Baseline correction methods based on penalized least squares are successfully applied to various spectral analyses. The methods change the weights iteratively by estimating a baseline. If a signal is below a previously fitted baseline, large weight is given. On the other hand, no weight or small weight is given when a signal is above a fitted baseline as it could be assumed to be a part of the peak. As noise is distributed above the baseline as well as below the baseline, however, it is desirable to give the same or similar weights in either case. For the purpose, we propose a new weighting scheme based on the generalized logistic function. The proposed method estimates the noise level iteratively and adjusts the weights correspondingly. According to the experimental results with simulated spectra and measured Raman spectra, the proposed method outperforms the existing methods for baseline correction and peak height estimation.

Early and differential diagnosis of Alzheimer's disease (AD) is a problem that puzzled many doctors. Reliable markers in easy-assembling samples are of considerable clinical diagnostic value. In this work, laser Raman spectroscopy (LRS) was developed a new method that potentially allows early and differential diagnosis of AD from the platelet sample. Raman spectra of platelets isolated from different ages of AD transgenic mice and non-transgenic controls were collected and analyzed. Multilayer perceptron networks (MLP) classification method was used to classify spectra and establish the diagnostic models. For differential diagnosis, spectra of platelets isolated from AD, Parkinson's disease (PD) and vascular dementia (VD) mice were also discriminated. Two notable spectral differences at 740 and 1654 cm −1 were revealed in the mean spectrum of platelets isolated from AD transgenic mice and the controls. MLP displayed a powerful ability in the classifying of early, advanced AD and the control group, and in differential diagnosis of PD and advanced AD, as well as VD and advanced AD. The results suggest that platelet detecting by LRS coupled with MLP analysis appears to be an easy and accurate method for early and differential diagnosis of AD. This technique could be rapidly promoted from laboratory to the hospital.

A total of 1,537 domestic and imported food products were examined for the incidence of Listeria monocytogenes between 1993 and 1997 in Korea. L. monocytogenes was detected using the U.S. Department of Agriculture isolation method. Isolated L. monocytogenes was confirmed by polymerase chain reaction with hly1 and hly2 primers designed from the listeriolysin O. Overall, 122 samples (7.9%) contained L. monocytogenes. The rate of isolation was 4.3% for beef, 19.1% for pork, 30.2% for chicken, 1.2% for shellfish, 4.4% for raw milk, 4.4% for frozen smoked mussels, and 6.1% for ice cream. No L. monocytogenes was found in pasteurized milk, pasteurized processed cheese, saltwater fish, dried seafoods, or ham. The overall incidence was lower than that reported in previous studies from other countries. Most isolates were serotype 1/2b except for chicken, in which serotype 1/2a was predominant. The serotyping results might imply the presence of food or geography-specific L. monocytogenes strains.

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In this paper, we consider a new background elimination method for Raman spectra. As a background is usually slowly varying with respect to wavelength, it could be approximated by a slowly varying curve. However, the usual curve-fitting method cannot be applied because there is a constraint that the estimated background must be beneath a measured spectrum. To meet the requirement, we adopt a polynomial as an approximating function and show that background estimation could be converted to a linear programming problem which is a special case of constrained optimization. In addition, we present an order selection algorithm for automatic baseline elimination. According to the experimental results, it is shown that the proposed method could be successfully applied to experimental Raman spectra as well as synthetic spectra.