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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.1
- 수록면
- 362 - 375 (14page)
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This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.
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등록된 정보가 없습니다.
참고문헌 (51)
참고문헌 신청
Ahmed FE / 2005 / Artificial neural networks for diagnosis and survival prediction in colon cancer / Mol Cancer 4:29
Chen Q / 2016 / Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array / J Food Eng 168:259~266
Chen Q / 2014 / Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost-OLDA classification algorithm / LWT - Food Sci Technol 57:502~507
Chen Q / 2015 / Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion / LWT - Food Sci Technol 60:781~787
Cheng JH / 2016 / Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet / J Food Eng 182:9~17
Chen Q / 2016 / Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array / J Food Eng 168:259~266
Chen Q / 2014 / Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost-OLDA classification algorithm / LWT - Food Sci Technol 57:502~507
Chen Q / 2015 / Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion / LWT - Food Sci Technol 60:781~787
Cheng JH / 2016 / Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet / J Food Eng 182:9~17
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