Analytical chemistry kernel library for spectroscopic profiling data

The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit inc...
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Autor*in:	Yinsheng Zhang [verfasserIn] Ling Jin [verfasserIn] XiaoFeng Ni [verfasserIn] Zhengyong Zhang [verfasserIn] Yongbo Cheng [verfasserIn] Haiyan Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Kernel Spectroscopic profiling Open-source toolkit

Übergeordnetes Werk:	In: Food Chemistry Advances - Elsevier, 2022, 3(2023), Seite 100342-
Übergeordnetes Werk:	volume:3 ; year:2023 ; pages:100342-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.focha.2023.100342

Katalog-ID:	DOAJ098946544

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allfields	10.1016/j.focha.2023.100342 doi (DE-627)DOAJ098946544 (DE-599)DOAJ9cf7f706b0564899a6ddb1bb7e1f316d DE-627 ger DE-627 rakwb eng TP368-456 Yinsheng Zhang verfasserin aut Analytical chemistry kernel library for spectroscopic profiling data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection. Kernel Spectroscopic profiling Open-source toolkit Food processing and manufacture Ling Jin verfasserin aut XiaoFeng Ni verfasserin aut Zhengyong Zhang verfasserin aut Yongbo Cheng verfasserin aut Haiyan Wang verfasserin aut In Food Chemistry Advances Elsevier, 2022 3(2023), Seite 100342- (DE-627)1799510484 2772753X nnns volume:3 year:2023 pages:100342- https://doi.org/10.1016/j.focha.2023.100342 kostenfrei https://doaj.org/article/9cf7f706b0564899a6ddb1bb7e1f316d kostenfrei http://www.sciencedirect.com/science/article/pii/S2772753X23001648 kostenfrei https://doaj.org/toc/2772-753X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2023 100342-
spelling	10.1016/j.focha.2023.100342 doi (DE-627)DOAJ098946544 (DE-599)DOAJ9cf7f706b0564899a6ddb1bb7e1f316d DE-627 ger DE-627 rakwb eng TP368-456 Yinsheng Zhang verfasserin aut Analytical chemistry kernel library for spectroscopic profiling data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection. Kernel Spectroscopic profiling Open-source toolkit Food processing and manufacture Ling Jin verfasserin aut XiaoFeng Ni verfasserin aut Zhengyong Zhang verfasserin aut Yongbo Cheng verfasserin aut Haiyan Wang verfasserin aut In Food Chemistry Advances Elsevier, 2022 3(2023), Seite 100342- (DE-627)1799510484 2772753X nnns volume:3 year:2023 pages:100342- https://doi.org/10.1016/j.focha.2023.100342 kostenfrei https://doaj.org/article/9cf7f706b0564899a6ddb1bb7e1f316d kostenfrei http://www.sciencedirect.com/science/article/pii/S2772753X23001648 kostenfrei https://doaj.org/toc/2772-753X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2023 100342-
allfields_unstemmed	10.1016/j.focha.2023.100342 doi (DE-627)DOAJ098946544 (DE-599)DOAJ9cf7f706b0564899a6ddb1bb7e1f316d DE-627 ger DE-627 rakwb eng TP368-456 Yinsheng Zhang verfasserin aut Analytical chemistry kernel library for spectroscopic profiling data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection. Kernel Spectroscopic profiling Open-source toolkit Food processing and manufacture Ling Jin verfasserin aut XiaoFeng Ni verfasserin aut Zhengyong Zhang verfasserin aut Yongbo Cheng verfasserin aut Haiyan Wang verfasserin aut In Food Chemistry Advances Elsevier, 2022 3(2023), Seite 100342- (DE-627)1799510484 2772753X nnns volume:3 year:2023 pages:100342- https://doi.org/10.1016/j.focha.2023.100342 kostenfrei https://doaj.org/article/9cf7f706b0564899a6ddb1bb7e1f316d kostenfrei http://www.sciencedirect.com/science/article/pii/S2772753X23001648 kostenfrei https://doaj.org/toc/2772-753X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2023 100342-
allfieldsGer	10.1016/j.focha.2023.100342 doi (DE-627)DOAJ098946544 (DE-599)DOAJ9cf7f706b0564899a6ddb1bb7e1f316d DE-627 ger DE-627 rakwb eng TP368-456 Yinsheng Zhang verfasserin aut Analytical chemistry kernel library for spectroscopic profiling data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection. Kernel Spectroscopic profiling Open-source toolkit Food processing and manufacture Ling Jin verfasserin aut XiaoFeng Ni verfasserin aut Zhengyong Zhang verfasserin aut Yongbo Cheng verfasserin aut Haiyan Wang verfasserin aut In Food Chemistry Advances Elsevier, 2022 3(2023), Seite 100342- (DE-627)1799510484 2772753X nnns volume:3 year:2023 pages:100342- https://doi.org/10.1016/j.focha.2023.100342 kostenfrei https://doaj.org/article/9cf7f706b0564899a6ddb1bb7e1f316d kostenfrei http://www.sciencedirect.com/science/article/pii/S2772753X23001648 kostenfrei https://doaj.org/toc/2772-753X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2023 100342-
allfieldsSound	10.1016/j.focha.2023.100342 doi (DE-627)DOAJ098946544 (DE-599)DOAJ9cf7f706b0564899a6ddb1bb7e1f316d DE-627 ger DE-627 rakwb eng TP368-456 Yinsheng Zhang verfasserin aut Analytical chemistry kernel library for spectroscopic profiling data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection. Kernel Spectroscopic profiling Open-source toolkit Food processing and manufacture Ling Jin verfasserin aut XiaoFeng Ni verfasserin aut Zhengyong Zhang verfasserin aut Yongbo Cheng verfasserin aut Haiyan Wang verfasserin aut In Food Chemistry Advances Elsevier, 2022 3(2023), Seite 100342- (DE-627)1799510484 2772753X nnns volume:3 year:2023 pages:100342- https://doi.org/10.1016/j.focha.2023.100342 kostenfrei https://doaj.org/article/9cf7f706b0564899a6ddb1bb7e1f316d kostenfrei http://www.sciencedirect.com/science/article/pii/S2772753X23001648 kostenfrei https://doaj.org/toc/2772-753X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2023 100342-
language	English
source	In Food Chemistry Advances 3(2023), Seite 100342- volume:3 year:2023 pages:100342-
sourceStr	In Food Chemistry Advances 3(2023), Seite 100342- volume:3 year:2023 pages:100342-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Kernel Spectroscopic profiling Open-source toolkit Food processing and manufacture
isfreeaccess_bool	true
container_title	Food Chemistry Advances
authorswithroles_txt_mv	Yinsheng Zhang @@aut@@ Ling Jin @@aut@@ XiaoFeng Ni @@aut@@ Zhengyong Zhang @@aut@@ Yongbo Cheng @@aut@@ Haiyan Wang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	1799510484
id	DOAJ098946544
language_de	englisch
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callnumber-first	T - Technology
author	Yinsheng Zhang
spellingShingle	Yinsheng Zhang misc TP368-456 misc Kernel misc Spectroscopic profiling misc Open-source toolkit misc Food processing and manufacture Analytical chemistry kernel library for spectroscopic profiling data
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topic_title	TP368-456 Analytical chemistry kernel library for spectroscopic profiling data Kernel Spectroscopic profiling Open-source toolkit
topic	misc TP368-456 misc Kernel misc Spectroscopic profiling misc Open-source toolkit misc Food processing and manufacture
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title	Analytical chemistry kernel library for spectroscopic profiling data
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title_full	Analytical chemistry kernel library for spectroscopic profiling data
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title_sort	analytical chemistry kernel library for spectroscopic profiling data
callnumber	TP368-456
title_auth	Analytical chemistry kernel library for spectroscopic profiling data
abstract	The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection.
abstractGer	The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection.
abstract_unstemmed	The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection.
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title_short	Analytical chemistry kernel library for spectroscopic profiling data
url	https://doi.org/10.1016/j.focha.2023.100342 https://doaj.org/article/9cf7f706b0564899a6ddb1bb7e1f316d http://www.sciencedirect.com/science/article/pii/S2772753X23001648 https://doaj.org/toc/2772-753X
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Analytical chemistry kernel library for spectroscopic profiling data

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