:E1069. [PMID: 32781687 PMCID: PMC7466239 DOI: 10.3390/foods9081069] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/01/2020] [Indexed: 12/27/2022] Open
Abstract
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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Affiliation(s)
- Abdo Hassoun
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Ingrid Måge
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Walter F. Schmidt
- United States Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705-2325, USA;
| | - Havva Tümay Temiz
- Department of Food Engineering, Bingol University, 12000 Bingol, Turkey;
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China;
| | - Hae-Yeong Kim
- Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea;
| | - Heidi Nilsen
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, 67100 Via Vetoio, Coppito, L’Aquila, Italy;
| | | | - Marek Sikorski
- Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland;
| | - Ewa Sikorska
- Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland;
| | - Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via Celoria, 2, 20133 Milano, Italy;
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, 39 Kessels Rd, Coopers Plains, QLD 4108, Australia;
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34
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Barragán-Hernández W, Mahecha-Ledesma L, Angulo-Arizala J, Olivera-Angel M. Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance. Foods 2020; 9:foods9080984. [PMID: 32721995 PMCID: PMC7466230 DOI: 10.3390/foods9080984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/06/2020] [Accepted: 07/10/2020] [Indexed: 01/25/2023] Open
Abstract
This study was conducted to evaluate the feasibility of using near-infrared spectroscopy (NIRS) to predict beef consumers’ perceptions. Photographs of 200 raw steaks were taken, and NIRS data were collected (transmittance and reflectance). The steak photographs were used to conduct a face-to-face survey of 400 beef consumers. Consumers rated beef color, visible fat, and overall appearance, using a 5-point Likert scale (where 1 indicated “Dislike very much” and 5 indicated “Like very much”), which later was simplified in a 3-point Likert scale. Factor analysis and structural equation modeling (SEM) were used to generate a beef consumer index. A partial least square discriminant analysis (PLS-DA) was used to predict beef consumers’ perceptions using NIRS data. SEM was used to validate the index, with root mean square errors of approximation ≤0.1 and comparative fit and Tucker–Lewis index values <0.9. PLS-DA results for the 5-point Likert scale showed low prediction (accuracy < 42%). A simplified 3-point Likert scale improved discrimination (accuracy between 52% and 55%). The PLS-DA model for purchasing decisions showed acceptable prediction results, particularly for transmittance NIRS (accuracy of 76%). Anticipating beef consumers’ willingness to purchase could allow the beef industry to improve products so that they meet consumers’ preferences.
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Affiliation(s)
- Wilson Barragán-Hernández
- Centro de Investigación Turipaná, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), Montería 230001, Colombia;
| | - Liliana Mahecha-Ledesma
- Facultad de Ciencias Agrarias, GRICA research group, Universidad de Antioquia, Medellin 1226, Colombia;
- Correspondence: ; Tel.: +57-4-2199101
| | - Joaquín Angulo-Arizala
- Facultad de Ciencias Agrarias, GRICA research group, Universidad de Antioquia, Medellin 1226, Colombia;
| | - Martha Olivera-Angel
- Facultad de Ciencias Agrarias, Biogénesis research group, Universidad de Antioquia, Medellin 1226, Colombia;
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35
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Leng T, Li F, Xiong L, Xiong Q, Zhu M, Chen Y. Quantitative detection of binary and ternary adulteration of minced beef meat with pork and duck meat by NIR combined with chemometrics. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107203] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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36
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Li YC, Liu SY, Meng FB, Liu DY, Zhang Y, Wang W, Zhang JM. Comparative review and the recent progress in detection technologies of meat product adulteration. Compr Rev Food Sci Food Saf 2020; 19:2256-2296. [PMID: 33337107 DOI: 10.1111/1541-4337.12579] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/06/2020] [Accepted: 05/06/2020] [Indexed: 12/11/2022]
Abstract
Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.
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Affiliation(s)
- Yun-Cheng Li
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Shu-Yan Liu
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China
| | - Fan-Bing Meng
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Da-Yu Liu
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Yin Zhang
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Wei Wang
- Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Jia-Min Zhang
- Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
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37
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A soft discriminant model based on mid-infrared spectra of bovine meat purges to detect economic motivated adulteration by the addition of non-meat ingredients. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01795-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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38
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Creydt M, Fischer M. Food authentication in real life: How to link nontargeted approaches with routine analytics? Electrophoresis 2020; 41:1665-1679. [PMID: 32249434 DOI: 10.1002/elps.202000030] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/19/2020] [Accepted: 03/23/2020] [Indexed: 12/20/2022]
Abstract
In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.
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Affiliation(s)
- Marina Creydt
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Hamburg, Germany
| | - Markus Fischer
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Hamburg, Germany
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39
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Weng S, Guo B, Tang P, Yin X, Pan F, Zhao J, Huang L, Zhang D. Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 230:118005. [PMID: 31951866 DOI: 10.1016/j.saa.2019.118005] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 12/08/2019] [Accepted: 12/27/2019] [Indexed: 06/10/2023]
Abstract
High economic returns induce the continuous occurrence of meat adulteration. In this study, visible/near-infrared (Vis/NIR) reflectance spectroscopy with multivariate methods was used for the rapid detection of adulteration in minced beef. First, the reflectance spectra of different adulterated minced beef samples were measured at 350-2500 nm. Standardization and Savitzky-Golay (SG) smoothing were applied to reduce spectral interference and noise. Then, support vector machine (SVM), random forest (RF), partial least squares regression (PLSR), and deep convolutional neural network (DCNN) were adopted for adulteration type identification and level prediction. Moreover, principal component analysis (PCA), locally linear embedding (LLE), subwindow permutation analysis (SPA), and competitive adaptive reweighted sampling (CARS) were performed to eliminate redundant information. SG smoothing performed better on interference reduction. DCNN and PCA identified adulteration type with the accuracy above 99%. In adulteration level prediction, the RF with spectra of important wavelengths selected by CARS provided optimal performance for beef adulterated with pork, and coefficient of determination of prediction (R2P) and the root mean square error of prediction (RMSEP) were 0.973 and 2.145. The best prediction for beef adulterated with beef heart was obtained using PLSR and CARS with R2P of 0.960 and RMSEP of 2.758. Accordingly, Vis/NIR reflectance spectroscopy coupled with multivariate methods can provide the rapid and accurate detection of adulterated minced beef.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China.
| | - Bingqing Guo
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China
| | - Peipei Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China
| | - Xun Yin
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China
| | - Fangfang Pan
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China
| | - Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China.
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China
| | - Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China
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40
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Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer. Molecules 2020; 25:molecules25071651. [PMID: 32260173 PMCID: PMC7180573 DOI: 10.3390/molecules25071651] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/01/2020] [Accepted: 04/01/2020] [Indexed: 11/16/2022] Open
Abstract
Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky-Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.
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41
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Zia Q, Alawami M, Mokhtar NFK, Nhari RMHR, Hanish I. Current analytical methods for porcine identification in meat and meat products. Food Chem 2020; 324:126664. [PMID: 32380410 DOI: 10.1016/j.foodchem.2020.126664] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 03/20/2020] [Accepted: 03/20/2020] [Indexed: 12/21/2022]
Abstract
Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.
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Affiliation(s)
- Qamar Zia
- A New Mind, Ash Shati, Al Qatif 32617-3732, Saudi Arabia.
| | - Mohammad Alawami
- A New Mind, Ash Shati, Al Qatif 32617-3732, Saudi Arabia; Depaartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
| | | | | | - Irwan Hanish
- Halal Product Research Institute, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
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42
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Han F, Huang X, H. Aheto J, Zhang D, Feng F. Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors. Foods 2020; 9:foods9020193. [PMID: 32075051 PMCID: PMC7073938 DOI: 10.3390/foods9020193] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 02/05/2020] [Accepted: 02/10/2020] [Indexed: 12/22/2022] Open
Abstract
The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.
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Affiliation(s)
- Fangkai Han
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, China (D.Z.)
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China;
- Correspondence:
| | - Joshua H. Aheto
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China;
| | - Dongjing Zhang
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, China (D.Z.)
| | - Fan Feng
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, China (D.Z.)
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