Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets
文献类型：期刊论文
| 作者 | Wang, W ; Yoon, S; Zhuang, H; Jiang, HZ; Yang, Y; Li, YF; Li YF(李玉锋)
|
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2018 |
| 卷号 | 8期号:2页码:256 |
| 关键词 | cooking loss broiler breast fillet hyperspectral imaging VNIR chemometrics |
| ISSN号 | 2076-3417 |
| DOI | 10.3390/app8020256 |
| 文献子类 | Article |
| 英文摘要 | Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400-1000 nm) range. Mean spectra were extracted from regions of interest (ROIs) determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1) of VNIR full spectra, performed best with correct classification rates (CCRs) of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS). Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories. |
| WOS关键词 | WATER-HOLDING CAPACITY ; PARTIAL LEAST-SQUARES ; REFLECTANCE SPECTROSCOPY ; NONDESTRUCTIVE DETERMINATION ; CHICKEN MEAT ; MULTIVARIATE CALIBRATION ; SENSORY CHARACTERISTICS ; PREDICTION ; PORK ; QUALITY |
| WOS研究方向 | Chemistry ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000427510300108 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/285718]  |
| 专题 | 高能物理研究所_实验物理中心 高能物理研究所_多学科研究中心 |
| 通讯作者 | Li YF(李玉锋) |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | Wang, W,Yoon, S,Zhuang, H,et al. Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets[J]. APPLIED SCIENCES-BASEL,2018,8(2):256. |
| APA | Wang, W.,Yoon, S.,Zhuang, H.,Jiang, HZ.,Yang, Y.,...&李玉锋.(2018).Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets.APPLIED SCIENCES-BASEL,8(2),256. |
| MLA | Wang, W,et al."Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets".APPLIED SCIENCES-BASEL 8.2(2018):256. |
入库方式： OAI收割
来源：高能物理研究所
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