Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample

Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Chenhui [verfasserIn] Shi, Zhuangwei [verfasserIn] Shen, Haoqi [verfasserIn] Fang, Yifei [verfasserIn] He, Songgui [verfasserIn] Bi, Hai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data

Übergeordnetes Werk:	Enthalten in: Journal of food composition and analysis - Orlando, Fla. : Academic Press, 1987, 118
Übergeordnetes Werk:	volume:118

DOI / URN:	10.1016/j.jfca.2023.105217

Katalog-ID:	ELV009311882

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520			\|a Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.
650		4	\|a Liquor
650		4	\|a Raman spectroscopy
650		4	\|a Rapid analysis
650		4	\|a Feature extraction
650		4	\|a LDA
650		4	\|a Ensemble learning
650		4	\|a Spectral preprocessing
650		4	\|a Auxiliary data
700	1		\|a Shi, Zhuangwei \|e verfasserin \|4 aut
700	1		\|a Shen, Haoqi \|e verfasserin \|0 (orcid)0000-0002-8368-385X \|4 aut
700	1		\|a Fang, Yifei \|e verfasserin \|4 aut
700	1		\|a He, Songgui \|e verfasserin \|4 aut
700	1		\|a Bi, Hai \|e verfasserin \|0 (orcid)0000-0002-2017-3668 \|4 aut
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936	b	k	\|a 58.34 \|j Lebensmitteltechnologie
951			\|a AR
952			\|d 118

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allfields	10.1016/j.jfca.2023.105217 doi (DE-627)ELV009311882 (ELSEVIER)S0889-1575(23)00091-1 DE-627 ger DE-627 rda eng 630 640 540 660 DE-600 58.34 bkl Wang, Chenhui verfasserin (orcid)0000-0002-4854-5072 aut Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication. Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data Shi, Zhuangwei verfasserin aut Shen, Haoqi verfasserin (orcid)0000-0002-8368-385X aut Fang, Yifei verfasserin aut He, Songgui verfasserin aut Bi, Hai verfasserin (orcid)0000-0002-2017-3668 aut Enthalten in Journal of food composition and analysis Orlando, Fla. : Academic Press, 1987 118 Online-Ressource (DE-627)267328400 (DE-600)1469801-8 (DE-576)259483710 0889-1575 nnns volume:118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie AR 118
spelling	10.1016/j.jfca.2023.105217 doi (DE-627)ELV009311882 (ELSEVIER)S0889-1575(23)00091-1 DE-627 ger DE-627 rda eng 630 640 540 660 DE-600 58.34 bkl Wang, Chenhui verfasserin (orcid)0000-0002-4854-5072 aut Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication. Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data Shi, Zhuangwei verfasserin aut Shen, Haoqi verfasserin (orcid)0000-0002-8368-385X aut Fang, Yifei verfasserin aut He, Songgui verfasserin aut Bi, Hai verfasserin (orcid)0000-0002-2017-3668 aut Enthalten in Journal of food composition and analysis Orlando, Fla. : Academic Press, 1987 118 Online-Ressource (DE-627)267328400 (DE-600)1469801-8 (DE-576)259483710 0889-1575 nnns volume:118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie AR 118
allfields_unstemmed	10.1016/j.jfca.2023.105217 doi (DE-627)ELV009311882 (ELSEVIER)S0889-1575(23)00091-1 DE-627 ger DE-627 rda eng 630 640 540 660 DE-600 58.34 bkl Wang, Chenhui verfasserin (orcid)0000-0002-4854-5072 aut Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication. Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data Shi, Zhuangwei verfasserin aut Shen, Haoqi verfasserin (orcid)0000-0002-8368-385X aut Fang, Yifei verfasserin aut He, Songgui verfasserin aut Bi, Hai verfasserin (orcid)0000-0002-2017-3668 aut Enthalten in Journal of food composition and analysis Orlando, Fla. : Academic Press, 1987 118 Online-Ressource (DE-627)267328400 (DE-600)1469801-8 (DE-576)259483710 0889-1575 nnns volume:118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie AR 118
allfieldsGer	10.1016/j.jfca.2023.105217 doi (DE-627)ELV009311882 (ELSEVIER)S0889-1575(23)00091-1 DE-627 ger DE-627 rda eng 630 640 540 660 DE-600 58.34 bkl Wang, Chenhui verfasserin (orcid)0000-0002-4854-5072 aut Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication. Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data Shi, Zhuangwei verfasserin aut Shen, Haoqi verfasserin (orcid)0000-0002-8368-385X aut Fang, Yifei verfasserin aut He, Songgui verfasserin aut Bi, Hai verfasserin (orcid)0000-0002-2017-3668 aut Enthalten in Journal of food composition and analysis Orlando, Fla. : Academic Press, 1987 118 Online-Ressource (DE-627)267328400 (DE-600)1469801-8 (DE-576)259483710 0889-1575 nnns volume:118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie AR 118
allfieldsSound	10.1016/j.jfca.2023.105217 doi (DE-627)ELV009311882 (ELSEVIER)S0889-1575(23)00091-1 DE-627 ger DE-627 rda eng 630 640 540 660 DE-600 58.34 bkl Wang, Chenhui verfasserin (orcid)0000-0002-4854-5072 aut Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication. Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data Shi, Zhuangwei verfasserin aut Shen, Haoqi verfasserin (orcid)0000-0002-8368-385X aut Fang, Yifei verfasserin aut He, Songgui verfasserin aut Bi, Hai verfasserin (orcid)0000-0002-2017-3668 aut Enthalten in Journal of food composition and analysis Orlando, Fla. : Academic Press, 1987 118 Online-Ressource (DE-627)267328400 (DE-600)1469801-8 (DE-576)259483710 0889-1575 nnns volume:118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie AR 118
language	English
source	Enthalten in Journal of food composition and analysis 118 volume:118
sourceStr	Enthalten in Journal of food composition and analysis 118 volume:118
format_phy_str_mv	Article
bklname	Lebensmitteltechnologie
institution	findex.gbv.de
topic_facet	Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data
dewey-raw	630
isfreeaccess_bool	false
container_title	Journal of food composition and analysis
authorswithroles_txt_mv	Wang, Chenhui @@aut@@ Shi, Zhuangwei @@aut@@ Shen, Haoqi @@aut@@ Fang, Yifei @@aut@@ He, Songgui @@aut@@ Bi, Hai @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	267328400
dewey-sort	3630
id	ELV009311882
language_de	englisch
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author	Wang, Chenhui
spellingShingle	Wang, Chenhui ddc 630 bkl 58.34 misc Liquor misc Raman spectroscopy misc Rapid analysis misc Feature extraction misc LDA misc Ensemble learning misc Spectral preprocessing misc Auxiliary data Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample
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topic_title	630 640 540 660 DE-600 58.34 bkl Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample Liquor Raman spectroscopy Rapid analysis Feature extraction LDA Ensemble learning Spectral preprocessing Auxiliary data
topic	ddc 630 bkl 58.34 misc Liquor misc Raman spectroscopy misc Rapid analysis misc Feature extraction misc LDA misc Ensemble learning misc Spectral preprocessing misc Auxiliary data
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title	Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample
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title_full	Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample
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title_sort	towards robustness and sensitivity of rapid baijiu (chinese liquor) discrimination using raman spectroscopy and chemometrics: dimension reduction, machine learning, and auxiliary sample
title_auth	Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample
abstract	Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.
abstractGer	Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.
abstract_unstemmed	Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.
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title_short	Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample
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author2	Shi, Zhuangwei Shen, Haoqi Fang, Yifei He, Songgui Bi, Hai
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up_date	2024-07-06T22:44:15.398Z
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Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample

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