| Project/Area Number |
07409011
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
広領域
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| Research Institution | Iwate University, Faculty of Agriculture |
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Principal Investigator |
MIURA Makoto Iwate University, Faculty of Agriculture, Associate Professor, 農学部, 助教授 (50261459)
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| Co-Investigator(Kenkyū-buntansha) |
USHIO Osamu System SACOM Co., Ltd., R&D Department, Senior Scientist, 開発本部, 部長研究員
YOSHIDA Hitoaki Iwate University, Computer Center, Associate Professor, 情報処理センター, 助教授 (00220666)
TSUNEKAWA Yoshitaka Iwate University, Faculty of Engineering, Lecturer, 工学部, 講師 (80163856)
SHINGAI Ryuzo Iwate University, Faculty of Engineering, Professor, 工学部, 教授 (00089088)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 1997: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1996: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | porous foods / porous structure / image analysis / quality / auto discrimination / process control / quality control / neural network / ファジィ・ニューラルネットワーク / 力学特性 / 粘弾性 |
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| Research Abstract |
The aim of this study was to develop an auto discrimination system of mechanical properties of food material as indices of process control and qualities of final products as indices for quality control for porous foods.
Mechanical loss tangent and relaxation time were suitable indices for bread doughs and cake batters in process control. In baking process, storage modulus and loss modulus at 65-85ﾟC were good indices. 'Good dough and batter' had a long relaxation time. Original images were pretreated with 4-neighbor sharping in 3*3 matrix. Newly developed digital image processing algorithms ; 'Multiple Image Binarization Method' made it possible to obtain a clear figure of each air cell from composite binary image with five different thresholds. Two dimensional discrete Fourier transform (2D-DFT) method made it possible to characterize each image of crumb by power spectrum of frequency and orientation for cells. Time-frequency analysis of jagged compressive force-deformation curves using the wavelet transformation method can be applicable to evaluation of crispness of puffed corn snacks.
Digital image analysis methods developed for commercial white pan breads and time-frequency analysis method developed for commercial puffed snacks were applicable to quality evaluation of extruded foods. Sixteen frequency components andsixteen direetion components of 2D-DFT power spectra obtained from crumb grain images in 3 categories (i.e.good, ordinary, and bad) of open top type breads were used for image recognition. Crumb grains of the breads could be discriminated with three-layr neural network learning by back-propagation algorithm.
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