Comparison Between Portable and Bench‐Top Near‐Infrared Spectroscopy for Corn Silage Characterization Using Partial Least Square and Support Vector Regression Methods

ABSTRACT In this work, bench‐top and portable near‐infrared (NIR) spectroscopy equipment are compared in the context of generating predictive models for the quantification of phosphorus (P), potassium (K), and nitrogen (N) components from corn silage samples. For this, 200 spectral samples resulting from bench‐top and portable NIR are preprocessed by the following sequence of approaches: mean centering application for removing the spectral bias/offset, Savitzky–Golay filter for highlighting signal energy absorption in relation to spectral noise, interval partial least square (iPLS) for selection of spectral region, and Monte Carlo method for outlier detection and removal. Then, from the preprocessed spectra, predictive models were built using the partial least squares (PLS) and support vector regression (SVR) methods for each chemical component and NIR equipment. In this sense, six models are generated, three for each NIR spectroscopy (or two for each element). As a result, considering all components and machine learning (ML) methods, bench‐top models achieved R 2 values between 0.66 (quantification of P using PLS or SVR) and 0.81 (prediction of K and N using SVR regressors) during the validation, whereas portable ones achieved values between 0.50 (prediction of K using SVR) and 0.67 (quantification of N using PLS). Our results can be considered competitive, as robust and accurate predictors are also generated.