Effect of spinach powder on the physico-chemical, rheological, nutritional and sensory characteristics on chapati premixes was studied by incorporating spinach powder at different concentrations from 1 % to 10 % based on wheat flour. Addition of 5 % of spinach powder to wheat flour was found to be optimum for chapati preparation. Effect of incorporation of spinach powder on the alveo-consistographic, mixographic and pasting characteristics were studied. It was observed that peak viscosity, breakdown viscosity significantly decreases whereas, peak time, tenacity increases with the increase in the concentration of spinach powder from 1 % to 10 % in chapati premixes. Addition of spinach powder also significantly affects the textural qualities of the chapaties. Premixes and prepared chapaties were also studied for chlorophyll content, total carotenoids,vitamins and minerals.

Vitamin losses during processing of cereal grains and their food products play an important role in human vitamin intake. Therefore, experiments were conducted to optimise the baking parameters for chapati, a flat unleavened, hot-plate-baked product of Asian diet. In the experiments, whole-wheat flour (100% extraction rate) was used for the preparation of chapaties. Central composite rotatable design with three independent variables (thickness of dough sheet, time as well as temperature of baking) at five levels and nine dependent variables, i.e. vitamin B 1 and B 2 content, textural properties and overall acceptability scores, were used to conduct the experiments. The ranges for three independent variables were: thickness, 2.5-4.5 mm; time, 2.0-3.0 min; and temperature, 200-240°C. Thickness of the dough sheet had a significant (P £ 0.05) positive effect on vitamin retention, while the temperature had greater negative effect as compared with the time of baking. Based on compromise optimisation, optimum conditions for baking of chapati, especially with respect to vitamin retention were: thickness of dough sheet 3.2 mm, time of baking 2.1 min and baking temperature 216.3°C.

No abstract

Convenient, ready-to consume thermally processed potato stuffed parothas retaining their natural sensory attributes have been developed. Response Surface Methodology was used to optimize the Stuffing: dough ratio along with other ingredients. Optimized Stuffed parothas were packed in indigenously developed retortable pouches and processed in a steam-air retort. The time-temperature history was recorded during heat processing using an Ellab data cum Fo recorder. The total processing time was 25 min with a Fo value of 3.5. The quality of the developed product was evaluated chemically, sensorily and microbiologically to assess the shelf-life. After 12 months of storage, peroxide value (PV), thiobarbituric acid value (TBA) and free fatty acid value (FFA) increases significantly (p≤0.05) from 9.98 to 29.16 meqO 2 /Kg fat, 0.101 to 0.171 mg MA/Kg sample and 1.01 to 2.66 % oleic acid respectively. The chemical changes and overall acceptability scores were found to be negatively correlated during storage. Textural and colour values also had an impact on the sensory scores of stuffed parothas during storage. After 12 months of storage, sensory scores decreases significantly (p≤0.05) from 8.6 to 7.1 on a nine point hedonic scale. Microbiologically stuffed parothas remained stable during entire period of storage.

A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as possible, automatic segmentation of retinal arteries is used to help the doctors. Also, it is a challenge for the medical community to analyze the image with the right procedure to diagnose the disorders with high accuracy. Furthermore, this will help the doctor to make the right decision on effective treatment. Hence, the authors have implemented an enhanced architecture called U-Net to segment retinal vessels in this paper. The proposed conventional U-Net permits using all the accessible spatial setting information by adding the multiscale input layer and a thick square to the conventional U-Net in terms of improving the accuracy level of image segmentation. It achieved 95.6% accuracy with a comparatively traditional U-Net model. Moreover, the segmentation results have proved that the proposed approach outperformed in detecting most complex low-contrast blood vessels even when they are very thin. The task of segmenting vessels in retinal images is known as retinal vessel segmentation. Blood vessel density can be assessed using dense pixel values. Data augmentation and analytics play a major role in building the true value of eye blood vessels for medical diagnosis. The proposed method is very promising in the automatic segmentation of retinal arteries.