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In rapid scan Fourier transform spectrometry, we show that the noise in the wavelet coefficients resulting from the filter bank decomposition of the complex insertion loss function is linearly related to the noise power in the sample interferogram by a noise amplification factor. By maximizing an objective function composed of the power of the wavelet coefficients divided by the noise amplification factor, optimal feature extraction in the wavelet domain is performed. The performance of a classifier based on the output of a filter bank is shown to be considerably better than that of an Euclidean distance classifier in the original spectral domain. An optimization procedure results in a further improvement of the wavelet classifier. The procedure is suitable for enhancing the contrast or classifying spectra acquired by either continuous wave or THz transient spectrometers as well as for increasing the dynamic range of THz imaging systems.

Shellfish are readily contaminated with viruses present in water containing sewage because of the concentration effect of filter feeding. Hepatitis A virus (HAV) is the main cause of acute hepatitis worldwide and may lead to severe illness or even death. It is transmitted through fecal and oral routes and causes widespread endemic and asymptomatic infections in young children. Here we describe a method for the detection of HAV RNA in shellfish involving the extraction of total RNA from oyster meat followed by reverse transcription-polymerase chain reaction (RT-PCR). Virus recovery from oyster extracts artificially seeded with HAV strain HM 175 was examined by RT-PCR. The minimum detection limit was 3.3 focus-forming units of HAV, and the recovery rate was 75.7%. This method was used to assess the viral contamination of four shellfish beds in Santa Catarina State, Brazil, over a 1-year period. Six (22%) of 27 samples collected in autumn and winter from one shellfish bed tested positive for HAV.

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a b s t r a c tBrazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.