Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging

Jiang, Hongzhe; Yoon, Seung-Chul; Zhuang, Hong; Wang, Wei; Lawrence, Kurt C.; Yi, Yang

doi:10.1016/j.meatsci.2018.01.013

Cited by 47 publications

(26 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2015 19 , Park et al., 2017 20 ) 23 Fluorescence (3) 6,7,8 Reflectance (12) 1,2,3,4,5,9,11,12,13,14,15,16 Transmittance (1) 10,17,18,19,20 Vis (3) 6,7,8 Vis-NIR (11) 1,2,9,10,11,12,13,14,15,16,17,18,19,20 SWIR (3) 3,4,5 ANOVA (2) 8,18 Band ratio (3) 6,11,12,14,16 Fuzzy logic (2) 1,2 kNN (2) 20 LDA (2) 19,20 Mahalanobis distance (2) 19,20 PCA (9) 6,7,8,13,15,16,18,20 PLS-DA (1) 20 PLSR (5) 3,4,5,18 QDA (3) 17,19,20 Qualitative analysis (2) 9,10 SIMCA (1) 18 SVM (2) 17,19,20 Physical defects ( Chao et al., 2002 1 ; Du et al., 2007 2 ; Fletcher and Kong, 2003 3 ; I. Kim, Kim et al., 2004 4 ; T. Kim et al., 2010b 5 ; Kong, 2003 6 ; Kong et al., 2004 7 ; Nakariyakul and Casasent, 2004 , Nakariyakul and Casasent, 2009 8,9 ; Yoon et al., 2008 10 ) 10 Fluorescence (6) 2,3,45,6,7 Reflectance (4) 1,8,9,10 Transmittance (1) 10 Vis (7) 2,3,4,5,6,7,8 Vis-NIR (3) 1,9,10 ABB algorithm (1) 9 Fuzzy logic (4) 1,4,6,7 LDA (1) 5 PCA (5) 1,3,7,8,10 SVM (1) 2,3 Product quality ( Elmasry et al., 2010 1 ; Iqbal et al., 2013 2 ; Jia et al., 2017 3 ; Jiang, Yoon, Zhuang, Wang, Lawrence, et al., 2018 4 ; Jiang, Yoon, Zhuang, Wang, Li, et al., 2018 5 ; Kandpal et al., 2013 6 ; Khulal et al., 2016 7 ; Khulal et al., 2017 8 ; Xiong et al., 2014 9 ; Xiong, Sun, Xie, Han and Wang, 2015 10 ; Y. Yang, Wang e...…”

Section: Resultsmentioning

confidence: 99%

Literature review: spectral imaging applied to poultry products

Falkovskaya

Gowen

2020

Poultry Science

View full text Add to dashboard Cite

Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging ( HSI ), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: “poultry” and “hyperspectral imaging”. A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Literature review: spectral imaging applied to poultry products

Falkovskaya

Gowen

2020

Poultry Science

View full text Add to dashboard Cite

show abstract

“…A variety of kernel functions exist, namely, linear kernel function, radial basis function (RBF), polynomial kernel function, and polynomial function. In the present study, SVM was built using RBF because previous researches confirm that RBF is a more compatible supported kernel function . The penalty coefficient ( c ) of the SVM model and the kernel width ( g ) of the kernel function must be determined.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, SVM was built using RBF because previous researches confirm that RBF is a more compatible supported kernel function. [26][27][28] The penalty coefficient (c) of the SVM model and the kernel width (g) of the kernel function must be determined. The optimal combination of (c, g) was determined by a grid-search procedure and the ranges of c and g were both 2 −10 to 2 10 .…”

Section: Multivariate Data Analysismentioning

confidence: 99%

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

Xie

Ning

et al. 2018

J Sci Food Agric

View full text Add to dashboard Cite

show abstract

“…However, they could not accurately display the detailed differences among the different pixels within the sample. The third method was to develop a qualitative or quantitative model first, and then predict the categories or values of each pixel within the samples in a pixel-based manner so that the variation from sample to sample and even spot to spot could be easily interpretable [40][41][42][43][44][45]. Therefore, the third method was adopted to describe the procedure of creating visual images for quantitative spatial distribution of adulteration levels in chicken meat.…”

Section: Visualization Of Carrageenan Adulteration Levelsmentioning

confidence: 99%

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019

Self Cite

View full text Add to dashboard Cite

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

show abstract

Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging

Cited by 47 publications

References 43 publications

Literature review: spectral imaging applied to poultry products

Literature review: spectral imaging applied to poultry products

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

Contact Info

Product

Resources

About