Internal Fresnel reflectance from meat microstructure in relation to pork paleness and pH

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

The surface of carp is easily damaged during the descaling process, which jeopardizes the quality and safety of carp products. Damage recognition realized by manual detection is an important factor restricting the automation in the pretreatment. For the commonly used methods of mechanical and water-jet descaling, damage area recognition according to the hyperspectral data was proposed. Two discrimination models, including decision tree (DT) and self-organizing feature mapping (SOM), were established to recognize the damaged and normal descaling area with the average spectral value. The damage-discrimination model based on DT was determined to be the optimal one, which possessed the best model performance (accuracy = 96.7%, sensitivity = 96.7%, specificity = 96.7%, F1-score = 96.7%). Considering the efficiency and precision of damage-area recognition and visualization, the principal component analysis (PCA) combined with pixel values statistical analysis was used to reduce the dimension of hyperspectral images at the image level. Through statistical analysis, the value 0 was used as the threshold to distinguish the normal area and the damaged area in the PC image to achieve preliminary segmentation. Then, the spectral values of the initially discriminated damage area were input into the DT discrimination model to realize the final discriminant of damaged area. On this basis, the position information of the damaged area could be used to realize the visualization. The final visualization maps for mechanical and water-jet descaling damage were obtained by image morphology processing. The average recognition accuracy can reach 94.9% and 90.3%, respectively. The results revealed that the hyperspectral imaging technique has great potential to recognize the carp damage area nondestructively and accurately under descaling processing. PRACTICAL APPLICATION: This study demonstrated that hyperspectral imaging technique can realize the carp damage area detection nondestructively and accurately under descaling processing. With the advantages of nondestructive and rapid, hyperspectral imaging system and the method can be widely expanded and applied to the quality detection of other freshwater fish pretreatment.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat.

Meat quality assessment using biophysical methods related to meat structure

This paper overviews the biophysical methods developed to gain access to meat structure information. The meat industry needs reliable meat quality information throughout the production process in order to guarantee high-quality meat products for consumers. Fast and non-invasive sensors will shortly be deployed, based on the development of biophysical methods for assessing meat structure. Reliable meat quality information (tenderness, flavour, juiciness, colour) can be provided by a number of different meat structure assessment either by means of mechanical (i.e., Warner-Bratzler shear force), optical (colour measurements, fluorescence) electrical probing or using ultrasonic measurements, electromagnetic waves, NMR, NIR, and so on. These measurements are often used to construct meat structure images that are fusioned and then processed via multi-image analysis, which needs appropriate processing methods. Quality traits related to mechanical properties are often better assessed by methods that take into account the natural anisotropy of meat due to its relatively linear myofibrillar structure. Biophysical methods of assessment can either measure meat component properties directly, or calculate them indirectly by using obvious correlations between one or several biophysical measurements and meat component properties. Taking these calculations and modelling the main relevant biophysical properties involved can help to improve our understanding of meat properties and thus of eating quality.