RESUMO
Using near-infrared (NIR) spectroscopy for poultry litter characterization can be a rapid, non-destructive, and low-cost alternative. This study aims to estimate the C, N, P, and K content in poultry litter samples using for first time NIR spectroscopy. For these purposes, the building models were carried out using Partial Least Squares (PLS) and Support Vector Machines (SVM) methods. A total of 160 litter samples were analyzed in poultry houses of different rearing systems, seeking the highest possible variability in their chemical composition. NIR spectroscopy, combined with PLS and SVM methods, is an alternative method for non-destructive C, N, P, and K determination in poultry samples. The regression models using SVM provide better accuracy for all elements, laying the basis for the nonlinear regression approach's application. The K determination on poultry litter using NIR was possible only by the SVM model (R2 = 0.8620 and RPD = 2.7330). Conclusively, the predictive ability was improved using the SVM method.
RESUMO
Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan-about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).