Microdetermination of diosgenin from fenugreek (Trigonella foenum-graecum) seeds.

Sulfuric acid hydrolysis of steroidal glycosides of Amber fenugreek was studied by capillary gas chromatographic analysis of diosgenin [(25R)-spirost-5-en-3-ol] and isomeric spirostadiene artifacts from 100 mg samples of seed material. Following extraction with 80% ethanol, highest recoveries of diosgenin occurred when hydrolyses were conducted in sulfuric acid, prepared at 1 molar (M) concentration in water containing 60-80% 2-propanol. Compared to a previous method with aqueous hydrochloric acid, the selected conditions of hydrolysis at 100 degrees C for 2 h with sulfuric acid in 70% 2-propanol reduced diene formation but did not completely eliminate these artifacts. Extraction of steroidal saponins with various alcohol/water mixtures prior to sulfuric acid hydrolysis gave similar recoveries of diosgenin. Application of the quantitative method to experimental samples of Amber, Quatro, and ZT-5 fenugreek, using 10 mg subsamples of crushed seed that had been defatted with petroleum ether and dried at 60 degrees C, gave diosgenin levels of 0.55, 0.42, and 0.75%, respectively. Levels of smilagenin and sarsasapogenin were very low in hydrolyzed seed extracts from ZT-5, a Canadian breeder line of fenugreek.

Color image correction for scanner and printer using B-spline CMAC neural networks.

The process of eliminating the color errors from the gamut mismatch, resolution conversion, and nonlinearity between scanner and printer is usually recognized as an essential issue of color reproduction. This paper presents a new formulation based on the generalized inverse plant control for the color error reduction process. In our formulation, the printer input and scanner output correspond to the input and output of a system plant, respectively. Obviously, if the printer input equals the scanner output, then there are no color errors involved in the entire system. In other words, the plant becomes an identity system. To achieve this goal, a plant generalized inverse should be identified and added to the original system. Since the system of a combination of both scanner and printer is highly nonlinear, CMAC-based neural networks, which have the capability to learn arbitrary nonlinearity, are applied to identify the plant generalized inverse. CMAC network is a perceptron-like feedforward structure with associative memory properties. Its memory requirements can be greatly reduced by the use of hash coding techniques. In order for CMAC networks to construct high-order, smooth, nonlinear plant inverse, more general CMAC addressing schemes have been proposed in conjunction with use of B-spline receptive functions. It is shown that B-spline CMAC networks learn orders of magnitude more rapidly than typical implementations of back propagation in the multilayered neural networks, due to the local nature of its weighting updating and the finite support of B-spline receptive field functions. Finally, a number of test samples are conducted to verify the effectiveness of the proposed method.

Constrained nonlinear optimization approaches to color-signal separation.

Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.