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Yu J, Li X, Qi X, Ding Z, Su S, Yu L, Zhou L, Li Y. Translatomics reveals the role of dietary calcium addition in regulating muscle fat deposition in pigs. Sci Rep 2024; 14:12295. [PMID: 38811812 PMCID: PMC11136974 DOI: 10.1038/s41598-024-62986-0] [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: 02/15/2024] [Accepted: 05/23/2024] [Indexed: 05/31/2024] Open
Abstract
Intramuscular fat (IMF) in pork holds significant importance for economic performance within the pig industry and dietary calcium supplementation enhances the accumulation of intramuscular fat. Additionally, calcium ions inhibit translation and reduce protein synthesis. However, the mechanism by which calcium regulates IMF deposition in muscle through translation remains largely unknown. In this study, we compared the ribosome profiles of the longissimus dorsi muscles of Duroc × Landrace × Large white pigs from the normal calcium (NC) group or calcium supplement (HC) group by Ribo-seq, and RNA-seq. By integrating multiple-omics analysis, we further discovered 437 genes that were transcriptionally unchanged but translationally altered and these genes were significantly enriched in the oxidative phosphorylation signaling pathway. Furthermore, experimental data showed that inhibiting the expression of COX10 and mtND4L increased triglyceride accumulation in C2C12 cells, providing new targets for intramuscular fat deposition. Finally, this work links dietary calcium, translation regulation and IMF deposition, providing a new strategy for both meat quality and economic performance within the pig industry.
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Affiliation(s)
- Jingsu Yu
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China
| | - Xiangling Li
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China
| | - Xinyu Qi
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China
| | - Zhaoxuan Ding
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China
| | - Songtao Su
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China
| | - Lin Yu
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China
| | - Lei Zhou
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China.
| | - Yixing Li
- Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Zhuang Autonomous Region, China.
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2
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Shen Y, Chen Y, Zhang S, Wu Z, Lu X, Liu W, Liu B, Zhou X. Smartphone-based digital phenotyping for genome-wide association study of intramuscular fat traits in longissimus dorsi muscle of pigs. Anim Genet 2024; 55:230-237. [PMID: 38290559 DOI: 10.1111/age.13401] [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: 12/11/2023] [Revised: 12/11/2023] [Accepted: 01/17/2024] [Indexed: 02/01/2024]
Abstract
Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time-consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image-based IMF traits through embedded deep-learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White × Tongcheng pigs. Genome-wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user-friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.
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Affiliation(s)
- Yang Shen
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yuxi Chen
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Shufeng Zhang
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Ze Wu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Xiaoyu Lu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Weizhen Liu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Bang Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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3
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Xu Z, Han Y, Zhao D, Li K, Li J, Dong J, Shi W, Zhao H, Bai Y. Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies. Foods 2024; 13:469. [PMID: 38338604 PMCID: PMC10855881 DOI: 10.3390/foods13030469] [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: 01/05/2024] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.
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Affiliation(s)
- Zeyu Xu
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Yu Han
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Dianbo Zhao
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Ke Li
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Junguang Li
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Junyi Dong
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
| | - Wenbo Shi
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
| | - Huijuan Zhao
- Henan Lianduoduo Supply Chain Management Co., Ltd., Hebi 458000, China;
| | - Yanhong Bai
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
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4
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Čandek-Potokar M, Lebret B, Gispert M, Font-I-Furnols M. Challenges and future perspectives for the European grading of pig carcasses - A quality view. Meat Sci 2024; 208:109390. [PMID: 37977057 DOI: 10.1016/j.meatsci.2023.109390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 11/03/2023] [Accepted: 11/05/2023] [Indexed: 11/19/2023]
Abstract
This study sought to evaluate pig carcass grading, describing the existing approaches and definitions, and highlighting the vision for overall quality grading. In particular, the current state of pig carcass grading in the European Union (SEUROP system), its weaknesses, and the challenges to achieve more uniformity and harmonization across member states were described, and a broader understanding of pig carcass value, which includes a vision for the inclusion of meat quality aspects in the grading, was discussed. Finally, the noninvasive methods for the on-line evaluation of pig carcass and meat quality (hereafter referred to as pork quality), and the conditions for their application were discussed. As the way pigs are raised (especially in terms of animal welfare and environmental impact), and more importantly, their perception of pork quality, is becoming increasingly important to consumers, the ideal grading of pigs should comprise pork quality aspects. As a result, a forward-looking "overall quality" approach to pork grading was proposed herein, in which grading systems would be based on the shared vision for pork quality (carcass and meat quality) among stakeholders in the pig industry and driven by consumer expectations with respect to the product. Emerging new technologies provide the technical foundation for such perspective; however, integrating all knowledge and technologies for their practical application to an "overall quality" grading approach is a major challenge. Nonetheless, such approach aligns with the recent vision of Industry 5.0, i.e. a model for the next level of industrialization that is human-centric, resilient, and sustainable.
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Affiliation(s)
- Marjeta Čandek-Potokar
- Agricultural Institute of Slovenia (KIS), Hacquetova ulica 17, 1000 Ljubljana, Slovenia.
| | | | - Marina Gispert
- IRTA-Food Quality and Technology, Finca Camps i Armet, E-17121 Monells, Girona, Spain
| | - Maria Font-I-Furnols
- IRTA-Food Quality and Technology, Finca Camps i Armet, E-17121 Monells, Girona, Spain
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5
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Yan X, Liu S, Wang S, Cui J, Wang Y, Lv Y, Li H, Feng Y, Luo R, Zhang Z, Zhang L. Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging. Foods 2024; 13:424. [PMID: 38338559 PMCID: PMC10855435 DOI: 10.3390/foods13030424] [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: 11/17/2023] [Revised: 12/26/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.
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Affiliation(s)
- Xiuwei Yan
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Sijia Liu
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Songlei Wang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Jiarui Cui
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Yongrui Wang
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Yu Lv
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Hui Li
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Yingjie Feng
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Ruiming Luo
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Zhifeng Zhang
- College of Aquaculture, Huazhong Agricultural University, Wuhan 430070, China;
| | - Lei Zhang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
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6
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Zhang H, He Q, Yang C, Lu M, Liu Z, Zhang X, Li X, Dong C. Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:9684. [PMID: 38139529 PMCID: PMC10748152 DOI: 10.3390/s23249684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.
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Affiliation(s)
- Haowen Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Qinghai He
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China;
| | - Chongshan Yang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Min Lu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Zhongyuan Liu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Xiaojia Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China;
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
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7
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Xu J, Liu X, Geng H, Liu R, Li F, Ma J, Liu M, Liu B, Sun H, Ma S, Wang Z, Zhu X, Li D, Wang C, Shi Y, Cui Y. Alfalfa Silage Diet Improves Meat Quality by Remodeling the Intestinal Microbes of Fattening Pigs. Foods 2023; 12:3209. [PMID: 37685141 PMCID: PMC10486512 DOI: 10.3390/foods12173209] [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: 07/22/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Because the demand for pork is increasing, it is crucial to devise efficient and green methods to improve the quality and quantity of meat. This study investigated the improvement in pork quality after the inclusion of alfalfa meal or alfalfa silage in pig diet. Our results indicated that alfalfa silage improved meat quality more effectively in terms of water-holding capacity, drip loss, and marbling score. Besides, an alfalfa silage diet can affect the level of fatty acids and amino acids in pork. Further, alfalfa silage was found to improve meat quality by remodeling intestinal microbiota and altering the level of SCFAs, providing a viable option for improving meat quality through forage.
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Affiliation(s)
- Junying Xu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Xiao Liu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Hongmin Geng
- National Engineering Research Center of Wheat and Corn Further Processing, Henan University of Technology, Zhengzhou 450002, China
| | - Rui Liu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Fang Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Jixiang Ma
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Mengqi Liu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Boshuai Liu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Hao Sun
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
| | - Sen Ma
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
| | - Zhichang Wang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
| | - Xiaoyan Zhu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
| | - Defeng Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
| | - Chengzhang Wang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
| | - Yinghua Shi
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
| | - Yalei Cui
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China; (J.X.)
- Henan Key Laboratory of Innovation and Utilization of Grassland Resources, Zhengzhou 450002, China
- Henan Forage Engineering Technology Research Center, Zhengzhou 450002, China
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8
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Wang Y, Zhang H, Yan E, He L, Guo J, Zhang X, Yin J. Carcass and meat quality traits and their relationships in Duroc × Landrace × Yorkshire barrows slaughtered at various seasons. Meat Sci 2023; 198:109117. [PMID: 36689802 DOI: 10.1016/j.meatsci.2023.109117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023]
Abstract
To understand characteristics of carcass traits and meat quality in pig population, 22 indicators of carcass characteristics and meat quality traits were measured on 278 Duroc × Landrace × Yorkshire barrows that were slaughtered in different seasons (spring, summer, autumn and winter). The effects of body weight and season on carcass characteristics and meat quality were analyzed by GLM procedure, followed the Bonferroni multiple test. The phenotypic correlations among those traits were calculated by employing the CORR procedure. In addition, the linear regression equations were constructed by stepwise regression model in REG procedure. The results showed that pigs slaughtered in spring had the heaviest body weight among the four seasons (P < 0.05), pigs slaughtered in summer had the lowest backfat depth and shear force (P < 0.05), and pigs slaughtered in winter had the lowest drip loss (P < 0.05). The results showed more variation in backfat depth, drip loss, intramuscular fat content, and shear force, compared with other indicators across pigs. Body weight had a significant association with loin eye area, average backfat depth and L⁎24 h (P < 0.05). Furthermore, regression equations for drip loss, cooking loss, shear force, and intramuscular fat content were constructed using more accessible indicators. Collectively, this study provided an overall view of carcass and meat quality traits in a commercial pig population in China, and illustrated that season significantly affected carcass characteristics and meat quality traits independently of body weight.
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Affiliation(s)
- Yubo Wang
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Hailiang Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Enfa Yan
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Linjuan He
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jianxin Guo
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xin Zhang
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jingdong Yin
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
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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.
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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
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Do DN, Hu G, Davoudi P, Shirzadifar A, Manafiazar G, Miar Y. Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources. Animals (Basel) 2022; 12:ani12182386. [PMID: 36139246 PMCID: PMC9495069 DOI: 10.3390/ani12182386] [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: 06/13/2022] [Revised: 09/08/2022] [Accepted: 09/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Aleutian disease (AD) is a major infectious disease found in mink farms, and it causes financial losses to the mink industry. Controlling AD often requires a counterimmunoelectrophoresis (CIEP) method, which is relatively expensive for mink farmers. Therefore, predicting AD infected mink without using CIEP records will be important for controlling AD in mink farms. In the current study, we applied nine machine learning algorithms to classify AD-infected mink. We indicated that the random forest could be used to classify AD-infected mink (accuracy of 0.962) accurately. This result could be used for implementing machine learning in controlling AD in the mink farms. Abstract American mink (Neogale vison) is one of the major sources of fur for the fur industries worldwide, whereas Aleutian disease (AD) is causing severe financial losses to the mink industry. A counterimmunoelectrophoresis (CIEP) method is commonly employed in a test-and-remove strategy and has been considered a gold standard for AD tests. Although machine learning is widely used in livestock species, little has been implemented in the mink industry. Therefore, predicting AD without using CIEP records will be important for controlling AD in mink farms. This research presented the assessments of the CIEP classification using machine learning algorithms. The Aleutian disease was tested on 1157 individuals using CIEP in an AD-positive mink farm (Nova Scotia, Canada). The comprehensive data collection of 33 different features was used for the classification of AD-infected mink. The specificity, sensitivity, accuracy, and F1 measure of nine machine learning algorithms were evaluated for the classification of AD-infected mink. The nine models were artificial neural networks, decision tree, extreme gradient boosting, gradient boosting method, K-nearest neighbors, linear discriminant analysis, support vector machines, naive bayes, and random forest. Among the 33 tested features, the Aleutian mink disease virus capsid protein-based enzyme-linked immunosorbent assay was found to be the most important feature for classifying AD-infected mink. Overall, random forest was the best-performing algorithm for the current dataset with a mean sensitivity of 0.938 ± 0.003, specificity of 0.986 ± 0.005, accuracy of 0.962 ± 0.002, and F1 value of 0.961 ± 0.088, and across tenfold of the cross-validation. Our work demonstrated that it is possible to use the random forest algorithm to classify AD-infected mink accurately. It is recommended that further model tests in other farms need to be performed and the genomic information needs to be used to optimize the model for implementing machine learning methods for AD detection.
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Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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