Physicochemical,nutritional and functional properties of marama bean flour

Marama bean is a protein-rich, underutilised, drought-tolerant legume in sub-Saharan Africa. Its utilisation may be increased through value addition into protein-rich flours. Defatted flour from unheated and dry-heated (150 °C/20 min) whole marama beans (UMF and HMF) were analysed for physicochemical and protein-based functional properties. Heating significantly increased in vitro protein digestibility of marama bean flour. Of functional importance are the high levels of tyrosine present in marama bean flours. Heating significantly reduced protein solubility and emulsifying capacity of marama bean flours whilst water absorption capacity was improved. Foaming capacity was not affected by heating. UMF could be useful in food systems requiring high emulsifying capacity, but would not be suitable for applications requiring high water absorption and foaming capacity. Due to its high protein contents, marama bean flour could be used to increase the protein quality of cereal-based foods to help alleviate protein-energy nutrition in the region.     

Validity and Meaning: Comment.

Comments on an article by Robert L. Ebel (see record 1962-05654-001). This article discusses basic problems in psychological testing and measurement, and as such the author touches upon basic problems of scientific psychology as well. According to the commentator the problems the author raises are very well taken. For a while it seems that he will come up with what points to a solution but some reflection shows that although he approached the "truth," he failed to reach it. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An Energy Efficient Clustered Gravitational and Fuzzy Based Routing Algorithm in WSNs

Wireless Personal Communications - Wireless sensor networks consist of many tiny sensor nodes which are deployed in various geographical locations for sensing the normal spectacles and also to...     

Allocation of functions between man and machines in automated systems.

With the growing complexity of the man-machine systems the problem of allocation becomes more critical. Little progress has been made towards its solution since the publication of Fitts' article in 1951 which has dominated thinking in this area. Fitts recommended that man be compared to machines and be chosen for those functions which he does better than machines and vice versa. To do so is wrong; when we can compare a man to a machine, we find that we can also build a machine for the function involved. Hence the lack of progress. Men and machines are complementary, rather than comparable. Once the problem is so reformulated, new ways of thinking which appear to be promising open up. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computer‐aided diagnosis system for the detection of bronchiectasis in chest computed tomography images

A computer‐aided diagnosis (CAD) system has been developed for the detection of bronchiectasis from computed tomography (CT) images of chest. A set of CT images of the chest with known diagnosis were collected and these images were first denoised using Wiener filter. The lung tissue was then segmented using optimal thresholding. The Pathology Bearing Regions (PBRs) were then extracted by applying pixel‐based segmentation. For each PBR, a gray level co‐occurrence matrix (GLCM) was constructed. From the GLCM texture features were extracted and feature vectors were constructed. A probabilistic neural network (PNN) was constructed and trained using this set of feature vectors. The images together with the PBRs and the corresponding feature vector and diagnosis were stored in an image database. Rules for diagnosis and for determining the severity of the disease were generated by analyzing the images known to be affected by bronchiectasis. The rules were then validated by a human expert. The validated rules were stored in the Knowledge Base. When a physician gives a CT image to the CAD system, it first transforms the image into a set of feature vectors, one for each PBR in the image. It then performs the diagnosis using two techniques: PNN and mahalanobis distance measure. The final diagnosis and the severity of the disease are determined by correlating the diagnosis determined by both the techniques in consultation with the knowledge base. The system also retrieves similar cases from the database. Thus, this system would aid the physicians in diagnosing bronchiectasis. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 290–298, 2009     

Headspace volatile composition and oxidative storage stability of pressed marama bean (Tylosema esculentum) oil

Marama bean (Tylosema esculentum) is an underutilised legume indigenous to the Kalahari Desert region of southern Africa. The bean has high lipid content and hence is a potential source for production of edible oil. The headspace volatile composition of freshly pressed marama bean oil was explored and the oil was further studied during 7 months of storage under different light and temperature conditions. The oxidative stability of the oil was examined by measuring peroxide value, vitamin E content and FT-IR spectra. Additionally, the headspace volatile composition of the oil was investigated during storage by use of dynamic headspace GC–MS. The results showed that marama oil is highly stable and has good natural antioxidant properties; enzymatic lipid oxidation does not take place in marama oil. Light has a greater effect on the lipid oxidation than has temperature, and hence marama oil should preferably be stored in darkness and rather at 25 °C than 35 °C. Under these conditions, the marama oil has a shelf life of at least 7 months.