Green methodology for soil organic matter analysis using a national near infrared spectral library in tandem with learning machine.

Precision agriculture requires faster and automatic responses for fertility parameters, especially regarding soil organic matter (SOM). In Brazil, the standard methodology for SOM determination is a wet procedure based on the oxidation of the sample by an excess of potassium dichromate based on Walkley-Black method. This methodology has serious drawbacks, since, at a national level, generates approximately 600,000 L/year of toxic acid waste containing Cr3+ and possibly Cr6+, besides time consuming and expensive. Herein, we present a faster green methodology that can eliminate the generation of these hazardous wastes and reduces the costs of analysis by approximately 80%, democratizing the soil fertility information and increasing the productivity. The methodology is based on the use of a national near infrared spectral library with approximately 43,000 samples and learning machine data analysis based on a random forest algorithm. The methodology was validated by submitting the prediction results of 12 blind soil samples to a proficiency assay used for fertility soil laboratories qualification, receiving the maximum quality excellence index, indicating that it is suitable for use in routine analysis.

Analysis of pharmaceutical pellets: an approach using near-infrared chemical imaging.

Pharmaceutical pellets are spherical or nearly spherical multi-unit dosage forms designed to optimize pharmacokinetics and pharmacodynamics features of drug release. The distribution of the pharmaceutical ingredients in the layers and core is a very important parameter for appropriate drug release, especially for pellets manufactured by the process of layer gain. Physical aspects of the sample are normally evaluated by Scanning Electron Microscopy (SEM), but it is in many cases unsuitable to provide conclusive chemical information about the distribution of the pharmaceutical ingredients in both layers and core. On the other hand, methods based on spectroscopic imaging can be very promising for this purpose. In this work, a Near-Infrared Chemical Imaging (NIR-CI) method was developed and applied to the analysis of diclophenac sodium pellets. Since all the compounds present in the sample were known in advance, Classical Least Squares (CLS) was used for calculations. The results have shown that the method was capable of providing chemical information about the distribution of the active ingredient and excipients in the core and coating layers and therefore can be complementary to SEM for the pharmaceutical development of pellets.