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Confocal and multiphoton optical imaging techniques have been powerful tools for evaluating the performance of and monitoring experiments within microfluidic devices, but this application suffers from two pitfalls. The first is that obtaining the necessary imaging contrast often requires the introduction of an optical label which can potentially change the behavior of the system. The emerging analytical technique stimulated Raman scattering (SRS) microscopy promises a solution, as it can rapidly measure 3D concentration maps based on vibrational spectra, label-free; however, when using any optical imaging technique, including SRS, there is an additional problem of optical aberration due to refractive index mismatch between the fluid and the device walls. New approaches such as 3D printing are extending the range of materials from which microfluidic devices can be fabricated; thus, the problem of aberration can be obviated simply by selecting a chip material that matches the refractive index of the desired fluid. To demonstrate complete chemical imaging of a geometrically complex device, we first use sacrificial molding of a freeform 3D printed template to create a round-channel, 3D helical micromixer in a low-refractive-index polymer. We then use SRS to image the mixing of aqueous glucose and salt solutions throughout the entire helix volume. This fabrication approach enables truly nonperturbative 3D chemical imaging with low aberration, and the concentration profiles measured within the device agree closely with numerical simulations.

Soybeans are a commodity crop of significant economic and nutritional interest. As an important source of protein, buyers of soybeans are interested in not only the total protein content but also in the specific amino acids that comprise the total protein content. Raman spectroscopy has the chemical specificity to measure the twenty common amino acids as pure substances. An unsolved challenge, however, is to quantify varying levels of amino acids mixed together and bound in soybeans at relatively low concentrations. Here we report the use of transmission Raman spectroscopy as a secondary analytical approach to nondestructively measure specific amino acids in intact soybeans. With the employment of a transmission-based Raman instrument, built specifically for nondestructive measurements from bulk soybeans, spectra were collected from twenty-four samples to develop a calibration model using a partial least-squares approach with a random-subset cross validation. The calibration model was validated on an independent set of twenty-five samples for oil, protein, and amino acid predictions. After Raman measurements, the samples were reduced to a fine powder and conventional wet chemistry methods were used for quantifying reference values of protein, oil, and 18 amino acids. We found that the greater the concentrations (% by weight component of interest), the better the calibration model and prediction capabilities. Of the 18 amino acids analyzed, 13 had R(2) values greater than 0.75 with a standard error of prediction c.a. 3-4% by weight. Serine, histidine, cystine, tryptophan, and methionine showed poor predictions (R(2)0.75), which were likely a result of the small sampling range and the low concentration of these components. It is clear from the correlation plots and root-mean-square error of prediction that Raman spectroscopy has sufficient chemical contrast to nondestructively quantify protein, oil, and specific amino acids in intact soybeans.

The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods.