Texture analysis and grade discriminant of sausages based on ultrasound imaging

Abstract: Sausage is an emulsification-type, popular meat product, because of its unique flavor, high in nutrition and easy to store procedures. According to national standards of China (GB/T 20712-2006), sausage can be divided into three types of grades (general, excellent and premium). Traditional sausage grade detection methods are laborious and time consuming.So, it is imperative to develop a rapid and simple detection method. In this research, ultrasound imaging system was evaluated as rapid and precise detection method to differentiate between different grades of sausage. And, the texture of the sausage was also analyzed simultaneously. A total of 120 sausage samples from 2 different manufacturers were collected from local supermarkets of Zhenjiang, Jiangsu, China. From each sausage, small sample (2.5 cm×1.5 cm) were obtained for ultrasound imaging, moisture, starch and protein measurement. These measurements were utilized to divide sausages into general, excellent and premium quality grades. Ultrasound imaging system worked with the UTEX 320 equipment in pulse echo mode. The parameters of ultrasound imaging system were as follow: the pulse voltage; 300 V, the pulse repetition frequency; 800 Hz, the gain; 35 dB, and the scanning speed was 5 mm/s. Total of 240 ultrasound images (2 brands 3 grades, each had 40 samples) were collected by ultrasound imaging system. Images generated from different grades had obvious difference, however, different brands' images with the same grade were similar. Grey level co-occurrence matrix (GLCM) was generated in 0, 45, 90 and 135° directions, respectively. The commonly used angular second moment (ASM), contrast (CON), correlation (COR) and homomorphity (HOM) were extracted in all directions, and a total of 16 texture feature variables were generated. Combined with the average average image (AVG), variance (VAR) of the image, 18 texture feature variables were finally obtained. Furthermore, the textural features of different grades had significant difference (P<0.05). All the texture feature values were randomly divided into calibration set (162 samples data) and prediction set (78 samples data) to build calibration model and predication model. Principal component analysis (PCA) was performed to simple variable because that texture feature always carried redundant data and examined the qualitative difference of these sausage grades using the first 3 score vectors. From the results of PCA, all the samples of sausage were divided into three classes according to the grade of the sample. However, the brand of the sausages failed to be distinguished. The 3 groups of different class of sausages were almost apart from each other in the space of the first 3 principal components (PCs), although there were some overlaps among the groups, because the emulsification-type meat product hardly to achieve well-distributed in each part. Results from PCA was in accordance to the results of image analysis. The first 3 PCs accounted for the all variations of 97.54%, representing all the information of the variables. Therefore, all the samples were divided into 3 classes based on different grades. The linear discriminant analysis (LDA), used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events, and support vector machine(SVM), as a learning algorithm used for classification and regression tasks, was used to get the identification model. All the models relatively had high recognition rate. The identification results of the SVM were compared with the LDA. From the comparison, it showed that the discrimination accuracy of all the 3 classes of sausages using the SVM was up to 100% in prediction set and 100% in calibration set, respectively. From the results, it can be concluded that the ultrasound imaging technology can be used as a powerful and attractive tool to identify and discriminate different grades of sausages. The study could provide a reference for ultrasonic imaging technology's appalication in discriminating different grades of sausages.