Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy
Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potenti...
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description | Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potential technique to detect adulterations in pistachio. The objective of this study was to generate a non-targeted method for portable FT-IR and UV–Vis spectrometers to authenticate pistachio and detect green pea and peanut adulterations. Pistachio granules were adulterated with green pea and peanut at different concentrations (5 to 40% w/w). Spectra were collected by a portable FT-IR spectrometer and by a conventional UV–Vis spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio, and Partial Least Square Regression (PLSR) to predict the concentrations of adulterants. SIMCA showed very distinct clusters for pure samples. Moreover, adulterated pistachio samples were discriminated by SIMCA even in low levels of adulteration (5%). Portable FTIR showed excellent performance (r
val
> 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV–VIS predicted (r
val
> 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV–Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio. |
doi_str_mv | 10.1007/s11694-020-00710-y |
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val
> 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV–VIS predicted (r
val
> 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV–Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio.</description><identifier>ISSN: 2193-4126</identifier><identifier>EISSN: 2193-4134</identifier><identifier>DOI: 10.1007/s11694-020-00710-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Additives ; Adulterants ; Algorithms ; Chemistry ; Chemistry and Materials Science ; Chemistry/Food Science ; Economic importance ; Engineering ; Food Science ; Food Science & Technology ; Fourier transforms ; Infrared spectroscopy ; Life Sciences & Biomedicine ; Original Paper ; Peanuts ; Peas ; Portability ; Predictions ; Regression analysis ; Science & Technology ; Spectrometers ; Spectroscopy ; Spectrum analysis ; Standard error</subject><ispartof>Journal of food measurement & characterization, 2021-04, Vol.15 (2), p.1075-1082</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>9</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000580496200004</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c319t-ab1719bbfe055800f3c3e009db0068ef8fc99a09994381bedfa47fdd5671e8e93</citedby><cites>FETCH-LOGICAL-c319t-ab1719bbfe055800f3c3e009db0068ef8fc99a09994381bedfa47fdd5671e8e93</cites><orcidid>0000-0003-2454-7898</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11694-020-00710-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11694-020-00710-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,39263,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Menevseoglu, Ahmed</creatorcontrib><creatorcontrib>Aykas, Didem Peren</creatorcontrib><creatorcontrib>Adal, Eda</creatorcontrib><title>Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy</title><title>Journal of food measurement & characterization</title><addtitle>Food Measure</addtitle><addtitle>J FOOD MEAS CHARACT</addtitle><description>Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potential technique to detect adulterations in pistachio. The objective of this study was to generate a non-targeted method for portable FT-IR and UV–Vis spectrometers to authenticate pistachio and detect green pea and peanut adulterations. Pistachio granules were adulterated with green pea and peanut at different concentrations (5 to 40% w/w). Spectra were collected by a portable FT-IR spectrometer and by a conventional UV–Vis spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio, and Partial Least Square Regression (PLSR) to predict the concentrations of adulterants. SIMCA showed very distinct clusters for pure samples. Moreover, adulterated pistachio samples were discriminated by SIMCA even in low levels of adulteration (5%). Portable FTIR showed excellent performance (r
val
> 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV–VIS predicted (r
val
> 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV–Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio.</description><subject>Additives</subject><subject>Adulterants</subject><subject>Algorithms</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Economic importance</subject><subject>Engineering</subject><subject>Food Science</subject><subject>Food Science & Technology</subject><subject>Fourier transforms</subject><subject>Infrared spectroscopy</subject><subject>Life Sciences & Biomedicine</subject><subject>Original Paper</subject><subject>Peanuts</subject><subject>Peas</subject><subject>Portability</subject><subject>Predictions</subject><subject>Regression analysis</subject><subject>Science & Technology</subject><subject>Spectrometers</subject><subject>Spectroscopy</subject><subject>Spectrum analysis</subject><subject>Standard error</subject><issn>2193-4126</issn><issn>2193-4134</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNkM9O3DAQxqOqlYqAF-jJEsdiGMfeJD5WK2iREJUq4Go5znhrtNip7ajKjT5D35Anwbup6K3qacbj7zd_vqr6wOCMAbTnibFGCgo10PJkQOc31UHNJKeCcfH2Na-b99VxSg8AwFgrRMMPql83wdOs4wYzDkSPYwzafCc5kKFUTCabiOjJiJpoP-yinzLRw7TNGHV2wRNXvl3KBXOB9DOZkvMbMoaYdb9FcnlLr76d7um7--en3_cukTSW1jEkE8b5qHpn9Tbh8Z94WN1dXtyuv9Drr5-v1p-uqeFMZqp71jLZ9xZhteoALDccAeTQAzQd2s4aKTVIKQXvWI-D1aK1w7BqWoYdSn5YnSx9y4k_JkxZPYQp-jJS1SsoM7q63anqRWXKeimiVWN0jzrOioHaua0Wt1VxW-3dVnOBugX6iX2wyTj0Bl_BYndZWMimLhmItct739Zh8rmgH_8fLWq-qFNR-A3Gvzf8Y70XTu2lsg</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Menevseoglu, Ahmed</creator><creator>Aykas, Didem Peren</creator><creator>Adal, Eda</creator><general>Springer US</general><general>Springer Nature</general><general>Springer Nature B.V</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><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>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</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>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-2454-7898</orcidid></search><sort><creationdate>20210401</creationdate><title>Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy</title><author>Menevseoglu, Ahmed ; Aykas, Didem Peren ; Adal, Eda</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-ab1719bbfe055800f3c3e009db0068ef8fc99a09994381bedfa47fdd5671e8e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Additives</topic><topic>Adulterants</topic><topic>Algorithms</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Economic importance</topic><topic>Engineering</topic><topic>Food Science</topic><topic>Food Science & Technology</topic><topic>Fourier transforms</topic><topic>Infrared spectroscopy</topic><topic>Life Sciences & Biomedicine</topic><topic>Original Paper</topic><topic>Peanuts</topic><topic>Peas</topic><topic>Portability</topic><topic>Predictions</topic><topic>Regression analysis</topic><topic>Science & Technology</topic><topic>Spectrometers</topic><topic>Spectroscopy</topic><topic>Spectrum analysis</topic><topic>Standard error</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Menevseoglu, Ahmed</creatorcontrib><creatorcontrib>Aykas, Didem Peren</creatorcontrib><creatorcontrib>Adal, Eda</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><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 (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</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>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Journal of food measurement & characterization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Menevseoglu, Ahmed</au><au>Aykas, Didem Peren</au><au>Adal, Eda</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy</atitle><jtitle>Journal of food measurement & characterization</jtitle><stitle>Food Measure</stitle><stitle>J FOOD MEAS CHARACT</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>15</volume><issue>2</issue><spage>1075</spage><epage>1082</epage><pages>1075-1082</pages><issn>2193-4126</issn><eissn>2193-4134</eissn><abstract>Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potential technique to detect adulterations in pistachio. The objective of this study was to generate a non-targeted method for portable FT-IR and UV–Vis spectrometers to authenticate pistachio and detect green pea and peanut adulterations. Pistachio granules were adulterated with green pea and peanut at different concentrations (5 to 40% w/w). Spectra were collected by a portable FT-IR spectrometer and by a conventional UV–Vis spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio, and Partial Least Square Regression (PLSR) to predict the concentrations of adulterants. SIMCA showed very distinct clusters for pure samples. Moreover, adulterated pistachio samples were discriminated by SIMCA even in low levels of adulteration (5%). Portable FTIR showed excellent performance (r
val
> 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV–VIS predicted (r
val
> 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV–Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11694-020-00710-y</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-2454-7898</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Additives Adulterants Algorithms Chemistry Chemistry and Materials Science Chemistry/Food Science Economic importance Engineering Food Science Food Science & Technology Fourier transforms Infrared spectroscopy Life Sciences & Biomedicine Original Paper Peanuts Peas Portability Predictions Regression analysis Science & Technology Spectrometers Spectroscopy Spectrum analysis Standard error |
title | Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy |
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