{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/93060"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/93060","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Developing a spectroscopy-based high throughput assay for anthocyanin content in corn","abstract":"Anthocyanins are one of the only natural colorants approved by the US Food & Drug Administration (FDA) and hence, are highly sought after as natural color pigment for the food industry. However, anthocyanins are susceptible to gradual degradation when exposed to certain food processing techniques or even due to prolonged/improper storage of food products that contain them. Although chemical assays to determine total anthocyanin content (TAC) exist, they are cumbersome, time consuming, and often requires destroying the sample. Spectroscopy-based assays are simple, fast, and nondestructive analytical tools that may be used in determining TAC in fruits and cereal grains, and near infrared (NIR) spectroscopy is widely used in analyzing the chemical composition of raw materials in the food, agricultural and pharmaceutical industries. In this study, tristimulus colorimetry and NIR spectroscopy were explored as a means of detecting and estimating anthocyanins in whole corn kernels and in ground corn. Results of the study showed that L*a*b* measurements were not useful for predicting TAC of whole corn samples, despite having multiple linear regression (MLR) models with 0.60 > R2 > 0.70. The poor predictive performance was due to the presence of water insoluble, red colored pigments called phlobaphenes which exhibited similar L*a*b* values as anthocyanins. The first partial least squares regression (PLSR) models were developed to predict TAC in ground corn samples that were blended with cyanidin-3-glucoside (C3G) to yield 0-1154 mg/kg TAC. Of the 51 blended corn samples, 12 contained phlobaphenes. The best PLSR model was based on NIR spectra that had been pretreated with a combination of multiplicative scatter correction (MSC) and second order Savitzky-Golay (SG) derivative. The scores plot of the model showed a prominent separation between red and yellow corn blends as compared to other models. With RPD = 1.6 and RER = 4.7, the model was useful for rough screening purposes. When the same PLSR approach was applied to the NIR spectra of whole corn samples, the best PLSR model was based on first order SG (using 13 smoothing points) pretreated spectra and was also useful for rough screening purposes only. Model performance improved when phlobaphene-containing samples were removed from the calibration and validation sets and with RER = 10.9 and RPD = 3.6, this model can be used for full screening purposes. This work demonstrates the potential of NIR spectroscopy as a method for rapidly estimating TAC and to discriminate corn samples containing phlobaphenes when a wider scanning range (1000-2500 nm) is used.","abstract_html":"Anthocyanins are one of the only natural colorants approved by the US Food & Drug Administration (FDA) and hence, are highly sought after as natural color pigment for the food industry. However, anthocyanins are susceptible to gradual degradation when exposed to certain food processing techniques or even due to prolonged/improper storage of food products that contain them. Although chemical assays to determine total anthocyanin content (TAC) exist, they are cumbersome, time consuming, and often requires destroying the sample. Spectroscopy-based assays are simple, fast, and nondestructive analytical tools that may be used in determining TAC in fruits and cereal grains, and near infrared (NIR) spectroscopy is widely used in analyzing the chemical composition of raw materials in the food, agricultural and pharmaceutical industries. In this study, tristimulus colorimetry and NIR spectroscopy were explored as a means of detecting and estimating anthocyanins in whole corn kernels and in ground corn. Results of the study showed that L*a*b* measurements were not useful for predicting TAC of whole corn samples, despite having multiple linear regression (MLR) models with 0.60 > R2 > 0.70. The poor predictive performance was due to the presence of water insoluble, red colored pigments called phlobaphenes which exhibited similar L*a*b* values as anthocyanins. The first partial least squares regression (PLSR) models were developed to predict TAC in ground corn samples that were blended with cyanidin-3-glucoside (C3G) to yield 0-1154 mg/kg TAC. Of the 51 blended corn samples, 12 contained phlobaphenes. The best PLSR model was based on NIR spectra that had been pretreated with a combination of multiplicative scatter correction (MSC) and second order Savitzky-Golay (SG) derivative. The scores plot of the model showed a prominent separation between red and yellow corn blends as compared to other models. With RPD = 1.6 and RER = 4.7, the model was useful for rough screening purposes. When the same PLSR approach was applied to the NIR spectra of whole corn samples, the best PLSR model was based on first order SG (using 13 smoothing points) pretreated spectra and was also useful for rough screening purposes only. Model performance improved when phlobaphene-containing samples were removed from the calibration and validation sets and with RER = 10.9 and RPD = 3.6, this model can be used for full screening purposes. This work demonstrates the potential of NIR spectroscopy as a method for rapidly estimating TAC and to discriminate corn samples containing phlobaphenes when a wider scanning range (1000-2500 nm) is used.","abstract_has_math":false,"creators":["Mangalvedhe, Ankita Anil"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Biological Engr","degree_department":null,"school":null,"contributors":["Danao, Mary-Grace C.","Rausch, Kent D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T18:42:59Z","date_published":"2016-11-10T18:42:59Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Near infrared","Anthocyanin"],"languages":["en"],"rights":["Copyright 2016 Ankita Mangalvedhe"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/93060","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Danao, Mary-Grace C.","Rausch, Kent D."]},{"key":"dc:creator","label":"Author","values":["Mangalvedhe, Ankita Anil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T18:42:59Z","2018-11-11T10:15:36Z","2016-07-15","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Biological Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Near infrared","Anthocyanin"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Ankita Mangalvedhe"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/93060"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Anthocyanins are one of the only natural colorants approved by the US Food & Drug Administration (FDA) and hence, are highly sought after as natural color pigment for the food industry. However, anthocyanins are susceptible to gradual degradation when exposed to certain food processing techniques or even due to prolonged/improper storage of food products that contain them. Although chemical assays to determine total anthocyanin content (TAC) exist, they are cumbersome, time consuming, and often requires destroying the sample. Spectroscopy-based assays are simple, fast, and nondestructive analytical tools that may be used in determining TAC in fruits and cereal grains, and near infrared (NIR) spectroscopy is widely used in analyzing the chemical composition of raw materials in the food, agricultural and pharmaceutical industries. In this study, tristimulus colorimetry and NIR spectroscopy were explored as a means of detecting and estimating anthocyanins in whole corn kernels and in ground corn. Results of the study showed that L*a*b* measurements were not useful for predicting TAC of whole corn samples, despite having multiple linear regression (MLR) models with 0.60 > R2 > 0.70. The poor predictive performance was due to the presence of water insoluble, red colored pigments called phlobaphenes which exhibited similar L*a*b* values as anthocyanins. The first partial least squares regression (PLSR) models were developed to predict TAC in ground corn samples that were blended with cyanidin-3-glucoside (C3G) to yield 0-1154 mg/kg TAC. Of the 51 blended corn samples, 12 contained phlobaphenes. The best PLSR model was based on NIR spectra that had been pretreated with a combination of multiplicative scatter correction (MSC) and second order Savitzky-Golay (SG) derivative. The scores plot of the model showed a prominent separation between red and yellow corn blends as compared to other models. With RPD = 1.6 and RER = 4.7, the model was useful for rough screening purposes. When the same PLSR approach was applied to the NIR spectra of whole corn samples, the best PLSR model was based on first order SG (using 13 smoothing points) pretreated spectra and was also useful for rough screening purposes only. Model performance improved when phlobaphene-containing samples were removed from the calibration and validation sets and with RER = 10.9 and RPD = 3.6, this model can be used for full screening purposes. This work demonstrates the potential of NIR spectroscopy as a method for rapidly estimating TAC and to discriminate corn samples containing phlobaphenes when a wider scanning range (1000-2500 nm) is used.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Ankita Mangalvedhe, accepted the attached license on 2016-07-12 at 17:34.","The student, Ankita Mangalvedhe, submitted this Thesis for approval on 2016-07-12 at 17:48.","This Thesis was approved for publication on 2016-07-15 at 08:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9906 on 2016-11-10 at 12:25:21","Made available in DSpace on 2016-11-10T18:42:59Z (GMT). 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However, anthocyanins are susceptible to gradual degradation when exposed to certain food processing techniques or even due to prolonged/improper storage of food products that contain them. Although chemical assays to determine total anthocyanin content (TAC) exist, they are cumbersome, time consuming, and often requires destroying the sample. Spectroscopy-based assays are simple, fast, and nondestructive analytical tools that may be used in determining TAC in fruits and cereal grains, and near infrared (NIR) spectroscopy is widely used in analyzing the chemical composition of raw materials in the food, agricultural and pharmaceutical industries. In this study, tristimulus colorimetry and NIR spectroscopy were explored as a means of detecting and estimating anthocyanins in whole corn kernels and in ground corn. Results of the study showed that L*a*b* measurements were not useful for predicting TAC of whole corn samples, despite having multiple linear regression (MLR) models with 0.60 > R2 > 0.70. The poor predictive performance was due to the presence of water insoluble, red colored pigments called phlobaphenes which exhibited similar L*a*b* values as anthocyanins. The first partial least squares regression (PLSR) models were developed to predict TAC in ground corn samples that were blended with cyanidin-3-glucoside (C3G) to yield 0-1154 mg/kg TAC. Of the 51 blended corn samples, 12 contained phlobaphenes. The best PLSR model was based on NIR spectra that had been pretreated with a combination of multiplicative scatter correction (MSC) and second order Savitzky-Golay (SG) derivative. The scores plot of the model showed a prominent separation between red and yellow corn blends as compared to other models. With RPD = 1.6 and RER = 4.7, the model was useful for rough screening purposes. When the same PLSR approach was applied to the NIR spectra of whole corn samples, the best PLSR model was based on first order SG (using 13 smoothing points) pretreated spectra and was also useful for rough screening purposes only. Model performance improved when phlobaphene-containing samples were removed from the calibration and validation sets and with RER = 10.9 and RPD = 3.6, this model can be used for full screening purposes. This work demonstrates the potential of NIR spectroscopy as a method for rapidly estimating TAC and to discriminate corn samples containing phlobaphenes when a wider scanning range (1000-2500 nm) is used.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Ankita Mangalvedhe, accepted the attached license on 2016-07-12 at 17:34.","The student, Ankita Mangalvedhe, submitted this Thesis for approval on 2016-07-12 at 17:48.","This Thesis was approved for publication on 2016-07-15 at 08:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9906 on 2016-11-10 at 12:25:21","Made available in DSpace on 2016-11-10T18:42:59Z (GMT). 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