• Asian-Australasian Journal of Animal Sciences
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• 제32권7호
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• KOREAN JOURNAL OF CROP SCIENCE
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• 제35권2호
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• pp.109-116
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• 1990

In order for investigating the variability of the characters related to malting barley quality of the different seed lots, about 150 seed lots sampled from the six districts in the southern parts of Cheonnam were analysed. The percentages of the first, second, and off grade of different seed lots of inspected malting barley were 25.9%, 62.7% and 11.4%, respectively. The coefficients of variation (CV) for years in the first grade was 25.0% and the off grade 43.4%. The CVs for 1,000 grain weight, assortment rate, grain colour, germination energy and capacity of different seed lots were 9.1%, 13.2%, 35.7%, 6.2% and 5.0%, respectively. Also, the CV for starch content of different seed lots was 4.8% and 9.6% for ${\beta}$-glucan, 14.0% for crude protein, 14.7% for husk rate, respectively. Seventy five percentage of seed lots in the first grade was fitted for the Agricultural Inspection Law of malting barley and the fitted rates in the second and off grade were 90% and 83%, respectively. The grade of malting barley were positively correlated with grain colour, crude protein, crude fiber, husk rate and ${\beta}$-glucan, but negatively with assortment rate, 1,000 grain weight, germination energy and capacity and starch content.