Use of visible spectroscopy for quality classification of intact pork meat

Xing, Juan; Ngadi, Michael; Gunenc, Aynur; Prasher, Shiv O.; Gariépy, C.

doi:10.1016/j.jfoodeng.2007.01.020

Cited by 35 publications

(13 citation statements)

References 22 publications

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“…The differences observed across studies may be related to differences in instrument calibration, sample size, sampling location, statistical methodology, and physical conditions at the sampling locations (industry or laboratory) (Kapper et al, 2012a). Dther studies used NORS for meat color evaluation (Kang et al, 2001;Liu et al, 2004;Roza-Delgado et al, 2013;Xing et al, 2007). On addition, NORS was used to predict the pH of pork meat (Prieto et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

et al. 2019

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The potential of near-infrared spectroscopy (NORS) to predict the physicochemical characteristics of the porcine longissimus dorsi (LD) muscle was evaluated in comparison to the standard methods of pH and color for meat quality analysis compared to the pH results with Colorimeter and pH meter. Spectral information from each sample (n = 77) was obtained as the average of 32 successive scans acquired over a spectral range from 400 -2498 nm with a 2 -nm gap for calibration and validation models. Partial least squares (PLS) regression was used for each individual model. An R 2 and a residual predictive deviation (RPD) of 0.67/1.7, 0.86/2, and 0.76/1.9 were estimated for color parameters L*, a*, and b*, respectively. Final pH had an R 2 of 0.67 and a RPD of 1.6. NORS showed great potential to predict color parameter a* of porcine LD muscle. Further studies with larger samples should help improve model quality.Keywords: carcasses; color analysis; longissimus dorsi; partial least squares regression; pH analysis.Practical Application: Development of prediction curves for evaluation of pork meat quality.

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Section: Introductionmentioning

confidence: 99%

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

et al. 2019

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“…Colorimetric test have been widely used on several application such as industrial control [1], immunoassays [2], food industry [3], water quality evaluation [4]. Color evaluations are mainly performed by exploring optical absorption or reflection spectroscopy techniques, which may require expensive devices, controlled illumination and the use of color calibrators.…”

Section: Introductionmentioning

confidence: 99%

Self-Referencing Method for Relative Color Intensity Analysis Using Mobile-Phone

Souza¹,

Oliveira²,

Oliveira³

et al. 2018

OPJ

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Mobile-phones have been widely explored on colorimetric evaluations. However, their use with different built-in image systems and acquisition configuration, in an environment with non-controlled illumination, limits the establishment of an accurate color analysis. To overcome this restriction, the determination of the absolute color of an object can be overlooked and a relative color value determined. In this work, we establish a new approach for spectroscopic evaluation based on cell-phone imaging, with no previous calibration, determining relative color values. The standalone relative color intensity method is evaluated under the use of four distinct mobile-phones and different illumination conditions. The capability to distinguish different color shades exploring the proposed self-referenced relative color intensity technique is appraised. Moreover, the potential use of the method is demonstrated by evaluating the chemical-adsorption process of Cysteamine molecules on gold nanoparticle surfaces. The proposed self-referenced technique can improve and expand the use of mobile-phones in spectroscopic applications.

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“…The technique is considered as fast, low-cost, nondestructive and environmental friendly (Batten, 1998;Xing, Ngadi, Gunenc, Prasher, & Gariepy, 2007 composition of raw and processed agro-products, but also discriminate food origins (Arana, Jaren, & Arazuri, 2005;Cen, Bao, He, & Sun, 2007;Cozzolino, Smuth, & Gishen, 2003;Cozzolino et al, 2005;Liu, Cozzolino, Cynkar, Gishen, & Colby, 2006;Liu, He, Wang, & Pan, 2007).…”

Section: Introductionmentioning

confidence: 99%

Classification of Chinese honeys according to their floral origin by near infrared spectroscopy

Chen

Wang

et al. 2012

Food Chemistry

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Use of visible spectroscopy for quality classification of intact pork meat

Cited by 35 publications

References 22 publications

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

Self-Referencing Method for Relative Color Intensity Analysis Using Mobile-Phone

Classification of Chinese honeys according to their floral origin by near infrared spectroscopy

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