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Soil testing requires the analysis of large numbers of samples in the laboratory that is often time consuming and expensive. Mid-infrared spectroscopy (mid-IR) and near infrared (NIR) spectroscopy are fast, nondestructive and inexpensive analytical methods that have been used for soil analysis, in the laboratory and in the field, to reduce the need for measurements using complex chemical/physical analyses. A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed. This study presents an overview of the use of infrared spectroscopy for the prediction of five physical (sand, silt and clay) and chemical (total carbon and total nitrogen) soil parameters with near and mid-infrared units in bench top and field setups. Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares (PLS), least squares support vector machines (LS-SVM) and locally weighted regression (LWR) for the development of the calibration models showed that the LS-SVM did not out-perform linear methods for most components while LWR that creates simpler models performed well. The present results tend to show that soil models are quite sensitive to the complexity of the model. The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench-top instruments. This was true for both near infrared and mid-infrared technology. Finally, analysis of field moist samples was not as satisfactory as using dried-ground samples regardless of the chemometrics methods applied.

Eight calibration transfer methods based on the removal of orthogonal signal were compared for the standardization of whole soybean protein and oil models. Dynamic orthogonal projection (DOP), transfer by orthogonal projection (TOP), error removal by orthogonal subtraction (EROS), orthogonal signal correction (OSC), and orthogonal projections to latent structures (O-PLS) as well as the modification and extension of some of these methods were compared in the transfer of models in intra and inter brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units of the second brand. Calibration models were transferable from brand to brand with similar or better

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

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