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.
{"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}
Pub Date : 2023-12-01DOI: 10.56530/spectroscopy.wu2080k9
Fran Adar
The formation of spherulites in polymers is a well-known phenomenon; when the polymer is crystallized by cooling from the melt, crystal lamellae grow out from a nucleation site in a spherical pattern. If the material is annealed on a planar surface, and viewed between crossed polarizers in a microscope, a Maltese cross with a banding pattern is observed. Where the crystals grow in a direction not parallel to the polarizers, the sample lights up. Often banding of the lit regions is observed, and is believed to be due to rotations of the crystal lamellae around the growth direction. Because it is well known that polarized Raman spectra respond to crystal orientation, we thought it would be interesting to try to document the relationship between the banding behavior and Raman polarization/orientation behavior. In this column I will show results of such an investigation of spherulites of poly(hydroxybutyate-co-hydroxyhexanoate) (PHBHx) with varying composition.
{"title":"Raman Spectra Used to Understand the Origins of Banding in Spherulites","authors":"Fran Adar","doi":"10.56530/spectroscopy.wu2080k9","DOIUrl":"https://doi.org/10.56530/spectroscopy.wu2080k9","url":null,"abstract":"The formation of spherulites in polymers is a well-known phenomenon; when the polymer is crystallized by cooling from the melt, crystal lamellae grow out from a nucleation site in a spherical pattern. If the material is annealed on a planar surface, and viewed between crossed polarizers in a microscope, a Maltese cross with a banding pattern is observed. Where the crystals grow in a direction not parallel to the polarizers, the sample lights up. Often banding of the lit regions is observed, and is believed to be due to rotations of the crystal lamellae around the growth direction. Because it is well known that polarized Raman spectra respond to crystal orientation, we thought it would be interesting to try to document the relationship between the banding behavior and Raman polarization/orientation behavior. In this column I will show results of such an investigation of spherulites of poly(hydroxybutyate-co-hydroxyhexanoate) (PHBHx) with varying composition.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":" 20","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620236","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}
Pub Date : 2023-12-01DOI: 10.56530/spectroscopy.sr2771p7
A. Tercier, E. Vasileva, C. Alvarez-Llamas, C. Fabre, S. Hermelin, B. Soula, F. Trichard, C. Dujardin, V. Motto-Ros
Laser-induced breakdown spectroscopy (LIBS) imaging instruments typically utilize lasers with repetition rates below 100 Hz since in most cases this regime provides a good balance of sampling frequency and laser pulse energy. However, measuring large sample areas of a several cm2 with high spatial resolution (<10 μm), at this frequency rate would be very time consuming since millions of spectral data points need to be collected in order to create a high-resolution image (3 h–15 h per cm2). In this work, we explore the approach to reduce, the acquisition time for high resolution LIBS imaging, or so-called μ-LIBS imaging, by using a laser operating in the kHz pulse repetition frequency (PRF) range. As a result, we describe and demonstrate a μ-LIBS imaging microscope which can image >6 cm2 sample areas with about 10 μm resolution in significantly shorter time (<20 min/cm2). The developed system opens a potential for variety of application fields where knowledge of elemental composition and elemental distribution is needed to perform conclusive analysis.
{"title":"High-Resolution High-Speed LIBS Microscopy","authors":"A. Tercier, E. Vasileva, C. Alvarez-Llamas, C. Fabre, S. Hermelin, B. Soula, F. Trichard, C. Dujardin, V. Motto-Ros","doi":"10.56530/spectroscopy.sr2771p7","DOIUrl":"https://doi.org/10.56530/spectroscopy.sr2771p7","url":null,"abstract":"Laser-induced breakdown spectroscopy (LIBS) imaging instruments typically utilize lasers with repetition rates below 100 Hz since in most cases this regime provides a good balance of sampling frequency and laser pulse energy. However, measuring large sample areas of a several cm2 with high spatial resolution (<10 μm), at this frequency rate would be very time consuming since millions of spectral data points need to be collected in order to create a high-resolution image (3 h–15 h per cm2). In this work, we explore the approach to reduce, the acquisition time for high resolution LIBS imaging, or so-called μ-LIBS imaging, by using a laser operating in the kHz pulse repetition frequency (PRF) range. As a result, we describe and demonstrate a μ-LIBS imaging microscope which can image >6 cm2 sample areas with about 10 μm resolution in significantly shorter time (<20 min/cm2). The developed system opens a potential for variety of application fields where knowledge of elemental composition and elemental distribution is needed to perform conclusive analysis.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":" 76","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611977","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}
Pub Date : 2023-12-01DOI: 10.56530/spectroscopy.rp3780c4
Brian C. Smith
Despite what some people think, inorganics do have mid-infrared spectra. In this column, we will prove this and discuss what inorganic compounds are and describe the general characteristics of their infrared spectra.
{"title":"Inorganics I: Introduction","authors":"Brian C. Smith","doi":"10.56530/spectroscopy.rp3780c4","DOIUrl":"https://doi.org/10.56530/spectroscopy.rp3780c4","url":null,"abstract":"Despite what some people think, inorganics do have mid-infrared spectra. In this column, we will prove this and discuss what inorganic compounds are and describe the general characteristics of their infrared spectra.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":" 10","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616734","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}
Pub Date : 2023-12-01DOI: 10.56530/spectroscopy.tc3777t8
R. D. McDowall
Computerized System Validation (CSV) is usually associated with great mountains of paper (GMP). Why should this be the case? Ask yourself, am I lazy? Why write multiple documents if only one is required to validate a spectrometer with a simple intended use? Interested? Read on…
{"title":"Simple Spectrometer System, Simple Validation?","authors":"R. D. McDowall","doi":"10.56530/spectroscopy.tc3777t8","DOIUrl":"https://doi.org/10.56530/spectroscopy.tc3777t8","url":null,"abstract":"Computerized System Validation (CSV) is usually associated with great mountains of paper (GMP). Why should this be the case? Ask yourself, am I lazy? Why write multiple documents if only one is required to validate a spectrometer with a simple intended use? Interested? Read on…","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"112 33","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138608692","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}
Pub Date : 2023-11-01DOI: 10.56530/spectroscopy.xg9883t4
Yukihiro Ozaki, M. Ishigaki
After a brief introduction to the technique, this review will discuss the state-of-the-art NIR imaging instrumentation and its applications, including applications of an ordinary NIR imaging system to solvent diffusion into a polymer. Other imaging applications discussed include the use of a portable NIR imaging system in the pharmaceutical industry, high-speed and wide-area monitoring of polymers, and imaging for fish embryos development research. Also discussed is an imaging-type two-dimensional Fourier spectroscopy (ITFS) system and its application to medaka (Japanese rice fish, Oryzias latipes) egg development. Finally, general perspectives on NIR imaging will be provided.
{"title":"Frontiers of NIR Imaging","authors":"Yukihiro Ozaki, M. Ishigaki","doi":"10.56530/spectroscopy.xg9883t4","DOIUrl":"https://doi.org/10.56530/spectroscopy.xg9883t4","url":null,"abstract":"After a brief introduction to the technique, this review will discuss the state-of-the-art NIR imaging instrumentation and its applications, including applications of an ordinary NIR imaging system to solvent diffusion into a polymer. Other imaging applications discussed include the use of a portable NIR imaging system in the pharmaceutical industry, high-speed and wide-area monitoring of polymers, and imaging for fish embryos development research. Also discussed is an imaging-type two-dimensional Fourier spectroscopy (ITFS) system and its application to medaka (Japanese rice fish, Oryzias latipes) egg development. Finally, general perspectives on NIR imaging will be provided.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"16 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291654","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}
Pub Date : 2023-11-01DOI: 10.56530/spectroscopy.uj1082r1
Zachary D. Schultz
Raman spectroscopy provides chemical information by detecting light scattered from a monochromatic source (such as a laser) at energies that correspond to molecular vibrations. Because Raman spectroscopy commonly uses visible lasers, the spatial resolution is approximately the same as what can be seen with an optical microscope. First demonstrated in the 1970s, coupling Raman spectroscopy with microscopes enabled the chemical information to be obtained from a focused laser spot. By moving the laser across the sample and recording the Raman spectrum at each location, images can be generated from changes in intensity at different Raman shifts that spatially characterize the molecules present. From the development of the Raman microprobe to today, advances in instrumentation have increased the speed, sensitivity, and spatial resolution of Raman microscopy. This article covers the fundamentals of Raman microscopy and how technological advances are enabling a variety of applications.
{"title":"Raman Scattering for Label-Free Chemical Imaging","authors":"Zachary D. Schultz","doi":"10.56530/spectroscopy.uj1082r1","DOIUrl":"https://doi.org/10.56530/spectroscopy.uj1082r1","url":null,"abstract":"Raman spectroscopy provides chemical information by detecting light scattered from a monochromatic source (such as a laser) at energies that correspond to molecular vibrations. Because Raman spectroscopy commonly uses visible lasers, the spatial resolution is approximately the same as what can be seen with an optical microscope. First demonstrated in the 1970s, coupling Raman spectroscopy with microscopes enabled the chemical information to be obtained from a focused laser spot. By moving the laser across the sample and recording the Raman spectrum at each location, images can be generated from changes in intensity at different Raman shifts that spatially characterize the molecules present. From the development of the Raman microprobe to today, advances in instrumentation have increased the speed, sensitivity, and spatial resolution of Raman microscopy. This article covers the fundamentals of Raman microscopy and how technological advances are enabling a variety of applications.","PeriodicalId":21957,"journal":{"name":"Spectroscopy","volume":"5 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297317","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}
Pub Date : 2023-11-01DOI: 10.56530/spectroscopy.dr5881c1
Xinchao Gao, Fei Hao, W. Pi, Xiangbing Zhu, Tao Zhang, Yuge Bi, Yanbin Zhang
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.
{"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}
Pub Date : 2023-11-01DOI: 10.56530/spectroscopy.fz7077a2
Shaofang He, Qing Zhou, Fang Wang, Luming Shen, Jing Yang
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.
为了利用土壤高光谱方法快速、准确地估算土壤有机质（SOM），我们开发了一种基于多尺度分形特征并结合高光谱数据主成分分析（PCA）的新型智能反演模型。首先，我们通过多尺度多分形去趋势波动分析（MMA）计算了光谱反射率的局部广义赫斯特指数，同时确定了敏感光谱波段。采用 PCA 方法获取敏感波段的最大主成分特征作为模型输入。最后，利用随机森林（RF）和支持向量机（SVM）这两种智能算法建立 SOM 估算模型。土壤高光谱数据具有典型的长程相关性，在不同尺度和波动下呈现出不同的分形结构。敏感波段为 359 nm 至 405 nm，且不受窗口拟合大小的影响。基于 MMA 的敏感带模型的精度优于原始敏感带。PCA 处理进一步提高了模型的性能。建议将基于 MMA 的模型与射频相结合用于 SOM 估算。
{"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}
Pub Date : 2023-11-01DOI: 10.56530/spectroscopy.ai1281l4
Barry K. Lavine, T. Hancewicz
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.
在分析红外和拉曼图像光谱数据时，修正交替最小二乘法（MALS）优于交替最小二乘法（ALS）。由于采用了脊回归技术，MALS 具有出色的稳定性，而且由于算法的核性质，MALS 在速度上具有很大的优势，从而减少了计算开销。MALS 在解析基向量方面表现出色，即使是信噪比低、近乎共线的数据也不例外，而 ALS 则往往达不到这一点。对于光谱成像，MALS 和其他 ALS 方法都依赖于样本成分之间的空间分辨率，因为低空间分辨率会导致成分混合增加。光谱成像结合了光谱学和数字成像技术来提取化学成分。多变量曲线分辨率 (MCR) 是 ALS 回归的基础，使其成为这种分析的重要工具，能够对复杂的光谱图像进行全面检查。本教程深入探讨了从红外和拉曼光谱图像中提取化学成分所需的数学技术。虽然讨论的重点是二维空间数据，但该方法可扩展到三维数据。
{"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}
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