1
|
Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
Collapse
Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
| |
Collapse
|
2
|
Yu Y, Chen W, Zhang H, Liu R, Li C. Discrimination among Fresh, Frozen-Stored and Frozen-Thawed Beef Cuts by Hyperspectral Imaging. Foods 2024; 13:973. [PMID: 38611279 PMCID: PMC11011688 DOI: 10.3390/foods13070973] [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: 02/15/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 04/14/2024] Open
Abstract
The detection of the storage state of frozen meat, especially meat frozen-thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen-stored (F-S), frozen-thawed three times (F-T-3) and frozen-thawed five times (F-T-5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze-thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.
Collapse
Affiliation(s)
- Yuewen Yu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Wenliang Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Hanwen Zhang
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Rong Liu
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Chenxi Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
| |
Collapse
|
3
|
Shrivastava A, Nag MK. Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach. Surg Innov 2024; 31:58-70. [PMID: 38059371 DOI: 10.1177/15533506231220968] [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] [Indexed: 12/08/2023]
Abstract
Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.
Collapse
|
4
|
Wu X, Liang X, Wang Y, Wu B, Sun J. Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 2022; 11:foods11223713. [PMID: 36429304 PMCID: PMC9689883 DOI: 10.3390/foods11223713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.
Collapse
Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Xinyue Liang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yixuan Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| |
Collapse
|
5
|
Li Y, Man S, Ye S, Liu G, Ma L. CRISPR-Cas-based detection for food safety problems: Current status, challenges, and opportunities. Compr Rev Food Sci Food Saf 2022; 21:3770-3798. [PMID: 35796408 DOI: 10.1111/1541-4337.13000] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/23/2022] [Accepted: 05/26/2022] [Indexed: 12/12/2022]
Abstract
Food safety is one of the biggest public issues occurring around the world. Microbiological, chemical, and physical hazards can lead to food safety issues, which may occur at all stages of the supply chain. In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, and field-deployable detection methods meeting diverse requirements are one of the imperative measures for food safety assurance. CRISPR-Cas system, a newly emerging technology, has been successfully repurposed in biosensing and has demonstrated huge potential to establish conceptually novel detection methods with high sensitivity and specificity. This review focuses on CRISPR-Cas-based detection and its current status and huge potential specifically for food safety inspection. We firstly illustrate the pending problems in food safety and summarize the popular detection methods. We then describe the potential applications of CRISPR-Cas-based detection in food safety inspection. Finally, the challenges and futuristic opportunities are proposed and discussed. Generally speaking, the current food safety detection methods are still unsatisfactory in some ways such as being time-consuming, displaying unmet sensitivity and specificity standards, and there is a comparative paucity of multiplexed testing and POCT. Recent studies have shown that CRISPR-Cas-based biosensing is an innovative and fast-expanding technology, which could make up for the shortcomings of the existing methods or even replace them. To sum up, the implementation of CRISPR-Cas and the integration of CRISPR-Cas with other techniques is promising and desirable, which is expected to provide "customized" and "smart" detection methods for food safety inspection in the coming future.
Collapse
Affiliation(s)
- Yaru Li
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Industrial Microbiology, Ministry of Education, Tianjin Key Laboratory of Industry Microbiology, National and Local United Engineering Lab of Metabolic Control Fermentation Technology, China International Science and Technology Cooperation Base of Food Nutrition/Safety and Medicinal Chemistry, College of Biotechnology, Tianjin University of Science & Technology, Tianjin, China
| | - Shuli Man
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Industrial Microbiology, Ministry of Education, Tianjin Key Laboratory of Industry Microbiology, National and Local United Engineering Lab of Metabolic Control Fermentation Technology, China International Science and Technology Cooperation Base of Food Nutrition/Safety and Medicinal Chemistry, College of Biotechnology, Tianjin University of Science & Technology, Tianjin, China
| | - Shengying Ye
- Pharmacy Department, The 983th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Tianjin, China
| | - Guozhen Liu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
| | - Long Ma
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Industrial Microbiology, Ministry of Education, Tianjin Key Laboratory of Industry Microbiology, National and Local United Engineering Lab of Metabolic Control Fermentation Technology, China International Science and Technology Cooperation Base of Food Nutrition/Safety and Medicinal Chemistry, College of Biotechnology, Tianjin University of Science & Technology, Tianjin, China
| |
Collapse
|
6
|
Brunner A, Schmidt VM, Zelger B, Woess C, Arora R, Zelger P, Huck CW, Pallua J. Visible and Near-Infrared hyperspectral imaging (HSI) can reliably quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot study on formalin-fixed paraffin-embedded specimens from myocard obtained during autopsy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121092. [PMID: 35257987 DOI: 10.1016/j.saa.2022.121092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION To implement Hyperspectral Imaging (HSI) as a tool for quantifying inflammatory cells in tissue specimens by the example of myocarditis in a collective of forensic patients. MATERIAL AND METHODS 44 consecutive patients with suspected myocardial inflammation at autopsy, diagnosed between 2013 and 2018 at the Institute of ForensicMedicine, Medical University of Innsbruck, were selected for this study. Using the IMEC SNAPSCAN camera, visible and near infrared hyperspectral images were collected from slides stained with CD3 and CD45 to assess quantity and spatial distribution of positive cells. Results were compared with visual assessment (VA) and conventional digital image analysis (DIA). RESULTS Finally, specimens of 40 patients were evaluated, of whom 36 patients (90%) suffered from myocarditis, two patients (5%) had suspected healing/healed myocarditis, and two did no have myocarditis (5%). The amount of CD3 and CD45 positive cells did not differ significantly between VA, HSI, and DIA (pVA/HSI/DIA = 0.46 for CD3 and 0.81 for CD45). Coheńs Kappa showed a very high correlation between VA versus HSI, VA versus DIA, and HSI versus DIA for CD3 (Coheńs Kappa = 0.91, 1.00, and 0.91, respectively). For CD45 an almost as high correlation was seen for VA versus HSI and HSI versus DIA (Coheńs Kappa = 0.75 and 0.70) and VA versus DIA (Coheńs Kappa = 0.89). CONCLUSION HSI is a reliable and objective method to count inflammatory cells in tissue slides of suspected myocarditis. Implementation of HSI in digital pathology might further expand the possibility of a sophisticated method.
Collapse
Affiliation(s)
- A Brunner
- Innsbruck Medical University, Institute of Pathology, Neuropathology, and Molecular Pathology, Muellerstrasse 44, 6020 Innsbruck, Austria
| | - V M Schmidt
- Institute of Forensic Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria
| | - B Zelger
- Innsbruck Medical University, Institute of Pathology, Neuropathology, and Molecular Pathology, Muellerstrasse 44, 6020 Innsbruck, Austria
| | - C Woess
- Institute of Forensic Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria.
| | - R Arora
- University Hospital for Orthopedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - P Zelger
- University Clinic for Hearing, Voice and Speech Disorders, Medical University of Innsbruck, Anichstrasse 35, Innsbruck, Austria
| | - C W Huck
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, Innsbruck, Austria
| | - J Pallua
- Innsbruck Medical University, Institute of Pathology, Neuropathology, and Molecular Pathology, Muellerstrasse 44, 6020 Innsbruck, Austria; Institute of Forensic Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria; University Hospital for Orthopedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| |
Collapse
|
7
|
Fan KJ, Su WH. Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review. BIOSENSORS 2022; 12:bios12020076. [PMID: 35200337 PMCID: PMC8869398 DOI: 10.3390/bios12020076] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 05/12/2023]
Abstract
Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.
Collapse
|
8
|
Spyrelli ED, Papachristou CK, Nychas GJE, Panagou EZ. Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis. Foods 2021; 10:foods10112723. [PMID: 34829004 PMCID: PMC8624579 DOI: 10.3390/foods10112723] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 11/21/2022] Open
Abstract
Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.
Collapse
|
9
|
Zhang T, Chen C, Xie K, Wang J, Pan Z. Current State of Metabolomics Research in Meat Quality Analysis and Authentication. Foods 2021; 10:2388. [PMID: 34681437 PMCID: PMC8535928 DOI: 10.3390/foods10102388] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/23/2022] Open
Abstract
In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.
Collapse
Affiliation(s)
- Tao Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; (T.Z.); (C.C.); (K.X.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China;
| | - Can Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; (T.Z.); (C.C.); (K.X.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China;
| | - Kaizhou Xie
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; (T.Z.); (C.C.); (K.X.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China;
| | - Jinyu Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; (T.Z.); (C.C.); (K.X.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China;
| | - Zhiming Pan
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China;
- Jiangsu Key Laboratory of Zoonosis, Key Laboratory of Prevention and Control of Biological Hazard Factors (Animal Origin) for Agrifood Safety and Quality, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Yangzhou University, Yangzhou 225009, China
| |
Collapse
|
10
|
Banana spoilage benchmark determination method and early warning potential based on hyperspectral data during its storage. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00948-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
11
|
Shi Y, Wang X, Borhan MS, Young J, Newman D, Berg E, Sun X. A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies. Food Sci Anim Resour 2021; 41:563-588. [PMID: 34291208 PMCID: PMC8277176 DOI: 10.5851/kosfa.2021.e25] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 11/09/2022] Open
Abstract
Increasing meat demand in terms of both quality and quantity in conjunction with
feeding a growing population has resulted in regulatory agencies imposing
stringent guidelines on meat quality and safety. Objective and accurate rapid
non-destructive detection methods and evaluation techniques based on artificial
intelligence have become the research hotspot in recent years and have been
widely applied in the meat industry. Therefore, this review surveyed the key
technologies of non-destructive detection for meat quality, mainly including
ultrasonic technology, machine (computer) vision technology, near-infrared
spectroscopy technology, hyperspectral technology, Raman spectra technology, and
electronic nose/tongue. The technical characteristics and evaluation methods
were compared and analyzed; the practical applications of non-destructive
detection technologies in meat quality assessment were explored; and the current
challenges and future research directions were discussed. The literature
presented in this review clearly demonstrate that previous research on
non-destructive technologies are of great significance to ensure
consumers’ urgent demand for high-quality meat by promoting automatic,
real-time inspection and quality control in meat production. In the near future,
with ever-growing application requirements and research developments, it is a
trend to integrate such systems to provide effective solutions for various grain
quality evaluation applications.
Collapse
Affiliation(s)
- Yinyan Shi
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA.,College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Md Saidul Borhan
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
| | - Jennifer Young
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - David Newman
- Department of Animal Science, Arkansas State University, Jonesboro, AR 72467, USA
| | - Eric Berg
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Xin Sun
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
| |
Collapse
|
12
|
Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
Collapse
Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| |
Collapse
|
13
|
Tong Y, Ach T, Curcio CA, Smith RT. Hyperspectral autofluorescence characterization of drusen and sub-RPE deposits in age-related macular degeneration. ACTA ACUST UNITED AC 2021; 6. [PMID: 33791592 PMCID: PMC8009528 DOI: 10.21037/aes-20-12] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Background: Soft drusen and basal linear deposit (BLinD) are two forms of the same extracellular lipid rich material that together make up an Oil Spill on Bruch’s membrane (BrM). Drusen are focal and can be recognized clinically. In contrast BLinD is thin and diffusely distributed, and invisible clinically, even on highest resolution OCT, but has been detected on en face hyperspectral autofluorescence (AF) imaging ex vivo. We sought to optimize histologic hyperspectral AF imaging and image analysis for recognition of drusen and sub-RPE deposits (including BLinD and basal laminar deposit), for potential clinical application. Methods: Twenty locations specifically with drusen and 12 additional locations specifically from fovea, perifovea and mid-periphery from RPE/BrM flatmounts from 4 AMD donors underwent hyperspectral AF imaging with 4 excitation wavelengths (λex 436, 450, 480 and 505 nm), and the resulting image cubes were simultaneously decomposed with our published non-negative matrix factorization (NMF). Rank 4 recovery of 4 emission spectra was chosen for each excitation wavelength. Results: A composite emission spectrum, sensitive and specific for drusen and presumed sub-RPE deposits (the SDr spectrum) was recovered with peak at 510–520 nm in all tissues with drusen, with greatest amplitudes at excitations λex 436, 450 and 480 nm. The RPE spectra of combined sources Lipofuscin (LF)/Melanolipofuscin (MLF) were of comparable amplitude and consistently recapitulated the spectra S1, S2 and S3 previously reported from all tissues: tissues with drusen, foveal and extra-foveal locations. Conclusions: A clinical hyperspectral AF camera, with properly chosen excitation wavelengths in the blue range and a hyperspectral AF detector, should be capable of detecting and quantifying drusen and sub-RPE deposits, the earliest known lesions of AMD, before any other currently available imaging modality.
Collapse
Affiliation(s)
- Yuehong Tong
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Ach
- Department of Ophthalmology, University Hospital Bonn, Germany
| | - Christine A Curcio
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, AL, USA
| | - R Theodore Smith
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
14
|
Spyrelli ED, Ozcan O, Mohareb F, Panagou EZ, Nychas GJE. Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis. Curr Res Food Sci 2021; 4:121-131. [PMID: 33748779 PMCID: PMC7961306 DOI: 10.1016/j.crfs.2021.02.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 02/19/2021] [Accepted: 02/19/2021] [Indexed: 01/07/2023] Open
Abstract
The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n = 215) were conducted at 0, 5, 10, and 15 °C for up to 480 h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets. Poultry meat’s vulnerability to spoilage demands rapid quality assessment LWT-Food Sci. Technol.methods. FT-IR and MSI are non-invasive methods applied in a variety of meat products. SorfML is a web platform providing diverse machine learning algorithms. FT-IR analysis via lars predicted efficiently microbial loads of TVC.
Collapse
Affiliation(s)
- Evgenia D Spyrelli
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece
| | - Onur Ozcan
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK
| | - Fady Mohareb
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK
| | - Efstathios Z Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece
| | - George-John E Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece
| |
Collapse
|
15
|
Yang Y, Wang W, Zhuang H, Yoon SC, Jiang H. Prediction of quality traits and grades of intact chicken breast fillets by hyperspectral imaging. Br Poult Sci 2020; 62:46-52. [PMID: 32875810 DOI: 10.1080/00071668.2020.1817326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
1. In this study, hyperspectral imaging was evaluated for its usefulness to predict quality traits and grading of intact chicken breast fillets. 2. Lightness of colour (L*) and pH of the fillets were measured as quality traits, and samples were then selected and graded to three different quality categories, i.e., dark, firm and dry (DFD), normal (NORM), and pale, soft and exudative (PSE) based on these two quality traits. Based on the prediction performance of full wavelength partial least square regression (PLSR) models, the spectral range of visible and near-infrared (Vis-NIR) was more suitable for the evaluation of quality traits and grading than the range of near-infrared (NIR). Key wavelengths of each quality trait and grade value were selected by the regression coefficient (RC) method. 3. The new key wavelength PLSR models showed good predictive performances (Rp = 0.85 and RMSEp = 2.18 for L*, Rp = 0.84, and RMSEp = 0.13 for pH, and Rp = 0.80 and RMSEp = 0.44 for quality grading). The classification accuracy for grades was 85.71% (calibration set) and 81.82% (prediction set), respectively. Finally, distribution maps showed that quality traits and grades of samples were able to be visualised. 4. These results suggested that hyperspectral imaging has the potential for quality prediction of fresh chicken meat.
Collapse
Affiliation(s)
- Y Yang
- College of Engineering, China Agricultural University , Beijing, China
| | - W Wang
- College of Engineering, China Agricultural University , Beijing, China
| | - H Zhuang
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS , Athens, GA, USA
| | - S-C Yoon
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS , Athens, GA, USA
| | - H Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University , Nanjing, China
| |
Collapse
|
16
|
Spyrelli ED, Doulgeraki AI, Argyri AA, Tassou CC, Panagou EZ, Nychas GJE. Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products. Microorganisms 2020; 8:E552. [PMID: 32290382 PMCID: PMC7232414 DOI: 10.3390/microorganisms8040552] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 03/31/2020] [Accepted: 04/09/2020] [Indexed: 12/03/2022] Open
Abstract
The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.
Collapse
Affiliation(s)
- Evgenia D. Spyrelli
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855 Athens, Greece; (E.D.S.); (E.Z.P.)
| | - Agapi I. Doulgeraki
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization “Demeter”, Sof. Venizelou 1, Lycovrissi, 14123 Attica, Greece; (A.I.D.); (A.A.A.); (C.C.T.)
| | - Anthoula A. Argyri
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization “Demeter”, Sof. Venizelou 1, Lycovrissi, 14123 Attica, Greece; (A.I.D.); (A.A.A.); (C.C.T.)
| | - Chrysoula C. Tassou
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization “Demeter”, Sof. Venizelou 1, Lycovrissi, 14123 Attica, Greece; (A.I.D.); (A.A.A.); (C.C.T.)
| | - Efstathios Z. Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855 Athens, Greece; (E.D.S.); (E.Z.P.)
| | - George-John E. Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855 Athens, Greece; (E.D.S.); (E.Z.P.)
| |
Collapse
|
17
|
Li W, Zhang XY, Du J, Li YF, Chen YJ, Cao Y. RNA-seq-based quanitative transcriptome analysis of meat color and taste from chickens administered by eucalyptus leaf polyphenols extract. J Food Sci 2020; 85:1319-1327. [PMID: 32175699 DOI: 10.1111/1750-3841.15082] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/23/2020] [Accepted: 01/27/2020] [Indexed: 01/13/2023]
Abstract
To evaluate how eucalyptus leaf polyphenol extract (EPE) affects chicken meat color and taste, we added different levels of EPE (0%, 0.06%, 0.09%, and 0.12%) to chicken feed. The redness (a* value) and the myoglobin content of breast muscle in EPE group were remarkably higher. Furthermore, the guanosine monophosphate, histidine, and glycine muscle contents were also enhanced. Transcriptome analysis showed that 10 candidate genes related to meat quality were affected by EPE treatment. The identified genes, with functions critical to chicken meat color and taste, will help to determine the molecular mechanisms of EPE.
Collapse
Affiliation(s)
- Wei Li
- College of Food Science, South China Agricultural University, Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, Guangdong Research Center for Engineering Technology in Bioactive Natural Products, Guangzhou, 510642, China
| | - Xiao-Ying Zhang
- College of Food Science, South China Agricultural University, Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, Guangdong Research Center for Engineering Technology in Bioactive Natural Products, Guangzhou, 510642, China
| | - Jie Du
- College of Food Science, South China Agricultural University, Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, Guangdong Research Center for Engineering Technology in Bioactive Natural Products, Guangzhou, 510642, China
| | - Yi-Feng Li
- College of Food Science, South China Agricultural University, Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, Guangdong Research Center for Engineering Technology in Bioactive Natural Products, Guangzhou, 510642, China
| | - Yun-Jiao Chen
- College of Food Science, South China Agricultural University, Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, Guangdong Research Center for Engineering Technology in Bioactive Natural Products, Guangzhou, 510642, China
| | - Yong Cao
- College of Food Science, South China Agricultural University, Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, Guangdong Research Center for Engineering Technology in Bioactive Natural Products, Guangzhou, 510642, China
| |
Collapse
|
18
|
Fu X, Chen J. A Review of Hyperspectral Imaging for Chicken Meat Safety and Quality Evaluation: Application, Hardware, and Software. Compr Rev Food Sci Food Saf 2019; 18:535-547. [DOI: 10.1111/1541-4337.12428] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 01/09/2019] [Accepted: 01/10/2019] [Indexed: 01/13/2023]
Affiliation(s)
- Xiaping Fu
- Faculty of Mechanical Engineering and Automation; Zhejiang Sci-Tech Univ.; 928 Second Avenue, Xiasha Higher Education Zone Hangzhou 310018 China
| | - Jinchao Chen
- Faculty of Mechanical Engineering and Automation; Zhejiang Sci-Tech Univ.; 928 Second Avenue, Xiasha Higher Education Zone Hangzhou 310018 China
| |
Collapse
|
19
|
Su WH, Sun DW. Advanced Analysis of Roots and Tubers by Hyperspectral Techniques. ADVANCES IN FOOD AND NUTRITION RESEARCH 2018; 87:255-303. [PMID: 30678816 DOI: 10.1016/bs.afnr.2018.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
Collapse
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland.
| |
Collapse
|
20
|
Vargas E, Espitia O, Arguello H, Tourneret JY. Spectral Image Fusion from Compressive Measurements. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 28:2271-2282. [PMID: 30507510 DOI: 10.1109/tip.2018.2884081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This work introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different CS imagers, allow the quality of the proposed fusion method to be appreciated.
Collapse
|
21
|
Su WH, Sun DW. Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review. Compr Rev Food Sci Food Saf 2017; 17:104-122. [DOI: 10.1111/1541-4337.12314] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 09/12/2017] [Accepted: 09/14/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| |
Collapse
|
22
|
Grading of Chinese Cantonese Sausage Using Hyperspectral Imaging Combined with Chemometric Methods. SENSORS 2017; 17:s17081706. [PMID: 28757578 PMCID: PMC5579875 DOI: 10.3390/s17081706] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 07/20/2017] [Accepted: 07/24/2017] [Indexed: 12/02/2022]
Abstract
Fast and accurate grading of Chinese Cantonese sausage is an important concern for customers, organizations, and the industry. Hyperspectral imaging in the spectral range of 874–1734 nm, combined with chemometric methods, was applied to grade Chinese Cantonese sausage. Three grades of intact and sliced Cantonese sausages were studied, including the top, first, and second grades. Support vector machine (SVM) and random forests (RF) techniques were used to build two different models. Second derivative spectra and RF were applied to select optimal wavelengths. The optimal wavelengths were the same for intact and sliced sausages when selected from second derivative spectra, while the optimal wavelengths for intact and sliced sausages selected using RF were quite similar. The SVM and RF models, using full spectra and the optimal wavelengths, obtained acceptable results for intact and sliced sausages. Both models for intact sausages performed better than those for sliced sausages, with a classification accuracy of the calibration and prediction set of over 90%. The overall results indicated that hyperspectral imaging combined with chemometric methods could be used to grade Chinese Cantonese sausages, with intact sausages being better suited for grading. This study will help to develop fast and accurate online grading of Cantonese sausages, as well as other sausages.
Collapse
|
23
|
Raman imaging for food quality and safety evaluation: Fundamentals and applications. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.01.012] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
24
|
Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0475-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
25
|
Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.05.006] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
26
|
Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats. Lebensm Wiss Technol 2015. [DOI: 10.1016/j.lwt.2014.10.021] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|