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Applying the reliability concept as a new and profitable design approach can optimize the design and manufacturing of tillage machines. In this study, the Monte Carlo method was used to perform reliability analysis on the whole chassis of a moldboard plow. For this, a complete model of a three-bottom moldboard plow was assumed as the limit mode function. Stochastic soil parameters were applied and the interaction forces of the plow with soil were simulated with finite element method (FEM). The desired output was the soil reaction forces on the plow at the most critical plowing speed and depth. Then, by applying the forces obtained from the FEM model and considering Young's modulus of the chassis as a random variable, chassis static analysis was performed in different iterations and the stress concentration at different locations was determined. The probability of failure (Pf) and reliability index (β) of different locations of chassis were calculated. The results of the Monte Carlo simulation showed that the highest Pf occurred in crossbars, standards, braces and, masts with the values of 1, 0.296, 0.165 and, 0.033, respectively, which indicates the uncertainty of the design in these parts and the needs to strengthen or optimize the aforementioned parts of the moldboard plow chassis. [ABSTRACT FROM AUTHOR]

Rapid and non-destructive adulteration detection is of particular importance to oil industries. This paper presents an application of visible and near-infrared (VNIR) spectroscopy for detection of adulteration levels in olive oil. Sunflower oil was used as an adulterant to olive oil, and the adulteration samples with different levels ranging from 0% to 40% were prepared and used for the experiments. The spectra were first considered in the range of 500-900 nm and then smoothened and normalized to reduce the light scattering effects. Principal component analysis (PCA) was performed on the spectra to have a primary data visualization and feature extraction. The extracted PCA scores were used to calculate the Mahalanobis distances of the adulterated samples from the pure sample. Further, the PCA scores were fed to the multi-class support vector machine (SVM) model to perform classification on the basis of different adulteration levels. The results showed that the spectral normalization highlighted different regions over the spectrum affected due to the adulteration. The PCA score biplots showed differences in the samples based on the different amounts of the adulteration. Moreover, the Mahalanobis distance provided a quantitative measure of the differences between the adulterated oil and the pure oil samples. The SVM modelling further supported the classification of the different levels of the adulteration. Consequently, the VNIR spectroscopy in combination with the SVM could support the development of the classification protocols for detection of adulteration in olive oils. [ABSTRACT FROM AUTHOR]