Physical and chemical grain properties of new registered common bean cv. ‘Kantar-05’

Moisture-dependent physical grain properties of a new registered common bean cultivar ‘Kantar-05’ were determined. Some important chemical parameters of the grain were also investigated. The average length, width and thickness of the grain were 12.48, 7.92 and 5.00 mm at 7.82% db (dry basis) moisture content. The values of bulk density and true density of the grains decreased from 793.37 to 683.62 kg/m3 (P<0.01) and from 1269.37 to 1206.55 kg/m3 (P<0.05) with increasing moisture content. The coefficients of dynamic friction increased from 0.180 to 0.316, 0.173 to 0.276, and 0.226 to 0.331 on steel, plywood and wood friction surface, respectively with increasing moisture content. The force of rupture decreased from 121.88 to 68.93 N with increase in moisture content. Phosphorus, potassium, calcium, sodium and magnesium contents were 5020 ppm, 5576 ppm, 3562 ppm, 780 ppm and 372 ppm, respectively wb% (wet basis) at the initial moisture content. The antioxidant activity and phenolic content of the grains were found to be 56.62 % and 24.82 μg GAE/mg db., respectively at the initial moisture content

Lifetime prediction of components for reuse: an overview

A state-of-the-art prediction of the remaining life of a product is discussed in detail in this paper on the basis of a literature study. The objective is to develop a robust model for estimating the remaining life of a product on the basis of data collected during its operating life. Very little research has been devoted to the remaining life prediction as a whole, but most research has been focused on predicting the mean time between failures (MTBF) and/or mean time to failure (MTTF). The authors of this paper have gathered this information to reflect the nature of the work of researchers to date and use it as a basis for model development

Determining the Reuse Potential of Components Based on Life Cycle Data

Reuse of components is one of the most efficient strategies for product recovery, which requires reliable methods for assessing the quality and the remaining life of used components. A new methodology, presented in this paper, is based on the trend analysis of lifetime monitoring data. Data with similar trends were grouped and a number of analysis techniques such as Linear Multiple Regression, Dynamic Ordinary Kriging, Universal Kriging and Neural Networks were applied in order to find the most suitable methodology for each group. The methodology was validated by using lifetime monitoring data from a consumer product

Elliptic Fourier analysis for shape distinction of Turkish hazelnut cultivars

SAYINCI, BAHADIR/0000-0001-7148-0855WOS: 000350242300001Shape is a crucial physical property of agricultural products and hence is an important parameter for assessing the quality standard. In the present study, shape variations among 17 hazelnut cultivars grown in Turkey were revealed from their digital images using shape descriptors obtained from elliptic Fourier analysis (EFA), which is a shape-based methodology. Subsequently, principal component analysis (PCA) was performed to summarize the variations among the hazelnut cultivars. This was followed by linear discriminant analysis using the first four principal components, representing 93.9 % of the total variance, obtained from the PCA to discriminate the 17 hazelnut cultivars. Estimates of Hotelling's pairwise comparisons from the multivariate analysis based on the shape variables obtained from the EFA revealed ideal shape differences between the hazelnut cultivars. Hierarchical cluster analysis divided the cultivars into six clusters according to their shape characteristics. In addition, size (projected area, length, width, thickness, surface area, geometric mean diameter), shape (shape index, sphericity, roundness, and elongation), and gravimetric (mass and volume) features of the 17 common hazelnut cultivars were also determined via an image processing technique.An analysis of variance was performed to test the differences among these variables in a descriptive method. We found that EFA provided excellent discrimination between the hazelnut cultivars with respect to their shape features.Ataturk University, Erzurum, TurkeyAtaturk UniversityThe authors are grateful for financial support from the Research Fund of Ataturk University, Erzurum, Turkey

Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. To competitively exploit these products, one option is to incorporate used components in ‘‘new’’ or remanufactured products. However, this option is partly limited by a firm’s ability to assess the reliability of used components. A comprehensive two-step approach is proposed. The first stage phase statistically analyzes the behavior of components for reuse. A well-known reliability assessment method, the Weibull analysis, is applied to the time-to-failure data to assess the mean life of components. In the second phase, the degradation and condition monitoring data are analyzed by developing an artificial neural network (ANN) model. The advantages of this approach over traditional approaches employing multiple regression analysis are highlighted with empirical data from a consumer product. Finally, the Weibull analysis and the ANN model are then integrated to assess the remaining useful life of components for reuse. This is a critical advance in sustainable management of supply chains since it allows for a better understanding of not only service requirements of product, but the remaining life in a product and hence its suitability for reuse or remanufacture. Future work should assess: (1) reduction in downtime of process equipment through the implementation of this technique as a means to better manage preventative maintenance; (2) reduce field failure of remanufactured product; (3) selling-service strategy through implementation of the proposed methodology