{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:23:44Z","timestamp":1792797824488,"version":"4.5.0"},"reference-count":52,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2020,4,18]],"date-time":"2020-04-18T00:00:00Z","timestamp":1587168000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"Major Innovation Project of Science, Engineering and Agriculture in Western First-class University in 2017"},{"name":"National Natural Science Foundation of China Program in 2017","award":["31760435"],"award-info":[{"award-number":["31760435"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["ift.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Food Science"],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>\n                      In this study, the ENVI 4.6 software was used to obtain the spectral reflection value of samples. The outlier samples were eliminated by the Monte Carlo method, and then SPXY (sample set partitioning based on be\n                      <jats:italic>x<\/jats:italic>\n                      \u2013\n                      <jats:italic>y<\/jats:italic>\n                      distances) was used to divide the calibration set and prediction set. The spectral images were pretreated and characteristic wavelengths were extracted. The spectral models of full and pretreated spectra and characteristic bands were established by partial least squares regression (PLSR) and principle component regression (PCR), and the optimal modeling combination was selected. The results showed that the modeling effect of the original spectrum was the best. In full\u2010PLSR model, the determination coefficient of the calibration set (Rc\n                      <jats:sup>2<\/jats:sup>\n                      ), the determination coefficient of prediction set (Rp\n                      <jats:sup>2<\/jats:sup>\n                      ), and the determination coefficient of interactive verification set (Rcv\n                      <jats:sup>2<\/jats:sup>\n                      ) were 0.8804, 0.7375, and 0.7422, and root\u2010mean\u2010square error of calibration set (RMSEC), root\u2010mean\u2010square error of prediction (RMSEP), and root mean square error of interactive validation set (RMSECV) were 2.3630, 2.9607, and 3.4209, respectively. PLSR and PCR models were established to obtain the optimal models of CARS\u2010PLSR and PCR\u2010PLSR. In the CARS\u2010PLSR model, the Rc\n                      <jats:sup>2<\/jats:sup>\n                      , Rp\n                      <jats:sup>2<\/jats:sup>\n                      , and Rcv\n                      <jats:sup>2<\/jats:sup>\n                      were 0.9135, 0.7654, and 0.8171, respectively, while RMSEC, RMSEP, and RMSECV were 2.0275, 2.9306, and 2.9262, respectively. In the iRF\u2010PCR model, Rc\n                      <jats:sup>2<\/jats:sup>\n                      , Rp\n                      <jats:sup>2<\/jats:sup>\n                      , and Rcv\n                      <jats:sup>2<\/jats:sup>\n                      were 0.7952, 0.7372, and 0.7280, respectively, while RMSEC, RMSEP, and RMSECV were 3.0207, 2.8278, and 3.4288, respectively. This study has demonstrated that visible and near\u2010infrared hyperspectral imaging system can rapidly predict the content of metmyoglobin in cooked tan mutton.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical Application<\/jats:title>\n                    <jats:p>This study has demonstrated that visible and near\u2010infrared (Vis\/NIR) hyperspectral imaging system can rapidly predict the content of MetMb in cooked tan mutton. With the advantages of nondestructive, rapid, real\u2010time, Vis\/NIR, hyperspectral imaging system can be widely expanded and applied to the detection of myoglobin in meat to evaluate the color of meat.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1750-3841.15137","type":"journal-article","created":{"date-parts":[[2020,4,18]],"date-time":"2020-04-18T05:27:48Z","timestamp":1587187668000},"page":"1403-1410","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Determination of metmyoglobin in cooked tan mutton using Vis\/NIR hyperspectral imaging system"],"prefix":"10.1111","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7008-8202","authenticated-orcid":false,"given":"Ruirui","family":"Yuan","sequence":"first","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guishan","family":"Liu","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"He","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Physics and Electrical and Electronic Engineering Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naiyun","family":"Fan","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Ban","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yourui","family":"Sun","sequence":"additional","affiliation":[{"name":"Non\u2010Destructive Detection Laboratory of Agricultural Products, School of Agriculture Ningxia University  Yinchuan 750021 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,4,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2018.10.022"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(01)00119-8"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meatsci.2011.07.011"},{"key":"e_1_2_8_5_1","first-page":"285","article-title":"Sucrose content nondestructive detection of lingwu long jujube by hyperspectral imaging technique","volume":"40","author":"Cheng L. 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