Determination of ripeness stages of Mazafati variety of date fruit by Raman spectroscopy

Introduction: The economical yield of date fruits depends on many factors (Al-Shahib and Marshall, 2003). One of them is harvesting in optimum stage. Generally, date fruits have four distinct stages of ripeness to satisfy different consumption requirements (e.g., fresh and processed). They are known throughout the world by their Arabic names which are Kimri, Khalal, Rutab and Tamr in order of ripeness (Imad and Abdul Wahab, 1995; Al-Shahib and Marshall, 2003; Sahari et al., 2007). Decreasing moisture content and increasing sugar content happens gradually while the date ripeness approaches to Tamr stage. From Kimri to Khalal stage, the size and acidity decreases when the color of Mazafati variety changes from green to red. The change in acidity continues from Rutab to Tamr stage while color transforms from brown to black. At the final stage of ripeness, Mazafati variety is soft and has a good storability (Al-Shahib and Marshall, 2003). The main Raman techniques commonly applied in agricultural product and food analyzing include dispersive Raman spectroscopy, Fourier Transform (FT), Raman spectroscopy, Surface-Enhanced Raman Spectroscopy (SERS) and Spatially Offset Raman Spectroscopy (SORS). Synytsya et al. (2003) illustrated that FT-Raman spectroscopy is a valuable tool in structural analysis of commercial citrus and sugar beet pectin. Yang and Irudayaraj (2003) employed an FT-Raman approach to detect and classify foodborne microorganisms on the whole apple surface for the first time. Schulz et al., (2005) revealed the potential of FT-Raman spectroscopy in natural carotenoid analysis. Also, many researchers have attempted to apply FT-Raman spectra on the whole fruits and vegetables. FT-Raman spectroscopy was used by Veraverbeke et al. (2005) to evaluate the natural, intact wax layers on the surface of whole fruits. Nikbakht et al. (2011) used a FT-Raman spectroscopy for qualitative and quantitative analysis of tomato ripeness parameters. The scope of this study was to evaluate the feasibility of a nondestructive method based on FT-Raman spectroscopy in distinction of Mazafati date fruits according to four mentioned ripeness stages. Materials and Methods: Sample preparation: Mazafati variety of date fruit was used for this study. During the harvest seasons of 2012 (July-August), the samples from each four stages of ripening namely Kimri, Khalal, Rutab and Tamr were collected from two different orchards in Bam, Kerman province, Iran. A number of 100 date samples were tested in this study, and the external features of the four stages are exemplified in Fig.1. To characterize the physical properties of studied samples, the selected physical properties such as initial moisture content, mass, geometric mean diameter, sphericity and density of studied samples were measured using represented methods by Mohsenin (1896), Jahromi et al. (2008) and Shakeri and Khodabakhshian (2011). At least, the samples were kept at 5C in a refrigerator for 7 days to distribute the moisture uniformly throughout the sample. Before spectral acquisition, the required quantities of date fruits in each ripeness stage was taken out of the frig and allowed to warm with room temperature for approximately 2 hr (Khodabakhshian et al., 2012). Chemical properties measurements: Tissue samples were cut from each fruit separately and were macerated with a commercial juice extractor, filtered and centrifuged. The supernatant juice was used for the determination of sugar content with a manual refractometer, and expressed as percent Brix in the juice. Dry weight percentage of samples (Between 3-5 g) was determined by weighing them first, then dried them at 105ºC in a forced-air oven for 4 h and finally reweighed. PH value of date fruits was determined by a pH meter. Raman spectroscopic set-up: FT-Raman spectra on the whole fruits in the region 200-2500 cm-1 were recorded using a Thermo Nicolet NEXUS 870 spectrometer (Thermo Electron Corp, Madison, Wis., U.S.A) equipped with a Deuterated Triglycine Sulfate (DTGS) detector and a solid substrate beam splitter. The spectra were collected with rapid scan software running under OMNIC (Nicolet, Madion, Wis., U.S.A) and a resolution of 4 cm-1 by coadding of 128 scans. FT-Raman has three main advantages over dispersive Raman systems: (1) reducing the laser-induced fluorescence that a number of samples exhibit; (2) easing the operation as with a Fourier transform infrared (FTIR) spectrometer; and (3) showing a high spectral resolution with a good wavelength accuracy (Yang and Ying, 2011). Furthermore, the Raman spectra of pure tannin were measured as a reference spectrum. The original data were used for further analysis only after subtracting dark current spectra. For obtaining dark current spectra, the laser was set to zero. Results and Discussion: Physical properties of date fruits: The results of some physical parameters of the studied date fruit are shown in Table1. The changes in the physical properties were dependent on the internal quality in different ripeness stages. This justification also was revealed for date fruits by Al-Hooti et al. (1995). The obtained relations between ripening stages and internal quality of studied samples are represented in the next part. Raman spectra of tannin: Raman features of the tannin in the wavelength range of 200-2500 cm-1 are shown in Figure 3. As shown in the figure, major Raman features of the tannin were observed in the spectral region of 600-1600 cm-1. Three main Raman peaks were identified in this region. The tannin showed its highest Raman intensity at 1590 cm-1, which was higher than that at 1357 cm-1. The other peak (650 cm-1) showed low intensity. As stated by many researchers (Shahidi and Naczk, 2004; Al-Farsi et al., 2005; Biglari et al., 2008), these bands are assigned to stretching C-C, C=C and C-H bonds which compose the structure of phytochemicals. Beyond 1600 cm-1, no notable Raman scattering signals were observed. Themain Raman features of tannin were revealed in the wavelength range of 600 to 1600 cm-1 since the main Raman features of tannin are in the wavelength range of 600-1600 cm-1, this region was used for calculating the spectral information divergence to evaluate the ripeness degree of the date fruits. Conclusions: This study reports the potential of FT Raman spectroscopy for nondestructive discriminating of Mazafati date fruits according to the four ripeness stages. The analysis of the Raman signal changes that happening during date ripening and its relationship with the ripeness degree of the date fruits was studied. In this regard, changes of pure tannin content in the wavelength range of 200-2500 cm-1 as a good ripeness index for date fruits was investigated. A modified polynomial, Self-Modeling mixture Analysis (SMA( and the Spectral Information Divergence (SID) was performed on different samples at four ripeness stages

Power system observability enhancement for parallel restoration of subsystems considering renewable energy resources

The observability of power systems during the parallel restoration of subsystems is one of the most important issues for system operators to accomplish the restoration task as quick as possible. Thus, this article proposes a coordinated optimal plan to solve the observability and sectionalizing problems by determining the locations of phasor measurement units (PMUs) and subsystems. Also, the impact of renewable energy resources on power system sectionalizing and the reliability value of power generation are taken into account in the proposed model. The objective functions that are considered in the optimization problem are the cost of wide-area measurement system (WAMS), the worst observability index among all subsystems and the lowest value of quality among all subsystems based on the reliability of subsystems. Since there are three contradictory objective functions, a multi-objective problem (MOP) is proposed as a mixed-integer nonlinear problem (MINLP). The Pareto curve of the proposed MOP is extracted by using a particle swarm optimization (PSO) algorithm. Two standard power grids are considered to validate the suggested technique. The outcomes of simulations confirm that the observability value of all sections is enhanced during the parallel restoration of the system. Also, the results show that the quality of subsystems in the presence of renewable energy resources is enhanced

Prioritization and Evaluation of Mechanical Components Failure of CNC Lathe Machine based on Fuzzy FMEA Approach

Introduction In recent years, with development of industrial products with complex and precise systems, the demand for CNC machines has been increasing, and as its technology has been progressed, more failure modes have been developed with complex and multi-purpose structures. The necessity of CNC machines’ reliability is also more evident than ever due to its impact on production and its implementation costs. Aiming at reducing the risks and managing the performance of the CNC machine parts in order to increase the reliability and reduce the stop time, it is important to identify all of the failure modes and prioritize them to determine the critical modes and take the proper cautionary maintenance actions approach. Materials and Methods      In this study, conventional and fuzzy FMEA, which is a method in the field of reliability applications, was used to determine the risks in mechanical components of CNC lathe machine and all its potential failure modes. The extracted information was mainly obtained by asking from CNC machine experts and analysts, who provided detailed information about the CNC machining process. These experts used linguistic terms to prioritize the S, O and D parameters. In the conventional method, the RPN numbers were calculated and prioritized for different subsystems. Then in the fuzzy method, first the working process of the CNC machine and the mechanism of its components were studied. Also, in this step, all failure modes of mechanical components of the CNC and their effects were determined. Subsequently, each of the three parameters S, O, and D were evaluated for each of the failure modes and their rankings. For ranking using the crisp data, usually, the numbers in 1-10 scale are used, then using linguistic variables, the crisp values are converted into fuzzy values (fuzzification). 125 rules were used to control the output values for correcting the input parameters (Inference). For converting input parameters to fuzzy values and transferring qualitative rules into quantitative results, Fuzzy Mamdani Inference Algorithm was used (Inference). In the following, the inference output values are converted into non-fuzzy values (defuzzification). In the end, the fuzzy RPNs calculated by the fuzzy algorithm and defuzzified are ranked. Results and Discussion In conventional FMEA method, after calculating the RPNs and prioritizing them, the results showed that this method grouped 30 subsystems into 30 risk groups due to the RPN equalization of some subsystems, while it is evident that by changing the subsystem, the nature of its failure and its severity would vary. Therefore, this result is not consistent with reality. According to the weaknesses of this method, fuzzy logic was used for better prioritization. In the fuzzy method, the results showed that, in the 5-point scale, with the Gaussian membership function and the Centroid defuzzification method, it was able to prioritize subsystems in 30 risk groups. In this method, gearboxes, linear guideway, and fittings had the highest priority in terms of the criticality of failure, respectively. Conclusions The results of the fuzzy FMEA method showed that, among the mechanical systems of CNC lathe machine, the axes components and the lubrication system have the highest FRPNs and degree of criticality, respectively. Using the fuzzy FMEA method, the experts' problems in prioritizing critical modes were solved. In fact, using the linguistic variables enabled experts to have a more realistic judgment of CNC machine components, and thus, compared to the conventional method, the results of the prioritization of failure modes are more accurate, realistic and sensible. Also, using this method, the limitations of the conventional method were reduced, and failure modes were prioritized more effectively and efficiently. Fuzzy FMEA is found to be an effective tool for prioritizing critical failure modes of mechanical components in CNC lathe machines. The results can also be used in arranging maintenance schedule to take corrective measures, and thereby, it can increase the reliability of the machining process

A new robust multi-machine power system stabilizer design using quantitative feedback theory

Small-signal oscillations is one of the important problems in power system operation that caused by insufficient natural damping in the system. This paper uses the Quantitative Feedback Theory (QFT) to design a new robust PSS for multi-machine power systems able to provide acceptable damping over a wide range of operating points. In the design procedure the main purpose is to reject the load fluctuations and, therefore, a particular transfer function is used as the nominal plant. The parametric uncertainty in power system is readily handled using QFT. The decentralized design with a simple structure is easily applied to multi-machine power systems. The nonlinear time-domain simulations are carried out to validate the effectiveness of the proposed controller. Results clearly show the benefits of the proposed controller for stability enhancement of power systems

Multi-Objective Model for Allocation of Gas Turbines with the Aim of Black-Start Capability Enhancement in Smart Grids

Installation of new energy sources as redundant black-start (BS) units is an efficient way to enhance the speed of power system restoration, especially when there is a high risk that the available power plants considered as BS units fail to operate. In this regard, this paper provides a new optimal design for the placement of the Gas Turbine (GT) as the redundant energy source to improve the smart grid performance during both restoration and normal conditions. To this end, there will be contradictory objective functions to be minimized. Therefore, a multi-objective problem (MOP), as a mixed integer linear programming (MILP), is defined. The Pareto optimal solutions of the MOP are obtained by using a new population-based meta-heuristic technique, called crow search algorithm (CSA). A typical test system is used for validation of the proposed method. The simulation results reveal that the system can benefit from this method not only to increase the capability of black-start generation, but also to improve the power system performance in normal conditions. It also provides the optimal start-up sequences of non-blackstart (NBS) units with the optimal transmission paths during the restoration process

Multi-objective model for allocation of gas turbines with the aim of black-start capability enhancement in smart grids

Installation of new power generating units as backup black-start (BBS) sources is a vital issue to improve the acceleration of power network restoration, especially when a serious problem is occurred in main BS units (BSUs) and leads to fail in operation. Accordingly, this work address a new design for the optimal locating of the Gas-based Turbine (GT) as BBS to improve the smart grid performance during both restoration and normal conditions. To this end, there will be incompatible fitness functions to be minimized. Therefore, a multi-objective problem (MOP) including a mixed integer Non-linear programming (MINLP), is formulated. The Pareto answers of the proposed MOP as the best solutions are modified and extracted by utilizing a meta-heuristic method, called crow search algorithm (CSA). A typical test system is employed for evaluation of the given plan. The extracted outcomes reveal that the network can desirably operate from this design not only to favorably enhance the capability of BSUs, but also to improve the power system performance in normal conditions. It also provides the better start-up program of non-black-start (NBS) power sources with the optimal paths during the restoration process.fi=vertaisarvioitu|en=peerReviewed