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Xu C, Yin Z. Unraveling the flavor profiles of chicken meat: Classes, biosynthesis, influencing factors in flavor development, and sensory evaluation. Compr Rev Food Sci Food Saf 2024; 23:e13391. [PMID: 39042376 DOI: 10.1111/1541-4337.13391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/04/2024] [Accepted: 05/19/2024] [Indexed: 07/24/2024]
Abstract
Chicken is renowned as the most affordable meat option, prized by consumers worldwide for its unique flavor, and universally recognized for its essential savory flavor. Current research endeavors are increasingly dedicated to exploring the flavor profile of chicken meat. However, there is a noticeable gap in comprehensive reviews dedicated specifically to the flavor quality of chicken meat, although existing reviews cover meat flavor profiles of various animal species. This review aims to fill this gap by synthesizing knowledge from published literature to describe the compounds, chemistry reaction, influencing factors, and sensory evaluation associated with chicken meat flavor. The flavor compounds in chicken meat mainly included water-soluble low-molecular-weight substances and lipids, as well as volatile compounds such as aldehydes, ketones, alcohols, acids, esters, hydrocarbons, furans, nitrogen, and sulfur-containing compounds. The significant synthesis pathways of flavor components were Maillard reaction, Strecker degradation, lipid oxidation, lipid-Maillard interaction, and thiamine degradation. Preslaughter factors, including age, breed/strain, rearing management, muscle type, and sex of chicken, as well as postmortem conditions such as aging, cooking conditions, and low-temperature storage, were closely linked to flavor development and accounted for the significant differences observed in flavor components. Moreover, the sensory methods used to evaluate the chicken meat flavor were elaborated. This review contributes to a more comprehensive understanding of the flavor profile of chicken meat. It can serve as a guide for enhancing chicken meat flavor quality and provide a foundation for developing customized chicken products.
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Affiliation(s)
- Chunhui Xu
- College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhaozheng Yin
- College of Animal Science, Zhejiang University, Hangzhou, China
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2
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Mustapha A, Ishak I, Zaki NNM, Ismail-Fitry MR, Arshad S, Sazili AQ. Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review. Heliyon 2024; 10:e32189. [PMID: 38975107 PMCID: PMC11225673 DOI: 10.1016/j.heliyon.2024.e32189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/20/2024] [Accepted: 05/29/2024] [Indexed: 07/09/2024] Open
Abstract
Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.
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Affiliation(s)
- Abdul Mustapha
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Iskandar Ishak
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, 43400, Malaysia
| | - Nor Nadiha Mohd Zaki
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Mohammad Rashedi Ismail-Fitry
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Syariena Arshad
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Awis Qurni Sazili
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
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3
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Douglas SL, Bernardez-Morales GM, Nichols BW, Johnson GF, Barahona-Dominguez LS, Jessup AP, Belk AD, Ball JJ, Cho S, Sawyer JT. Inclusion of Beef Heart in Ground Beef Patties Alters Quality Characteristics and Consumer Acceptability as Assessed by the Application of Electronic Nose and Tongue Technology. Foods 2024; 13:811. [PMID: 38472924 DOI: 10.3390/foods13050811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024] Open
Abstract
Consumer purchasing of beef is often driven by the trinity of flavor, palatability, and convenience. Currently, beef patties in the United States are manufactured with fat and lean trimmings derived from skeletal muscles. A reduction in total beef supply may require the use of animal by-product utilization such as variety meats to achieve patty formulations. The current study aimed to assess textural, color, and flavor characteristics in addition to volatile compounds through electronic technology, e-nose and e-tongue, of ground beef patties formulated with beef heart. Ground beef patties were manufactured with 0%, 6%, 12%, or 18% beef heart, with the remainder of the meat block being shoulder clod-derived ground beef. Patties (n = 65/batch/treatment) within each batch (n = 3) with each treatment were randomly allocated to cooked color (n = 17/batch/treatment), Allo-Kramer shear force (AKSF; n = 17/batch/treatment), texture profile analysis (TPA; n = 6/batch/treatment), cooking loss (n = 17/batch/treatment), consumer panel (n = 3/batch/treatment), e-nose (n = 1/batch/treatment), and e-tongue (n = 1/batch/treatment) analysis groups. Patties containing beef heart did not require additional cooking time (p = 0.1325) nor exhibit greater cooking loss (p = 0.0803). Additionally, inclusion rates of beef heart increased hardness (p = 0.0030) and chewiness values (p = 0.0316) in TPA, were internally redder (p = 0.0001), and reduced overall liking by consumer panelists (p = 0.0367). Lastly, patties containing beef heart exhibited greater red-to-brown (p = 0.0003) and hue angle (p = 0.0001) values than control patties. The results suggest that beef heart inclusion does alter ground beef quality characteristics and consumer acceptability.
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Affiliation(s)
| | | | - Brooks W Nichols
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
| | | | | | - Ainsley P Jessup
- Department of Poultry Sciences, Auburn University, Auburn, AL 36849, USA
| | - Aeriel D Belk
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
| | - Jase J Ball
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
| | - Sungeun Cho
- Department of Poultry Sciences, Auburn University, Auburn, AL 36849, USA
| | - Jason T Sawyer
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
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4
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, 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, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 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, China
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Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5410-5440. [PMID: 37818969 DOI: 10.1039/d3ay01132a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.
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Affiliation(s)
- Hala Abi-Rizk
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
| | | | - Julien Chamberland
- Department of Food Sciences, STELA Dairy Research Center, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada
| | - Christophe B Y Cordella
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
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6
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Li H, Wang Y, Zhang J, Li X, Wang J, Yi S, Zhu W, Xu Y, Li J. Prediction of the freshness of horse mackerel (Trachurus japonicus) using E-nose, E-tongue, and colorimeter based on biochemical indexes analyzed during frozen storage of whole fish. Food Chem 2023; 402:134325. [DOI: 10.1016/j.foodchem.2022.134325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 11/30/2022]
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Munekata PES, Finardi S, de Souza CK, Meinert C, Pateiro M, Hoffmann TG, Domínguez R, Bertoli SL, Kumar M, Lorenzo JM. Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:672. [PMID: 36679464 PMCID: PMC9860605 DOI: 10.3390/s23020672] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/21/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.
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Affiliation(s)
- Paulo E. S. Munekata
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sarah Finardi
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Carolina Krebs de Souza
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Caroline Meinert
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Mirian Pateiro
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Tuany Gabriela Hoffmann
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
- Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
| | - Rubén Domínguez
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sávio Leandro Bertoli
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR–Central Institute for Research on Cotton Technology, Mumbai 400019, India
| | - José M. Lorenzo
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
- Facultade de Ciencias, Universidade de Vigo, Área de Tecnoloxía dos Alimentos, 32004 Ourense, Spain
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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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9
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Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm. Foods 2022; 11:foods11152278. [PMID: 35954045 PMCID: PMC9368686 DOI: 10.3390/foods11152278] [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: 07/12/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g−1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.
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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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Detection of adulteration in mutton using digital images in time domain combined with deep learning algorithm. Meat Sci 2022; 192:108850. [DOI: 10.1016/j.meatsci.2022.108850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 04/17/2022] [Accepted: 05/12/2022] [Indexed: 11/19/2022]
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Zhang L, Hao Z, Zhao C, Zhang Y, Li J, Sun B, Tang Y, Yao M. Taste compounds, affecting factors, and methods used to evaluate chicken soup: A review. Food Sci Nutr 2021; 9:5833-5853. [PMID: 34646550 PMCID: PMC8498081 DOI: 10.1002/fsn3.2501] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/29/2021] [Accepted: 07/14/2021] [Indexed: 11/29/2022] Open
Abstract
The taste of chicken soup is dependent upon various taste substances and human senses. More than 300 nonvolatile compounds reportedly exist in chicken/chicken soup. The primary purpose of this review was to elaborate on the prominent taste substances, the taste evaluation methods, and the factors affecting the taste of chicken soup. Most taste-active compounds with taste descriptions and thresholds in chicken soup were summarized. The application of sensory evaluation, liquid chromatography, electronic tongue, and other evaluation methods in chicken soup taste analysis were elaborated. The effects of genetic constitution, preslaughter, processing, and storage on chicken soup taste had been discussed. Nucleotides (especially inosine 5'-monophosphate), amino acids and their derivatives, organic acids, sugars, and peptides play a vital role in the taste attributes of chicken soup. Combining of liquid chromatography and mass spectrometry enables qualitative and quantitative analysis of taste-active compounds in chicken soup, aiding the exploration of key taste-active compounds. The electronic tongue application helps the overall taste perception of the soluble taste-active compounds present in chicken soup samples. Postmortem aging and stewing for a prolonged duration are effective techniques for improving the taste quality of chicken soup. The washing of preprocessing, the cooking temperature of processing, and the storage conditions also exert a significant impact on the taste of chicken soup.
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Affiliation(s)
- Lili Zhang
- College of Food Science and EngineeringTianjin University of Science and TechnologyTianjinChina
- Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business UniversityBeijingChina
| | - Zhilin Hao
- Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business UniversityBeijingChina
| | - Chao Zhao
- College of Food ScienceFujian Agriculture and Forestry UniversityFujianChina
| | - Yuyu Zhang
- Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business UniversityBeijingChina
| | - Jian Li
- Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business UniversityBeijingChina
| | - Baoguo Sun
- College of Food Science and EngineeringTianjin University of Science and TechnologyTianjinChina
- Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business UniversityBeijingChina
| | - Yizhuang Tang
- Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business UniversityBeijingChina
| | - Meixiang Yao
- Jiangxi Jiangzhong Diet Therapy Technology Co., LtdJiujiangChina
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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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14
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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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15
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Galvan D, Aquino A, Effting L, Mantovani ACG, Bona E, Conte-Junior CA. E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review. Crit Rev Food Sci Nutr 2021; 62:6605-6645. [PMID: 33779434 DOI: 10.1080/10408398.2021.1903384] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
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Affiliation(s)
- Diego Galvan
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Luciane Effting
- Chemistry Department, State University of Londrina (UEL), Londrina, PR, Brazil
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Federal University of Technology Paraná (UTFPR), Campo Mourão, PR, Brazil
| | - Carlos Adam Conte-Junior
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
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16
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Tsakanikas P, Karnavas A, Panagou EZ, Nychas GJ. A machine learning workflow for raw food spectroscopic classification in a future industry. Sci Rep 2020; 10:11212. [PMID: 32641761 PMCID: PMC7343812 DOI: 10.1038/s41598-020-68156-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 06/07/2020] [Indexed: 12/24/2022] Open
Abstract
Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure quality and affordability, reducing at the same time the costs of keeping the food fresh and increase productivity. A system, as the one presented herein, for raw food categorization is needed in future food industries to automate food classification according to type, the process of algorithm approaches that will be applied to every different food origin and also for serving disabled people. The purpose of this work was to develop a machine learning workflow based on supervised PLS regression and SVM classification, towards automated raw food categorization from FTIR. The system exhibited high efficiency in multi-class classification of 7 different types of raw food. The selected food samples, were diverse in terms of storage conditions (temperature, storage time and packaging), while the variability within each food was also taken into account by several different batches; leading in a classifier able to embed this variation towards increased robustness and efficiency, ready for real life applications targeting to the digital transformation of the food industry.
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Affiliation(s)
- Panagiotis Tsakanikas
- School of Food and Nutritional Sciences, Department of Food Science and Human Nutrition, Laboratory of Microbiology and Biotechnology of Foods, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece.
| | - Apostolos Karnavas
- School of Food and Nutritional Sciences, Department of Food Science and Human Nutrition, Laboratory of Microbiology and Biotechnology of Foods, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
| | - Efstathios Z Panagou
- School of Food and Nutritional Sciences, Department of Food Science and Human Nutrition, Laboratory of Microbiology and Biotechnology of Foods, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
| | - George-John Nychas
- School of Food and Nutritional Sciences, Department of Food Science and Human Nutrition, Laboratory of Microbiology and Biotechnology of Foods, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece.
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18
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Liu J, Zuo M, Low SS, Xu N, Chen Z, Lv C, Cui Y, Shi Y, Men H. Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model. SENSORS 2020; 20:s20030686. [PMID: 32012652 PMCID: PMC7038490 DOI: 10.3390/s20030686] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 01/23/2020] [Accepted: 01/24/2020] [Indexed: 11/16/2022]
Abstract
As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.
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Affiliation(s)
- Jingjing Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
- Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30602, USA
- Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
- Correspondence: (J.L.); (H.M.); Tel.: +86-432-6480-7283 (J.L. & H.M.); Fax: +86-432-6480-6201 (J.L. & H.M.)
| | - Mingxu Zuo
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
| | - Sze Shin Low
- Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
| | - Ning Xu
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
| | - Zhiqing Chen
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
| | - Chuang Lv
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
| | - Ying Cui
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
| | - Yan Shi
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
| | - Hong Men
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (M.Z.); (N.X.); (Z.C.); (C.L.); (Y.C.); (Y.S.)
- Correspondence: (J.L.); (H.M.); Tel.: +86-432-6480-7283 (J.L. & H.M.); Fax: +86-432-6480-6201 (J.L. & H.M.)
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Abstract
Taste is of five basic types, namely, sourness, saltiness, sweetness, bitterness and umami. In this review, we focus on a potentiometric taste sensor that we developed and fabricated using lipid polymer membranes. The taste sensor can measure the taste perceived by humans and is called an electronic tongue with global selectivity, which is the property to discriminate taste qualities and quantify them without discriminating each chemical substance. This property is similar to the gustatory system; hence, the taste sensor is a type of biomimetic device. In this paper, we first explain the sensing mechanism of the taste sensor, its application to beer evaluation and the measurement mechanism. Second, results recently obtained are introduced; i.e., the application of the senor to high-potency sweeteners and the improvement of the bitterness sensor are explained. Last, quantification of the bitterness-masking effect of high-potency sweeteners is explained using a regression analysis based on both the outputs of bitterness and sweetness sensors. The taste sensor provides a biomimetic method different from conventional analytical methods.
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Affiliation(s)
- Xiao Wu
- Research and Development Center for Five-Sense Devices, Kyushu University
| | - Yusuke Tahara
- Research and Development Center for Five-Sense Devices, Kyushu University
| | - Rui Yatabe
- Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University
| | - Kiyoshi Toko
- Research and Development Center for Five-Sense Devices, Kyushu University.,Institute for Advanced Study, Kyushu University
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Bonah E, Huang X, Aheto JH, Osae R. Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review. Journal of Food Science and Technology 2019; 57:1977-1990. [PMID: 32431324 DOI: 10.1007/s13197-019-04143-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 10/17/2019] [Accepted: 10/24/2019] [Indexed: 01/16/2023]
Abstract
Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.
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Affiliation(s)
- Ernest Bonah
- 1School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People's Republic of China.,Laboratory Services Department, Food and Drugs Authority, P. O. Box CT 2783, Cantonments - Accra, Ghana
| | - Xingyi Huang
- 1School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People's Republic of China
| | - Joshua Harrington Aheto
- 1School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People's Republic of China
| | - Richard Osae
- 1School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People's Republic of China
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Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork. J FOOD QUALITY 2019. [DOI: 10.1155/2019/4342509] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
An E-panel, comprising an electronic nose (E-nose) and an electronic tongue (E-tongue), was used to distinguish the organoleptic characteristics of minced mutton adulterated with different proportions of pork. Meanwhile, the normalization, stepwise linear discriminant analysis (step-LDA), and principle component analysis were employed to merge the data matrix of E-nose and E-tongue. The discrimination results were evaluated and compared by canonical discriminant analysis (CDA) and Bayesian discriminant analysis (BAD). It was shown that the capability of discrimination of the combined system (classification error 0%∼1.67%) was superior or equable to that obtained with the two instruments separately, and E-tongue system (classification error for E-tongue 0∼2.5%) obtained higher accuracy than E-nose (classification error 0.83%∼10.83% for E-nose). For the combined system, the combination of extracted data of 6 PCs of E-nose and 5 PCs of E-tongue was proved to be the most effective method. In order to predict the pork proportion in adulterated mutton, multiple linear regression (MLR), partial least square analysis (PLS), and backpropagation neural network (BPNN) regression models were used, and the results were compared, aiming at building effective predictive models. Good correlations were found between the signals obtained from E-tongue, E-nose, and fusion data of E-nose and E-tongue and proportions of pork in minced mutton with correlation coefficients higher than 0.90 in the calibration and validation data sets. And BPNN was proved to be the most effective method for the prediction of pork proportions with R2 higher than 0.97 both for the calibration and validation data set. These results indicated that integration of E-nose and E-tongue could be a useful tool for the detection of mutton adulteration.
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