Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.     

In situ recording of particle network formation in liquids by ion conductivity measurements

The formation of fractal silica networks from a colloidal initial state was followed in situ by ion conductivity measurements. The underlying effect is a high interfacial lithium ion conductivity arising when silica particles are brought into contact with Li salt-containing liquid electrolytes. The experimental results were modeled using Monte Carlo simulations and tested using confocal fluorescence laser microscopy and ζ-potential measurements.     

Fabrication of SAPO‐34 with Tuned Mesopore Structure

Crystalline SAPO‐34 molecular sieves with hierarchical network were synthesized employing polyethylene glycol (PEG) as the meso‐generating agent via a self‐assembly strategy. XRD, FESEM, N₂ adsorption‐desorption and FT‐IR spectroscopic analyses showed that PEG co‐template has a decisive role in tailoring the pore structure and producing a tuned structure from microporous towards the mesoporous structure. Also, addition of PEG favored the formation of more uniform and smaller crystals than the conventional SAPO‐34. In fact, PEG did not only control the size of crystals due to its crystal growth inhibiting (CGI) effect but also modified the morphology of the crystals and improved CSD (crystal size distribution) along with induction of mesopores into the porous structure. The modified SAPO‐34 would be recommended for selective formation of light olefins through the acid‐catalyzed reactions, such as the conversion of methanol to olefins/propylene (MTO/MTP) and propane dehydrogenation (PDH) to produce olefins with higher selectivity and catalyst stability than the conventional SAPO‐34.     

Synthesis,crystal structure and silver complexation of a novel saddle-shaped stilbenophane: NMR and theoretical study on the complex

A new Z,Z-stilbenophane was synthesised and characterised. According to an X-ray structure analysis, the structure has a saddle shape, with the π-electrons of the double bonds and the oxygen atoms pointing towards the centre of a cavity. The ligand forms a 1:1 complex with Ag⁺. Both NMR spectra and theoretical analysis (Gauge-independent atomic orbitals (GIAO) and Quantum theory of atoms in molecules (QTAIM)) suggest that the silver cation is bound within the molecular cavity. The metal is coordinated by the two olefinic double bonds and the four oxygen atoms in an approximately octahedral environment. The coordination motif is unusual because the soft silver cation prefers the interaction with the four hard oxygen atoms over the bonding to the arene units, which is frequently observed in Ag⁺ arene complexes.     

Vitali-Hahn-Saks theorem for hyperbolic logics

An analog to the Vitali-Hahn-Saks theorem for indefinite measures on hyperbolic logics of projections in indefinite metric spaces is proved.     

Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid

Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the dynamic viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the dynamic viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the dynamic viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the dynamic viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.

     

Investigation of the effect of using various HFC refrigerants in geothermal heat pump with residential heating applications

Journal of Thermal Analysis and Calorimetry - In the present investigation, the impact of various refrigerants on the efficiency of the geothermal heat pump operation is investigated. Appropriate...     

Magnetic amino‐functionalized hollow silica‐titania microsphere as an efficient sorbent for extraction of pesticides in green and roasted coffee beans

This study describes the synthesis and application of a magnetic amino‐functionalized hollow silica‐titania microsphere as a new sorbent for magnetic dispersive micro‐solid phase extraction of selected pesticides in coffee bean samples. The sorbent was fully characterized by Fourier‐transform infrared spectroscopy, field emission scanning electron microscopy, transition electron microscopy, energy‐dispersive X‐ray spectroscopy, and vibrating sample magnetometry techniques. Significant extraction parameters affecting the proposed method, such as extraction time, sorbent amount, sample solution pH, salt amount, and desorption conditions (desorption solvent and time) were investigated and optimized. All the figures of merits were validated in coffee bean samples under the matrix‐matched calibration method. Linear dynamic ranges were 5–250 µg/kg with the determination coefficients (R²) > 0.9980. The limits of detection for the pesticides of chlorpyrifos, malathion, hexaconazole, and atrazine were 1.42, 1.43, 1.35, and 1.33 µg/kg, respectively. Finally, the method was successfully applied for the determination of the pesticides in green and roasted coffee bean samples, and the obtained recoveries were in the range of 74–113% for spiked samples. The prepared sorbent could be used for the magnetic dispersive micro‐solid phase extraction of pesticides in the plant‐derived food matrix.