In the first paragraph of the experimental section, the paper says:
Larger apertures tend to allow light that is diffusely reflected from all layers of the sample (including the surface and subsurface layers) to reach the detectors. In contrast, smaller apertures allow primarily that light reflected from more shallow surface layers to reach the detectors. Thus a difference spectrum (spectrum collected with larger aperture - spectrum collected with smaller aperture) provides a spectrum of the deeper layers of the sample. By using a series of apertures, and calculating the appropriate difference spectra, depth-resolved spectra may be selectively collected from various layers of the sample.

I suspect that the noise level and baseline location for spectra collected with different aperture sizes would be quite different. A simple difference of spectra seems likely to introduce many artifacts. Indeed, the authors themselves do not appear to employ this simplistic approach alone in locating concentrations in layers in their actual data (multiplicative scatter correction, or MSC, for example, is mentioned in the Data Preprocessing and Analysis section). Perhaps the manuscript should provide some sort of warning at this point in the text that the subtraction approach, while a useful model for describing the concept, may be too simple for reliable analytical use in biological imaging.

I would like to see a detailed diagram of a Franz cell, or a literature reference to the Franz cell.

In the section entitled Calibration with An Artificial Neural Network (ANN) the text says:
Calibration with the ANN yielded an R-squared of 0.935 with a standard error of 66 g/ml for the training group and a standard error of 123 for the test group, proving the ANN was reasonably effective for prediction of salicylic acid concentration at any time and any depth in this experiment. The variances of the calibration and prediction samples were not significantly different in an F test (p < 0.05). It would be useful to reiterate the number of samples used in the F test here because the variances look rather different (one is almost double the other).

In the following section,
Equation 9 was used in conjunction with a nonlinear modeling program (TableCurve 3D, Jandel Scientific, Inc., San Rafael, CA) to estimate all of the parameters (, A, B and D). Using this equation relating the concentration to time and distance, the diffusion coefficient of SA in an agarose matrix was determined to be 25.7 x 10 cm/sec. Coefficients , A, and B are 1.41 x 10 sec, 1524.2 and 1575.1 respectively. The correlation coefficient for the nonlinear regression equation was estimated to be 0.9641. At the 95% confidence level, the model shown in Eq (9) was statistically significant (p < 0.05). A surface fit of Eq. (9) to the actual data is seen in Figure 12, which displays correlation of the model to the data.
Was the hypothesis tested statistically (at p < 0.05) the hypothesis that rho=0 or that the ratio of variances was one?

One comment on style: I still prefer the use of quantification (which is found in my Webster s Unabridged Dictionary) to quantitation (which is not).

Return to Cover of Wave of the Future