{"title":"便携式光谱成像(443-726 nm)与RGB成像通过包装薄膜预测家禽产品“待用”状态的比较","authors":"Anastasia Swanson, A. Herrero-Langreo, A. Gowen","doi":"10.1255/jsi.2021.a6","DOIUrl":null,"url":null,"abstract":"The objective of this study is to compare portable visible spectral imaging (443–726 nm) and conventional RGB imaging for detecting products stored beyond the recommended “use-by” date and predicting the number of days poultry products have been stored. Packages of chicken thighs with skin on were stored at 4 °C and imaged daily in pack through plastic lidding film using spectral and RGB imaging over 10 days. K-nearest neighbour (KNN) models were built to detect poultry stored beyond its recommended “use-by” date and partial least squares regression (PLSR) models were built to predict the storage day of samples. Model overfitting in the spectral PLSR model was prevented using a geostatistical approach to estimate the number of latent variables (LV). All models were built at the object level by using mean spectra and colour values per image. The KNN model built using spectral images (acc. = 93 %, sen. = 75 %, spec. = 100 %) was more suitable than the model built using RGB images (acc. = 80 %, sen. = 42 %, spec. = 96 %) for detecting poultry stored beyond its “use-by” date. The PLSR model built using spectral images (R2 = 0.78 RMSEC = 0.92, RMSEV = 1.11, RMSEP = 1.34 day) was more suitable than the model built using RGB images (R2 = 0.60, RMSEC = 1.66, RMSEV = 1.67, RMSEP = 1.92 day) for predicting storage day of poultry products.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Comparison of portable spectral imaging (443–726 nm) and RGB imaging for predicting poultry product “use-by” status through packaging film\",\"authors\":\"Anastasia Swanson, A. Herrero-Langreo, A. Gowen\",\"doi\":\"10.1255/jsi.2021.a6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this study is to compare portable visible spectral imaging (443–726 nm) and conventional RGB imaging for detecting products stored beyond the recommended “use-by” date and predicting the number of days poultry products have been stored. Packages of chicken thighs with skin on were stored at 4 °C and imaged daily in pack through plastic lidding film using spectral and RGB imaging over 10 days. K-nearest neighbour (KNN) models were built to detect poultry stored beyond its recommended “use-by” date and partial least squares regression (PLSR) models were built to predict the storage day of samples. Model overfitting in the spectral PLSR model was prevented using a geostatistical approach to estimate the number of latent variables (LV). All models were built at the object level by using mean spectra and colour values per image. The KNN model built using spectral images (acc. = 93 %, sen. = 75 %, spec. = 100 %) was more suitable than the model built using RGB images (acc. = 80 %, sen. = 42 %, spec. = 96 %) for detecting poultry stored beyond its “use-by” date. The PLSR model built using spectral images (R2 = 0.78 RMSEC = 0.92, RMSEV = 1.11, RMSEP = 1.34 day) was more suitable than the model built using RGB images (R2 = 0.60, RMSEC = 1.66, RMSEV = 1.67, RMSEP = 1.92 day) for predicting storage day of poultry products.\",\"PeriodicalId\":37385,\"journal\":{\"name\":\"Journal of Spectral Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Spectral Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1255/jsi.2021.a6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Chemistry\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Spectral Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1255/jsi.2021.a6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Chemistry","Score":null,"Total":0}
Comparison of portable spectral imaging (443–726 nm) and RGB imaging for predicting poultry product “use-by” status through packaging film
The objective of this study is to compare portable visible spectral imaging (443–726 nm) and conventional RGB imaging for detecting products stored beyond the recommended “use-by” date and predicting the number of days poultry products have been stored. Packages of chicken thighs with skin on were stored at 4 °C and imaged daily in pack through plastic lidding film using spectral and RGB imaging over 10 days. K-nearest neighbour (KNN) models were built to detect poultry stored beyond its recommended “use-by” date and partial least squares regression (PLSR) models were built to predict the storage day of samples. Model overfitting in the spectral PLSR model was prevented using a geostatistical approach to estimate the number of latent variables (LV). All models were built at the object level by using mean spectra and colour values per image. The KNN model built using spectral images (acc. = 93 %, sen. = 75 %, spec. = 100 %) was more suitable than the model built using RGB images (acc. = 80 %, sen. = 42 %, spec. = 96 %) for detecting poultry stored beyond its “use-by” date. The PLSR model built using spectral images (R2 = 0.78 RMSEC = 0.92, RMSEV = 1.11, RMSEP = 1.34 day) was more suitable than the model built using RGB images (R2 = 0.60, RMSEC = 1.66, RMSEV = 1.67, RMSEP = 1.92 day) for predicting storage day of poultry products.
期刊介绍:
JSI—Journal of Spectral Imaging is the first journal to bring together current research from the diverse research areas of spectral, hyperspectral and chemical imaging as well as related areas such as remote sensing, chemometrics, data mining and data handling for spectral image data. We believe all those working in Spectral Imaging can benefit from the knowledge of others even in widely different fields. We welcome original research papers, letters, review articles, tutorial papers, short communications and technical notes.