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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]

In this study, coupled mechanical-biospeckle imaging method was performed on potato tubers subjected to creep and uniaxial compression tests. Using this method, we analyzed the characteristics of biospeckle images in relation to the structural changes of potato tissue on loading. During mechanical tests, the speckle pattern images were recorded. The biospeckle activity indices including entropy of the wavelet transform, contrast of successive correlations (CSC), inertia moment, and absolute value of differences were calculated to extract features from the captured videos. The results revealed that the entropy could be an acceptable parameter to assess structural changes and locate rupture point of tuber in the compression test. Analysis of the biospeckle images also showed that the CSC has a decreasing trend during the creep test. Prediction models of mechanical properties were developed by fuzzy c-means clustering algorithm and were optimized using the ANFIS and genetic algorithm. The results can provide the confidence to use this method in drying, frying, and all processes that cannot be taken out samples of it. [ABSTRACT FROM AUTHOR]

Influence of microwave power on dehydration kinetics as well as quantity, chemical composition and antioxidant potential of peppermint essential oil was evaluated. The leaves were dried using microwave powers of 200, 400 and 800 W, and the essential oils extracted by hydro-distillation. The antioxidant potential of the essential oils was quantified by DPPH radical-scavenging activity method. The obtained results showed that increasing microwave power caused significant decrement (p ≤ 0.01) in drying duration. Drying over the considered microwave powers reduced essential oil yield significantly (p ≤ 0.01). In total, 36 compounds were identified in the essential oils belonging mainly to oxygenated monoterpenes. The main constituents namely menthol, menthone and menthyl acetate showed significant variation (p ≤ 0.01 or p ≤ 0.05) with the drying process. Antioxidant assaying indicated significant effect (p ≤ 0.05) on radical scavenging activity of the essential oil by drying and essential oil concentration. Compared to the fresh samples (with an IC50 of 103 μg/ml), the leaves dried at microwave power levels of 200, 400 and 800 W resulted in significant lower antioxidant activities with IC50 values of 201, 291 and 615 μg/ml, respectively. [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]

Food adulteration is a widespread illegitimate procedure involving contamination of food with chemical and physical substances. The adulterated food products are not only of decreased quality but also may cause pathogenic effects that jeopardize the human health. Adulteration of liquid foods is majorly performed for economic gains by utilizing cheap adulterants which do not necessarily change the color, taste and appearance of the food to be easily detectable by human senses. In the present study, two different dielectric spectroscopy sensors (parallel plate capacitor (PPC) and cylindrical stub resonator (CSR)) were examined and compared for detection of adulteration in grape syrup. The aim was to address which sensor could be a more precise instrument for detecting the type and level of a variety of common adulterants in grape syrup. The different adulterants tested were the date syrup, grape paste and sugar-water solution mixed at 5, 10, 15, 20, 25 and 30% with pure grape syrup. The multivariate dielectric spectral data were visualized with principal component analysis (PCA). Furthermore, similarity between different adulterants and their levels was identified with the hierarchal cluster analysis (HCA). To perform classification analysis, two different classification techniques i.e., linear discriminant analysis (LDA) and multi-class support vector machines (SVM) were utilized and compared. The results showed that PCA provided clear visualization identifying different types of adulterants over the score plots. Classification of adulteration type using SVM and LDA resulted in 100% accuracy for either of the sensors. For classifying the level of adulteration, PPC sensor associated with SVM classifier resulted in the highest accuracy (100%). In conclusion, the adulteration detection in grape syrup was satisfactorily addressed by the dielectric spectroscopy techniques. As diagnosistic tools, both the instruments could be implemented with standards executed for food security assessments. [ABSTRACT FROM AUTHOR]