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Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging
A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted me...
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Published in: | Food and bioprocess technology 2014-11, Vol.7 (11), p.3100-3108 |
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description | A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination (R ² cᵥ) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process. |
doi_str_mv | 10.1007/s11947-014-1327-5 |
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This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination (R ² cᵥ) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process.</description><identifier>ISSN: 1935-5130</identifier><identifier>EISSN: 1935-5149</identifier><identifier>DOI: 10.1007/s11947-014-1327-5</identifier><language>eng</language><publisher>Boston: Springer-Verlag</publisher><subject>Agriculture ; Biotechnology ; Chemistry ; Chemistry and Materials Science ; Chemistry/Food Science ; Color ; Food Science ; hyperspectral imagery ; Hyperspectral imaging ; Infrared imaging ; least squares ; Mathematical models ; Meat ; meat quality ; Nondestructive testing ; Original Paper ; pH effects ; pork ; prediction ; principal component analysis ; Principal components analysis ; Quality control ; rapid methods ; Regression analysis ; Regression models ; Salting ; Statistical analysis ; Statistical models ; swine ; Wavelengths</subject><ispartof>Food and bioprocess technology, 2014-11, Vol.7 (11), p.3100-3108</ispartof><rights>Springer Science+Business Media New York 2014</rights><rights>Springer Science+Business Media New York 2014.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-7ee784571c2e20ed0b8ff321d155e6b75fa92276d723cca174b071e8e58d67bf3</citedby><cites>FETCH-LOGICAL-c476t-7ee784571c2e20ed0b8ff321d155e6b75fa92276d723cca174b071e8e58d67bf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Liu, Dan</creatorcontrib><creatorcontrib>Ma, Ji</creatorcontrib><creatorcontrib>Sun, Da-Wen</creatorcontrib><creatorcontrib>Pu, Hongbin</creatorcontrib><creatorcontrib>Gao, Wenhong</creatorcontrib><creatorcontrib>Qu, Jiahuan</creatorcontrib><creatorcontrib>Zeng, Xin-An</creatorcontrib><title>Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging</title><title>Food and bioprocess technology</title><addtitle>Food Bioprocess Technol</addtitle><description>A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination (R ² cᵥ) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process.</description><subject>Agriculture</subject><subject>Biotechnology</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Color</subject><subject>Food Science</subject><subject>hyperspectral imagery</subject><subject>Hyperspectral imaging</subject><subject>Infrared imaging</subject><subject>least squares</subject><subject>Mathematical models</subject><subject>Meat</subject><subject>meat quality</subject><subject>Nondestructive testing</subject><subject>Original Paper</subject><subject>pH effects</subject><subject>pork</subject><subject>prediction</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>Quality control</subject><subject>rapid methods</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Salting</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>swine</subject><subject>Wavelengths</subject><issn>1935-5130</issn><issn>1935-5149</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKxEAQRYMo-PwAVza4jnb1I5UsZVBnwBeM47bpJNVjhpiO3XHh35sxojtXVQXn3oKTJKfAL4BzvIwAhcKUg0pBCkz1TnIAhdSpBlXs_u6S7yeHMW44z7gCeZC8PgWqm2pofMe8YzPf-sBsV7N-vr2Xth2oZk8-VE1H7J7sENkqNt2avTSxKVv6hh_IhnTRuWDHNjb_7CnEnqoh2JYt3ux65I-TPWfbSCc_8yhZ3Vw_z-bp3ePtYnZ1l1YKsyFFIsyVRqgECU41L3PnpIAatKasRO1sIQRmNQpZVRZQlRyBctJ5nWHp5FFyPvX2wb9_UBzMxn-EbnxphAKOiEWejRRMVBV8jIGc6UPzZsOnAW62Qs0k1IxCzVao0WNGTJk4st2awl_zf6GzKeSsN3YdmmhWSzECnANKJbX8AvRSgR8</recordid><startdate>20141101</startdate><enddate>20141101</enddate><creator>Liu, Dan</creator><creator>Ma, Ji</creator><creator>Sun, Da-Wen</creator><creator>Pu, Hongbin</creator><creator>Gao, Wenhong</creator><creator>Qu, Jiahuan</creator><creator>Zeng, Xin-An</creator><general>Springer-Verlag</general><general>Springer US</general><general>Springer Nature B.V</general><scope>FBQ</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>AEUYN</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>20141101</creationdate><title>Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging</title><author>Liu, Dan ; Ma, Ji ; Sun, Da-Wen ; Pu, Hongbin ; Gao, Wenhong ; Qu, Jiahuan ; Zeng, Xin-An</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-7ee784571c2e20ed0b8ff321d155e6b75fa92276d723cca174b071e8e58d67bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agriculture</topic><topic>Biotechnology</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Color</topic><topic>Food Science</topic><topic>hyperspectral imagery</topic><topic>Hyperspectral imaging</topic><topic>Infrared imaging</topic><topic>least squares</topic><topic>Mathematical models</topic><topic>Meat</topic><topic>meat quality</topic><topic>Nondestructive testing</topic><topic>Original Paper</topic><topic>pH effects</topic><topic>pork</topic><topic>prediction</topic><topic>principal component analysis</topic><topic>Principal components analysis</topic><topic>Quality control</topic><topic>rapid methods</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Salting</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>swine</topic><topic>Wavelengths</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Dan</creatorcontrib><creatorcontrib>Ma, Ji</creatorcontrib><creatorcontrib>Sun, Da-Wen</creatorcontrib><creatorcontrib>Pu, Hongbin</creatorcontrib><creatorcontrib>Gao, Wenhong</creatorcontrib><creatorcontrib>Qu, Jiahuan</creatorcontrib><creatorcontrib>Zeng, Xin-An</creatorcontrib><collection>AGRIS</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 Database (Proquest)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Agriculture 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>Liu, Dan</au><au>Ma, Ji</au><au>Sun, Da-Wen</au><au>Pu, Hongbin</au><au>Gao, Wenhong</au><au>Qu, Jiahuan</au><au>Zeng, Xin-An</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging</atitle><jtitle>Food and bioprocess technology</jtitle><stitle>Food Bioprocess Technol</stitle><date>2014-11-01</date><risdate>2014</risdate><volume>7</volume><issue>11</issue><spage>3100</spage><epage>3108</epage><pages>3100-3108</pages><issn>1935-5130</issn><eissn>1935-5149</eissn><abstract>A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400–1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination (R ² cᵥ) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process.</abstract><cop>Boston</cop><pub>Springer-Verlag</pub><doi>10.1007/s11947-014-1327-5</doi><tpages>9</tpages></addata></record> |
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subjects | Agriculture Biotechnology Chemistry Chemistry and Materials Science Chemistry/Food Science Color Food Science hyperspectral imagery Hyperspectral imaging Infrared imaging least squares Mathematical models Meat meat quality Nondestructive testing Original Paper pH effects pork prediction principal component analysis Principal components analysis Quality control rapid methods Regression analysis Regression models Salting Statistical analysis Statistical models swine Wavelengths |
title | Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging |
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