Multivariate Curve Resolution of Wavelet and Fourier Compressed Spectra Peter de B. Harrington,* ,² Paul J. Rauch, ‡ and Chunsheng Cai § Center for Intelligent Chemical Instrumentation, Chemistry Department, Ohio University, Athens, Ohio 45701-2979, Schaffner Manufacturing Company, Inc., 21 Herron Avenue, Pittsburgh, Pennsylvania 15202, and Aventis Pharmaceuticals, Mail Stop: C1-M0336 10236 Marion Park Drive, Kansas City, Missouri 64137. The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion- mobility spectra. The spectra obtained from the SIM- PLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPLIS- MA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium γ-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and con- centration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean- square errors of reconstruction <3%. The daublet wavelet filters were selected, because they worked well when compared to coiflet and symmlet. The effects of the daublet filter width and compression ratio were evaluated with respect to reconstruction errors of the data sets and SIMPLISMA spectra. For these experiments, the daublet 14 filter performed well for the two data sets. Advances in analytical instrumentation have led to measure- ment systems that generate large quantities of data for single samples. Examples include hyphenated systems that include separation stages coupled to multichannel detectors, such as liquid chromatography-mass spectrometry (LC-MS) and LC-nuclear magnetic resonance spectrometry (LC-NMR). Some advances, such as the use of time-of-flight MS, furnish more resolution elements and can collect spectra at faster rates. Large volumes of data may be unwieldy for further online processing, especially if the processing is to be accomplished in real time. Another area of advancement is the miniaturization of chemical sensors. As sensors decrease in size and cost, they will find widespread use outside of the controlled environment of the analytical laboratory. The analysis of complex samples in intricate environments will also force the replacement of the classical approach of signal averaging a stable sensor response with dynamic modeling methods. The advantage of modeling the dynamic sensor response is that temporal information may be exploited to resolve analytes in a mixture or to correct instrument drift resulting from changing ambient conditions. The use of dynamic modeling requires storing individual spectra or data objects. As sensors decrease in size, their storage and processing capabilities may become limited. For some applications, the data may be transmitted from sensors using wireless communication. Bandwidth limits may be encountered that may restrict the rate at which data can be conveyed, thereby necessitating the use of compression. IMS sensors are amenable to miniaturization. These instru- ments are routinely used at airports for screening hand luggage for explosives, and have broad application for forensic, environ- mental, process, and industrial hygiene monitoring. Hand-held IMS sensors are commercially available that furnish detection limits below one part per million. By modeling a data set using methods such as principal component analysis (PCA) and SIMPLISMA, the model can provide a clear and complete perspective of large and intricate sets of measurements. Multivariate curve resolution methods build mathematical models from variations in a data set that resolve spectra (i.e., curves). The models estimate the number of independent components (i.e., analytes). Overlapping peaks in the spectra may be modeled as separate components. Models furnish clear perspectives into complex trends in data. 1 Modeling methods should be used routinely for spectrometric and multichannel measurements. Compression methods may be used to reduce the size of the spectra so that low capacity storage can be maintained and computational burdens alleviated. In many cases, compressing the data can also improve its quality by removing high-frequency noise components. In many compression methods, the data when * Corresponding author. ² Ohio University. ‡ Shaffner Manufacturing Company, Inc. § Aventis Pharmaceuticals. (1) Harrington, P. B.; Reese, E. S.; Rauch, P. J.; Hu, L.; Davis, D. M. Appl. Spectrosc. 1997, 51, 808-816. Anal. Chem. 2001, 73, 3247-3256 10.1021/ac000956s CCC: $20.00 © 2001 American Chemical Society Analytical Chemistry, Vol. 73, No. 14, July 15, 2001 3247 Published on Web 05/26/2001