Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing
In Baijiu brewing process, suppliers may adulterate glutinous sorghum with commercially inferior japonica sorghum, which can affect the yield and quality of the final Baijiu production. Currently, sorghum adulteration detection in Baijiu brewing process in China is carried out manually by sampling a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
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| Online Access: | https://ieeexplore.ieee.org/document/9826840/ |
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| _version_ | 1849304173661650944 |
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| author | Shanglin Yang Yang Lin Yong Li Defu Xu Suyi Zhang Lihui Peng |
| author_facet | Shanglin Yang Yang Lin Yong Li Defu Xu Suyi Zhang Lihui Peng |
| author_sort | Shanglin Yang |
| collection | DOAJ |
| description | In Baijiu brewing process, suppliers may adulterate glutinous sorghum with commercially inferior japonica sorghum, which can affect the yield and quality of the final Baijiu production. Currently, sorghum adulteration detection in Baijiu brewing process in China is carried out manually by sampling and observation, which strongly depends on the experiences of workers. In this paper, we proposed a method that uses sorghum images as input and combines image processing and deep neural networks to identify grain varieties and calculate the adulteration ratio. Two derivative networks of CNN, i.e., ResNet and SqueezeNet are used to implement the deep neural networks for sorghum grain identification and adulteration ratio calculation. The classification accuracy of the ResNet and SqueezeNet based models reached 93.34% and 87.98% on test set, respectively. The root mean squared error (RSME) for adulteration ratio estimation is 4.95% and 7.73%, respectively. The mean absolute error (MAE) is 4.20% and 6.29% accordingly. The proposed pipeline is capable of realizing rapid and non-destructive adulteration detection of raw materials in industrial production, thus conducing to the industrial digital transformation and efficiency improvement. |
| format | Article |
| id | doaj-art-516730df582642c8be0c0fbd5691c4fa |
| institution | Kabale University |
| issn | 2768-7236 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj-art-516730df582642c8be0c0fbd5691c4fa2025-08-20T03:55:48ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362022-01-0111810.1109/OJIM.2022.31900249826840Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu BrewingShanglin Yang0https://orcid.org/0000-0002-1591-0846Yang Lin1https://orcid.org/0000-0002-5124-8650Yong Li2https://orcid.org/0000-0002-8304-4089Defu Xu3https://orcid.org/0000-0002-3948-0692Suyi Zhang4https://orcid.org/0000-0002-1898-3805Lihui Peng5https://orcid.org/0000-0001-7363-6374Department of Automation, Tsinghua University, Beijing, ChinaLuzhou Laojiao Company Ltd., Luzhou, ChinaLuzhou Laojiao Company Ltd., Luzhou, ChinaLuzhou Laojiao Company Ltd., Luzhou, ChinaLuzhou Laojiao Company Ltd., Luzhou, ChinaDepartment of Automation, Tsinghua University, Beijing, ChinaIn Baijiu brewing process, suppliers may adulterate glutinous sorghum with commercially inferior japonica sorghum, which can affect the yield and quality of the final Baijiu production. Currently, sorghum adulteration detection in Baijiu brewing process in China is carried out manually by sampling and observation, which strongly depends on the experiences of workers. In this paper, we proposed a method that uses sorghum images as input and combines image processing and deep neural networks to identify grain varieties and calculate the adulteration ratio. Two derivative networks of CNN, i.e., ResNet and SqueezeNet are used to implement the deep neural networks for sorghum grain identification and adulteration ratio calculation. The classification accuracy of the ResNet and SqueezeNet based models reached 93.34% and 87.98% on test set, respectively. The root mean squared error (RSME) for adulteration ratio estimation is 4.95% and 7.73%, respectively. The mean absolute error (MAE) is 4.20% and 6.29% accordingly. The proposed pipeline is capable of realizing rapid and non-destructive adulteration detection of raw materials in industrial production, thus conducing to the industrial digital transformation and efficiency improvement.https://ieeexplore.ieee.org/document/9826840/Adulteration detectionbrewing processdeep learningimage processingmachine vision |
| spellingShingle | Shanglin Yang Yang Lin Yong Li Defu Xu Suyi Zhang Lihui Peng Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing IEEE Open Journal of Instrumentation and Measurement Adulteration detection brewing process deep learning image processing machine vision |
| title | Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing |
| title_full | Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing |
| title_fullStr | Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing |
| title_full_unstemmed | Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing |
| title_short | Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing |
| title_sort | deep neural network based sorghum adulteration detection in baijiu brewing |
| topic | Adulteration detection brewing process deep learning image processing machine vision |
| url | https://ieeexplore.ieee.org/document/9826840/ |
| work_keys_str_mv | AT shanglinyang deepneuralnetworkbasedsorghumadulterationdetectioninbaijiubrewing AT yanglin deepneuralnetworkbasedsorghumadulterationdetectioninbaijiubrewing AT yongli deepneuralnetworkbasedsorghumadulterationdetectioninbaijiubrewing AT defuxu deepneuralnetworkbasedsorghumadulterationdetectioninbaijiubrewing AT suyizhang deepneuralnetworkbasedsorghumadulterationdetectioninbaijiubrewing AT lihuipeng deepneuralnetworkbasedsorghumadulterationdetectioninbaijiubrewing |