Real-time inspection of pork quality attributes using dual-band spectroscopy

Wang, Wenxiu; Peng, Yankun; Sun, Hui; Zheng, Xiaochun; Wei, Wensong

doi:10.1016/j.jfoodeng.2018.05.022

Cited by 27 publications

(9 citation statements)

References 36 publications

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“…The differences in absorbance can be explained by the texture structure when meat is minced, which interferes with light absorbance, as suggested by Cozzolino et al (2000) and Fan et al (2018). Although several studies carried out with the NIRS methodology in other pig breeds focused on evaluating some of the meat quality traits, such as instrumental colour and water holding capacity (Čandek-Potokar et al, 2006;Kapper et al, 2012b;Savenije et al, 2006;Wang et al, 2018), few references reported on the comparison between intact and minced samples. Cáceres-Nevado et al (2019) also observed slightly higher absorbance of intact samples than minced ones when they analysed moisture, protein and fat content in Iberian pig loins.…”

Section: Nirs Spectra Characteristicsmentioning

confidence: 99%

Use of NIRS for the assessment of meat quality traits in open-air free-range Iberian pigs

Fernández-Barroso

Parrini

Muñoz

et al. 2021

Journal of Food Composition and Analysis

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Near infrared spectroscopy (NIRS) can be useful in order to determine meat quality traits as a rapid and nondestructive technique. The aim of the present study is to assess the accuracy of NIRS technique to determine meat quality traits on Longissimus thoracis et lumborum muscle of open-air free-range Iberian pig (n = 287) both in intact and minced samples. Traits were measured by instrumental-chemical techniques: colour (L*, a* and b*), myoglobin content, centrifuge force water loss and texture: shear force and texture profile analysis (TPA: hardness, cohesiveness, springiness, chewiness). Calibration models between instrumental-chemical measures and NIRS spectral data were developed employing partial least square regressions (PLS). The samples were split in two random datasets (80 % in training set, 20 % in external validation set). An internal full cross-validation method was applied. Results were evaluated in terms of coefficient of determination (R 2 ), root-mean-square error (RMSE), residual prediction deviation (RPD) and range error ratio (RER). Full spectral range was used to develop mathematical equations. As regard external validation procedure, the highest coefficients of determination (R 2 p) in intact loin samples were achieved for hardness, redness (a*) and yellowness (b*) (0.70.7. The models developed suggest the ability of NIRS for quantitative prediction of shear force and for a rough classification of colour parameters L*, a*, b* in minced samples as well as hardness in intact samples.

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Section: Nirs Spectra Characteristicsmentioning

confidence: 99%

Use of NIRS for the assessment of meat quality traits in open-air free-range Iberian pigs

Fernández-Barroso

Parrini

Muñoz

et al. 2021

Journal of Food Composition and Analysis

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“…The SNV method is used to correct the spectral difference caused by particle scattering among different experimental samples. Similar to the SNV, MSC is a technique for eliminating the influence of scattering caused by the particle size, density, and uniformity of the distribution of samples [ 28 , 29 ]. SG smoothing adopts a fix-size time domain window moving full wavelength to smooth the noise as much as possible without compromising the original spectrum [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

Weng

Wang

et al. 2021

Biosensors

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The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

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“…These methods (chemical analysis and instrumentation tests) are considered valid, consistent, precise, and reliable compared to subjective sensory evaluation (Peng & Dhakal, 2015). Nonetheless, there are many drawbacks to the application of these methods in meat quality detection as they are destructive, have complex sample preparation, require highly skilled operators, are unsuitable for on/in-line monitoring, and require long processing times (Khulal et al, 2017;Pophiwa et al, 2020;Wang et al, 2018a). The downsides of all three traditional methods highlight the need for more rapid, accurate, and nondestructive methods that can be used on meat and meat products for assessing the quality and safety, from animal breeding through consumption.…”

Section: Meat Quality Evaluationmentioning

confidence: 99%

“…In response to the above drawbacks, nondestructive techniques have gained much attention in recent years, and rapid advances have been seen. Scientists have developed various advanced techniques including electronic nose (e‐nose) (Jia et al., 2018; Timsorn et al., 2016), computer vision (CV) (Bhargava & Bansal, 2020; Geronimo et al., 2019; Taheri‐Garavand, Fatahi, Omid, et al., 2019), spectroscopy (de Nadai Bonin et al., 2020; Rady & Adedeji, 2018; Wang et al., 2018a), hyperspectral imaging (HSI) (Rady & Adedeji, 2020; Siripatrawan, 2018), and multispectral imaging (MSI) (Sendin et al., 2018; Su & Sun, 2018). These emerging techniques have merits above the conventional methods in that they can be nondestructive, rapid, and have the potential to be applied as an on‐site detection method (Balage et al., 2015; Li et al., 2016; Velásquez et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Emerging nondestructive approaches for meat quality and safety evaluation—A review

Khaled

Parrish

Adedeji

2021

Comp Rev Food Sci Food Safe

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Meat is one of the most consumed agro‐products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor‐intensive, time‐consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e‐nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e‐nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

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Real-time inspection of pork quality attributes using dual-band spectroscopy

Cited by 27 publications

References 36 publications

Use of NIRS for the assessment of meat quality traits in open-air free-range Iberian pigs

Use of NIRS for the assessment of meat quality traits in open-air free-range Iberian pigs

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

Emerging nondestructive approaches for meat quality and safety evaluation—A review

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