The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts

Marchi, Massimo De; Penasa, M.; Cecchinato, Alessio; Bittante, Giovanni

doi:10.1016/j.meatsci.2012.09.013

Cited by 39 publications

(28 citation statements)

References 45 publications

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“…Kapper et al (2012aKapper et al ( , 2012b hypothesized that recent improvements in NIRS technology have improved the ability and potential development of prediction equations for pork quality. Indeed, recent reports have shown significant improvements in the confidence of the generated models (Alamprese, Casale, Sinelli, Lanteri, & Casiraghi, 2013;De Marchi, 2013;De Marchi, Penasa, Cecchinato, & Bittante, 2013;Prieto, Dugan, López-Campos, Aalhus, & Uttaro, 2013;Prieto et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Predicting pork quality using Vis/NIR spectroscopy

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Predicting pork quality using Vis/NIR spectroscopy

et al. 2015

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“…The bands at 2110 and 2180 nm likely correspond to protein present in the muscle samples [2,7]. The bands at 1230-1440 nm likely correspond to C-H bonds and appeared many times in the previous reports [7,43]. …”

Section: Selection Of Key Wavelengthsmentioning

confidence: 67%

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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“…Vis/NIR spectroscopy has been applied for detection of multiple quality attributes of beef, such as chemical composition (Prieto et al, 2006), characterization of beef muscle on the basis of cooked WBSF (Xia et al, 2007), and measurement of quality attributes of ground and intact beef in terms of pH, color (L*, a*, b*), aging loss, cooking loss, and WBSF (Marchi et al, 2013). While these studies reported the potential of Vis/NIR for measurement of beef quality attributes, the measurement accuracy was generally not satisfactory because scanning a single point at a time on each sample can cause poor performance of the model.…”

Section: Beefmentioning

confidence: 99%