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Wang R, Wang X, Liu B, Zhang L, Li J, Chen D, Ma Y, He H, Liu J, Liu Y, Zhang Y. Estimation of Genetic Parameters of Early Growth Traits in Dumeng Sheep. Animals (Basel) 2024; 14:2298. [PMID: 39199832 PMCID: PMC11350660 DOI: 10.3390/ani14162298] [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: 07/14/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 09/01/2024] Open
Abstract
This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits.
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Affiliation(s)
- Ruijun Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China; (R.W.); (X.W.); (B.L.); (L.Z.); (J.L.)
| | - Xinle Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China; (R.W.); (X.W.); (B.L.); (L.Z.); (J.L.)
| | - Baodong Liu
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China; (R.W.); (X.W.); (B.L.); (L.Z.); (J.L.)
| | - Lifei Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China; (R.W.); (X.W.); (B.L.); (L.Z.); (J.L.)
| | - Jing Li
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China; (R.W.); (X.W.); (B.L.); (L.Z.); (J.L.)
| | - Dayong Chen
- Inner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab 011800, China; (D.C.); (Y.M.); (H.H.); (J.L.)
| | - Yunhui Ma
- Inner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab 011800, China; (D.C.); (Y.M.); (H.H.); (J.L.)
| | - Huijie He
- Inner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab 011800, China; (D.C.); (Y.M.); (H.H.); (J.L.)
| | - Jie Liu
- Inner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab 011800, China; (D.C.); (Y.M.); (H.H.); (J.L.)
| | - Yongbin Liu
- School of Life Sciences, Inner Mongolia University, Hohhot 010020, China
| | - Yanjun Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China; (R.W.); (X.W.); (B.L.); (L.Z.); (J.L.)
- Key Laboratory of Mutton Sheep Genetics and Breeding, Ministry of Agriculture, Hohhot 010018, China
- Key Laboratory of Goat and Sheep Genetics, Breeding and Reproduction, Inner Mongolia Autonomous Region, Hohhot 010018, China
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [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: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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Wang S, Zhu R, Huang Z, Zheng M, Yao X, Jiang X. Synergetic application of thermal imaging and CCD imaging techniques to detect mutton adulteration based on data-level fusion and deep residual network. Meat Sci 2023; 204:109281. [PMID: 37467680 DOI: 10.1016/j.meatsci.2023.109281] [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: 07/04/2022] [Revised: 07/07/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
To improve the performance of single thermal imaging and single CCD imaging in detecting unknown adulterated meat samples, these two imaging techniques combined with a deep residual network were synergistically applied to detect mutton adulteration. Considering the importance of spatial and detailed information in improving stability and accuracy, three data-level fusion methods, namely, colour image stitching, grey image stitching and grey channel stacking, were proposed for the fusion of thermal images and CCD images. Classification and prediction models were further developed based on fusion images. The results showed that the models with colour image stitching achieved the best performance. For the external validation set, the accuracy of the best classification model in discriminating five categories was 99.30%. In predicting pork proportions, the R2, RMSE, RPD and RER of the best prediction model were 0.9717, 0.0238, 7.8696 and 21.28, respectively. The best prediction model for duck proportions had a R2 of 0.9616, RMSE of 0.0277, RPD of 5.1015, and RER of 14.44. Therefore, the synergetic application of thermal imaging and CCD imaging can provide a novel and promising tool to detect mutton adulteration and the quality of other food items.
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Affiliation(s)
- Shichang Wang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, Xinjiang, China
| | - Rongguang Zhu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, Xinjiang, China; Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China.
| | - Zhongtao Huang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, Xinjiang, China
| | - Minchong Zheng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, Xinjiang, China
| | - Xuedong Yao
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, Xinjiang, China
| | - Xunpeng Jiang
- Bluestar Adisseo Nanjing Co. Ltd, Nanjing 210000, Jiangsu, China
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Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image. Foods 2023; 12:foods12061178. [PMID: 36981105 PMCID: PMC10048714 DOI: 10.3390/foods12061178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/14/2023] Open
Abstract
The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.
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Xu Y, Kou J, Zhang Q, Tan S, Zhu L, Geng Z, Yang X. Visual Detection of Water Content Range of Seabuckthorn Fruit Based on Transfer Deep Learning. Foods 2023; 12:foods12030550. [PMID: 36766080 PMCID: PMC9914117 DOI: 10.3390/foods12030550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/14/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
To realize the classification of sea buckthorn fruits with different water content ranges, a convolution neural network (CNN) detection model of sea buckthorn fruit water content ranges was constructed. In total, 900 images of seabuckthorn fruits with different water contents were collected from 720 seabuckthorn fruits. Eight classic network models based on deep learning were used as feature extraction for transfer learning. A total of 180 images were randomly selected from the images of various water content ranges for testing. Finally, the identification accuracy of the network model for the water content range of seabuckthorn fruit was 98.69%, and the accuracy on the test set was 99.4%. The program in this study can quickly identify the moisture content range of seabuckthorn fruit by collecting images of the appearance and morphology changes during the drying process of seabuckthorn fruit. The model has a good detection effect for seabuckthorn fruits with different moisture content ranges with slight changes in characteristics. The migration deep learning can also be used to detect the moisture content range of other agricultural products, providing technical support for the rapid nondestructive testing of moisture contents of agricultural products.
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Affiliation(s)
- Yu Xu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Jinmei Kou
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832000, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832000, China
| | - Shudan Tan
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Lichun Zhu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Zhihua Geng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832000, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832000, China
- Correspondence:
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