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     The growing global consumption of non-alcoholic drinks has brought attention to the characterization and quality control of popular beverages such as malt beverages. Organic acids remarkably impact on the microbial control, stability and organoleptic characteristics (flavor, color and aroma) of beverages. This study focuses on the determination of organic acids, including oxalic, citric, tartaric, malic, succinic, lactic, fumaric, acetic, propionic, and gallic acid, in 100 commercial malt beverages from different brands (five Iranian and five various imported brands) and flavored variants (classic, pomegranate, peach, tropical and lemon). In addition, the contents of total phenols, total flavonoids, ascorbic acid, and free amino acids were measured to assess the overall composition. Liquid chromatography (LC) was employed to develop a method for analyzing the organic acids, while spectrophotometric techniques were used for quantifying other bioactive compounds.  The results revealed significant variations in the organic acid profiles, with succinic acid being the most abundant, while tartaric acid was absent in all samples. For better data analysis, chemometrics technique (PCA method) was applied to classify achieved results. The results show that PCA can classify the malt drinks based on the additive values with a very high precision. In order to improve the quality control of malt beverages, it is recommended that some extra assessments like organic acids and free amino nitrogen determination tests would better to be considered at Iranian national standard.
 

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Today, nanosilver is one of the most commercialized nanomaterials. The demand for synthesis of Nanosilver through biocompatible routs due to wide biomedical application has increased. Use of plants and plant products as sustainable and renewable resources in the synthesis of nanoparticles is more advantageous over other biological routes. In this study, biosynthesis of silver nanoparticles (AgNPs) using aqueous extract of Withania somnifera as reducing agent is reported. Effect of parameters such as AgNO3 concentration, aqueous extract, pH and formation time were investigated and optimized by UV-visible spectroscopy in the synthesis of nanoparticles. At room temperature, the solution color started to change from pale yellow to dark brown due to the reduction of silver ion. The transmission electron microscopy (TEM) was applied for size and morphological analysis of nanoparticles. TEM result shows a spherical structure with an average size ranging from 24-35 nm for silver nanoparticles.
 

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Today, the increasing process of food waste and agricultural products is one of the serious challenges in the most countries, especially in developing countries, so one of the serious policies of governments in the food security is to reduce the waste and maintain the quality of agricultural products. So far, several methods have been used to measure the quality of agricultural products, only some of which are technically and industrially justified. Vis / NIR Spectrophotometry method is one of the methods that has been considered and used in evaluating the qualitative characteristics of agricultural products due to its high speed and accuracy. In this regard, in the present study, visible/near infrared Spectrophotometry was used to measure the qualitative changes and classification of K-Lime samples of lemon during the storage period (10, 20 and 30 days). In order to analyze the qualitative characteristics and classify the data extracted from NIR, the pattern recognition methods including principal component analysis (PCA), linear Discriminant analysis (LDA) and support vector machine (SVM) were used. The results showed that Visible/Near Infrared (Vis/NIR) Spectrophotometry was able to differentiate its lemon samples based on storage time. Although PCA, LDA and SVM methods were able to classify lemon samples with good accuracy according to qualitative characteristics, but LDA and SVM methods with 100% accuracy had better accuracy and fit. Also, according to the results, the quadratic function has been determined and introduced as the best function for constructing classification models by LDA and SVM methods.
 

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Rice is one of the most popular staple food of population. . It can absorb contaminants and heavy metals from the soil and affect human health.. Therefore it is necessary to have information about the heavy metals in rice and intake of them by human. Based on this, heavy metals cadmium, chromium, lead and nickel in rice samples were studied from three sources: Iran, Pakistan, India and atomic absorption spectrophotometer The amount of heavy metals of Cd, Cr, Pb and Ni in various rice samples ranged from 0.04±0.008 to 0.40±0.03, 0.19±0.10 to 0.50±0.0, 0.092±0.04 to 1.28±0.1 and 0.19±0.01 to 0.89±0.01 respectively. The highest total heavy metals was observed in Taj mahal and the lowest in Abdalsalam. Estimated daily intake (EDI) value was calculated for different rice types.The concentration of cadmium and lead were above limit of quantification (LOQ) defined by FAO/WHO except one brand, whilst the chromium amount was significantly lower than LOQ. From recent rice consumption data, the estimated daily intakes of toxic compounds were computed for Iranian population. Estimated daily intake (EDI) for all heavy metals through imported and domestic cultivated rice consumption was considerably lower than the ADI.