Coffee Bean Grade Determination Based on Image Parameter

Quality standard for coffee as an agriculture commodity in Indonesia uses defect system which is regulated in Standar Nasional Indonesia (SNI) for coffee bean, No: 01-2907-1999. In the Defect System standard, coffee bean is classified into six grades, from grade I to grade VI depending on the number of defect found in the coffee bean. Accuracy of this method heavily depends on the experience and the expertise of the human operators. The objective of the research is to develop a system to determine the coffee bean grading based on SNI No: 01-2907-1999. A visual sensor, a webcam connected to a computer, was used for image acquisition of coffee bean image samples, which were placed under uniform illumination of 414.5+2.9 lux. The computer performs feature extraction from parameters of coffee bean image samples in the term of texture (energy, entropy, contrast, homogeneity) and color (R mean, G mean, and B mean) and determines the grade of coffee bean based on the image parameters by implementing neural network algorithm. The accuracy of system testing for the coffee beans of grade I, II, III, IVA, IVB, V, and VI have the value of 100, 80, 60, 40, 100, 40, and 100%, respectively

Nanoparticle image velocimetry at topologically structured surfaces

Nanoparticle image velocimetry ͑nano-PIV͒, based on total internal reflection fluorescent microscopy, is very useful to investigate fluid flows within ϳ100 nm from a surface; but so far it has only been applied to flow over smooth surfaces. Here we show that it can also be applied to flow over a topologically structured surface, provided that the surface structures can be carefully configured not to disrupt the evanescent-wave illumination. We apply nano-PIV to quantify the flow velocity distribution over a polydimethylsiloxane surface, with a periodic gratinglike structure ͑with 215 nm height and 2 m period͒ fabricated using our customized multilevel lithography method. The measured tracer displacement data are in good agreement with the computed theoretical values. These results demonstrate new possibilities to study the interactions between fluid flow and topologically structured surfaces

Nanoparticle image velocimetry at topologically structured surfaces

Nanoparticle image velocimetry (nano-PIV), based on total internal reflection fluorescent microscopy, is very useful to investigate fluid flows within ∼100 nm from a surface; but so far it has only been applied to flow over smooth surfaces. Here we show that it can also be applied to flow over a topologically structured surface, provided that the surface structures can be carefully configured not to disrupt the evanescent-wave illumination. We apply nano-PIV to quantify the flow velocity distribution over a polydimethylsiloxane surface, with a periodic gratinglike structure (with 215 nm height and 2 μm period) fabricated using our customized multilevel lithography method. The measured tracer displacement data are in good agreement with the computed theoretical values. These results demonstrate new possibilities to study the interactions between fluid flow and topologically structured surfaces