基于残差网络模型的速溶全脂奶粉分散性与堆积密度检测方法
TS207.3; 针对传统的奶粉品质国际标准检测方法中存在的主观性和滞后性等问题,本研究提出了一种基于残差网络(residual network,ResNet)的奶粉分散性和堆积密度的快速分类检测方法.在本研究中,使用的数据集包括499张在10倍光学显微镜下拍摄的速溶全脂奶粉颗粒微观分布图像,这些图像来自10个不同的样本组.首先,按照国际标准方法检测这10组样本的分散性和堆积密度,进而基于测试结果划分不同的分散性和堆积密度级别.随后,利用这些微观图像对ResNet模型进行训练,以实现对不同样本的有效分类.最终,通过分类结果预测速溶全脂奶粉的分散性、松散密度和振实密度.此外,本研究还对比了Res...
Saved in:
| Published in | 食品科学 Vol. 45; no. 10; pp. 9 - 18 |
|---|---|
| Main Authors | , , , , |
| Format | Magazine Article |
| Language | Chinese |
| Published |
江南大学人工智能与计算机学院,江苏无锡 214122%江南大学人工智能与计算机学院,江苏无锡 214122%江南大学未来食品科学中心,江苏无锡 214122
25.05.2024
武汉大学国家网络安全学院,湖北武汉 430072%嘉兴未来食品研究院,浙江嘉兴 314005 江南大学未来食品科学中心,江苏无锡 214122 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6630 |
| DOI | 10.7506/spkx1002-6630-20240129-262 |
Cover
| Abstract | TS207.3; 针对传统的奶粉品质国际标准检测方法中存在的主观性和滞后性等问题,本研究提出了一种基于残差网络(residual network,ResNet)的奶粉分散性和堆积密度的快速分类检测方法.在本研究中,使用的数据集包括499张在10倍光学显微镜下拍摄的速溶全脂奶粉颗粒微观分布图像,这些图像来自10个不同的样本组.首先,按照国际标准方法检测这10组样本的分散性和堆积密度,进而基于测试结果划分不同的分散性和堆积密度级别.随后,利用这些微观图像对ResNet模型进行训练,以实现对不同样本的有效分类.最终,通过分类结果预测速溶全脂奶粉的分散性、松散密度和振实密度.此外,本研究还对比了ResNet、EfficientNetV2和Swin Transformer等不同深度学习模型的预测效果.结果表明,基于ResNet 152的深度学习模型在预测速溶全脂奶粉的分散性、松散密度和振实密度方面表现最佳,其在测试集上的准确率分别达到97.50％、98.75％和95.00％.这些深度学习模型在奶粉品质检测中的出色性能不仅证明了该方法能够实时、准确地预测奶粉的分散性和堆积密度,同时也为奶粉品质的在线检测提供了新的技术途径. |
|---|---|
| AbstractList | TS207.3; 针对传统的奶粉品质国际标准检测方法中存在的主观性和滞后性等问题,本研究提出了一种基于残差网络(residual network,ResNet)的奶粉分散性和堆积密度的快速分类检测方法.在本研究中,使用的数据集包括499张在10倍光学显微镜下拍摄的速溶全脂奶粉颗粒微观分布图像,这些图像来自10个不同的样本组.首先,按照国际标准方法检测这10组样本的分散性和堆积密度,进而基于测试结果划分不同的分散性和堆积密度级别.随后,利用这些微观图像对ResNet模型进行训练,以实现对不同样本的有效分类.最终,通过分类结果预测速溶全脂奶粉的分散性、松散密度和振实密度.此外,本研究还对比了ResNet、EfficientNetV2和Swin Transformer等不同深度学习模型的预测效果.结果表明,基于ResNet 152的深度学习模型在预测速溶全脂奶粉的分散性、松散密度和振实密度方面表现最佳,其在测试集上的准确率分别达到97.50％、98.75％和95.00％.这些深度学习模型在奶粉品质检测中的出色性能不仅证明了该方法能够实时、准确地预测奶粉的分散性和堆积密度,同时也为奶粉品质的在线检测提供了新的技术途径. |
| Abstract_FL | To address the problems of the traditional international standard methods for milk powder quality detection such as subjectivity and lag,this study proposed a rapid method for the detection of the dispersibility and bulk density of milk powder based on residual network(ResNet).The dataset used in this study included 499 particle distribution images taken for 10 groups of instant whole milk powder samples under a 10 × optical microscope.Initially,these sample groups were tested for dispersibility and bulk density using the international standard methods,and classified into different levels of dispersibility and bulk density based on the test results.Subsequently,these microscopic images were used to train the ResNet to facilitate effective classification of different samples.Ultimately,the classification results were used to predict the dispersibility,loose density,and tapped density of instant whole milk powder.Additionally,this study compared the predictive performance of different deep learning models,including ResNet,EfficientNetV2,and Swin Transformer.The results indicated that the deep learning model based on ResNet 152 exhibited the best performance in predicting the dispersibility,loose density,and tapped density of instant whole milk powder,with accuracy rates of 97.50％,98.75％,and 95.00％,respectively for the test set.The exceptional performance of these deep learning models in milk powder quality detection not only proves that this method can predict the dispersibility and bulk density of milk powder in real time and accurately,but also provides a new technological approach for online quality detection of milk powder. |
| Author | 沈嵩 丁浩晗 崔晓晖 谢祯奇 王震宇 |
| AuthorAffiliation | 江南大学未来食品科学中心,江苏无锡 214122;江南大学人工智能与计算机学院,江苏无锡 214122%江南大学人工智能与计算机学院,江苏无锡 214122%江南大学未来食品科学中心,江苏无锡 214122;武汉大学国家网络安全学院,湖北武汉 430072%嘉兴未来食品研究院,浙江嘉兴 314005 |
| AuthorAffiliation_xml | – name: 江南大学未来食品科学中心,江苏无锡 214122;江南大学人工智能与计算机学院,江苏无锡 214122%江南大学人工智能与计算机学院,江苏无锡 214122%江南大学未来食品科学中心,江苏无锡 214122;武汉大学国家网络安全学院,湖北武汉 430072%嘉兴未来食品研究院,浙江嘉兴 314005 |
| Author_FL | WANG Zhenyu SHEN Song XIE Zhenqi DING Haohan CUI Xiaohui |
| Author_FL_xml | – sequence: 1 fullname: DING Haohan – sequence: 2 fullname: SHEN Song – sequence: 3 fullname: XIE Zhenqi – sequence: 4 fullname: CUI Xiaohui – sequence: 5 fullname: WANG Zhenyu |
| Author_xml | – sequence: 1 fullname: 丁浩晗 – sequence: 2 fullname: 沈嵩 – sequence: 3 fullname: 谢祯奇 – sequence: 4 fullname: 崔晓晖 – sequence: 5 fullname: 王震宇 |
| BookMark | eNo9kMtKw0AARWdRwVr7E4LL6Mwk81pK8QUFEXRdJpNEfJCKQXQZpBYlUHBRFyJWKdquWlCsGvFvOhP9C1sVV3dx4Z7LmQK5sBr6AMwgOMcIpPPR_u4xghBblNrQwhA7EGFhYYpzIP9fTIJiFG270EECORyxPFjXrXSYNkwv0S-97OMie7823Tt9k2RXta-4ZdKBPu1-1k70_SB7PNdnddNsm7gzfG3o23rW6et-XacPph2b58Rcvpmn5jSYCORe5Bf_sgA2lxY3SitWeW15tbRQtqKfO4p5Hvc5x1BRYiNIlEcRQr7wbGkTyRV3CHWEDARhrsCKSOQp7jImic9VwOwCmP3dPZJhIMOtyk718CAcEStjF2MDIw7E9jdV5HEY |
| ClassificationCodes | TS207.3 |
| ContentType | Magazine Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.7506/spkx1002-6630-20240129-262 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitle_FL | Detection of Dispersibility and Bulk Density of Instant Whole Milk Powder Based on Residual Network |
| EndPage | 18 |
| ExternalDocumentID | spkx202410002 |
| GrantInformation_xml | – fundername: (国家重点研发计划); (中央高校基本科研业务费专项资金资助项目); (教育部工程研究中心开放基金) funderid: (国家重点研发计划); (中央高校基本科研业务费专项资金资助项目); (教育部工程研究中心开放基金) |
| GroupedDBID | -02 2B. 4A8 5XA 5XC 92H 92I 93N ABJNI ACGFS ALMA_UNASSIGNED_HOLDINGS CCEZO CDRFL CW9 GROUPED_DOAJ PSX TCJ TGT U1G U5L |
| ID | FETCH-LOGICAL-s1002-c7dd8e8820c653105cd6111e9d3a35a8c845649af957b92c5a1dc8b77a5e8cf73 |
| ISSN | 1002-6630 |
| IngestDate | Thu May 29 04:00:14 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Keywords | 堆积密度 deep learning instant whole milk powder bulk density 深度学习 dispersibility 速溶全脂奶粉 分散性 residual network 残差网络 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1002-c7dd8e8820c653105cd6111e9d3a35a8c845649af957b92c5a1dc8b77a5e8cf73 |
| PageCount | 10 |
| ParticipantIDs | wanfang_journals_spkx202410002 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-05-25 |
| PublicationDateYYYYMMDD | 2024-05-25 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-05-25 day: 25 |
| PublicationDecade | 2020 |
| PublicationTitle | 食品科学 |
| PublicationTitle_FL | Food Science |
| PublicationYear | 2024 |
| Publisher | 江南大学人工智能与计算机学院,江苏无锡 214122%江南大学人工智能与计算机学院,江苏无锡 214122%江南大学未来食品科学中心,江苏无锡 214122 武汉大学国家网络安全学院,湖北武汉 430072%嘉兴未来食品研究院,浙江嘉兴 314005 江南大学未来食品科学中心,江苏无锡 214122 |
| Publisher_xml | – name: 江南大学未来食品科学中心,江苏无锡 214122 – name: 武汉大学国家网络安全学院,湖北武汉 430072%嘉兴未来食品研究院,浙江嘉兴 314005 – name: 江南大学人工智能与计算机学院,江苏无锡 214122%江南大学人工智能与计算机学院,江苏无锡 214122%江南大学未来食品科学中心,江苏无锡 214122 |
| SSID | ssib041914817 ssib048970456 ssj0000579004 ssib001105556 ssib051376496 |
| Score | 1.4351395 |
| Snippet | TS207.3; 针对传统的奶粉品质国际标准检测方法中存在的主观性和滞后性等问题,本研究提出了一种基于残差网络(residual network,ResNet)的奶粉分散性和堆积密度的快速分类检... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 9 |
| Title | 基于残差网络模型的速溶全脂奶粉分散性与堆积密度检测方法 |
| URI | https://d.wanfangdata.com.cn/periodical/spkx202410002 |
| Volume | 45 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw3V3Na9RQEA-lvejFb_wsPfi8SDQf7_OYbbMUQUFoobeSTbIKwiq2BempSC1KoeChHkSsUrQ9taBYdcX_psnqf-HMJOmmomg9eglv5838MvN-2ey8bN48y7qolJJtydt24iXK5m4K98GkndjSpHHkOLE2Ea5Gvn5Djk_ya1NiamDoUu2tpbnZ1pV4_pfrSv6FVZABr7hK9gDM7oGCANrALxyBYTj-FccsFMw0WSNgIcejDlkoWRAy3cCuhsJ2qFhjjBmXGg1mRklHs8Al80JZMQPmnIWGXn1oog4ANiTqaIH6oUYF7aEkENQFgB7ThnSgV6KVgV4fG4ATKHJMk2Mgd0hHoTxokqRJEoHnCsgcbMEQzy7IMQCEtiGJz4qdMqtkGr0FfUNQBgzdEtxQaMEYYFYXVOWIW2IHBGkMM6qvIikeTQ6hSr9HswaE4xG-qJwHD2vGYMOZ4RWsXzVk_cGKx_GdgGIRNn0V6KRuGYMGnhRBcxq7MoaSXaS5IFWU2CiBmHwkuD_QGlkPirEICVAi64V5CWjQHOLxRmsOaBxxTdyDFbCFauCJe9lzueuBtvgv3aXzBQE1xsjdP19XZQDQxiCbGNWBnKslA5gtQEbu1LOFovhpdVd0ar_9ppZEFjnFz-kJZOe0oeq9Ow_2sG289PBpsO2Vacn-8u-ojCr4PxykmkMeZC9u7dERTXtwl9t-TSyONRt1_7EF10bhzK36LFzIKnhZZrPYA0EZh3Za3XOrqM-MDl_9vbu00LHTjjq3ajn5xFHrSFUcfyQobo3HrIH528etw7Uaqyesm9lad7e7km8tZx-3el-f9r68yDdfZy-Xe88Xvy-s5d2d7NHmt8WH2Zud3rsn2eOlfHU9X9jY_bSSvVrqbWxn20tZ922-vpB_WM6ffc7fr560JpvhxOi4Xe4jY8-Q37FKEp1qmOvEElIOR8SJhBQvNYkf-SLSscaaWiZqG6FaxotF5CaxbikViVTHbeWfsgY7dzvpaWuEp4lwsUYnFy3ueDKKlYARSUxbSpho-mes4XJEpsvfiZnpfQye_ZPCOetQ_2Z03hqcvT-XXoB5z2xrmEj_AaZq9cQ |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C%E6%A8%A1%E5%9E%8B%E7%9A%84%E9%80%9F%E6%BA%B6%E5%85%A8%E8%84%82%E5%A5%B6%E7%B2%89%E5%88%86%E6%95%A3%E6%80%A7%E4%B8%8E%E5%A0%86%E7%A7%AF%E5%AF%86%E5%BA%A6%E6%A3%80%E6%B5%8B%E6%96%B9%E6%B3%95&rft.jtitle=%E9%A3%9F%E5%93%81%E7%A7%91%E5%AD%A6&rft.au=%E4%B8%81%E6%B5%A9%E6%99%97&rft.au=%E6%B2%88%E5%B5%A9&rft.au=%E8%B0%A2%E7%A5%AF%E5%A5%87&rft.au=%E5%B4%94%E6%99%93%E6%99%96&rft.date=2024-05-25&rft.pub=%E6%B1%9F%E5%8D%97%E5%A4%A7%E5%AD%A6%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%AD%A6%E9%99%A2%2C%E6%B1%9F%E8%8B%8F%E6%97%A0%E9%94%A1+214122%25%E6%B1%9F%E5%8D%97%E5%A4%A7%E5%AD%A6%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%AD%A6%E9%99%A2%2C%E6%B1%9F%E8%8B%8F%E6%97%A0%E9%94%A1+214122%25%E6%B1%9F%E5%8D%97%E5%A4%A7%E5%AD%A6%E6%9C%AA%E6%9D%A5%E9%A3%9F%E5%93%81%E7%A7%91%E5%AD%A6%E4%B8%AD%E5%BF%83%2C%E6%B1%9F%E8%8B%8F%E6%97%A0%E9%94%A1+214122&rft.issn=1002-6630&rft.volume=45&rft.issue=10&rft.spage=9&rft.epage=18&rft_id=info:doi/10.7506%2Fspkx1002-6630-20240129-262&rft.externalDocID=spkx202410002 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fspkx%2Fspkx.jpg |