2018
DOI: 10.3390/app8040640
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Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

Abstract: This study investigated the fusion of spectra and texture data of hyperspectral imaging (HSI, 1000-2500 nm) for predicting the water-holding capacity (WHC) of intact, fresh chicken breast filets. Three physical and chemical indicators-drip loss, expressible fluid, and salt-induced water gain-were measured to be different WHC references of chicken meat. Different partial least squares regression (PLSR) models were established with corresponding input variables including the full spectra, key wavelengths, and te… Show more

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Cited by 32 publications
(16 citation statements)
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“…The final study to quantify sensory attributes of poultry products also fused textural and spectral data, but this time to predict water-holding capacity of whole chicken breast fillets using NIR HSI ( Yang et al., 2018b ). Water-holding capacity is directly related to sensory attributes such as tenderness and juiciness.…”
Section: Resultsmentioning
confidence: 99%
“…The final study to quantify sensory attributes of poultry products also fused textural and spectral data, but this time to predict water-holding capacity of whole chicken breast fillets using NIR HSI ( Yang et al., 2018b ). Water-holding capacity is directly related to sensory attributes such as tenderness and juiciness.…”
Section: Resultsmentioning
confidence: 99%
“…An imaging algorithm was developed to transfer the wavelengths selected based on a successive projection algorithm and partial least squares regression (SPA– PLSR) model to each pixel in the image, with the goal of developing distribution maps. Yang (2018) integrated spectra and texture features via hyperspectral imaging at 1000–2500 ​nm range and adopted the PLSR model to predict the water-holding capacity of chicken breast fillets.…”
Section: Machine Learning Approachesmentioning
confidence: 99%
“…157 Xiong et al. (2015) Chicken Grading PLSR RMSEp, multiple results Yang et al. (2018) Egg Grading SPA-SVR, SVC 96.3% for scattered yolk Zhang et al.…”
Section: Machine Learning Approachesmentioning
confidence: 99%
“…However, due to hyperspectral data having a high dimension, classification can be challenging, especially if there is a lack of training data available. Thus, texture can provide additional information for classification of agricultural land using three-dimensional wavelet texture features (Qian et al, 2012), or with a Gray-Level Co-Occurrence Matrix to assess either building materials (Lerma et al, 2000) and meat quality (Yang et al, 2018). The texture can be described using different approaches and the algorithm is chosen to suit the needs.…”
Section: Urban Materials Datasets and Classification Approachesmentioning
confidence: 99%