Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy

Ropodi, Athina; Panagou, Efstathios Ζ.; Nychas, George‐John E.

doi:10.1016/j.meatsci.2017.09.016

Cited by 44 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, reflectance at 570-700 nm is related to respiratory pigments such as myoglobin (570 nm), oxymyoglobin (590 nm) and metmyoglobin (630 nm) [23,24,59]. In the NIR region, absorption bands at 910 nm are linked to denaturation of proteins [60,64] while at 750 and 970 nm, O-H second overtones are related to the moisture content in the samples [26,38,53]. In addition, absorption bands observed in the NIR region (928 and 940 nm) are correlated to the presence of fatty acids and fat within the sample matrix [14,60,65].…”

Section: Discussionmentioning

confidence: 99%

“…As illustrated in Figure 3, batches used in external validation differed from the calibration dataset in all products and especially in the case of the chicken breast. These findings indicate the importance of performing validation with independent datasets (batches at different time points) and to include as much variability as possible in the developed model [64,66,67]. Additionally, the developed model addressed for at-line implementation must be validated by an independent dataset in order to construct an accurate and robust model [24,52].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Spyrelli

Doulgeraki²,

Argyri³

et al. 2020

Microorganisms

Self Cite

View full text Add to dashboard Cite

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the “time from slaughter” parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Spyrelli

Doulgeraki²,

Argyri³

et al. 2020

Microorganisms

Self Cite

View full text Add to dashboard Cite

show abstract

“…Had that been the case, e.g. Ropodi et al (2018), and Tsakanikas et al (2016) give good surveys of data-analytic tools for that, including support vector machines, partial least squares discriminant analysis, and Gaussian Mixture models.…”

Section: Texture Analysismentioning

confidence: 99%

“…Mendoza et al (2006) reviewed how different vision systems were employed to assess color and other attributes of agricultural foods. Image analysis has also proven useful in the analysis of ripening stages for fruits and vegetables (Mendoza and Aguilera, 2004;Xing and De Baerdemaeker, 2005;Steinmetz et al, 1999), identification of previously frozen products (Brosnan and Sun, 2004;Sharifzadeh et al, 2013;Pu et al, 2015;Ropodi et al, 2018), and spoilage detection in meat (Dissing et al, 2012;Tsakanikas et al, 2016). Feng and Sun (2013) investigated Pseudomonas loads in chicken fillets using near infrared hyperspectral imaging.…”

Section: Accepted Manuscript 1 Introductionmentioning

confidence: 99%

Investigation of pausing fermentation of salamis with multispectral imaging for optimal sensory evaluations

Trinderup

Møller

Dahl³

et al. 2018

Meat Science

View full text Add to dashboard Cite

The fermentation process of salamis involves several parameters influencing taste, texture, and color of the salami. One significant parameter is the fermentation time. It is difficult to conduct sensory evaluations to assess the effect of time without introducing variation between observation days. It may be possible to overcome this by stalling or pausing the fermentation by deep-chilling the salamis. This study investigates the difference of non- and deep-chilled salamis with the use of a multispectral imaging system. The statistical investigation, based on image features relating to size, visual texture, and color of the sausages over time, showed that it may be possible to stall the fermentation process. It was shown that a statistical difference in the two kinds of samples is present. For the size feature the difference could be quantified into a number of days. However, for the important color feature only a statistical difference was observed, whereas the visual difference expressed in terms of ΔE was barely present.

show abstract

“…Recently, it has been used in different studies in quality evaluation of different agro‐food products, for example, for predicting heme and nonheme iron contents in pork sausage (Ma et al., 2016), transgenic assessment of crop seeds (Liu et al., 2014a, 2016a), classification of storage period in tea (Xiong et al., 2015), quality evaluation of rocket (Løkke, Seefeldt, Skov, & Edelenbos, 2013), quality monitoring of in‐shell infestation in almonds and sunflower seeds (Ma et al., 2015; Yu et al., 2019), characterizing sensory properties and physicochemical parameters in strawberry and tomato fruits (Li et al., 2014; Liu, Liu, Chen, Yang, & Zheng, 2015; Liu et al, 2014b), pork microbial safety identification (Dissing et al., 2013; Ma et al., 2014), and adulteration prediction in meat, tomato paste, and infant formula powder (Liu et al., 2017; Liu, Liu, Yang, Chen, & Zheng, 2017; Ropodi, Pavlidis, Mohareb, Panagou, & Nychas, 2015a). Furthermore, spectral imaging holds high potential for quality assessment relevant to moisture contents, for example, for inspection of frozen minced beef followed by thawing process (Ropodi, Panagou, & Nychas, 2018), identification of moisture contents in carrots (Liu et al., 2016b), and dehydrated prawns (Wu et al., 2012). Although MSI technology is a promising solution, it still remains a challenge to monitor water status in mushroom during drying process.…”

Section: Introductionmentioning

confidence: 99%

Multispectral imaging for predicting the water status in mushroom during hot‐air dehydration

Younas

Liu

et al. 2020

Journal of Food Science

View full text Add to dashboard Cite

In-depth understanding of the shifting of water status during dehydration is crucial for obtaining better quality of dried food. In this work, we report a nondestructive method to measure the water status in hot-air dried mushroom via multispectral imaging (MSI) technology combined with chemometric methods. The low-field nuclear magnetic resonance (LF-NMR) measurements were performed as reference. During drying process, the moisture content changed dramatically with notable migration and conversion of different water phases. Partial least squares (PLS), back propagation neural network (BPNN), and least squares-support vector machine (LS-SVM) models were applied to develop quantitative models. Among all, BPNN model showed considerably better performance of prediction with coefficient of determination R 2 c = 0.9829, R 2 p = 0.9639. The results demonstrated that MSI technology combined with chemometric methods is an impressive approach for determination of the water status in hot-air dried mushrooms, which would facilitate infield of food processing by providing applicable and appropriate platform.Practical Application: Experimental investigation of different water status during food processing. Assessment of the potential of multispectral imaging to predict water status. Usage of novel measurement method for food processors.

show abstract

Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy

Cited by 44 publications

References 28 publications

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Investigation of pausing fermentation of salamis with multispectral imaging for optimal sensory evaluations

Multispectral imaging for predicting the water status in mushroom during hot‐air dehydration

Contact Info

Product

Resources

About