Determination of beef tenderness based on airflow pressure combined with structural light three-dimensional (3D) vision technology.

The main factor affecting beef quality, consumer satisfaction, and purchase decisions is beef tenderness. In this study, a rapid nondestructive testing method for beef tenderness based on airflow pressure combined with structural light 3D vision technology was proposed. The structural light 3D camera was used to scan the 3D point cloud deformation information of the beef surface after the airflow acted on it for 1.8 s. Six deformation characteristics and three point cloud characteristics of the beef surface depression region were obtained by using denoising, point cloud rotation, point cloud segmentation, point cloud descending sampling, alphaShape, and other algorithms. A total of nine characteristics were mainly concentrated in the first five principal components (PCs). Therefore, the first five PCs were put into three different models. The results showed that the Extreme Learning Machine (ELM) model had a comparatively higher prediction effect for the prediction of beef shear force, with a root mean square error of prediction (RMSEP) of 11.1389 and a correlation coefficient (R) of 0.8356. In addition, the correct classification accuracy of the ELM model for tender beef achieved 92.96%. The overall classification accuracy reached 93.33%. Consequently, the proposed methods and technology can be applied for beef tenderness detection.

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters.

Inner-injury fragrant pears are easily prone to rot during storage. Discriminating inner injury in the Korla fragrant pear from the normal pear is difficult as the flesh may be injured while the peel of the fruit remains intact. This study demonstrated the recognition of inner-injury pears based on their electric characteristics to pick out the inner-injury pears before storage. The electrical parameters parallel equivalent capacitance, quality factor, parallel equivalent inductance, parallel equivalent resistance, complex impedance, and phase angle were measured using the fruit electrical characteristic detection instrument. Principal component analysis and correlation analysis were used to determine the characteristic parameters, connected with the qualitative value of the fragrant pear to establish three discrimination models. When the measurement frequency was 100 kHz, compared with the Naïve Bayes and K-nearest neighbor models, the Support Vector Machine model with the characteristic parameters of quality factor, parallel equivalent resistance, and phase angle performed best. The recognition accuracy of the test set was 92.00%, the precision was 92.41%, the recall was 97.33%, and the F1 score was 0.95. Therefore, the electrical characteristic technique effectively detected the inner injury of fragrant pears and provided a new way to distinguish the inner injury of fruits.

Assessment of kiwifruit firmness by using airflow and laser technique.

Firmness is a valid and widely acknowledged indication of fruit quality that is directly connected to physical structure and mechanical qualities. The deformation signals of kiwifruit for firmness assessment were acquired using an assessment system based on airflow and laser technology in this investigation. Using partial least squares regression (PLSR), genetic algorithm optimization of bp neural network (GA-BP), and an extreme learning machine (ELM), deformation data from kiwifruit was used to create models of Magness-Taylor penetration firmness prediction. The ELM model outperformed the PLSR model, and GA-BP model in the prediction set, with a correlation coefficient of 0.876 and a root mean squared error of 3.576 N in the prediction set. These findings showed that an assessment system based on airflow and laser techniques can be utilized to assess the firmness of kiwifruit quickly and nondestructively.

Quantitative assessment and determinants of the papillary microvasculature in healthy subjects.

BACKGROUND: It is critical to monitor the optic disc's vessel density using Optical coherence tomography angiography (OCTA) and evaluate its determinants. In the current study, we investigate the superficial vessel density (VD) of the papillary microvasculature and its determinants in healthy subjects of Southern China. METHODS: This was a prospective, cross-sectional study. Superficial VD in healthy individuals' optic disc region was measured by OCTA. The factors associated with ocular and systemic parameters were analyzed using a generalized estimation equation (GEE) model. RESULTS: A total of 510 eyes of 260 healthy subjects were analyzed in the study. The total VD in the optic disc area was 17.21 ± 2.15 mm- 1 (95% CI, 17.02-17.40 mm- 1). The VD in the inner ring and the outer ring of the optic disc were significantly higher compared with the central ring, while the VD of the superior quadrant and inferior quadrant was significantly higher compared with the temporal and nasal quadrant. After adjusting for the ocular factors and systemic factors, AL (ß = - 0.4917, P = 0.0003), disc area (ß = - 0.3748, P = 0.0143), CMT (ß = - 0.0183, P = 0.0003) and SSI (ß = 1.0588, P < 0.001) were significantly associated with total VD of the optic disc. CONCLUSION: The mean total VD in the optic disc area was 17.21 ± 2.15 mm- 1 in healthy subjects, and the superior and inferior VD was significantly higher than the temporal and nasal VD. AL, disc area, CMT, and SSI may affect the total VD in the optic disc area and should be considered in clinical practice.

Evaluation of surface texture of dried Hami Jujube using optimized support vector machine based on visual features fusion.

The surface texture of dried jujube fruits is a significant quality grading criterion. This paper introduced a novel visual feature fusion based on connected region density, texture features, and color features. The single-scale Two-Dimensional Discrete Wavelet Transform was used to perform single-scale decomposition and reconstruction of dried Hami jujube image before visual features extraction. The connected region density was extracted by the two different algorithms, whereas the texture features were extracted by Gray Level Co-occurrence Matrix and the color features were extracted by image processing algorithms. Based on selected features which obtained by correlation analysis of visual features, the accuracy rate of the optimized Support Vector Machine classification model was 96.67%. In comparing with Extreme Learning Machine classification model and other fusion methods, the optimized Support Vector Machine based on selected visual features fusion was better.

Surface measurement based on chessboard-shaped 2-D Ronchi grating method.

A method for surface profile measurement using a chessboard-shaped 2-D Ronchi grating is proposed. The gradients in two orthogonal directions of the measured surface can be obtained simultaneously using a chessboard-shaped 2-D Ronchi grating, so that it is possible to reconstruct the surface profile by means of only one Ronchigram with high accuracy. The measuring principle and the design of the chessboard-shaped 2-D Ronchi grating are described. Measurements of both stationary and varying surfaces using an instrument constructed on the basis of the method were conducted with satisfactory results.