In book: Food Policy, Control and Research, Chapter: 6, Publisher: Nova Biomedical, pp.149-186


Rice is an important staple food throughout the world. Its market quality is mainly based on physical properties such as colour, chalkiness, length, width, shape, density and the number of broken rice kernels. These grain quality indicators can be determined by flatbed scanning (FBS) and image analysis (IA). The rice kernels were distributed in a single layer on the glass plate of the scanner and covered with a black sheet of paper. To facilitate the separation of touching rice kernels on the glass plate of the scanner, a sample matrix with small holes can be used. A fully automatic procedure was developed using freeware IA software and standard spreadsheet software. The FBS procedure requires a PC with standard desktop scanner. The FBS method is fast, easy to use and cheap. It allows the visualisation and quantitative physical analysis of rice. The method for the determination of the size and size distribution of rice and the amount of broken rice kernels was tested on parboiled and regular-milled white rice of different varieties and compared to manual analysis by weighing after visual separation of whole and broken kernels and by measuring the length and width of rice kernels using a sliding calliper. It yields the same accuracy and better precision than the more time-consuming manual method. Beside the colour of individual rice kernels also the colour of a thick layer of rice could be measured. This so-called bulk colour provides an objective assessment of the rice colour using the CIE L*a*b* colour space. Determination of the bulk colour was tested on milled raw rice and processed rice (puffed and popped) and compared to the analysis using a chromatic reflectometer. FBS-IA can monitor colour changes with the same accuracy and better precision than a chromatic reflectometer. Analysis by FBS-IA takes about 3 min per sample compared to about 30 min for manual analysis (broken rice kernels, length and width). FBS can also be used for the characterisation of other cereal grains and their products such as breakfast cereals, snacks, dry pasta and meals.

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    • "The most important steps are image data collection, feature extractions (such as shape, size, color, and orientation etc.) and their representation, model/algorithm selection and learning, and model testing. For example, Gerard van Dalen [8] extracted characteristics of rice using flatbed scanning and image analysis. Jose D Guzman et al. [9] investigated grain features extracted from each sample image. "
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