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A method for structure validation based on the simultaneous analysis of a 1D (1)H NMR and 2D (1)H - (13)C single-bond correlation spectrum such as HSQC or HMQC is presented here. When compared with the validation of a structure by a 1D (1)H NMR spectrum alone, the advantage of including a 2D HSQC spectrum in structure validation is that it adds not only the information of (13)C shifts, but also which proton shifts they are directly coupled to, and an indication of which methylene protons are diastereotopic. The lack of corresponding peaks in the 2D spectrum that appear in the 1D (1)H spectrum, also gives a clear picture of which protons are attached to heteroatoms. For all these benefits, combined NMR verification was expected and found by all metrics to be superior to validation by 1D (1)H NMR alone. Using multiple real-life data sets of chemical structures and the corresponding 1D and 2D data, it was possible to unambiguously identify at least 90% of the correct structures. As part of this test, challenging incorrect structures, mostly regioisomers, were also matched with each spectrum set. For these incorrect structures, the false positive rate was observed as low as 6%.

Chlorinated paraffins (CPs) can be mixtures of nearly a half-million possible isomers. Despite the extensive use of CPs, their isomer composition and effects on the environment remain poorly understood. Here, we reveal the isomeric distributions of nine CP mixtures with single-chain lengths (C 14/15 ) and varying degrees of chlorination. The molar distribution of C n H 2 n+ 2 –m Cl m in each mixture was determined using high-resolution mass spectrometry (MS). Next, the mixtures were analyzed by applying both one-dimensional 1 H, 13 C and two-dimensional nuclear magnetic resonance (NMR) spectroscopy. Due to substantially overlapping signals in the experimental NMR spectra, direct assignment of individual isomers was not possible. As such, a new NMR spectral matching approach that used massive NMR databases predicted by a neural network algorithm to provide the top 100 most likely structural matches was developed. The top 100 isomers appear to be an adequate representation of the overall mixture. Their modeled physicochemical and toxicity parameters agree with previous experimental results. Chlorines are not evenly distributed in any of the CP mixtures and show a general preference at the third carbon. The approach described here can play a key role in understanding of complex isomeric mixtures such as CPs that cannot be resolved by MS alone.

A unique opportunity exists when an experimental NMR spectrum is obtained for which a specific chemical structure is anticipated. A process of Verification--the confirmation of a postulated structure--is now possible, as opposed to Elucidation-the de novo determination of a structure. A method for automated structure verification is suggested, which compares the chemical shifts, intensities and multiplicities of signals in an experimental 1H NMR spectrum with those from a predicted spectrum for the proposed structure. A match factor (MF) is produced and used to classify the spectrum-structure match into one of three categories, correct, ambiguous, or incorrect. The verification result is also augmented by the spectrum assignment obtained as part of the verification process. This method was tested on a set of synthetic spectra and several sets of experimental spectra, all of which were automatically prepared from raw data. Taking into account even the most problematic structures, with many labile protons present and poor prediction accuracy, 50% of all spectra can still be automatically verified without any false positives or negatives. In a blind test on a typical set of data, it is shown that fewer than 31% of the structures would need manual evaluation. This means that a system is possible whereby 69% of the spectra are prepared and evaluated automatically, and never need to be seen or evaluated by a human.

The dependence of the values of NMR spin-spin coupling constants on molecular conformation can be a valuable tool in the structure determination process. The continuing increase in the resonance frequency of modern NMR spectrometers allows an increasing number of resonances to be examined using first-order multiplet analysis. While this can easily be done for the simplest patterns (doublets, triplets, quartets), more complex patterns can be extremely difficult to analyze. The task of deducing the coupling constant values from a multiplet is the reverse process of generating a conventional splitting tree from a single line (chemical shift) by sequential branching using a given set of coupling constants. We present a simple, straightforward method of deducing coupling constant values from first-order multiplets based on a general inverted splitting tree algorithm but also including a peak intensity normalization procedure that utilizes multiplet symmetry and generates a set of possible first-order intensity distribution patterns. When combined with an inverted splitting tree algorithm, it is possible to find an intensity pattern that allows the deduction of a proper set of coupling constants.