Selecting optimal combinations of preprocessing methods is a major holdup for chemometric analysis. The analyst decides which method(s) to apply to the data, frequently by highly subjective or inefficient means, such as user experience or trial and error. Here, we present a user-friendly method using optimal experimental designs for selecting preprocessing transformations. We applied this strategy to optimize partial least square regression (PLSR) analysis of Stokes Raman spectra to quantify hydroxylammonium (0–0.5 M), nitric acid (0–1 M), and total nitrate (0–1.5 M) concentrations. The best PLSR model chosen by a determinant (D)-optimal design comprising 26 samples (i.e., combinations of preprocessing methods) was compared with PLSR models built with no preprocessing, a user-selected preprocessing method (i.e., trial and error), and a user-defined design strategy (576 samples). The D-optimal selection strategy improved PLSR prediction performance by more than 50% compared with the raw data and reduced the number of combinations by more than 95.5%.

A new instrument is described that will employ buffer gas cooling with mm-wave rotational spectroscopy (60-90 GHz) to probe molecules desorbed from astrochemical ices prepared in an ultrahigh vacuum environment. Here the design and performance of the buffer gas cell, mm- wave spectrometer and data acquisition system are reported, while application to molecules desorbed from ice surfaces will be described in a future publication. The effective temperature of the neon-cooled buffer gas cell is determined by monitoring a range of rotational lines of propyl cyanide introduced into the cell. Its number density is estimated from comparison to room temperature measurements and the effective collision cross section with neon is estimated by monitoring the free induction decay (FID) lifetimes. The spectrometer and data acquisition system described are capable of acquiring and time-domain averaging the FIDs at 10 Gs/s, 10 bit vertical resolution and 98% duty cycle.

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