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Abstract Decades of research in exercise immunology have demonstrated the profound impact of exercise on the immune response, influencing an individual's disease susceptibility. Accurate prediction of white blood cells (WBCs) count during exercise can help to design effective training programs to maintain optimal the immune system function and prevent its suppression. In this regard, this study aimed to develop an easy-to-use and efficient modelling tool for predicting WBCs count during exercise. To achieve this goal, the predictive power of a range of machine-learning algorithms, including six standalone models (M5 prime (M5P), random forest (RF), alternating model trees (AMT), reduced error pruning tree (REPT), locally weighted learning (LWL), and support vector regression (SVR)) were assessed along with six types of hybrid models trained with a bagging (BA) algorithm (BA-M5P, BA-RF, BA-AMT, BA-REPT, BA-LWL, and BA- SVR). A comprehensive database was constructed from 200 eligible people. The models employed post-exercise training WBCs counts as the output parameter and seven WBCs-influencing factors, including intensity and duration of exercise, pre-exercise training WBCs counts, age, body fat percentage, maximal aerobic capacity, and muscle mass as input parameters. Comparing the prediction results of the models to the observed WBCs using standard statistics indicated that the BA-M5P model had the greatest potential to produce a robust prediction of the number of lymphocytes, neutrophils, monocytes, and WBC compared to other models. Moreover, pre-exercise training WBCs counts, intensity and duration of exercise and body fat percentage were the most important features in predicting WBCs counts. These findings hold significant implications for the advancement of exercise immunology and the promotion of public health.

Olive is a Mediterranean fruit that is cultivated mainly for its oil. Extra virgin olive oil has always been the attention and demand of users. Therefore, fraud in virgin and extra virgin olive oils is observed by adding valuable food oils and lower prices such as canola, sunflower, olive pomace, etc. Fraud detection with conventional methods such as gas chromatography is difficult, time-consuming, and requires sample and operator preparation. For this reason, the use of non-destructive technologies for fraud detection is important. In this research, detection of adulteration of pomace oil (olive pomace oil) was investigated using visible/near-infrared spectroscopy (Vis/NIR) technology. Three types of extra virgin olive oil, virgin and refined olive oil were used for sampling. Moreover, the samples were made in six categories of pure, 11, 20, 33, 50 and 100% fraud. Each treatment was prepared and tested in ten samples. Next, in order to analyze the qualitative features and classify the data extracted from the spectrometer, pattern recognition methods including linear discriminant analysis (LDA) and support vector machine (SVM) were used. The obtained results showed that visible/near infrared (Vis/NIR) spectroscopy is able to distinguish olive oil samples based on different percentages of pomace adulteration. Although the LDA method was able to classify olive oil samples with acceptable accuracy according to the adulteration rate, the SVM method had a better accuracy and fit with a training accuracy of 96.69% and a validation of 94.21%. According to the results, the linear function was suggested as the best function for building the classification models using the SVM method.