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Stachniuk A, Trzpil A, Czeczko R, Nowicki Ł, Ziomkowska M, Fornal E. Absolute quantification of targeted rabbit liver- and meat tissue-specific peptide markers in highly processed food products. Food Chem 2024; 438:138069. [PMID: 38007955 DOI: 10.1016/j.foodchem.2023.138069] [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: 09/07/2023] [Revised: 11/05/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023]
Abstract
A highly sensitive and selective method for the simultaneous absolute quantification of peptides unique to rabbit meat- and liver-specific tissue was developed using liquid chromatography - triple quadrupole mass spectrometry. Two rabbit skeletal muscle-specific peptides (SSVFVADPK and PHSHPALTPEQK), three rabbit liver tissue-specific peptides (FNLEALVTHTLPFEK, AILNYVANK, and TELAEPTSTR) and one peptide specific to both rabbit offal and skeletal muscle tissue (AFFGHYLYEVAR) were monitored. Analyses were performed using peptides labelled with stable isotopes (13C and 15N) as internal standards. Fifteen food samples containing rabbit meat and/or liver were analysed to verify compliance of the rabbit meat and liver composition with product labelling. One sample was adulterated with undeclared rabbit liver. The limit of detection and limit of quantification for the selected peptides of interest were in the range of 0.17 to 0.35 ng/mg and 0.57 to 1.17 ng/mg, respectively. The method may be useful for the determination of rabbit meat and liver tissue in highly processed food samples.
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Affiliation(s)
- Anna Stachniuk
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland.
| | - Alicja Trzpil
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Renata Czeczko
- Department of Chemistry, University of Live Sciences in Lublin, ul. Akademicka 15, 20-950 Lublin, Poland
| | - Łukasz Nowicki
- Altium International Sp. z o.o, ul. Puławska 303, 02-785 Warszawa, Poland
| | | | - Emilia Fornal
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
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2
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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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3
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Stachniuk A, Trzpil A, Montowska M, Fornal E. Heat-stable peptide markers specific to rabbit and chicken liver tissue for meat product authentication testing. Food Chem 2023; 424:136432. [PMID: 37245471 DOI: 10.1016/j.foodchem.2023.136432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 05/03/2023] [Accepted: 05/18/2023] [Indexed: 05/30/2023]
Abstract
A three-step analysis was used to detect and identify heat-stable peptide markers specific to liver tissue from rabbit and chicken. It involved peptide discovery by liquid chromatography coupled to high resolution mass spectrometer (LC-HRMS), followed by protein identification using Spectrum Mill software and multiple reaction monitoring (MRM) based confirmation of the discovered peptides using a liquid chromatography coupled to triple quadrupole mass spectrometer (LC-TQ). We identified 50 and 91 heat-stable peptide markers unique to chicken and rabbit liver, respectively. The markers were validated in commercial food samples with declared liver tissue contents ranging from 5% to 30%. The best candidate peptides for distinguishing liver tissue from skeletal muscle were selected and then confirmed using MRM-based approach. Limit of detection of liver was found to be in the range of 0.13 to 2.13% (w/w) for chicken liver-specific peptide markers, and from 0.04 to 0.6% (w/w) for rabbit liver-specific peptide markers.
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Affiliation(s)
- Anna Stachniuk
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland.
| | - Alicja Trzpil
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Magdalena Montowska
- Department of Meat Technology, Poznan University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznan, Poland
| | - Emilia Fornal
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
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4
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Jin H, Tu L, Wang Y, Zhang K, Lv B, Zhu Z, Zhao D, Li C. Rapid detection of waste cooking oil using low-field nuclear magnetic resonance. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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5
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Pu H, Yu J, Sun DW, Wei Q, Shen X, Wang Z. Distinguishing Fresh and Frozen-thawed Beef Using Hyperspectral Imaging Technology Combined with Convolutional Neural Networks. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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6
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Jiang H, Yuan W, Ru Y, Chen Q, Wang J, Zhou H. Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 282:121689. [PMID: 35914356 DOI: 10.1016/j.saa.2022.121689] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 07/14/2022] [Accepted: 07/26/2022] [Indexed: 05/10/2023]
Abstract
Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.
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Affiliation(s)
- Hongzhe Jiang
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
| | - Weidong Yuan
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Yu Ru
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Qing Chen
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Jinpeng Wang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hongping Zhou
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
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7
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Mortas M, Awad N, Ayvaz H. Adulteration detection technologies used for halal/kosher food products: an overview. DISCOVER FOOD 2022. [PMCID: PMC9020560 DOI: 10.1007/s44187-022-00015-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
AbstractIn the Islamic and Jewish religions, there are various restrictions that should be followed in order for food products to be acceptable. Some food items like pork or dog meat are banned to be consumed by the followers of the mentioned religions. However, illegally, some food producers in various countries use either the meat or the fat of the banned animals during food production without being mentioned in the label on the final products, and this considers as food adulteration. Nowadays, halal or kosher labeled food products have a high economic value, therefore deceiving the consumers by producing adulterated food is an illegal business that could make large gains. On the other hand, there is an insistent need from the consumers for getting reliable products that comply with their conditions. One of the main challenges is that the detection of food adulteration and the presence of any of the banned ingredients is usually unnoticeable and cannot be determined by the naked eye. As a result, scientists strove to develop very sensitive and precise analytical techniques. The most widely utilized techniques for the detection and determination of halal/kosher food adulterations can be listed as High-Pressure Liquid Chromatography (HPLC), Capillary Electrophoresis (CE), Gas Chromatography (GC), Electronic Nose (EN), Polymerase Chain Reaction (PCR), Enzyme-linked Immuno Sorbent Assay (ELISA), Differential Scanning Calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Near-infrared (NIR) Spectroscopy, Laser-induced Breakdown Spectroscopy (LIBS), Fluorescent Light Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy and Raman Spectroscopy (RS). All of the above-mentioned techniques were evaluated in terms of their detection capabilities, equipment and analysis costs, accuracy, mobility, and needed sample volume. As a result, the main purposes of the present review are to identify the most often used detection approaches and to get a better knowledge of the existing halal/kosher detection methods from a literature perspective.
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Affiliation(s)
- Mustafa Mortas
- Department Food Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun, 55139 Turkey
- Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210 USA
| | - Nour Awad
- Department Food Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun, 55139 Turkey
| | - Huseyin Ayvaz
- Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210 USA
- Department of Food Engineering, Faculty of Engineering, Canakkale Onsekiz Mart University, Canakkale, 17100 Turkey
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8
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Wu W, Zhang L, Zheng X, Huang Q, Farag MA, Zhu R, Zhao C. Emerging applications of metabolomics in food science and future trends. Food Chem X 2022; 16:100500. [DOI: 10.1016/j.fochx.2022.100500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/17/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
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9
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Pork liver tissue-specific peptide markers for food authenticity testing and adulteration detections. Food Chem 2022; 405:135013. [DOI: 10.1016/j.foodchem.2022.135013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
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10
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Pulluri KK, Kumar VN. Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose. SENSORS (BASEL, SWITZERLAND) 2022; 22:7789. [PMID: 36298140 PMCID: PMC9609363 DOI: 10.3390/s22207789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/23/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Food adulteration is the most serious problem found in the food industry as it harms people's healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.
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11
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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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12
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Yin S, Niu L, Liu Y. Recent Progress on Techniques in the Detection of Aflatoxin B1 in Edible Oil: A Mini Review. Molecules 2022; 27:molecules27196141. [PMID: 36234684 PMCID: PMC9573432 DOI: 10.3390/molecules27196141] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/12/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Contamination of agricultural products and foods by aflatoxin B1 (AFB1) is becoming a serious global problem, and the presence of AFB1 in edible oil is frequent and has become inevitable, especially in underdeveloped countries and regions. As AFB1 results from a possible degradation of aflatoxins and the interaction of the resulting toxic compound with food components, it could cause chronic disease or severe cancers, increasing morbidity and mortality. Therefore, rapid and reliable detection methods are essential for checking AFB1 occurrence in foodstuffs to ensure food safety. Recently, new biosensor technologies have become a research hotspot due to their characteristics of speed and accuracy. This review describes various technologies such as chromatographic and spectroscopic techniques, ELISA techniques, and biosensing techniques, along with their advantages and weaknesses, for AFB1 control in edible oil and provides new insight into AFB1 detection for future work. Although compared with other technologies, biosensor technology involves the cross integration of multiple technologies, such as spectral technology and new nano materials, and has great potential, some challenges regarding their stability, cost, etc., need further studies.
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Affiliation(s)
- Shipeng Yin
- School of Food Science and Technology, Jiangnan University, No. 1800 Lihu Road, Binhu District, Wuxi 214122, China
| | - Liqiong Niu
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Yuanfa Liu
- School of Food Science and Technology, Jiangnan University, No. 1800 Lihu Road, Binhu District, Wuxi 214122, China
- Correspondence: ; Tel.: 86–510-8587-6799
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13
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Grundy HH, Brown L, Rosario Romero M, Donarski J. Review: Methods to determine offal adulteration in meat products to support enforcement and food security. Food Chem 2022; 399:133818. [DOI: 10.1016/j.foodchem.2022.133818] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 02/07/2023]
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14
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Detection of chicken and fat adulteration in minced lamb meat by VIS/NIR spectroscopy and chemometrics methods. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2022. [DOI: 10.1515/ijfe-2021-0333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Meat fraud has been changed to an important challenge to both industry and governments because of the public health issue. The main purpose of this research was to inspect the possibility of using VIS/NIR spectroscopy, combined with chemometric techniques to detect the adulteration of chicken meat and fat in minced lamb meat. 180 samples of pure lamb, chicken and fat and adulterated samples at different levels: 5, 10, 15 and 20% (w/w) were prepared and analyzed after pre-processing techniques. In order to remove additive and multiplicative effects in spectral data, derivatives and scatter-correction preprocessing methods were applied. Principle Component Analysis (PCA) as unsupervised method was applied to compress data. Moreover, Support Vector Machine (SVM) and Soft Independent Modeling Class Analogies (SIMCA) as supervised methods was applied to estimate the discrimination power of these models for nine and three class datasets. The best classification results were 56.15 and 80.70% for classification of nine class and three class datasets respectively with SVM model. This study shows the applicability of VIS/NIR combined with chemometrics to detect the type of fraud in minced lamb meat.
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15
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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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16
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Yan Z, Liu H, Li T, Li J, Wang Y. Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113490] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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17
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Zhang J, Guo M, Liu G. Rapid identification of lamb freshness grades using visible and near-infrared spectroscopy (Vis-NIR). J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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18
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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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19
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Pandiselvam R, Mahanti NK, Manikantan M, Kothakota A, Chakraborty SK, Ramesh S, Beegum PS. Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108588] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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20
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Nieto-Ortega S, Melado-Herreros Á, Foti G, Olabarrieta I, Ramilo-Fernández G, Gonzalez Sotelo C, Teixeira B, Velasco A, Mendes R. Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods 2021; 11:foods11010055. [PMID: 35010181 PMCID: PMC8750308 DOI: 10.3390/foods11010055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 11/16/2022] Open
Abstract
The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.
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Affiliation(s)
- Sonia Nieto-Ortega
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
- Correspondence: ; Tel.: +34-667-174-323
| | - Ángela Melado-Herreros
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
| | - Giuseppe Foti
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
| | - Idoia Olabarrieta
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
| | - Graciela Ramilo-Fernández
- Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain; (G.R.-F.); (C.G.S.); (A.V.)
| | - Carmen Gonzalez Sotelo
- Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain; (G.R.-F.); (C.G.S.); (A.V.)
| | - Bárbara Teixeira
- Portuguese Institute for the Sea and Atmosphere, IPMA, R. Alfredo Magalhães Ramalho, 6, 1449-006 Lisbon, Portugal; (B.T.); (R.M.)
- Interdisciplinary Center of Marine and Environmental Research (CIIMAR), University of Porto, Rua das Bragas 289, 4050-123 Porto, Portugal
| | - Amaya Velasco
- Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain; (G.R.-F.); (C.G.S.); (A.V.)
| | - Rogério Mendes
- Portuguese Institute for the Sea and Atmosphere, IPMA, R. Alfredo Magalhães Ramalho, 6, 1449-006 Lisbon, Portugal; (B.T.); (R.M.)
- Interdisciplinary Center of Marine and Environmental Research (CIIMAR), University of Porto, Rua das Bragas 289, 4050-123 Porto, Portugal
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Gopalakrishnan K, Sharma A, Emanuel N, Prabhakar PK, Kumar R. Sensors for Non‐Destructive Quality Evaluation of Food. Food Chem 2021. [DOI: 10.1002/9781119792130.ch13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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22
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Maduro Dias C, Nunes H, Melo T, Rosa H, Silva C, Borba A. Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104772] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Siddiqui MA, Khir MHM, Witjaksono G, Ghumman ASM, Junaid M, Magsi SA, Saboor A. Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy. Foods 2021; 10:foods10102405. [PMID: 34681455 PMCID: PMC8535455 DOI: 10.3390/foods10102405] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 11/16/2022] Open
Abstract
Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as "Pure (80 samples)" and "Adulterated (90 samples)". Lard was mixed with the ratio of 10-50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700-1800 cm-1 and 2800-3000 cm-1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.
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Affiliation(s)
- Muhammad Aadil Siddiqui
- Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; (M.H.M.K.); (S.A.M.)
- Correspondence:
| | - Mohd Haris Md Khir
- Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; (M.H.M.K.); (S.A.M.)
| | - Gunawan Witjaksono
- BRI Research Institute, Jl. Harsono RM No. 2, Ragunan, Passsar Minggu, Jakarta 12550, Indonesia;
| | | | - Muhammad Junaid
- Department of Electronics Engineering, FICT, BUITEMS, Quetta 87300, Pakistan;
| | - Saeed Ahmed Magsi
- Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; (M.H.M.K.); (S.A.M.)
| | - Abdul Saboor
- High Performance Cloud Computing Centre (HPC), Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia;
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24
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Su N, Pan F, Wang L, Weng S. Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods. BIOSENSORS-BASEL 2021; 11:bios11080261. [PMID: 34436063 PMCID: PMC8395004 DOI: 10.3390/bios11080261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 12/27/2022]
Abstract
The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.
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Affiliation(s)
- Ning Su
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
| | - Fangfang Pan
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
| | - Liusan Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
- Correspondence: (L.W.); (S.W.)
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
- Correspondence: (L.W.); (S.W.)
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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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Leng T, Li F, Chen Y, Tang L, Xie J, Yu Q. Fast quantification of total volatile basic nitrogen (TVB-N) content in beef and pork by near-infrared spectroscopy: Comparison of SVR and PLS model. Meat Sci 2021; 180:108559. [PMID: 34049182 DOI: 10.1016/j.meatsci.2021.108559] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
With application of PLS regression and SVR, quantitation models of near infrared diffuse reflectance spectroscopy were established for the first time to predict the content of volatile basic nitrogen (TVB-N) content in beef and pork. Results indicated that the best PLS model based on the raw spectra showed an excellent prediction performance with a high value of correlation coefficient at 0.9366 and a low root-mean-square error of prediction value of 3.15, and none of those pretreatment methods could improve the prediction performance of the PLS model. Moreover, comparatively the model obtained by SVR showed inferior quantitative predictive ability (R = 0.8314, RMSEP = 4.61). Analysis on VIP selected wavelengths inferred amino bond containing compounds and lipid may play important roles in the development of PLS models for TVB-N. Results from this study demonstrated the potential of using NIR spectroscopy and PLS for the prediction of TVB-N in beef and pork while more efforts are required to improve the performance of SVR models.
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Affiliation(s)
- Tuo Leng
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China
| | - Feng Li
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China
| | - Yi Chen
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China.
| | - Lijun Tang
- Food Inspection and Testing Institute of Jiangxi Province, Nanchang 330046, People's Republic of China
| | - Jianhua Xie
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China
| | - Qiang Yu
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People's Republic of China
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Detection of Meat Adulteration Using Spectroscopy-Based Sensors. Foods 2021; 10:foods10040861. [PMID: 33920872 PMCID: PMC8071343 DOI: 10.3390/foods10040861] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 03/31/2021] [Accepted: 04/13/2021] [Indexed: 11/16/2022] Open
Abstract
Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.
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28
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Jiang H, Ru Y, Chen Q, Wang J, Xu L. Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 249:119307. [PMID: 33348095 DOI: 10.1016/j.saa.2020.119307] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/04/2020] [Accepted: 12/01/2020] [Indexed: 06/12/2023]
Abstract
Hyperspectral imaging (HSI) technique was investigated to explore a feasible protocol for detecting the potential offal (lung) adulteration in ground pork. Tested samples (176 adulterated and 2 controls) were first prepared with adulterant of ground lung in range of 0%-100% (w/w) at 10% intervals. After hyperspectral images were acquired and calibrated in reflectance mode (400-1000 nm), full spectra were extracted from identified regions of interests (ROIs) and then transformed into absorbance and Kubelka-Munck spectral units, respectively. Partial least squares regression (PLSR) models based on full spectra showed that raw reflectance spectra with no preprocessings performed best with coefficient of determination (Rp2) of 0.98, root mean square error (RMSEP) of 4.25%, and ratio performance deviation (RPD) of 7.53 in prediction set. To reduce the high dimensionality of spectra, data was further explored using principal component loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC), respectively. The optimal performance of established simplified PLSR model were acquired using eleven featured wavelengths selected by PC loadings with Rp2 of 0.98, RMSEP of 4.47% and RPD of 7.16. Finally, the limit of detection (LOD) was calculated to be a satisfactory 7.58%, and readily discernible visualization procedure using preferred simplified PLSR model yielded satisfactory spatial distribution of adulteration situation. Control samples with known distribution were also visualized to successfully prove the validity. Consequently, this research offers an alternative assay for visually and rapidly detecting offal of lung adulteration in ground pork.
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Affiliation(s)
- Hongzhe Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
| | - Yu Ru
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Qing Chen
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Jinpeng Wang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Linyun Xu
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
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29
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Lanza I, Conficoni D, Balzan S, Cullere M, Fasolato L, Serva L, Contiero B, Trocino A, Marchesini G, Xiccato G, Novelli E, Segato S. Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics. FOOD QUALITY AND SAFETY 2021. [DOI: 10.1093/fqsafe/fyaa032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Abstract
Objectives
Near-infrared (NIR) spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated, especially for portable devices. The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times (RT), coupled with multivariate classifier models.
Materials and Methods
Ninety-six samples were analysed by both NIR tools at 2, 6, 10 and 14 days post mortem. NIR data were subsequently submitted to partial least squares discriminant analysis (PLS-DA) and canonical discriminant analysis (CDA). The latter was preceded by double feature selection based on Boruta and Stepwise procedures.
Results
PLS-DA sorted moderate separation of RT theses, while shelf life assessment was more accurate on application of Stepwise-CDA. Bench-top tool had better performance than portable one, probably because it captured more informative spectral data as shown by the variable importance in projection (VIP) and restricted pool of Stepwise-CDA predictive scores (SPS).
Conclusions
NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage. Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts, suitable for routine at-line application for screening of meat quality.
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Affiliation(s)
- Ilaria Lanza
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
| | - Daniele Conficoni
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
| | - Stefania Balzan
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy
| | - Marco Cullere
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
| | - Luca Fasolato
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy
| | - Lorenzo Serva
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
| | - Barbara Contiero
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
| | - Angela Trocino
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy
| | - Giorgio Marchesini
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
| | - Gerolamo Xiccato
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Legnaro, Italy
| | - Enrico Novelli
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy
| | - Severino Segato
- Department of Animal Medicine, Productions and Health, University of Padova, Legnaro, Italy
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30
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Du Q, Zhu M, Shi T, Luo X, Gan B, Tang L, Chen Y. Adulteration detection of corn oil, rapeseed oil and sunflower oil in camellia oil by in situ diffuse reflectance near-infrared spectroscopy and chemometrics. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107577] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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31
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Chemical Sensors for Farm-to-Table Monitoring of Fruit Quality. SENSORS 2021; 21:s21051634. [PMID: 33652654 PMCID: PMC7956188 DOI: 10.3390/s21051634] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 02/03/2023]
Abstract
Farm-to-table operations produce, transport, and deliver produce to consumers in very different ways than conventional, corporate-scale agriculture operations. As a result, the time it takes to get a freshly picked fruit to the consumer is relatively short and the expectations of the consumer for freshness and quality are high. Since many of these operations involve small farms and small businesses, resources to deploy sensors and instruments for monitoring quality are scarce compared to larger operations. Within stringent power, cost, and size constraints, this article analyzes chemical sensor technologies suitable for monitoring fruit quality from the point of harvest to consumption in farm-to-table operations. Approaches to measuring sweetness (sugar content), acidity (pH), and ethylene gas are emphasized. Not surprisingly, many instruments developed for laboratory use or larger-scale operations are not suitable for farm-to-table operations. However, there are many opportunities still available to adapt pH, sugar, and ethylene sensing to the unique needs of localized farm-to-table operations that can help these operations survive and expand well into the future.
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32
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Edwards K, Manley M, Hoffman LC, Williams PJ. Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud. Foods 2021; 10:foods10020448. [PMID: 33670564 PMCID: PMC7922372 DOI: 10.3390/foods10020448] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 12/04/2022] Open
Abstract
In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.
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Affiliation(s)
- Kiah Edwards
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (K.E.); (M.M.)
| | - Marena Manley
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (K.E.); (M.M.)
| | - Louwrens C. Hoffman
- Department of Animal Sciences, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; or
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Health and Food Sciences Precinct, 39 Kessels Rd, Coopers Plains 4108, Australia
| | - Paul J. Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (K.E.); (M.M.)
- Correspondence: ; Tel.: +27-21-808-3155
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33
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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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34
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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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35
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Yuan R, Liu G, He J, Ma C, Cheng L, Fan N, Ban J, Li Y, Sun Y. Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system. J Food Sci 2020; 85:1403-1410. [PMID: 32304238 DOI: 10.1111/1750-3841.15137] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 03/14/2020] [Accepted: 03/30/2020] [Indexed: 11/30/2022]
Abstract
In this study, the ENVI 4.6 software was used to obtain the spectral reflection value of samples. The outlier samples were eliminated by the Monte Carlo method, and then SPXY (sample set partitioning based on be x-y distances) was used to divide the calibration set and prediction set. The spectral images were pretreated and characteristic wavelengths were extracted. The spectral models of full and pretreated spectra and characteristic bands were established by partial least squares regression (PLSR) and principle component regression (PCR), and the optimal modeling combination was selected. The results showed that the modeling effect of the original spectrum was the best. In full-PLSR model, the determination coefficient of the calibration set (Rc2 ), the determination coefficient of prediction set (Rp2 ), and the determination coefficient of interactive verification set (Rcv2 ) were 0.8804, 0.7375, and 0.7422, and root-mean-square error of calibration set (RMSEC), root-mean-square error of prediction (RMSEP), and root mean square error of interactive validation set (RMSECV) were 2.3630, 2.9607, and 3.4209, respectively. PLSR and PCR models were established to obtain the optimal models of CARS-PLSR and PCR-PLSR. In the CARS-PLSR model, the Rc2 , Rp2 , and Rcv2 were 0.9135, 0.7654, and 0.8171, respectively, while RMSEC, RMSEP, and RMSECV were 2.0275, 2.9306, and 2.9262, respectively. In the iRF-PCR model, Rc2 , Rp2 , and Rcv2 were 0.7952, 0.7372, and 0.7280, respectively, while RMSEC, RMSEP, and RMSECV were 3.0207, 2.8278, and 3.4288, respectively. This study has demonstrated that visible and near-infrared hyperspectral imaging system can rapidly predict the content of metmyoglobin in cooked tan mutton. PRACTICAL APPLICATION: This study has demonstrated that visible and near-infrared (Vis/NIR) hyperspectral imaging system can rapidly predict the content of MetMb in cooked tan mutton. With the advantages of nondestructive, rapid, real-time, Vis/NIR, hyperspectral imaging system can be widely expanded and applied to the detection of myoglobin in meat to evaluate the color of meat.
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Affiliation(s)
- Ruirui Yuan
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Guishan Liu
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Jianguo He
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Chao Ma
- School of Physics and Electrical and Electronic Engineering, Ningxia University, Yinchuan, 750021, China
| | - Lijuan Cheng
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Naiyun Fan
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Jingjing Ban
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Yue Li
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Yourui Sun
- Non-Destructive Detection Laboratory of Agricultural Products, School of Agriculture, Ningxia University, Yinchuan, 750021, China
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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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37
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Varrà MO, Fasolato L, Serva L, Ghidini S, Novelli E, Zanardi E. Use of near infrared spectroscopy coupled with chemometrics for fast detection of irradiated dry fermented sausages. Food Control 2020. [DOI: 10.1016/j.foodcont.2019.107009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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38
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Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging. Foods 2020; 9:foods9020154. [PMID: 32041126 PMCID: PMC7073906 DOI: 10.3390/foods9020154] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/28/2020] [Accepted: 02/04/2020] [Indexed: 01/18/2023] Open
Abstract
Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.
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Rady A, Adedeji AA. Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01719-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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40
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Cheng W, Sørensen KM, Engelsen SB, Sun DW, Pu H. Lipid oxidation degree of pork meat during frozen storage investigated by near-infrared hyperspectral imaging: Effect of ice crystal growth and distribution. J FOOD ENG 2019. [DOI: 10.1016/j.jfoodeng.2019.07.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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41
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Wang W, Liu J, Zhang Q, Zhou X, Liu B. Multiplex PCR assay for identification and quantification of bovine and equine in minced meats using novel specific nuclear DNA sequences. Food Control 2019. [DOI: 10.1016/j.foodcont.2019.05.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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42
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Markiewicz-Keszycka M, Zhao M, Cama-Moncunill X, El Arnaout T, Becker D, O'Donnell C, Cullen PJ, Sullivan C, Casado-Gavalda MP. Rapid analysis of magnesium in infant formula powder using laser-induced breakdown spectroscopy. Int Dairy J 2019. [DOI: 10.1016/j.idairyj.2019.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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43
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Recent advances in detecting and regulating ethylene concentrations for shelf-life extension and maturity control of fruit: A review. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.06.010] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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44
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Zhu Z, Zhou Q, Sun DW. Measuring and controlling ice crystallization in frozen foods: A review of recent developments. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.05.012] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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45
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Mapping changes in sarcoplasmatic and myofibrillar proteins in boiled pork using hyperspectral imaging with spectral processing methods. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.04.095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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46
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Lin X, Xu JL, Sun DW. Investigation of moisture content uniformity of microwave-vacuum dried mushroom (Agaricus bisporus) by NIR hyperspectral imaging. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.03.034] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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47
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Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.04.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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48
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Qin P, Qiao D, Xu J, Song Q, Yao L, Lu J, Chen W. Rapid visual sensing and quantitative identification of duck meat in adulterated beef with a lateral flow strip platform. Food Chem 2019; 294:224-230. [PMID: 31126457 DOI: 10.1016/j.foodchem.2019.05.030] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 04/09/2019] [Accepted: 05/06/2019] [Indexed: 12/28/2022]
Abstract
A novel high-sensitivity authentication method has been demonstrated for the rapid visual detection of adulterated meat based on both the lateral flow strip (LFS) platform and on polymerase chain reaction (PCR). After the rapid extraction of genomic components from meat, the on-site amplification of the target DNA of adulterated duck meat is carried out with the rationally designed functional FITC- and biotin-modified primer set, thereby producing numerous double-stranded DNA (dsDNA) products dually labelled with FITC and biotin. The FITC-labelled terminal end of the products binds to the pre-immobilized FITC antibody on the test line of the strip, and the biotin-labelled terminal end binds to the streptavidin-conjugated gold nanoparticles, resulting in a visible test line on the LFS for the rapid identification of duck meat in adulterated beef. After optimization, an adulteration ratio as low as 0.05% can be easily measured, which is more sensitive than other common adulteration authentication methods and is even comparable to instrumental methods. Moreover, 22 commercial processed meat samples were tested with this new strategy, and 4 adulterated samples were successfully identified by both the classic method and our method. In essence, the present authentication method is simple in design, convenient in operation, and can be easily extended to the identification of other adulteration components just by replacing the modified primers.
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Affiliation(s)
- Panzhu Qin
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China
| | - Dongqing Qiao
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China
| | - Jianguo Xu
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China
| | - Qing Song
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China
| | - Li Yao
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China
| | - Jianfeng Lu
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China
| | - Wei Chen
- Engineering Research Centre of Bio-Process, MOE, School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, PR China.
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49
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Su WH, Sun DW. Mid-infrared (MIR) Spectroscopy for Quality Analysis of Liquid Foods. FOOD ENGINEERING REVIEWS 2019. [DOI: 10.1007/s12393-019-09191-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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50
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Ripeness Classification of Bananito Fruit (
Musa acuminata,
AA): a Comparison Study of Visible Spectroscopy and Hyperspectral Imaging. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01506-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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