Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network

Liu, Cunchuan; Chu, Zhaojie; Weng, Shizhuang; Zhu, Gongqin; Han, Kuang-Wei; Zhang, Zixi; Huang, Liusheng; Zhu, Zhen‐Gang; Zheng, Shouguo

doi:10.1016/j.foodchem.2022.132651

Cited by 43 publications

(20 citation statements)

References 38 publications

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“…They are often used for flavor evaluation and component analysis in the production of foods ( 44 ), flowers ( 45 ), and pharmaceuticals ( 46 ). In meat products, the electronic nose and electronic tongue are often used to evaluate the flavor and freshness ( 47 , 48 ). In this study, we used the electronic nose and electronic tongue to evaluate the flavor of goat meat.…”

Section: Discussionmentioning

confidence: 99%

Dietary supplementation with jasmine flower residue improves meat quality and flavor of goat

Wang

et al. 2023

Front. Nutr.

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Jasmine flower residue (JFR) is a by-product retained in the production process of jasmine tea and can be used as an unconventional feed due to its rich nutrient value. This study aimed to evaluate the effects of the addition of JFR to the diet of goats on their meat quality and flavor. Twenty-four castrated Nubian male goats were randomly divided into two groups and fed a mixed diet containing 10% JFR (JFR, n = 12) or a conventional diet (CON, n = 12) for 45 days. Meat quality and flavor were measured at the end of the treatment. The addition of JFR to the diet could reduce the shear force of the longissimus dorsi muscle, as well as, the cross-sectional area and diameter of muscle fibers, indicating that the addition of JFR improved meat quality. JFR also increased the content of glutamic acid and ω-3 polyunsaturated fatty acid (C18:3n3 and C20:5N3) and reduced the content of C24:1 and saturated fatty acid (C20:0 and C22:0). In addition, the use of JFR increased the content of acetaldehyde and hexanal in the meat. Furthermore, JFR introduced new volatile components in the meat. The umami, saltiness, and richness of the meat also improved. In conclusion, the addition of jasmine flower residue to the diet can improve the meat quality and flavor of goat.

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Section: Discussionmentioning

confidence: 99%

Dietary supplementation with jasmine flower residue improves meat quality and flavor of goat

Wang

et al. 2023

Front. Nutr.

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“…In addition, to further validate the feasibility of the HSI technique for predicting TVB-N, other techniques were used in some studies to build prediction models by fusing with the HSI technique. Liu et al [ 109 ] used a combination of electronic nose and HSI technique and proposed an input-modified convolution neural network (IMCNN) to predict the TVB-N content of lamb. The experimental results demonstrate that the fusion of e-nose and HSI techniques can achieve a more accurate TVB-N prediction of lamb, and IMCNN has good feature extraction and modeling capability for one-dimensional vector e-nose sensing data.…”

Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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“…Common technical methods used for mutton detection in recent years mainly include image processing, spectroscopic techniques combined with machine learning classification, and regressors. By extracting and analyzing multi-dimensional feature information such as color, texture, contour, protein, water content, and total volatile basic nitrogen (TVB-N) in mutton sample images, and establishing the relationship with mutton freshness [ 6 , 7 , 8 ], tenderness [ 9 , 10 , 11 ], authenticity [ 12 , 13 ], pH [ 14 , 15 ], storage time [ 16 , 17 ], and other indicators, these methods allow effective and nondestructive detection of mutton quality. Although the aforementioned technical methods can achieve high detection accuracy, they also have shortcomings such as cumbersome artificial extraction of sample features, poor generalization of models, and low adaptability, which are not suitable for the classification and detection of mutton with multiple categories, large quantities, and complex natural feature expression.…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer

Zhao

Bai

Wang

et al. 2023

Foods

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In order to realize the real-time classification and detection of mutton multi-part, this paper proposes a mutton multi-part classification and detection method based on the Swin-Transformer. First, image augmentation techniques are adopted to increase the sample size of the sheep thoracic vertebrae and scapulae to overcome the problems of long-tailed distribution and non-equilibrium of the dataset. Then, the performances of three structural variants of the Swin-Transformer (Swin-T, Swin-B, and Swin-S) are compared through transfer learning, and the optimal model is obtained. On this basis, the robustness, generalization, and anti-occlusion abilities of the model are tested and analyzed using the significant multiscale features of the lumbar vertebrae and thoracic vertebrae, by simulating different lighting environments and occlusion scenarios, respectively. Furthermore, the model is compared with five methods commonly used in object detection tasks, namely Sparser-CNN, YoloV5, RetinaNet, CenterNet, and HRNet, and its real-time performance is tested under the following pixel resolutions: 576 × 576, 672 × 672, and 768 × 768. The results show that the proposed method achieves a mean average precision (mAP) of 0.943, while the mAP for the robustness, generalization, and anti-occlusion tests are 0.913, 0.857, and 0.845, respectively. Moreover, the model outperforms the five aforementioned methods, with mAP values that are higher by 0.009, 0.027, 0.041, 0.050, and 0.113, respectively. The average processing time of a single image with this model is 0.25 s, which meets the production line requirements. In summary, this study presents an efficient and intelligent mutton multi-part classification and detection method, which can provide technical support for the automatic sorting of mutton as well as for the processing of other livestock meat.

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Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network

Cited by 43 publications

References 38 publications

Dietary supplementation with jasmine flower residue improves meat quality and flavor of goat

Dietary supplementation with jasmine flower residue improves meat quality and flavor of goat

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer

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