1
|
Djenane D, Aider M. The one-humped camel: The animal of future, potential alternative red meat, technological suitability and future perspectives. F1000Res 2024; 11:1085. [PMID: 38798303 PMCID: PMC11128057 DOI: 10.12688/f1000research.125246.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 05/29/2024] Open
Abstract
The 2020 world population data sheet indicates that world population is projected to increase from 7.8 billion in 2020 to 9.9 billion by 2050 (Increase of more than 25%). Due to the expected growth in human population, the demand for meats that could improve health status and provide therapeutic benefits is also projected to rise. The dromedary also known as the Arabian camel, or one-humped camel ( Camelus dromedarius), a pseudo ruminant adapted to arid climates, has physiological, biological and metabolic characteristics which give it a legendary reputation for surviving in the extreme conditions of desert environments considered restrictive for other ruminants. Camel meat is an ethnic food consumed across the arid regions of Middle East, North-East Africa, Australia and China. For these medicinal and nutritional benefits, camel meat can be a great option for sustainable meat worldwide supply. A considerable amount of literature has been published on technological aspects and quality properties of beef, lamb and pork but the information available on the technological aspects of the meat of the one humped camel is very limited. Camels are usually raised in less developed countries and their meat is as nutritionally good as any other traditional meat source. Its quality also depends on the breed, sex, age, breeding conditions and type of muscle consumed. A compilation of existing literature related to new technological advances in packaging, shelf-life and quality of camel meat has not been reviewed to the best of our knowledge. Therefore, this review attempts to explore the nutritional composition, health benefits of camel meat, as well as various technological and processing interventions to improve its quality and consumer acceptance. This review will be helpful for camel sector and highlight the potential for global marketability of camel meat and to generate value added products.
Collapse
Affiliation(s)
- Djamel Djenane
- Laboratory of Food Quality and Food Safety, Department of Food Science and Technology., University of Mouloud MAMMERI, Tizi-Ouzou, 15000, Algeria
| | - Mohammed Aider
- Department of Soil Sciences and Agri-Food Engineering, Université Laval, Quebec City, QC, Canada
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec City, QC, Canada
| |
Collapse
|
2
|
Tejerina D, Oliván M, García-Torres S, Franco D, Sierra V. Use of Near-Infrared Spectroscopy to Discriminate DFD Beef and Predict Meat Quality Traits in Autochthonous Breeds. Foods 2022. [PMCID: PMC9601313 DOI: 10.3390/foods11203274] [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] [Indexed: 11/16/2022] Open
Abstract
The potential of near-infrared reflectance spectroscopy (NIRS) to discriminate Normal and DFD (dark, firm, and dry) beef and predict quality traits in 129 Longissimus thoracis (LT) samples from three Spanish purebreeds, Asturiana de los Valles (AV; n = 50), Rubia Gallega (RG; n = 37), and Retinta (RE; n = 42) was assessed. The results obtained by partial least squares-discriminant analysis (PLS-DA) indicated successful discrimination between Normal and DFD samples of meat from AV and RG (with sensitivity over 93% for both and specificity of 100 and 72%, respectively), while RE and total sample sets showed poorer results. Soft independent modelling of class analogies (SIMCA) showed 100% sensitivity for DFD meat in total, AV, RG, and RE sample sets and over 90% specificity for AV, RG, and RE, while it was very low for the total sample set (19.8%). NIRS quantitative models by partial least squares regression (PLSR) allowed reliable prediction of color parameters (CIE L*, a*, b*, hue, chroma). Results from qualitative and quantitative assays are interesting in terms of early decision making in the meat production chain to avoid economic losses and food waste.
Collapse
Affiliation(s)
- David Tejerina
- Centro de Investigaciones Científicas y Tecnológicas de Extremadura (CICYTEX-La Orden), Junta de Extremadura, Guadajira, 06187 Badajoz, Spain
- Correspondence:
| | - Mamen Oliván
- Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Carretera AS-267, PK 19, 33300 Villaviciosa, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Avda. Roma s/n, 33011 Oviedo, Spain
| | - Susana García-Torres
- Centro de Investigaciones Científicas y Tecnológicas de Extremadura (CICYTEX-La Orden), Junta de Extremadura, Guadajira, 06187 Badajoz, Spain
| | - Daniel Franco
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, 32900 San Cibrao das Viñas, Spain
- Department of Chemical Engineering, Campus Vida, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Verónica Sierra
- Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Carretera AS-267, PK 19, 33300 Villaviciosa, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Avda. Roma s/n, 33011 Oviedo, Spain
| |
Collapse
|
3
|
Alvarenga TI, Hopkins DL, Morris S, McGilchrist P, Fowler SM. Intramuscular fat prediction of the semimembranosus muscle in hot lamb carcases using NIR. Meat Sci 2021; 181:108404. [DOI: 10.1016/j.meatsci.2020.108404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/27/2020] [Accepted: 11/29/2020] [Indexed: 11/25/2022]
|
4
|
Leighton PL, Segura JD, Lam SD, Marcoux M, Wei X, Lopez-Campos OD, Soladoye P, Dugan ME, Juarez M, PRIETO NURIA. Prediction of carcass composition and meat and fat quality using sensing technologies: A review. MEAT AND MUSCLE BIOLOGY 2021. [DOI: 10.22175/mmb.12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Consumer demand for high-quality healthy food is increasing, thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.
Collapse
|
5
|
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
|
6
|
Patel N, Toledo-Alvarado H, Bittante G. Performance of different portable and hand-held near-infrared spectrometers for predicting beef composition and quality characteristics in the abattoir without meat sampling. Meat Sci 2021; 178:108518. [PMID: 33866264 DOI: 10.1016/j.meatsci.2021.108518] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 04/02/2021] [Accepted: 04/05/2021] [Indexed: 11/18/2022]
Abstract
The availability of portable and handheld NIR instruments on the market opens up new possibilities in meat analysis. However, there is lack of research comparing different NIR instruments for evaluating beef characteristics from spectra obtained directly on the meat surface. Our aim, therefore, was to build and test calibration and prediction models for predicting beef characteristics, and to compare the performances of three NIR instruments differing in size and characteristics: a transportable visible-NIR spectrometer (Vis-NIRS), a portable (NIRS), and a hand-held Micro-NIRS. Spectra were collected from 178 beef samples (Longissimus thoracis muscle) from the meat surface in the abattoir. The spectra were subjected to different mathematical pretreatments then partial least square regressions. The results showed that all instruments predicted dry matter, protein and lipids with R2VAL 0.23 to 0.70; pH and cooking loss R2VAL 0.19 to 0.25; and color R2VAL 0.35 to 0.77. Overall, the prediction performances of the three instruments were similar, although Micro-NIRS performed better in some respects.
Collapse
Affiliation(s)
- Nageshvar Patel
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, School of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Ciudad Universitaria, 04510 Mexico City, Mexico
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell'Università 16, 35020 Legnaro (PD), Italy
| |
Collapse
|
7
|
Patel N, Toledo-Alvarado H, Cecchinato A, Bittante G. Predicting the Content of 20 Minerals in Beef by Different Portable Near-Infrared (NIR) Spectrometers. Foods 2020; 9:E1389. [PMID: 33019621 PMCID: PMC7600663 DOI: 10.3390/foods9101389] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/24/2020] [Accepted: 09/27/2020] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to test the predictability of a detailed mineral profile of beef using different portable near-infrared spectrometers (NIRS). These devices are rapid, chemical waste-free, cheap, nondestructive tools that can be used directly on the meat surface in the work environment without the need to take samples. We compared a transportable Visible-NIRS (weight 5.6 kg; wavelength 350-1830 nm), a portable NIRS (2.0 kg; 950-1650 nm), and a hand-held Micro-NIRS (0.06 kg; 905-1649 nm) to predict the contents of 20 minerals (measured by ICP-OES) in 178 beef samples (Longissimus thoracis muscle) using different mathematical pretreatments of the spectra and partial least square regressions. The externally validated results show that Fe, P, Mg, S, Na, and Pb have some potential for prediction with all instruments (R2VAL: 0.40-0.83). Overall, the prediction performances of the three instruments were similar, although the smallest (Micro-NIRS) exhibited certain advantages.
Collapse
Affiliation(s)
- Nageshvar Patel
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy; (H.T.-A.); (A.C.); (G.B.)
| | | | | | | |
Collapse
|
8
|
Current Advances in Meat Nutritional, Sensory and Physical Quality Improvement. Foods 2020; 9:foods9030321. [PMID: 32164289 PMCID: PMC7143788 DOI: 10.3390/foods9030321] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 03/09/2020] [Indexed: 01/06/2023] Open
Abstract
Meat is an important source of proteins, vitamins, minerals and fat, and these nutrients are important for their beneficial effects on human health. In recent years, meat quality has become a more relevant topic for consumers with regard to health and sensory characteristics, and for beef industry stakeholders because it affects their profitability. Therefore, the control of meat quality, including technological, sensory and nutritional quality traits, constitutes an important target for any farm animal production. What those qualities are and how we best evaluate them at the different levels of the continuum from the farm to fork are critical to understanding meat production and consumption patterns. However, despite the efforts of the industrials to control the eating and nutritional quality traits of meat and meat products, there remains a high level of variability, which is one reason for consumer dissatisfaction. This Special Issue focuses on the study of continuum aspects from farm to fork, which would have an impact on the control of the nutritional, sensory and technological aspects of carcass, muscle, meat and meat-product qualities. It groups fourteen original studies and one comprehensive review within five main topics that are (i) production systems and rearing practices, (ii) prediction of meat qualities, (iii) statistical approaches for meat quality prediction/management, (iv) muscle biochemistry and proteomics techniques and (v) consumer acceptability, development and characterisation of meat products.
Collapse
|