Sentence examples for mass spectral sample from inspiring English sources

It is worthwhile to note that each meta-sample in the subspace is the data locality persevered prototype of its corresponding high-dimensional mass spectral sample.

MICA seeks the low dimensional meta-sample (prototype) for each high-dimensional mass spectral sample in the subspace generated by the statistically independent components from a meta-profile of the input data.

Obviously, the mass spectral samples with similar global characteristics but different local characteristics will not be recognized in the following classification.

As we pointed out before, since some mass spectral samples may display very similar global-characteristics but different local-characteristics, a SVM classifier integrated with a global feature selection method may inevitably encounter difficulty in distinguishing these samples.

Although difficult to extract out, the local features are probably the key to attaining a high-performance mass spectral pattern classification for its subtle data behaviour capturing, especially because many mass spectral samples share very similar global characteristics but different local characteristics.

For mass spectral analysis, samples (10  μl) of eluate obtained from the HPLC column containing suspected curcumin-derived species were dried and solubilized in acetonitrile : water (7 : 3) and injected into the mass spectrometer using a back flow of 70/30 acetonitrile/water at 50  μl min−1 (Waters Alliance 2695 HPLC pump) using desolvation and source temperatures of 200 and 120°C, respectively.

One hundred and one adult patients (58 males and 43 females) with histologically confirmed as melanoma stage 2 (S2) or stage 3 (serasera were analysed, yielding mass spectral data for 99 samples (49 samples in S2 and 50 in S3).

Mass spectral analysis for each sample was based on the average of 300 laser shots.

Mass spectral acquisition for initial sample screening was programmed into four scan events running concurrently throughout the chromatographic separation.

The first is that we can detect many more small molecules as mass spectral signals in biological samples than we can presently identify [129], possibly as a result of unknown enzyme promiscuity [130 132].

The technique uses mass spectral signals from clinical samples, which are compared with a reference database, thus allowing swift and accurate identification.