Predicting mechanical properties of fried chicken nuggets using image processing and neural network techniques

Qiao, Junfei; Wang, N.; Ngadi, Michael; Kazemi, Seyed-Farzan

doi:10.1016/j.jfoodeng.2006.03.026

Cited by 35 publications

(16 citation statements)

References 17 publications

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“…On the other hand, the increasing trends of energy, correlation and homogeneity values revealed improvement of uniformity and smoothness of the images due to decrease of coalescence and increase of softness of bread texture. Similar trends were also reported by Qiao et al [12] for image textural properties of nugget. In this research an effort has been made to apply image texture analysis as a nondestructive and rapid method to predict mechanical properties of bread.…”

Section: Resultssupporting

confidence: 90%

“…In this study, four image texture features namely, contrast, correlation, entropy and homogeneity were calculated based on equations 1-4 [12]. Contrast measures the local variation in an image (ranging from 0 to [size (GLCM, 1)-1] 2 ) and a high contrast value indicates a high degree of local variation.…”

Section: Sensory Analysismentioning

confidence: 99%

“…Image textures are important image features and have been recently applied in food sector for quality evaluation as a nondestructive, objective and rapid method [7][8][9][10][11]. Published data revealed that image texture features can be used for determination of mechanical properties of food materials [12][13]. Image texture is defined as the spatial organization of intensity variations of pixels in gray level image, which corresponded to both brightness value and pixel locations [14].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Different Processing Parameters on Quality Factors and Image Texture Features of Bread

Karimi¹

2012

J Bioproces Biotechniq

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In this research, image texture analysis as a nondestructive and rapid method was applied for estimation of mechanical texture property of bread. Bread samples were formulated using different emulsifiers (i.e. SSL, DATEM and E471) in three concentrations (0.2, 0.4 and 0.6%) at three proofing times (25, 35 and 45min) and sensory properties, specific volume, color components (i.e. L*, a* and b*), porosity, hardness and four image texture features (i.e. contrast, correlation, energy and homogeneity) were determined. The results indicated that emulsifier treated samples showed better (p<0.05) sensory perceptions in compression to control samples. However, the proofing time did not show significant effect. Application of E471 significantly increased the specific volume of bread. Emulsifier treated samples had higher lightness values. Application of higher concentration and longer proofing time led to higher porosity of bread. Emulsifiers, their concentrations and proofing times had positive significant (p<0.05) effects on softness of bread. On the other side of this research, contrast, correlation, energy and homogeneity were calculated from Grey Level CoOccurrence Matrix (GLCM). They showed splendid correlations with hardness of bread (0.958, 0.973, 0.966 and 0.91, respectively). Multiple Linear Regression (MLR) was conducted between hardness and four image texture features and the mathematical equation could predict hardness with high correlation of coefficient of 0.994. These results strongly suggest that image texture analysis can be applied as a nondestructive and rapid method for estimation of hardness of bread.

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Section: Resultssupporting

confidence: 90%

Section: Sensory Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Different Processing Parameters on Quality Factors and Image Texture Features of Bread

Karimi¹

2012

J Bioproces Biotechniq

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“…However, the requirement of a distinctive imaging apparatus and cost is the major drawback concerning these techniques [13]. Simple imaging techniques using digital camera, scanners have also been adopted for products such as chicken nuggets [14], bread [15], and tea [4]. Researchers have obtained good performance in envisaging the mechanical properties of the products using image texture-based analysis techniques such as wavelet-transform, or GLCM.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive low-cost approach for fuzzy classification of tomato images based on firmness prediction using regression

Sehgal¹,

Goel²

2017

IJSWPM

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“…Hence, ANN is widely applied across industries for various distinct objectives. The industries pertain ANN to solve their production problems are steel [4], [5], powder metallurgy materials [6], [7], chemical process [8] and food industries [9], [10], [11], [12]. ANN model has also been applied in market share prediction [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to forecast the market share of Thai rice

Apichottanakul

Piewthongngam

Pathumnakul

2009

2009 IEEE International Conference on Industrial Engineering and Engineering Management

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In this paper, the artificial neural networks (ANN) is used to estimate the market share of Thai rice in the global market. Two models are formulated under two assumptions. First, the market share depending on exporting prices of rice of Thailand, Vietnam, India, USA, Pakistan, China. Second, only the export prices of rice from Thailand, Vietnam, USA, and China are considered. The export prices are used as input parameters, while the market share of Thai's rice in the global market is the only output parameter of the models. Annual data from 1980 to 2005 are gathered from United States Department of Agriculture (USDA) and Food and Agriculture Organization of the United Nations (FAO). The study showed that the second model provide more promising results with the minimum mean absolute percent error (MAPE) of 4.69% and the average MAPE of 10.92%.

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Predicting mechanical properties of fried chicken nuggets using image processing and neural network techniques

Cited by 35 publications

References 17 publications

Effect of Different Processing Parameters on Quality Factors and Image Texture Features of Bread

Effect of Different Processing Parameters on Quality Factors and Image Texture Features of Bread

Non-destructive low-cost approach for fuzzy classification of tomato images based on firmness prediction using regression

Using an artificial neural network to forecast the market share of Thai rice

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