Effect of chitosan on Bacillus cereus inhibition and quality of cooked rice during storage

Improper cooling of cooked rice at an inappropriate temperature or leaving cooked rice at room temperature can cause food poisoning attributed to Bacillus cereus. Natural food preservative of either squid or crab polymer chitosan solution was added to examine their antibacterial properties against Bacillus cereus in cooked rice during storage at 37 and 4 °C. Both types of chitosan could retard the growth of B. cereus and total aerobic counts in cooked rice stored at 37 °C up to 1 day. In addition, the effect of chitosans on the physical and textural properties of cooked rice during storage was studied. Both chitosans slightly increased the moisture content of cooked rice. However, chitosans had no effect on the whiteness and hardness of cooked rice during storage (P > 0.05). Therefore, both chitosans have a potential to be used as food preservative for cooked rice with no negative effects on rice quality.     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

Correlation between instrumental texture and colour quality attributes with sensory analysis of selected cheeses as affected by fat contents

This study evaluated several physical and sensory parameters of different types of cheese available in the Polish market. The measurements of textural properties were conducted in an Instron universal testing machine, while the colour properties of cheeses were measured using a Minolta chromameter. The chemical composition was determined by means of the near‐infrared spectroscopy (NIRs). Moreover, a trained sensory panel was invited to assess the cheese texture‐related properties. Generally, cheeses with reduced fat content were characterised by higher hardness, adhesiveness, cohesiveness and elasticity. Texture‐related parameters of cheese with canola oil were comparable to that of most of full‐fat cheeses. The correlation analysis between physical and sensory attributes related to cheese textural properties indicated the potential applications of TPA, shear and penetration tests (r = 0.766, r = 0.75 and r = 0.765, respectively) for the evaluation of sensory properties related to the hardness. Meanwhile, the elasticity of cheese obtained from sensory evaluation was strongly correlated with the elasticity determined from the shear test (r = 0.722) and moderately correlated with the elasticity from penetration test (r = 0.588), indicating a need to refine the method of penetration test. In addition, cheeses exhibited higher meltability during convection heating at 230 °C than microwave heating. The values of meltability for cheese with reduced fat content were lower than those of full‐fat cheese.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

The correlation of texture and magnetic properties in Co-Cr films

This investigation focuses on the correlation between the crystallographic orientation of grains with respect to magnetic properties in Co-Cr films. Based on a distribution measured for the (0002) crystallographic pole densities, modified formulae describing the texture were used to calculate the coercivity Hc, the squareness ratio Rs (Mr⊥/TMs) and the anisotropy field Ha for Co-Cr films. In general, calculated values for the coercive force He agreed well with the measured values. The calculated Rs⊥. values were much larger than the measured ones, however, indicating that the influence of the demagnetizing field and the magnetostriction on the orientation of the magnetization cannot be neglected. On leave from Institute of Computing Technology, Academia Sinica, P.O.Box 2704-6, Beijing, P.R. China.     

Texture Changes During the Ripening of Port Salut Argentino Cheese in 2 Sampling Zones

ABSTRACT: Texture changes during ripening of Port Salut Argentino cheese for different sampling zones were studied. Compression relaxation tests were performed and results were analyzed using both Maxwellian and Peleg's models. Elastic equilibrium modulus obtained from the Maxwellian model decreased from 1.22 to 0.11 10⁴Pa during ripening. The constants derived from Peleg's model, k₁ and k₂, diminished with ripening time from 1.18 to 0.71 min and from 1.27 to 1.12, respectively. Asymptotic equilibrium modulus from Peleg's model decreased from 0.95 to 0.07 10⁴Pa during ripening. Rate parameters derived from a 1st order kinetics applied to both equilibrium moduli showed that the decrease was faster in the external zone (0.0846 d⁻¹) than in the central zone (0.0368 d⁻¹). The correlation between equilibrium moduli, salt concentration, moisture content, and maturation indexes was obtained with a determination coefficient of 0.76.     

Molecular correlations in sheared thermotropic copolyesters showing banded textures

Banded textures produced in a thermotropic liquid crystal polymer by shearing between glass slides are examined by using both transmission electron and polarized light microscopy. The periodic variation in director orientation about the shear axis, as measured by light microscopy, is shown to be distinctly different from that indicated by electron diffraction. Measurements of birefringence and observation of Zernicke phase contrast indicate periodic variations in optical properties of the polymer, in step with the bands. Such effects are accounted for in terms of a synchronous rotation of the planar aromatic groups about the molecular chain axes. Evidence for an out-of-plane component of molecular orientation is also presented.     

The microstructure of Cu films deposited by the self-ion assisted technique

The microstructures of Cu films deposited by the self-ion assisted, partially ionized beam (PIB) deposition technique under two different accelerating potentials, 0 KeV and 6 KeV, are compared. The 6 KeV film shows a bimodal (111) fiber and (100) fiber texture with an abundance of twin boundaries and a relatively large average grain size with a typical lognormal distribution. The 0 KeV film consists of small, mostly (111) oriented grains with islands of abnormally large (100) grains. The controlling factors for the abnormal growth of the (100) grains are discussed in relation to the observed microstructures, showing that all factors necessary for abnormal (100) growth are present in the films.