Milk and dairy products as an animal protein source with a high nutritional value has a particular importance in human diet. Development of non-destructive and quick methods for assessing the quality and freshness of milk is of particular importance in ensuring product safety before consumption. The aim of this study was to measure the DIELECTRIC POWER with the use of a parallel-plate sensor to develop a new method for rapid assessment of milk freshness. Three milks with commercial names of Pak, Kaleh and Damdaran and a milk from dairy farm were DIELECTRICally measured during seven storage days. Sinusoidal voltage wad applied to the milk samples and capacitor POWER within the frequency range of 0-150 MHz was measured using a spectrum analyzer. Results proved that the frequency range of 75-100 MHz varied considerably with storage time so that the frequency of the valley within this frequency range increased with storage time. The frequency of the valley as a spectrum characteristic predicted the storage day by a linear regression model with R2 and RMSE of 0.84 and 0.8, respectively. The correlation of milk pH and the frequency of the valley showed that during the first four days of storage with a minor change in pH, the frequency of valley varied significantly indicating further physico-chemical changes in milk with storage that affects the DIELECTRIC properties of milk. The DIELECTRIC POWER at the frequencies of 2.7 and 89.4 as the most effective frequencies, predicted the storage day and pH, respectively with RMSEs of 0.4 and 0.22. Multiple linear regression models decreased the errors of prediction to 0.26 and 0.11 for storage day and pH, respectively. Based on the findings of this study, DIELECTRIC POWER SPECTROSCOPY can be implemented as a non-destructive, simple and accurate method to milk pH and freshness determination.

Sugarcane, an important crop in sugar production in the country, is widely produced in Khuzestan province. After harvesting the sugarcane and when delivered to the sugar factory, the cane is shredded. In this study, for measurement of sucrose concentration in shredded cane, the DIELECTRIC SPECTROSCOPY technique in the frequency range of 0-100 MHz was evaluated using a cylindrical parallel-plate sensor with compaction of the shredded cane. 55 samples of shredded cane were taken from the sugar production line of Salman Farsi factory during the harvesting season of 2021-2022 and their reference properties including sugar concentration with three indicators of Brix using refractometry method, Pol using simple polarimetry method and the sucrose measured using double polarimetry were measured. The samples were subjected to DIELECTRIC SPECTROSCOPY using the capacitance sensor and the DIELECTRIC spectra were analyzed for sugar concentration prediction. Multivariate regression analyses of partial least-sugare regresseion (PLSR), principal component regression (PCR) and support vector regression (SVR) were applied for development of prediction models of sugar concentration variables. Validation of the prediction model of PLSR showed a stronger prediction of sucrose (R2= 0.79, RMSE= 0.89, RPD= 2.22) measured using the double-polarimetry as compared to Pol (R2= 0.76, RMSE= 0.8, RPD= 2.07) and Brix (R2= 0.77, RMSE= 0.89, RPD= 2). This result was explained with higher number of OH groups of sucrose molecule as compared to the other sugars existing in the sugarcane juice and the dominant effect of sucrose on the DIELECTRIC characteristics of the juice. With optimization and improvement of the accuracy of this method, a simple and cost-effective instrument can be developed for use in sugar industries.

Regarding the importance of quality of meat and other daily consuming food stuffs in the growth and health of human society, development of quality diagnosing and monitoring systems for food materials are being paid increasing attention by investigators. In this study, 40 beef samples were subjected to macroscopic imaging and DIELECTRIC POWER SPECTROSCOPY at 20 frequencies in the range of 5-100 MHz during five days of storage at 5 ° C. It was hypothesized that combination of the two sensing methods would result in more information on physicochemical changes of meat during ageing. For any beef sample, 42 attributes (i. e. 20 DIELECTRIC variables including DIELECTRIC POWER at different frequencies and 22 texture and color features of the image) were extracted. Classification analyses for the day of storage were performed with five algorithms of neural networks including multi-layer perceptron (MLP), multinomial logistic regression (MRL), functional trees (FT), logistic model trees (LMT) and Bagging aggregation. The results showed that the DIELECTRIC POWER at different frequencies decreased with the storage day from e. g. 250 µ W at 5 MHz on the first day to 100 µ W at the same frequency on the fifth day. The results showed that image parameters of beef were more effective in classification than DIELECTRIC variables but combining the information of the both sensory techniques, after reduction using PCA, resulted in classification accuracies of %78 for functional tree (FT) algorithm and %77 for Bagging classification with MLP as the base classifier.

DIELECTRIC relaxation properties of solution grown foil samples of poly (vinylidene fluoride) (PVDF) of 20 mm thickness with similar (Al-Al) electrode combination have been studied in the low frequency range of 100 Hz to 10 kHz by varying the temperature from 30°C to 170°C. PVDF foil samples exhibit two DIELECTRIC loss maxima, one around 130±10°C and the other around 150±10°C. These peaks correspond to α- and α'-relaxation processes, respectively. The variation of permittivity with temperature is attributed to the thermal expansion, orientation of dipolar molecules and the chaotic random motion of molecules in the close vicinity of α and α' peaks, respectively. The thermally stimulated depolarization current (TSDC) spectra of PVDF foil sample are characterized by two current maximas, one at 70±10°C (b-peak) and the other centered at 135±10°C (a-peak). The first peak attributed to b-relaxation process was caused by local motions of side chain. However, a-relaxation peak appeared due to the formation of space charge at metal polymer interface. The peak position and activation energy do not change with the poling field (Ep), but it strongly depends upon the poling temperature (Tp). The location of the TSDC a-peak is found to be comparable with the a-peak obtained by DIELECTRIC relaxation SPECTROSCOPY.

IntroductionThe development of portable devices for real-time quality assessment of sugarcane is an essential necessity in the agricultural and industrial technology of sugarcane production and processing. Attributes of sugarcane such as sugar concentration and water content can be utilized for this purpose. Near infrared (NIR) SPECTROSCOPY has been one of the most widely applied techniques for quality evaluation of sugarcane. However, NIR spectrophotometers in the full NIR wavelength range (up to 2500 nm) are expensive devices that are not readily available for portable applications. Short-wave NIR devices in the range of 1100 nm are available at lower costs but need to be evaluated for specific applications. On the other hand, DIELECTRIC SPECTROSCOPY has attracted the attention of researchers for quality evaluation of agricultural and food products. In a previous study, a parallel-plate capacitance sensor was developed and evaluated for non-destructive measurement of sugarcane Brix (total soluble solids) and Pol (sucrose concentration) as well as water content, in the frequency range of 0-10 MHz. The results showed excellent prediction models with root mean square errors smaller than 0.3 for Brix and Pol. This study aimed to develop and evaluate a DIELECTRIC sensor that can be extended for portable measurements on standing sugarcane stalk in comparison with short-wave NIR (SWNIR) SPECTROSCOPY to address how the fusion of the two methods may improve the accuracy of models for predicting sugarcane Brix.Materials and MethodsA DIELECTRIC sensor in the form of a gadget was developed with metallic electrodes to encompass the sugarcane stalk samples. The DIELECTRIC sensor was excited with a sinusoidal voltage within 0-150 MHz frequency range by a function generator, and the conductive POWER through the electrodes was measured using a spectrum analyzer. 105 sugarcane stalk samples were prepared from seven sugarcane varieties and scanned with the DIELECTRIC sensor. The samples were also subjected to Vis-SWNIR radiation in the wavelength range of 400-1100 nm, and the reflectance spectra were captured. Reference Brix and water content of the samples were determined using a portable refractometer and oven-drying method, respectively. Regression analyses and artificial neural networks were performed on independent and combined data from DIELECTRIC and Vis-SWNIR SPECTROSCOPY to develop prediction models for Brix and water content.Results and DiscussionPartial least squares regression on independent data sets of each instrument resulted in RMSEP = 1.14 and RMSEP = 1.88 for Brix using Vis-SWNIR and DIELECTRIC SPECTROSCOPY, respectively. Moreover, data fusion of DIELECTRIC and Vis-SWNIR SPECTROSCOPY at a low level for the prediction of Brix significantly improved the prediction accuracy to R2P = 0.94 and RMSEP= 0.74. The medium-level data fusion resulted in R2P = 0.89 and RMSEP = 0.93 for prediction of water content.ConclusionIn this study, the accuracy of using Vis-SWNIR and DIELECTRIC SPECTROSCOPY data for predicting Brix and water content in sugarcane stalk samples was evaluated. To develop the prediction models, partial least squares (PLS) regression and artificial neural network (ANN) were compared. First, the prediction models were developed based on Vis-SWNIR and DIELECTRIC SPECTROSCOPY independently. Then, the two techniques were fused and the improvement in the prediction accuracy was investigated. Fusing the two methods at an intermediate level lowered the RMSE of Brix to 0.74, showing noticeable improvement compared to previous studies. Based on the achieved results, developing a fusion probe for SWNIR and DIELECTRIC SPECTROSCOPY and designing the measuring system could be the aim of future studies for in-situ evaluation of sugarcane quality parameters. Due to the importance of sugarcane quality evaluation, during growth and maturity, the results of this study can have a significant role in the development of a portable device that combines NIR and DIELECTRIC SPECTROSCOPY methods for fast and non-destructive evaluation of sugarcane quality parameters.AcknowledgmentThis article was extracted from a research project financially supported by the Research deputy of Shahrekord University. The grant number was 0GRD34M1614. The authors would like to appreciate the support of the Amir-Kabir Sugarcane Agro-Industry Co., Khuzestan, Iran for providing the sugarcane stalk samples.

Biodiesel is a clean and alternative fuel for diesel. It produces from renewable sources such as vegetable and animal oils. However, due to the high price of this clean fuel, it is mixed with other inexpensive fuels, such as diesel, and a fraud fuel was sold as a pure one. The purpose of this study is to evaluate an intelligent system for detecting the level of fraud in biodiesel fuels by integrating DIELECTRIC SPECTROSCOPY and image processing techniques. In order to classify fake biodiesel specimens using frequency and color properties, the principal component analysis, linear discriminant analysis, decision tree, Support vector machine, and Artificial neural network were used. The best network with structure 1-6-36 to predict blended biodiesel palm and diesel samples provided correlation coefficient and mean square error values of 0. 944 and 0. 006, respectively. In the final step, the combination of DIELECTRIC and color properties was used to model the problem. Correlation coefficient and mean squared error values for biodiesel and diesel mixed samples were 0. 962 and 0. 008 with a structure of 1-2-38. The results of the evaluations show that the designed device has the ability to detect biodiesel fuel fraud with high precision.

In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive POWER dispatch method in a wind-integrated POWER system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive POWER generation and active POWER loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive POWER resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

The properties of the biodiesel will be changed by changing the reaction parameters of biodiesel production from vegetable oils that heating value is one of important of them. The aim of this study is to evaluate a new system in order to detect changes in the properties of biodiesel, such as heating value by DIELECTRIC SPECTROSCOPY with the capacitive sensor. Transesterified palm-based biofuels were synthesized in different weather conditions using the heat loss of a linear parabolic trough solar collector POWER. At first, a sample of biodiesel was produced with optimized parameters as the basis of evaluation. Then, five samples of biodiesel with the same optimized parameters but with different temperatures were produced by the solar collector as one of the renewable energy systems. After that, the samples were placed in the device and information obtained by DIELECTRIC SPECTROSCOPY was transferred to software. In order to validate the analysis, some algorithms from the decision tree technique were used. According to the results, the Classification and Regression Tree (CART) algorithm had the highest accuracy (99%) in comparison with others. According to the accuracy of algorithms, it was deduced that the chosen techniques had good potential to determine biodiesel fuel properties.