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STUDIES OF HARDNESS AND WATER MOBILITY OF COOKED WILD RICE USING NUCLEAR MAGNETIC RESONANCE (1997)

RUAN, R. ROGER ; ZOU, C. ; WADHAWAN, C. ; [et al.]

Oxford, UK : Blackwell Publishing Ltd

Journal of food processing and preservation 21 (1997), S. 0

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ISSN: 1745-4549

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Process Engineering, Biotechnology, Nutrition Technology

Notes: The changes in hardness and molecular mobility of water of cooked wild rice during 10 days storage were investigated in this study. Seven grades of wild rice were subjected to various degrees of soaking and cooking and were stored at 5C. Nuclear magnetic resonance (NMR) technique was employed to determine the free induction decay (FID) signals and spin-spin relaxation time constants (T2) of protons, which was regarded as a measure of rotational molecular mobility of water in the samples. Multi-component behavior of FID signals was observed, based on which water molecules in the samples were classified into three fractions with different T2s. The hardness of the samples was assessed using a texture analyzer. All samples firmed over the storage period, which correlated well with the changes in NMR parameters. A multiple variable regression model was proposed to describe the relationships between firming and NMR parameters in the wild rice samples. The model demonstrates the possibility of using NMR parameters to assess and predict the firmness of the samples during storage.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1111/j.1745-4549.1997.tb00770.x

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Recursive-in-order least-squares parameter estimation algorithm for 2-D noncausal Gaussian Markov random field model (1995)

Zou, C. R. ; Plotkin, E. I. ; Swamy, M. N. S. ; [et al.]

Springer

Circuits, systems and signal processing 14 (1995), S. 87-110

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ISSN: 1531-5878

Source: Springer Online Journal Archives 1860-2000

Topics: Electrical Engineering, Measurement and Control Technology

Notes: Abstract We present in this paper a recursive-in-order least-squares (LS) algorithm to compute efficiently the parameters of a 2-D Gaussian Markov random field (GMRF) model. The algorithm is based on the fact that the least-squares estimation of the parameters of a 2-D noncausal GMRF model is consistent and the coefficient matrix in the normal equation has near-to-block-Toeplitz structure. Hence, it has a Levinson-like form for the updating of model parameters by introducing auxiliary variables. Moreover, this paper proposes the concept ofrecursive path for 2-D recursive-in-order algorithms, and points out that there exists a tradeoff between fast computation of the parameters and accurate choice of model support; a compromise recursive path is then suggested where the orders change alternately in two directions. The computational complexity of the developed algorithm is analyzed, and the results show that the algorithm is more efficient when either the image size or the model support is larger. It is found that the total number of multiplications (mps) involved in the new algorithm is only about 14% of that in the conventional LS method when the image size is 512 × 512 and the neighbor set of the model is a 17 × 17 window. Computer simulation results using the recursive-in-order algorithm developed in this paper and the conventional LS method are given to verify the correctness of the new algorithm.