Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy
BACKGROUND Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible–NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates. RESULTS Visible–NIR a...
Gespeichert in:
| Veröffentlicht in: | Journal of the science of food and agriculture 2018-03, Vol.98 (5), p.1703-1711 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1711 |
|---|---|
| container_issue | 5 |
| container_start_page | 1703 |
| container_title | Journal of the science of food and agriculture |
| container_volume | 98 |
| creator | Pomares‐Viciana, Teresa Martínez‐Valdivieso, Damián Font, Rafael Gómez, Pedro del Río‐Celestino, Mercedes |
| description | BACKGROUND
Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible–NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates.
RESULTS
Visible–NIR and wet chemical methods were used to determine individual sugars and starch in zucchini fruits. By applying a principal component analysis (PCA) with NIR spectral data a differentiation between the less sweet versus the sweetest zucchini accessions could be found. For the determination of carbohydrate content effective prediction models for individual sugars such as glucose, fructose, sucrose and starch by using partial least squares (PLS) regression have been developed.
CONCLUSION
The coefficients of determination in the external validation (R2VAL) ranged from 0.66 to 0.85. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for different quality compounds and showed values that were characteristic of equations suitable for screening purposes. From the study of the MPLS loadings of the first three terms of the different equations for sugars and starch, it can be concluded that some major cell components such as pigments, cellulose, organic acids highly participated in modelling the equations for carbohydrates. © 2017 Society of Chemical Industry |
| doi_str_mv | 10.1002/jsfa.8642 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1933938667</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1933938667</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3532-cc80938cf95eeaee694be0a1fa405f92541e5514c97071ac3ea86128a3f307383</originalsourceid><addsrcrecordid>eNp1kcFuEzEQhi1ERUPhwAsgS1zgsO3Y3vWuj1VEgapSD8DZmjhj4iixF3tXKDw9TlM4IPVkjebTN575GXsj4FIAyKtt8Xg56FY-YwsBpm8ABDxni9qTTSdaec5elrIFAGO0fsHO5TB0SnR6wbbLDWZ0E-VQcAopcoxrPmZaB_dQJs8d5lXaHNYZJ-IuxYnixEPkv2fnNiEG7vMcJj6XEH_wSJhr02esDl5GclNOxaXx8IqdedwVev34XrDvNx-_LT83d_efviyv7xqnOiUb5wYwanDedERIpE27IkDhsYXOG9m1grq6lDM99AKdIhy0kAMqr6BXg7pg70_eMaefM5XJ7kNxtNthpDQXK4xSdYDWfUXf_Ydu05xj_Z2V9YRaQ2ugUh9OlKublEzejjnsMR-sAHsMwB4DsMcAKvv20Tiv9rT-R_69eAWuTsCvsKPD0yZ7-_Xm-kH5BzGBkP0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2010660490</pqid></control><display><type>article</type><title>Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Pomares‐Viciana, Teresa ; Martínez‐Valdivieso, Damián ; Font, Rafael ; Gómez, Pedro ; del Río‐Celestino, Mercedes</creator><creatorcontrib>Pomares‐Viciana, Teresa ; Martínez‐Valdivieso, Damián ; Font, Rafael ; Gómez, Pedro ; del Río‐Celestino, Mercedes</creatorcontrib><description>BACKGROUND
Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible–NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates.
RESULTS
Visible–NIR and wet chemical methods were used to determine individual sugars and starch in zucchini fruits. By applying a principal component analysis (PCA) with NIR spectral data a differentiation between the less sweet versus the sweetest zucchini accessions could be found. For the determination of carbohydrate content effective prediction models for individual sugars such as glucose, fructose, sucrose and starch by using partial least squares (PLS) regression have been developed.
CONCLUSION
The coefficients of determination in the external validation (R2VAL) ranged from 0.66 to 0.85. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for different quality compounds and showed values that were characteristic of equations suitable for screening purposes. From the study of the MPLS loadings of the first three terms of the different equations for sugars and starch, it can be concluded that some major cell components such as pigments, cellulose, organic acids highly participated in modelling the equations for carbohydrates. © 2017 Society of Chemical Industry</description><identifier>ISSN: 0022-5142</identifier><identifier>EISSN: 1097-0010</identifier><identifier>DOI: 10.1002/jsfa.8642</identifier><identifier>PMID: 28853156</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Animal models ; Carbohydrates ; Carbohydrates - chemistry ; Cellulose ; Cellulose - analysis ; Cucurbita ; Cucurbita - chemistry ; Feasibility studies ; Fructose ; Fructose - analysis ; Fruit - chemistry ; Fruits ; glucose ; Glucose - analysis ; Infrared spectroscopy ; Mathematical models ; Near infrared radiation ; NIRS ; Nutrition ; Organic acids ; Pigments ; Prediction models ; Principal components analysis ; Regression analysis ; Spectroscopy, Near-Infrared - methods ; Standard error ; Starch ; Starch - analysis ; Sucrose ; Sucrose - analysis ; Sugar ; Sweet taste ; Vegetables - chemistry</subject><ispartof>Journal of the science of food and agriculture, 2018-03, Vol.98 (5), p.1703-1711</ispartof><rights>2017 Society of Chemical Industry</rights><rights>2017 Society of Chemical Industry.</rights><rights>2018 Society of Chemical Industry</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3532-cc80938cf95eeaee694be0a1fa405f92541e5514c97071ac3ea86128a3f307383</citedby><cites>FETCH-LOGICAL-c3532-cc80938cf95eeaee694be0a1fa405f92541e5514c97071ac3ea86128a3f307383</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjsfa.8642$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjsfa.8642$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,778,782,1414,27907,27908,45557,45558</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28853156$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pomares‐Viciana, Teresa</creatorcontrib><creatorcontrib>Martínez‐Valdivieso, Damián</creatorcontrib><creatorcontrib>Font, Rafael</creatorcontrib><creatorcontrib>Gómez, Pedro</creatorcontrib><creatorcontrib>del Río‐Celestino, Mercedes</creatorcontrib><title>Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy</title><title>Journal of the science of food and agriculture</title><addtitle>J Sci Food Agric</addtitle><description>BACKGROUND
Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible–NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates.
RESULTS
Visible–NIR and wet chemical methods were used to determine individual sugars and starch in zucchini fruits. By applying a principal component analysis (PCA) with NIR spectral data a differentiation between the less sweet versus the sweetest zucchini accessions could be found. For the determination of carbohydrate content effective prediction models for individual sugars such as glucose, fructose, sucrose and starch by using partial least squares (PLS) regression have been developed.
CONCLUSION
The coefficients of determination in the external validation (R2VAL) ranged from 0.66 to 0.85. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for different quality compounds and showed values that were characteristic of equations suitable for screening purposes. From the study of the MPLS loadings of the first three terms of the different equations for sugars and starch, it can be concluded that some major cell components such as pigments, cellulose, organic acids highly participated in modelling the equations for carbohydrates. © 2017 Society of Chemical Industry</description><subject>Animal models</subject><subject>Carbohydrates</subject><subject>Carbohydrates - chemistry</subject><subject>Cellulose</subject><subject>Cellulose - analysis</subject><subject>Cucurbita</subject><subject>Cucurbita - chemistry</subject><subject>Feasibility studies</subject><subject>Fructose</subject><subject>Fructose - analysis</subject><subject>Fruit - chemistry</subject><subject>Fruits</subject><subject>glucose</subject><subject>Glucose - analysis</subject><subject>Infrared spectroscopy</subject><subject>Mathematical models</subject><subject>Near infrared radiation</subject><subject>NIRS</subject><subject>Nutrition</subject><subject>Organic acids</subject><subject>Pigments</subject><subject>Prediction models</subject><subject>Principal components analysis</subject><subject>Regression analysis</subject><subject>Spectroscopy, Near-Infrared - methods</subject><subject>Standard error</subject><subject>Starch</subject><subject>Starch - analysis</subject><subject>Sucrose</subject><subject>Sucrose - analysis</subject><subject>Sugar</subject><subject>Sweet taste</subject><subject>Vegetables - chemistry</subject><issn>0022-5142</issn><issn>1097-0010</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kcFuEzEQhi1ERUPhwAsgS1zgsO3Y3vWuj1VEgapSD8DZmjhj4iixF3tXKDw9TlM4IPVkjebTN575GXsj4FIAyKtt8Xg56FY-YwsBpm8ABDxni9qTTSdaec5elrIFAGO0fsHO5TB0SnR6wbbLDWZ0E-VQcAopcoxrPmZaB_dQJs8d5lXaHNYZJ-IuxYnixEPkv2fnNiEG7vMcJj6XEH_wSJhr02esDl5GclNOxaXx8IqdedwVev34XrDvNx-_LT83d_efviyv7xqnOiUb5wYwanDedERIpE27IkDhsYXOG9m1grq6lDM99AKdIhy0kAMqr6BXg7pg70_eMaefM5XJ7kNxtNthpDQXK4xSdYDWfUXf_Ydu05xj_Z2V9YRaQ2ugUh9OlKublEzejjnsMR-sAHsMwB4DsMcAKvv20Tiv9rT-R_69eAWuTsCvsKPD0yZ7-_Xm-kH5BzGBkP0</recordid><startdate>201803</startdate><enddate>201803</enddate><creator>Pomares‐Viciana, Teresa</creator><creator>Martínez‐Valdivieso, Damián</creator><creator>Font, Rafael</creator><creator>Gómez, Pedro</creator><creator>del Río‐Celestino, Mercedes</creator><general>John Wiley & Sons, Ltd</general><general>John Wiley and Sons, Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QL</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T5</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>201803</creationdate><title>Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy</title><author>Pomares‐Viciana, Teresa ; Martínez‐Valdivieso, Damián ; Font, Rafael ; Gómez, Pedro ; del Río‐Celestino, Mercedes</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3532-cc80938cf95eeaee694be0a1fa405f92541e5514c97071ac3ea86128a3f307383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animal models</topic><topic>Carbohydrates</topic><topic>Carbohydrates - chemistry</topic><topic>Cellulose</topic><topic>Cellulose - analysis</topic><topic>Cucurbita</topic><topic>Cucurbita - chemistry</topic><topic>Feasibility studies</topic><topic>Fructose</topic><topic>Fructose - analysis</topic><topic>Fruit - chemistry</topic><topic>Fruits</topic><topic>glucose</topic><topic>Glucose - analysis</topic><topic>Infrared spectroscopy</topic><topic>Mathematical models</topic><topic>Near infrared radiation</topic><topic>NIRS</topic><topic>Nutrition</topic><topic>Organic acids</topic><topic>Pigments</topic><topic>Prediction models</topic><topic>Principal components analysis</topic><topic>Regression analysis</topic><topic>Spectroscopy, Near-Infrared - methods</topic><topic>Standard error</topic><topic>Starch</topic><topic>Starch - analysis</topic><topic>Sucrose</topic><topic>Sucrose - analysis</topic><topic>Sugar</topic><topic>Sweet taste</topic><topic>Vegetables - chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pomares‐Viciana, Teresa</creatorcontrib><creatorcontrib>Martínez‐Valdivieso, Damián</creatorcontrib><creatorcontrib>Font, Rafael</creatorcontrib><creatorcontrib>Gómez, Pedro</creatorcontrib><creatorcontrib>del Río‐Celestino, Mercedes</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the science of food and agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pomares‐Viciana, Teresa</au><au>Martínez‐Valdivieso, Damián</au><au>Font, Rafael</au><au>Gómez, Pedro</au><au>del Río‐Celestino, Mercedes</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy</atitle><jtitle>Journal of the science of food and agriculture</jtitle><addtitle>J Sci Food Agric</addtitle><date>2018-03</date><risdate>2018</risdate><volume>98</volume><issue>5</issue><spage>1703</spage><epage>1711</epage><pages>1703-1711</pages><issn>0022-5142</issn><eissn>1097-0010</eissn><abstract>BACKGROUND
Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible–NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates.
RESULTS
Visible–NIR and wet chemical methods were used to determine individual sugars and starch in zucchini fruits. By applying a principal component analysis (PCA) with NIR spectral data a differentiation between the less sweet versus the sweetest zucchini accessions could be found. For the determination of carbohydrate content effective prediction models for individual sugars such as glucose, fructose, sucrose and starch by using partial least squares (PLS) regression have been developed.
CONCLUSION
The coefficients of determination in the external validation (R2VAL) ranged from 0.66 to 0.85. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for different quality compounds and showed values that were characteristic of equations suitable for screening purposes. From the study of the MPLS loadings of the first three terms of the different equations for sugars and starch, it can be concluded that some major cell components such as pigments, cellulose, organic acids highly participated in modelling the equations for carbohydrates. © 2017 Society of Chemical Industry</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>28853156</pmid><doi>10.1002/jsfa.8642</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0022-5142 |
| ispartof | Journal of the science of food and agriculture, 2018-03, Vol.98 (5), p.1703-1711 |
| issn | 0022-5142 1097-0010 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1933938667 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Animal models Carbohydrates Carbohydrates - chemistry Cellulose Cellulose - analysis Cucurbita Cucurbita - chemistry Feasibility studies Fructose Fructose - analysis Fruit - chemistry Fruits glucose Glucose - analysis Infrared spectroscopy Mathematical models Near infrared radiation NIRS Nutrition Organic acids Pigments Prediction models Principal components analysis Regression analysis Spectroscopy, Near-Infrared - methods Standard error Starch Starch - analysis Sucrose Sucrose - analysis Sugar Sweet taste Vegetables - chemistry |
| title | Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T07%3A05%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Characterisation%20and%20prediction%20of%20carbohydrate%20content%20in%20zucchini%20fruit%20using%20near%20infrared%20spectroscopy&rft.jtitle=Journal%20of%20the%20science%20of%20food%20and%20agriculture&rft.au=Pomares%E2%80%90Viciana,%20Teresa&rft.date=2018-03&rft.volume=98&rft.issue=5&rft.spage=1703&rft.epage=1711&rft.pages=1703-1711&rft.issn=0022-5142&rft.eissn=1097-0010&rft_id=info:doi/10.1002/jsfa.8642&rft_dat=%3Cproquest_cross%3E1933938667%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2010660490&rft_id=info:pmid/28853156&rfr_iscdi=true |