Muffins were evaluated for color by visual examination and by development of a machine-reading system coupled with disrriminant analysis of the data acquired. A classification algorithm separated light from dark-colored muffins. The system's precision was assessed by evaluating the color of 4 cm diameter muffins pregraded prior to the evaluation of color and without pregrading. Applied to 200 samples, the automated system was able to correctb classifi 96% of the pregraded and 79% of the ungraded mufins. The algorithm procedure was able to classifi muffins at an accuracy level better than 88% in most cases whereas quality decisions among inspectors varied by 20 to 30%. Critical to precision by the machine-read procedure was control of the illumination.
In checking harvesting discipline and quality control for oil palm fruits, color has presumably been an important guide to whether the oil content has reached a maximum where the fruit bunch is ready for cutting. However, establishing a single and harmonious standard base on color is a very contentious issue in the oil palm industry because of the subjective nature of the human vision of color. This was further complicated due to the lack of information on fruit color upon which to base a definite ripeness criterion. We demonstrated in this paper that this problem can be solved using machine vision technology. Methods used were to treat color in HSI (Hue, Saturation and Intensity) color space and applied multivariate discriminant analysis. These have proven to be highly effective for color evaluation and image processing. The vision system was trained to classify oil palms into four quality grades according to PORIM (Palm Oil Research Institute of Malaysia) inspection standards. These are the unripe, the underripe, the optimally ripe and the overripe classes. Depending upon the quality feature evaluated, misclassification by the vision system varied from 5 to 12% but averaged at about 8%. Machine vision disagreement ranged from 2 to 19%.
This study was aimed to examine the physicochemical properties of starch extracted from two different points (base and mid heights) of the sago palms trunks (Metroxylon sagu) of different physiological growth stages namely, ‘Plawei’, ‘Bubul’, ‘Angau Muda’, ‘Angau Tua’ and ‘Late Angau Tua’ stages. The physicochemical properties of sago starch studied were the morphology of starch, amylose content, particle size and distribution profile, pasting, thermal and retrogradation profiles. The results showed significant differences in the amylose and amylopectin content as well as in the granule sizes of starch from the different growth stages. Variation was observed in the proportions of granule sizes and pasting properties of starch from base and mid heights of the different growth stages while slight or insignificant differences was observed in the thermal properties of sago starch.
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