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

Simple Spectrometer System, Simple Validation? 简单的光谱仪系统，简单的验证？

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-12-01 DOI: 10.56530/spectroscopy.tc3777t8

R. D. McDowall

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

Soil Organic Matter Estimation Modeling Using Fractal Feature of Soil for vis-NIR Hyperspectral Imaging 利用可见近红外高光谱成像的土壤分形特征建立土壤有机质估算模型

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-11-01 DOI: 10.56530/spectroscopy.fz7077a2

Shaofang He, Qing Zhou, Fang Wang, Luming Shen, Jing Yang

{"title":"Soil Organic Matter Estimation Modeling Using Fractal Feature of Soil for vis-NIR Hyperspectral Imaging","authors":"Shaofang He, Qing Zhou, Fang Wang, Luming Shen, Jing Yang","doi":"10.56530/spectroscopy.fz7077a2","DOIUrl":"https://doi.org/10.56530/spectroscopy.fz7077a2","url":null,"abstract":"To produce a fast, accurate estimation for soil organic matter (SOM) by soil hyperspectral methods, we developed a novel intelligent inversion model based on multiscale fractal features combined with principal component analysis (PCA) of hyperspectral data. First, we calculated the local generalized Hurst exponent of the spectral reflectivity by multiscale multifractal detrended fluctuation analysis (MMA) while determining the sensitive spectral bands. PCA was employed to access the maximum principal component features of the sensitive bands used as the model input. Finally, two intelligent algorithms, random forest (RF), and a support vector machine (SVM), were utilized for establishing the SOM estimation model. The soil hyperspectral data possesses the typical nature of long-range correlation, presenting distinct fractal structures at different scales and fluctuations. The sensitive bands were from 359 nm to 405 nm, and were not impacted by window fitting size. The accuracy of the models of MMA-based sensitive bands is superior to that of the original bands. The PCA processing brings additional model performance improvement. The MMA-based models combined with RF is recommended for SOM estimation.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"11 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297378","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

Analysis of Infrared and Raman Imaging Data Using Alternating and Modified Alternating Least Squares 利用交替最小二乘法和修正交替最小二乘法分析红外和拉曼成像数据

IF 0.5 4区化学

Spectroscopy Pub Date : 2023-11-01 DOI: 10.56530/spectroscopy.ai1281l4

Barry K. Lavine, T. Hancewicz

{"title":"Analysis of Infrared and Raman Imaging Data Using Alternating and Modified Alternating Least Squares","authors":"Barry K. Lavine, T. Hancewicz","doi":"10.56530/spectroscopy.ai1281l4","DOIUrl":"https://doi.org/10.56530/spectroscopy.ai1281l4","url":null,"abstract":"Modified alternating least squares (MALS) outperforms alternating least squares (ALS) in the analysis of infrared and Raman image spectral data. MALS offers superior stability thanks to ridge regression and a substantial speed advantage due to the kernel nature of the algorithm, reducing computational overhead. MALS excels in resolving basis vectors even in low signal-to-noise, nearly collinear data, whereas ALS often falls short. For spectroscopic imaging, both MALS and other ALS methods rely on spatial resolution between sample components, as low spatial resolution leads to increased mixing of components. Spectroscopic imaging combines spectroscopy and digital imaging to extract chemical composition. Multivariate curve resolution (MCR)’s foundation in ALS regression makes it a vital tool for this analysis, enabling a comprehensive examination of complex spectroscopic images. This tutorial delves into the mathematical techniques necessary for extracting chemical insights from infrared and Raman spectroscopic images. While this discussion focuses on two-dimensional spatial data, the methodology can be extended to three-dimensional data.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297221","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