Analyzing Milk Foam Using Machine Learning for Diverse Applications

Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. M...
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Autor*in:	Acharya, Saswata [verfasserIn] Dandigunta, Babuji Sagar, Harsh Rani, Jyoti Priyadarsini, Madhumita Verma, Shreyansh Kushwaha, Jeetesh Fageria, Pradeep Lahiri, Pratik Chattopadhyay, Pradipta Dhoble, Abhishek S.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Milk oam Machine earning Foaming properties dulterants everages Surfactants

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Food analytical methods - New York, NY : Springer, 2008, 15(2022), 12 vom: 06. Aug., Seite 3365-3378
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:12 ; day:06 ; month:08 ; pages:3365-3378

Links:	Volltext

DOI / URN:	10.1007/s12161-022-02379-z

Katalog-ID:	SPR048541036

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allfields	10.1007/s12161-022-02379-z doi (DE-627)SPR048541036 (SPR)s12161-022-02379-z-e DE-627 ger DE-627 rakwb eng Acharya, Saswata verfasserin aut Analyzing Milk Foam Using Machine Learning for Diverse Applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. Milk (dpeaa)DE-He213 oam (dpeaa)DE-He213 Machine (dpeaa)DE-He213 earning (dpeaa)DE-He213 Foaming properties (dpeaa)DE-He213 Milk (dpeaa)DE-He213 dulterants (dpeaa)DE-He213 Milk (dpeaa)DE-He213 everages (dpeaa)DE-He213 Surfactants (dpeaa)DE-He213 Dandigunta, Babuji aut Sagar, Harsh aut Rani, Jyoti aut Priyadarsini, Madhumita aut Verma, Shreyansh aut Kushwaha, Jeetesh aut Fageria, Pradeep aut Lahiri, Pratik aut Chattopadhyay, Pradipta aut Dhoble, Abhishek S. aut Enthalten in Food analytical methods New York, NY : Springer, 2008 15(2022), 12 vom: 06. Aug., Seite 3365-3378 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378 https://dx.doi.org/10.1007/s12161-022-02379-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 12 06 08 3365-3378
spelling	10.1007/s12161-022-02379-z doi (DE-627)SPR048541036 (SPR)s12161-022-02379-z-e DE-627 ger DE-627 rakwb eng Acharya, Saswata verfasserin aut Analyzing Milk Foam Using Machine Learning for Diverse Applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. Milk (dpeaa)DE-He213 oam (dpeaa)DE-He213 Machine (dpeaa)DE-He213 earning (dpeaa)DE-He213 Foaming properties (dpeaa)DE-He213 Milk (dpeaa)DE-He213 dulterants (dpeaa)DE-He213 Milk (dpeaa)DE-He213 everages (dpeaa)DE-He213 Surfactants (dpeaa)DE-He213 Dandigunta, Babuji aut Sagar, Harsh aut Rani, Jyoti aut Priyadarsini, Madhumita aut Verma, Shreyansh aut Kushwaha, Jeetesh aut Fageria, Pradeep aut Lahiri, Pratik aut Chattopadhyay, Pradipta aut Dhoble, Abhishek S. aut Enthalten in Food analytical methods New York, NY : Springer, 2008 15(2022), 12 vom: 06. Aug., Seite 3365-3378 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378 https://dx.doi.org/10.1007/s12161-022-02379-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 12 06 08 3365-3378
allfields_unstemmed	10.1007/s12161-022-02379-z doi (DE-627)SPR048541036 (SPR)s12161-022-02379-z-e DE-627 ger DE-627 rakwb eng Acharya, Saswata verfasserin aut Analyzing Milk Foam Using Machine Learning for Diverse Applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. Milk (dpeaa)DE-He213 oam (dpeaa)DE-He213 Machine (dpeaa)DE-He213 earning (dpeaa)DE-He213 Foaming properties (dpeaa)DE-He213 Milk (dpeaa)DE-He213 dulterants (dpeaa)DE-He213 Milk (dpeaa)DE-He213 everages (dpeaa)DE-He213 Surfactants (dpeaa)DE-He213 Dandigunta, Babuji aut Sagar, Harsh aut Rani, Jyoti aut Priyadarsini, Madhumita aut Verma, Shreyansh aut Kushwaha, Jeetesh aut Fageria, Pradeep aut Lahiri, Pratik aut Chattopadhyay, Pradipta aut Dhoble, Abhishek S. aut Enthalten in Food analytical methods New York, NY : Springer, 2008 15(2022), 12 vom: 06. Aug., Seite 3365-3378 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378 https://dx.doi.org/10.1007/s12161-022-02379-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 12 06 08 3365-3378
allfieldsGer	10.1007/s12161-022-02379-z doi (DE-627)SPR048541036 (SPR)s12161-022-02379-z-e DE-627 ger DE-627 rakwb eng Acharya, Saswata verfasserin aut Analyzing Milk Foam Using Machine Learning for Diverse Applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. Milk (dpeaa)DE-He213 oam (dpeaa)DE-He213 Machine (dpeaa)DE-He213 earning (dpeaa)DE-He213 Foaming properties (dpeaa)DE-He213 Milk (dpeaa)DE-He213 dulterants (dpeaa)DE-He213 Milk (dpeaa)DE-He213 everages (dpeaa)DE-He213 Surfactants (dpeaa)DE-He213 Dandigunta, Babuji aut Sagar, Harsh aut Rani, Jyoti aut Priyadarsini, Madhumita aut Verma, Shreyansh aut Kushwaha, Jeetesh aut Fageria, Pradeep aut Lahiri, Pratik aut Chattopadhyay, Pradipta aut Dhoble, Abhishek S. aut Enthalten in Food analytical methods New York, NY : Springer, 2008 15(2022), 12 vom: 06. Aug., Seite 3365-3378 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378 https://dx.doi.org/10.1007/s12161-022-02379-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 12 06 08 3365-3378
allfieldsSound	10.1007/s12161-022-02379-z doi (DE-627)SPR048541036 (SPR)s12161-022-02379-z-e DE-627 ger DE-627 rakwb eng Acharya, Saswata verfasserin aut Analyzing Milk Foam Using Machine Learning for Diverse Applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. Milk (dpeaa)DE-He213 oam (dpeaa)DE-He213 Machine (dpeaa)DE-He213 earning (dpeaa)DE-He213 Foaming properties (dpeaa)DE-He213 Milk (dpeaa)DE-He213 dulterants (dpeaa)DE-He213 Milk (dpeaa)DE-He213 everages (dpeaa)DE-He213 Surfactants (dpeaa)DE-He213 Dandigunta, Babuji aut Sagar, Harsh aut Rani, Jyoti aut Priyadarsini, Madhumita aut Verma, Shreyansh aut Kushwaha, Jeetesh aut Fageria, Pradeep aut Lahiri, Pratik aut Chattopadhyay, Pradipta aut Dhoble, Abhishek S. aut Enthalten in Food analytical methods New York, NY : Springer, 2008 15(2022), 12 vom: 06. Aug., Seite 3365-3378 (DE-627)566007320 (DE-600)2424728-5 1936-976X nnns volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378 https://dx.doi.org/10.1007/s12161-022-02379-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 12 06 08 3365-3378
language	English
source	Enthalten in Food analytical methods 15(2022), 12 vom: 06. Aug., Seite 3365-3378 volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378
sourceStr	Enthalten in Food analytical methods 15(2022), 12 vom: 06. Aug., Seite 3365-3378 volume:15 year:2022 number:12 day:06 month:08 pages:3365-3378
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Milk oam Machine earning Foaming properties dulterants everages Surfactants
isfreeaccess_bool	false
container_title	Food analytical methods
authorswithroles_txt_mv	Acharya, Saswata @@aut@@ Dandigunta, Babuji @@aut@@ Sagar, Harsh @@aut@@ Rani, Jyoti @@aut@@ Priyadarsini, Madhumita @@aut@@ Verma, Shreyansh @@aut@@ Kushwaha, Jeetesh @@aut@@ Fageria, Pradeep @@aut@@ Lahiri, Pratik @@aut@@ Chattopadhyay, Pradipta @@aut@@ Dhoble, Abhishek S. @@aut@@
publishDateDaySort_date	2022-08-06T00:00:00Z
hierarchy_top_id	566007320
id	SPR048541036
language_de	englisch
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author	Acharya, Saswata
spellingShingle	Acharya, Saswata misc Milk misc oam misc Machine misc earning misc Foaming properties misc dulterants misc everages misc Surfactants Analyzing Milk Foam Using Machine Learning for Diverse Applications
authorStr	Acharya, Saswata
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topic_title	Analyzing Milk Foam Using Machine Learning for Diverse Applications Milk (dpeaa)DE-He213 oam (dpeaa)DE-He213 Machine (dpeaa)DE-He213 earning (dpeaa)DE-He213 Foaming properties (dpeaa)DE-He213 dulterants (dpeaa)DE-He213 everages (dpeaa)DE-He213 Surfactants (dpeaa)DE-He213
topic	misc Milk misc oam misc Machine misc earning misc Foaming properties misc dulterants misc everages misc Surfactants
topic_unstemmed	misc Milk misc oam misc Machine misc earning misc Foaming properties misc dulterants misc everages misc Surfactants
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title	Analyzing Milk Foam Using Machine Learning for Diverse Applications
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title_full	Analyzing Milk Foam Using Machine Learning for Diverse Applications
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author_browse	Acharya, Saswata Dandigunta, Babuji Sagar, Harsh Rani, Jyoti Priyadarsini, Madhumita Verma, Shreyansh Kushwaha, Jeetesh Fageria, Pradeep Lahiri, Pratik Chattopadhyay, Pradipta Dhoble, Abhishek S.
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title_sort	analyzing milk foam using machine learning for diverse applications
title_auth	Analyzing Milk Foam Using Machine Learning for Diverse Applications
abstract	Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract In the beverages industry, milk foaming is done to enhance the flavor, texture, and visual appeal of milk-based beverages. It is very crucial to study milk foam properties not just to create visually appealing and rich in taste beverages but also to estimate the adulterants present in it. Machine learning is being used in every field nowadays as it can analyze large datasets quickly and help in making data-driven decisions. This paper is a demonstration of how a futuristic apparatus will detect the best type of milk for beverages and identify milk adulteration using machine learning. In the current study, machine learning methods are employed to assess milk foam properties. This study aims to choose the best type of milk for foam-based milk beverages preparations and detect surfactants often used in low concentrations for foaming but act as adulterants at high concentrations. Surfactants alter the foaming properties of milk in different ways depending on their charge and are therefore used in the dairy industry. By using machine learning techniques, the impact of three different surfactants, having distinct ionic properties, on three distinct types of milk have been analyzed. It was found that foaming properties of milk were highly correlated to each other. “Random forest classifier” turned out to be the most effective among all the machine learning models in both the tasks. Heating and addition of sodium dodecyl sulfate (SDS) improved foaming. The findings of this study can be used for deriving valuable insights about the dairy industry. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	12
title_short	Analyzing Milk Foam Using Machine Learning for Diverse Applications
url	https://dx.doi.org/10.1007/s12161-022-02379-z
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author2	Dandigunta, Babuji Sagar, Harsh Rani, Jyoti Priyadarsini, Madhumita Verma, Shreyansh Kushwaha, Jeetesh Fageria, Pradeep Lahiri, Pratik Chattopadhyay, Pradipta Dhoble, Abhishek S.
author2Str	Dandigunta, Babuji Sagar, Harsh Rani, Jyoti Priyadarsini, Madhumita Verma, Shreyansh Kushwaha, Jeetesh Fageria, Pradeep Lahiri, Pratik Chattopadhyay, Pradipta Dhoble, Abhishek S.
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doi_str	10.1007/s12161-022-02379-z
up_date	2024-07-03T19:53:25.457Z
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