Spectroscopy最新文献

Hyperspectral Imaging Combined with Convolutional Neural Network for Rapid and Accurate Evaluation of Tilapia Fillet Freshness 高光谱成像与卷积神经网络相结合，快速准确地评估罗非鱼片的新鲜度

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-12-08 DOI: 10.56530/spectroscopy.ae4768d1

Shuqi Tang, Peng Li, Shenghui Chen, Chunhai Li, Ling Zhang, Nan Zhong

{"title":"Hyperspectral Imaging Combined with Convolutional Neural Network for Rapid and Accurate Evaluation of Tilapia Fillet Freshness","authors":"Shuqi Tang, Peng Li, Shenghui Chen, Chunhai Li, Ling Zhang, Nan Zhong","doi":"10.56530/spectroscopy.ae4768d1","DOIUrl":"https://doi.org/10.56530/spectroscopy.ae4768d1","url":null,"abstract":"The purpose of this work is to achieve rapid and nondestructive determination of tilapia fillets storage time associated with its freshness. Here, we investigated the potential of hyperspectral imaging (HSI) combined with a convolutional neural network (CNN) in the visible and near-infrared region (vis-NIR or VNIR, 397−1003 nm) and the shortwave near-infrared region (SWNIR or SWIR, 935−1720 nm) for determining tilapia fillets freshness. Hyperspectral images of 70 tilapia fillets stored at 4 ℃ for 0–14 d were collected. Various machine learning algorithms were employed to verify the effectiveness of CNN, including partial least-squares discriminant analysis (PLS-DA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme learning machine (ELM). Their performance was compared from spectral preprocessing and feature extraction. The results showed that PLS-DA, KNN, SVM, and ELM require appropriate preprocessing methods and feature extraction to improve their accuracy, while CNN without the requirement of these complex processes achieved higher accuracy than the other algorithms. CNN achieved accuracy of 100% in the test set of VNIR, and achieved 87.30% in the test set of SWIR, indicating that VNIR HSI is more suitable for detection freshness of tilapia. Overall, HSI combined with CNN could be used to rapidly and accurately evaluating tilapia fillets freshness.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"145 ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Raman Spectra Used to Understand the Origins of Banding in Spherulites 利用拉曼光谱了解球粒云母带状结构的起源

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-12-01 DOI: 10.56530/spectroscopy.wu2080k9

Fran Adar

引用次数: 0

High-Resolution High-Speed LIBS Microscopy 高分辨率高速 LIBS 显微镜

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-12-01 DOI: 10.56530/spectroscopy.sr2771p7

A. Tercier, E. Vasileva, C. Alvarez-Llamas, C. Fabre, S. Hermelin, B. Soula, F. Trichard, C. Dujardin, V. Motto-Ros

引用次数: 0

Inorganics I: Introduction 无机物 I：导论

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-12-01 DOI: 10.56530/spectroscopy.rp3780c4

Brian C. Smith

引用次数: 0

Frontiers of NIR Imaging 近红外成像前沿

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-11-01 DOI: 10.56530/spectroscopy.xg9883t4

Yukihiro Ozaki, M. Ishigaki

引用次数: 0

Raman Scattering for Label-Free Chemical Imaging 用于无标记化学成像的拉曼散射

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-11-01 DOI: 10.56530/spectroscopy.uj1082r1

Zachary D. Schultz

引用次数: 0

Identification and Classification of Degradation-Indicator Grass Species in a Desertified Steppe Based on HSI-UAV 基于 HSI-UAV 的荒漠化草原退化指示草种的识别与分类

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-11-01 DOI: 10.56530/spectroscopy.dr5881c1

Xinchao Gao, Fei Hao, W. Pi, Xiangbing Zhu, Tao Zhang, Yuge Bi, Yanbin Zhang

{"title":"Identification and Classification of Degradation-Indicator Grass Species in a Desertified Steppe Based on HSI-UAV","authors":"Xinchao Gao, Fei Hao, W. Pi, Xiangbing Zhu, Tao Zhang, Yuge Bi, Yanbin Zhang","doi":"10.56530/spectroscopy.dr5881c1","DOIUrl":"https://doi.org/10.56530/spectroscopy.dr5881c1","url":null,"abstract":"The emergence and number of grassland degradation-indicator grass species are important in evaluating the extent of grassland degradation. Plant populations in desertified steppe are distributed randomly and at low density. Specifically, degradation-indicator grass species mainly exist as individuals, making spectrum-based identification difficult. Here, a low-altitude unmanned aerial vehicle (UAV) hyperspectral remote-sensing system was constructed to identify the typical degradation-indicator grass species of a desertified steppe in China. The ASI index (Artemisia frigida Willd. and Stipa breviflora Grisb. index) and classification rules were proposed and applied. We implemented a comprehensive application of amplified differences in spectral characteristics between vegetation communities and assigned plant senescence reflectance-index bands, using the characteristics of the plant populations under observation and UAV hyperspectral remote-sensing data, to solve the problems resulting from high similarity while identifying ground objects. Our results lay a solid foundation for monitoring and evaluating desertified steppe degradation-indicator grass species based on remote sensing.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"191 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key Steps in the Workflow to Analyze Raman Spectra 关键步骤在工作流程中分析拉曼光谱

4区化学

Spectroscopy Pub Date : 2023-10-01 DOI: 10.56530/spectroscopy.fl6984w5

Shuxia Guo, Jürgen Popp, Thomas Bocklitz

引用次数: 0

Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance 回顾与展望:指数信号与机器学习在核磁共振中的应用

4区化学

Spectroscopy Pub Date : 2023-09-01 DOI: 10.56530/spectroscopy.yx1073b8

Di Guo, Xianjing Chen, Mengli Lu, Wangfeng He, Sihui Luo, Yanqin Lin, Yuqing Huang, Lizhi Xiao, Xiaobo Qu

引用次数: 0

Guess Who’s Coming to Inspect R&D? 猜猜谁要来视察研发部门了?