Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats

Xiong, Zhenjie; Sun, Da‐Wen; Pu, Hongbin; Zhu, Zhiwei; Luo, Man

doi:10.1016/j.lwt.2014.10.021

Cited by 65 publications

(36 citation statements)

References 56 publications

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“…PCA is often referred to as a data compression technique because it reduces the dimensionality of the data to fewer components that describe a large portion of the variance [13]. The first principal component accounts for the largest variance, while subsequent components account for decreasingly smaller portions.…”

Section: Selection Of Inputs Of Ls-svmmentioning

confidence: 99%

Measurement of Diesel Cetane Number Using Near Infrared Spectra and Multivariate Calibration

Zhan¹,

Jianguo²

2017

Proceedings of the 2017 International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017)

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Abstract.Near infrared spectroscopy (NIRS) was investigated for measurement of diesel cetane. Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC) and Savitzky-Golay smoothing combining first derivative were adopted to eliminate the system noises and external disturbances. Then, partial least squares (PLS) and least squares-support vector machine (LS-SVM) methods were implemented for calibration models. Simultaneously, the performance of least squares-support vector machine (LS-SVM) method was compared with three kinds of dimension reduction input, including principal components (PCs), latent variables (LVs), and effective wavelengths (EWs). The best predictions were obtained with LS-SVM-LVs model for diesel cetane number (R 2 Pre =0.557, RMSEP = 2.169 and residual prediction deviation (RPD) =1.61), which was deemed as good model predictions. It is recommended to adopt LS-SVM-LVs model technique for higher accuracy measurement of the selected diesel cetane number with NIR, in comparison with LS-SVM-PCs and LS-SVM-EWs model techniques.

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Section: Selection Of Inputs Of Ls-svmmentioning

confidence: 99%

Measurement of Diesel Cetane Number Using Near Infrared Spectra and Multivariate Calibration

Zhan¹,

Jianguo²

2017

Proceedings of the 2017 International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017)

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“…However, most of these models were established based on spectral data without incorporating spatial information, which is also important for predicting the quality characteristics of meat. Recently, the importance of analyzing the spectral and spatial information of HSI simultaneously has been emphasized by several researchers (Cheng & Sun, 2015a;Liu, Pu, Sun, Wang, & Zeng, 2014;Xiong, Sun, Pu, Zhu, & Luo, 2015), and the results demonstrated that HSI combined with data fusion would be more accurate for nondestructive analysis and predictions.…”

Section: Introductionmentioning

confidence: 99%

Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat

Cheng

Sun

et al. 2016

LWT - Food Science and Technology

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100

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“…Of these methods, feature level fusion has the advantage of avoiding huge data pre-processing and potential information losses, and was thus used to integrate spectra and texture data in this study. However, during the procedure of data fusion, another potential problem is that the feature parameters have large disparities in values, and large-value parameters may hide the predictive ability of small-value parameters [28]. Therefore, a classical mean normalization procedure was applied to rescale the different values between the spectra and texture features as follows:…”

Section: Fusion Of Spectra and Texture Datamentioning

confidence: 99%

“…Spectral information can effectively reflect the chemical components within the meat [25][26][27], while spatial information can evaluate important physical qualities of meat, such as size, shape, texture, etc. In particular, texture can be used as an effective tool for reflecting the surface information of chicken breast filets, such as the distribution of muscle fibers, fat, and fascia [28,29]. Some studies have tested the fusion of spectral and spatial information of HSI for predicting the WHC of fish and red meat [8,30], and the results demonstrated that the prediction accuracy can be improved.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

Wang

Zhuang

et al. 2018

Applied Sciences

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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 texture variables, as well as the fusion data of key wavelengths and the corresponding texture variables, respectively. The results demonstrated that for drip loss and expressible fluid, texture data was an effective supplement to spectra data, and fusion data as an input variable could effectively improve the predictive ability of the independent prediction set (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). While the best model to predict salt-induced water gain was based on key wavelengths (Rp = 0.69, RMSEp = 18.04), this was mainly because salt-induced water gain was measured on mince samples, which lacked the important physical structure to represent the texture information of meat. Our results of this study demonstrated the potential to further improve the evaluation of the WHC of chicken meat by HSI.

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Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats

Cited by 65 publications

References 56 publications

Measurement of Diesel Cetane Number Using Near Infrared Spectra and Multivariate Calibration

Measurement of Diesel Cetane Number Using Near Infrared Spectra and Multivariate Calibration

Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat

Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

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