Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality
Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. ‘Big Top’ and cv. ‘Magique’, were analysed by visible and...
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| Veröffentlicht in: | Food and bioprocess technology 2017-10, Vol.10 (10), p.1755-1766 |
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| creator | Cortés, V. Blasco, J. Aleixos, N. Cubero, S. Talens, P. |
| description | Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. ‘Big Top’ and cv. ‘Magique’, were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (
R
2
P
of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification. |
| doi_str_mv | 10.1007/s11947-017-1943-y |
| format | Article |
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R
2
P
of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.</description><identifier>ISSN: 1935-5130</identifier><identifier>EISSN: 1935-5149</identifier><identifier>DOI: 10.1007/s11947-017-1943-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Agricultural products ; Agriculture ; Biotechnology ; Chemistry ; Chemistry and Materials Science ; Chemistry/Food Science ; Control methods ; Diffuse reflectance spectroscopy ; Food Science ; I.R. radiation ; Identification methods ; Infrared analysis ; Infrared spectra ; Infrared spectroscopy ; Least squares method ; Near infrared radiation ; Nickel ; Original Paper ; Quality assessment ; Quality control ; Reflectance ; Regression analysis ; Spectrum analysis</subject><ispartof>Food and bioprocess technology, 2017-10, Vol.10 (10), p.1755-1766</ispartof><rights>Springer Science+Business Media, LLC 2017</rights><rights>Springer Science+Business Media, LLC 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-9dbe9892a630c8046b6c00fc0a122844576d87eb2c2386ee048a89b47256ada3</citedby><cites>FETCH-LOGICAL-c359t-9dbe9892a630c8046b6c00fc0a122844576d87eb2c2386ee048a89b47256ada3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11947-017-1943-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11947-017-1943-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27926,27927,41490,42559,51321</link.rule.ids></links><search><creatorcontrib>Cortés, V.</creatorcontrib><creatorcontrib>Blasco, J.</creatorcontrib><creatorcontrib>Aleixos, N.</creatorcontrib><creatorcontrib>Cubero, S.</creatorcontrib><creatorcontrib>Talens, P.</creatorcontrib><title>Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality</title><title>Food and bioprocess technology</title><addtitle>Food Bioprocess Technol</addtitle><description>Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. ‘Big Top’ and cv. ‘Magique’, were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (
R
2
P
of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.</description><subject>Agricultural products</subject><subject>Agriculture</subject><subject>Biotechnology</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Control methods</subject><subject>Diffuse reflectance spectroscopy</subject><subject>Food Science</subject><subject>I.R. radiation</subject><subject>Identification methods</subject><subject>Infrared analysis</subject><subject>Infrared spectra</subject><subject>Infrared spectroscopy</subject><subject>Least squares method</subject><subject>Near infrared radiation</subject><subject>Nickel</subject><subject>Original Paper</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Reflectance</subject><subject>Regression analysis</subject><subject>Spectrum analysis</subject><issn>1935-5130</issn><issn>1935-5149</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1UMtOwzAQtBBIlMIHcLPE2bBOnIePVaFQqQIBFVfLcdYoVZoEO0XKjU_HVQqcOO3sah7aIeSSwzUHyG4851JkDHjGAojZcEQmXMYJS7iQx784hlNy5v0GIAXB4wn5eqt8VdRIdVPSR9SOLRvrtMOS3lbW7jzSF7Q1ml43BulrF5BrvWm7gdrW0YX2PX3e6brqdV99jj5hb_qfw8x79H6LTU9bGyKCk6saPIiGc3Jide3x4jCnZL24W88f2OrpfjmfrZiJE9kzWRYocxnpNAaTg0iL1ABYA5pHUS5EkqVlnmERmSjOU0QQuc5lIbIoSXWp4ym5Gm07137s0Pdq0-5cExJVJDjkIpNCBhYfWSb86B1a1blqq92gOKh9z2rsWYWe1b5nNQRNNGp84Dbv6P6c_xd9A5Mbgo4</recordid><startdate>20171001</startdate><enddate>20171001</enddate><creator>Cortés, V.</creator><creator>Blasco, J.</creator><creator>Aleixos, N.</creator><creator>Cubero, S.</creator><creator>Talens, P.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X2</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M0K</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20171001</creationdate><title>Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality</title><author>Cortés, V. ; Blasco, J. ; Aleixos, N. ; Cubero, S. ; Talens, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-9dbe9892a630c8046b6c00fc0a122844576d87eb2c2386ee048a89b47256ada3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Agricultural products</topic><topic>Agriculture</topic><topic>Biotechnology</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Control methods</topic><topic>Diffuse reflectance spectroscopy</topic><topic>Food Science</topic><topic>I.R. radiation</topic><topic>Identification methods</topic><topic>Infrared analysis</topic><topic>Infrared spectra</topic><topic>Infrared spectroscopy</topic><topic>Least squares method</topic><topic>Near infrared radiation</topic><topic>Nickel</topic><topic>Original Paper</topic><topic>Quality assessment</topic><topic>Quality control</topic><topic>Reflectance</topic><topic>Regression analysis</topic><topic>Spectrum analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cortés, V.</creatorcontrib><creatorcontrib>Blasco, J.</creatorcontrib><creatorcontrib>Aleixos, N.</creatorcontrib><creatorcontrib>Cubero, S.</creatorcontrib><creatorcontrib>Talens, P.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Agricultural Science Database</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>Food and bioprocess technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cortés, V.</au><au>Blasco, J.</au><au>Aleixos, N.</au><au>Cubero, S.</au><au>Talens, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality</atitle><jtitle>Food and bioprocess technology</jtitle><stitle>Food Bioprocess Technol</stitle><date>2017-10-01</date><risdate>2017</risdate><volume>10</volume><issue>10</issue><spage>1755</spage><epage>1766</epage><pages>1755-1766</pages><issn>1935-5130</issn><eissn>1935-5149</eissn><abstract>Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. ‘Big Top’ and cv. ‘Magique’, were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (
R
2
P
of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11947-017-1943-y</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural products Agriculture Biotechnology Chemistry Chemistry and Materials Science Chemistry/Food Science Control methods Diffuse reflectance spectroscopy Food Science I.R. radiation Identification methods Infrared analysis Infrared spectra Infrared spectroscopy Least squares method Near infrared radiation Nickel Original Paper Quality assessment Quality control Reflectance Regression analysis Spectrum analysis |
| title | Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality |
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