RESUMO
Ischemic preconditioning (IPC) has been an excellent strategy for enhancing sports performance recovery, although there is still no consensus on the ideal protocol. Thus, this study aimed to evaluate the effects of IPC with different cuff pressures (low pressure, medium pressure, and high pressure) on the attenuation of neuromuscular fatigue after an isometric test protocol. And to verify whether this improvement was related to muscle oxygen saturation during the test protocol. Thirty males (18-35 years old) with experience in resistance training were allocated to three different groups: low pressure (20 mmHg), medium pressure (100 mmHg), and high pressure (190 mmHg). The individual occlusion pressure of each participant was identified using ultrasound. Each participant performed two test protocols (8 maximal isometric contractions lasting 20-s with a 10-s rest interval) in an extension chair; after the first test protocol, the participant received the IPC intervention with a low, medium, or high cuff pressure or received the noncuff intervention (randomized order). Only the medium-pressure group showed a smaller decrease in mean force change compared to the no-cuff condition (-4.40% vs. -13.10%, p=0.01, respectively), and the low- and high-pressure groups did not exhibit significant pressure differences (IPC vs. noncuff: -8.40% vs. -13.10%, p=0.11 and -9.10% vs. -14.70%, p=0.12, respectively). Muscle oxygen saturation across test protocols showed no significant differences in all IPC conditions (p>0.05). Although, IPC with medium pressure was effective at optimizing the recovery of neuromuscular performance, this improvement is not related to an increase in muscle oxygen saturation during exercise.
RESUMO
This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.