The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle

Cozzolino, Daniel; Barlocco, N.; Vadell, A.; Ballesteros, Fernando; Gallieta, G.

doi:10.1016/s0023-6438(02)00199-8

Cited by 70 publications

(46 citation statements)

References 19 publications

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“…Thus it was understandable that the r value for a * was better than that for L * and b * values. Higher correlations were obtained in this study for the L * and b * values compared to those reported by Cozzolino et al (2003). The best r values they reported for L * , a * and b * prediction on the intact samples were 0.64, 0.87 and 0.58, respectively.…”

Section: Color Prediction Based On the Reflectance Spectracontrasting

confidence: 56%

“…Some factors like muscle structure and surface moisture can cause light scattering. Packing of tissue into the sample cup can also deform muscle fiber orientation effects and alter the penetration of the light through the meat sample (Cozzolino et al, 2003). FN meat is firmer and with less surface moisture than SE meat.…”

Section: Discriminating the Pale And Red Classesmentioning

confidence: 99%

“…Some work has been done to relate the visible and NIR spectroscopy to the L * , a * and b * values. Cozzolino, Barlocco, Vadell, Ballesteros, and Gallieta (2003) attempted to predict the color values of pork muscle using both visible and visible-NIR wavelength region. The results indicated that the performance of visible spectroscopy was better than that of visible-NIR spectroscopy in terms of r 2 value.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of visible spectroscopy for quality classification of intact pork meat

Xing

Ngadi

Gunenc

et al. 2007

Journal of Food Engineering

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Section: Color Prediction Based On the Reflectance Spectracontrasting

confidence: 56%

Section: Discriminating the Pale And Red Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of visible spectroscopy for quality classification of intact pork meat

Xing

Ngadi

Gunenc

et al. 2007

Journal of Food Engineering

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“…For pork, color is an especially important parameter for customers which make their buy/not buy partially based on it. Cozzolino et al [127] applied VIS/NIR for prediction.…”

Section: B14 Appearancementioning

confidence: 99%

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Alander

Bochko

Martinkauppi

et al. 2013

International Journal of Spectroscopy

131

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This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

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“…Optical techniques are one of the feasible ways to replace those conventional methods of WHC evaluation based on its advantages of fast, non-invasive, simultaneous detection of multiple traits [13,14]. Visible and near-infrared spectroscopy (Vis/NIRS) has been widely tested to measure quality traits of meat and meat product, and good results have been achieved [15][16][17].…”

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.

show abstract

The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle

Cited by 70 publications

References 19 publications

Use of visible spectroscopy for quality classification of intact pork meat

Use of visible spectroscopy for quality classification of intact pork meat

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

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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