Independent testing labs are under tremendous pressure to increase throughput (production) and detection limits. For samples that are complex these goals are not congruent with the task of providing indisputable identification of chemical entities under ultrafast conditions.

While labs must run analyses faster to be competitive, they cannot sacrifice data quality. This is especially true in highly-regulated markets such as environmental, food, and pharmaceutical. If an analyst can't document data quality, the sample analysis information is considered unusable.

Similarly, R&D and manufacturing support labs must produce accurate data to protect their company image. This is particularly true when dealing with complex matrices as found in food, beverage, flavor and fragrances.

Mass spectrometry manufacturers rely on ion extraction and bestfit, target compound "library matching" to determine compound identity. While this works well when dealing with pure standards or clean samples, the majority of customer samples are far from pure.

A new approach called "deconvolution" – a powerful and proven mathematical technique for extracting information from background signals and matrix noise – is revolutionizing the process. But be forewarned: Not all deconvolution is created equal. This paper examines the options and details the system requirements needed for labs to accurately identify compounds, reduce sample prep costs and reanalysis rates, create ultra-fast screening methods, and dramatically shorten quantitative run-times.