جستجوی مقالات مرتبط با کلیدواژه « طیف سنجی » در نشریات گروه « علوم پایه »

Modeling mineral potential based on the precise collection and processing of geological, geophysical, and satellite data enables us to predict the potential presence of mineral substances in a specific area. This process involves constructing complex mathematical models that, utilizing machine learning algorithms and thorough data analysis, assist authorities and decision-makers in the mining sector. It helps identify mineral-rich zones for optimal extraction and manage land resources in the best possible way. Given the diverse geological units present in the Saqez sheet, this sheet is considered one of the most promising areas for the formation of metallic deposits. The host rock for most Pb-Zn deposits in Iran is sedimentary, known as Sedimentary-Hosted Pb-Zn deposits. According to conducted surveys, it has been determined that the Pb-Zn mineralization occurring in the Saqez sheet is also of this type. Typically, calcite, dolomite, shale, sandstone, and igneous rocks serve as hosts for these deposits.

In this research, the exploratory layers of lithology, dolomite alteration, and geochemical of Pb-Zn were used to prepare a map of the prediction of Pb-Zn mineralization in the turpentine sheet. Utilizing the singularity technique on sediments stream, using various exploratory layers performing laboratory spectroscopy, and applying the spectral behavior curves obtained on Sentinel-2A images in this innovative and creative research. It shows its existence compared to another similar research. After fuzzification of exploration layers in GIS software, the prediction map of Pb-Zn mineralization was obtained by Fuzzy-Gamma function and gamma value of 0.85. The results of XRF and ICP-MS analysis on the discovered Pb-Zn samples showed a grade between 3 and 7%, which indicates the correct selection of the studied area and the exploratory layers and their correct integration. Further, by conducting laboratory spectroscopy in a dark room with a halogen lamp and obtaining the spectral behavior curve of the sphalerite mineral related to the samples of the study area, the SAM matching algorithm was applied to the Sentinel-2A satellite images.

According to the Pb-Zn mineralization prediction map of the Saqez sheet, this map is classified into four categories of low, moderate, high, and extreme mineralization potential. It is evident from this map that the northern, central, and southeastern regions have the highest potential for Pb-Zn mineralization. Upon examining the topography and road identification of the Saqez sheet, six areas were selected for exploratory drilling of Pb-Zn. Samples were collected for analysis and validation of identified points. XRF and ICP-MS analysis results indicated that the total Pb-Zn content ranged from 2 to 7% and 70,000 to 20,000 ppm. Finally, high-grade Pb-Zn samples were selected for petrographic examination. Petrographic studies revealed that minerals such as sphalerite, galena, and pyrite were predominant in the collected samples, with their texture filling the pore spaces. Specifically, sphalerite replaced galena, and galena replaced pyrite.The results obtained from laboratory spectral analysis and the application of spectral behavior curves for sphalerite minerals on Sentinel-2A satellite images were utilized to assess the accuracy of the work and identify promising new mineralized areas. By comparing the corrected spectra from the laboratory experiments with the USGS spectral library, it was determined that the obtained spectral behavior is similar to the USGS spectral library. Therefore, the predictive map of Pb-Zn mineralization resulting from the application of spectral behavior curves for sphalerite on Sentinel-2A satellite images indicates the correct selection of imagery, appropriate spectral analysis, and all processing steps.

Due to the fact that the host rocks of most Pb-Zn deposits in Iran are of sedimentary origin, the first step in modeling the mineral potential of these deposits is to accurately recognize the ore deposit type. Based on the evidence and samples observed in the study area, the ore deposit type in the study area can be considered as the Irish type. Therefore, based on this, modeling and prediction of Pb-Zn mineralization on the one hundred thousand scale map of Saqez were carried out according to the Irish type Pb-Zn mineralization.After evaluating the Pb-Zn mineral potential map, seven final areas were selected for exploration. Based on the exploration conducted in six areas (S1, S2, P, Q-Pb-Zn, Polygon12, and Polygon13), samples containing Pb-Zn mineralization were discovered, while Polygon5 was identified as lacking Pb-Zn mineralization, indicating the reliability of the exploration layers and integration method. XRF and Aqua Regia analysis results showed that the discovered Pb-Zn samples had grades ranging from two to seven percent, indicating economic-grade content for these metals.

Silver nanoparticles (AgNPs) are used in a variety of industries, including coatings, disinfectants, water purification, and medicine, due to their small size and unique physical and chemical properties. To avoid the use of chemicals, extracellular biosynthesis of AgNPs using fungal systems is an appropriate method. In this study, the extracellular biosynthesis of AgNPs by the resting cell of the fungus Aspergillus niger ZRS14 was investigated, and the synthesis process was optimized using Taguchi's statistical method.

The effect of biomass parameters, incubation time, pH and silver nitrate concentration in three levels was performed using Qualitek-4 software. The characteristics of the synthesized AgNPs were determined by spectroscopic analysis including UV-Vis spectroscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and field emission scanning electron microscopy (FESEM).

The results showed that the selected strain under optimal conditions including 2 mM silver nitrate, pH 6, temperature 32 ◦C, and after 72 h incubation, spherical silver nanoparticles with an average size of 24 to 38 nm in extracellular form synthesized under resting cell strategy. Because of the coating of fungi-secreted proteins, synthetic spherical elemental AgNPs have a crystalline nature, a small size, and high stability.

Nectarine is a plant that is cultivated as an important commercial product in some countries and is known as an important source of sugar and vitamins in the human diet. Due to the increase in expectations for food products with high quality and safety standards, accurate, fast and targeted determination of the characteristics of food products is necessary. In the nectarine product, quality evaluation after the harvesting stage is necessary to provide a reliable and uniform product to the market. The purpose of this study is to identify and classify nectarines by extracting characteristics from the response patterns of the spectrometer and using chemometrics methods. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties. The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive manner, and only some of them have been able to meet the above conditions and have been justified in terms of technical and industrial aspects. To be meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality. With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now essential. Because manual methods do not have automatic control, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, using spectrometry and chemometrics methods, the variety of nectarine fruit was identified.

For this study, 5 different nectarine cultivars were prepared from the gardens of Moghan city (Ardebil province) and were tested and data collected. A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, and portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer with the purpose of displaying and storing the acquired spectra in the Spectra Wiz software through the USB port. With the aim of creating optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented in order to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process. The data obtained from spectral imaging may be affected by the scattering of light by the detector with sample change, sample size change, surface roughness in the sample, the noise created due to the increase in temperature of the device and many other factors, and unwanted information affect the accuracy of calibration models. Therefore, to achieve stable, accurate and reliable calibration models, data pre-processing is needed (Rossel, 2008). In this research, Savitzky-Golay smoothing, first and second derivatives, baseline, standard normal distribution, and incremental scatter correction were applied to the data. The use of non-destructive methods based on spectroscopy in the full range of wavelengths requires spending time and very high costs, which makes the practical application of this method almost impossible; therefore, one should look for a way to find the optimal wavelengths and limit the wavelengths to the minimum possible value. Chemometrics uses multivariate statistics to extract useful information from complex analytical data. The chemometrics used in this study started with principal component analysis (PCA) to explore the output response of the sensors and reduce the dimensionality of the data. In the next step, linear diagnostic analysis (LDA) was also used to classify 5 varieties of Shail. (PCA) is one of the most common statistical data reduction methods. This method is an unsupervised technique used to explore and reduce the dimensionality of a dataset. The analysis itself involves the determination of variable components, which are linear combinations of many investigated characteristics. In this research, in order to construct the LDA model, the data were randomly divided into two parts: 70% of the samples were used for training and cross-validation, and the rest of the data were used for independent validation.

Based on the results of the PCA analysis presented in Figure 2, the first principal component (PC-1) describes 72% and the second principal component (PC-2) 13% of the variance of the tested samples. As a result, the first two principal components together express 85% of the data. Considering that it is possible that the degree of correlation between the properties of different samples during the tests, due to various reasons such as technical problems of the equipment, data collection, incorrect sampling, etc., in some samples, inappropriate or socalled outliers The LDA method is a supervised method that is used to find the most distinct eigenvectors and maximizes the between-class and intra-class variance ratios and is capable of classifying two or more groups of samples. The LDA method was used to identify the nectarine cultivars based on the output response of the spectrometer. Unlike the PCA method, the LDA method can extract the resulting information to optimize the resolution between classes. Therefore, this method was used to detect 5 nectarine cultivars based on the output response of the spectrometer. The results of the identification of figures equal to 100% were obtained.

Potato with the scientific name (Solanum tuberosum. L.) is a plant that is cultivated as an important crop in all countries and is known as a source of carbohydrates, proteins, and vitamins in the human diet. This is a native product of South America and its origin is from Peru. In the food industry, this product is transformed into various products such as baked potatoes, fried potatoes, potato chips, potato starch, dry fried potatoes, etc.Due to the increase in expectations for food products with high quality and safety standards, accurate, fast and targeted determination of the characteristics of food products is necessary. In the apple-potato product, quality assessment after the harvest stage is necessary to provide a reliable and uniform product to the market, because potatoes, like many other products, have uneven quality and processing during the harvest stage. - Be At the same time, the safety and desirability of food play an important role in the food industry and are directly related to people's health. In addition, a huge part of potatoes used in the processing industry is stored, so considering the importance of this food item and the demand of the people throughout the year, it is possible to meet the needs of the applicants only through long-term storage with optimal conditions was responsible. Potatoes for the processing industry must have some requirements such as low sugar content, high dry matter and specific weight, high antioxidants, light skin colour and no sprouting.The complexity of the reflectance spectrum of food makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, sensory analysis by experts is a costly process and requires trained people who can only work for a relatively short period. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties.The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented so far for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive way, and only some of them havebeen able to meet the above conditions and have been justified in terms of technical and industrial aspects.Meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality.With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now necessary. Because manual methods do not have automatic control, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, using spectrometry and chemometrics methods, changes in acidity and SSC of potato were investigated over time.

In each treatment period (in total 5 periods were considered and the intervals of periods were determined as one week), unripe walnut samples in addition to ripe samples (in the last period) were taken from one of the orchards around Ardabil (located in Shahrivar village) was prepared, tested and data collected.A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer to display and store the acquired spectra in the Spectra Wiz software through the USB port. To create optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process.To measure SSC, liquid refractometer model BPTR100 (Middle East System Control Company, brand name Prisma Tech, made in Iran) available at Mohaghegh Ardabili University is used. For this, first, some water is taken from the samples and after pouring it into the microtube, we allow it to reach the ambient temperature, and then it is placed on the refractometer and the amount of sugar is read in terms of Brix.For this purpose, a laboratory pH meter, which is also called a pH meter, was used. The pH meter is actually a potentiometer consisting of an ion-selective glass electrode that selectively responds to the activity of hydrogen ions in the solution and measures the potential difference between the external solution (sample) and the internal solution (reference electrode solution). The pH-sensitive part is made of a special thin glass membrane that is at the bottom of the electrode.

In this research, in order to estimate the amount of acidity and SSC of potato-potato and the amount of wavelength absorption in 5 different periods of storage (two-week periods), reflectance spectroscopy was performed in the wavelength range of 400 to 1100 nm. After removing the noises by PCA analysis, to improve the spectrum, different pre-processings were applied and their effects were investigated. The appropriate model was determined using the partial least squares (PLS) method. Important wavelengths were selected based on the regression coefficient of the best model. Based on PLS analysis, the best results were obtained with Savitzky-Golay smoothing preprocessing. As a result, it seems that the non-destructive method of ultraspectral imaging was able to detect the amount of SSC in potatoes, but no acceptable result was obtained in the case of acidity.

 Due to the growing need of industries for raw materials and high environmental hazards of chemical products, many researchers and manufacturers have turned their attention to the use of biological products, especially products of microbial origin. In this study, the potential of Aspergillus oryzae and Aspergillus flavus for resistance to ultra violet light in the range of 350-290 nm has been evaluated.

two species of fungi Aspergillus oryzae and Aspergillus flavus, after being purchased as lyophilized powder, were cultured in various culture media for 10 days to select the appropriate culture medium to produce the maximum biomass. Samples were cultured in selective culture medium, PDA, for 7 days and after drying the biomass and separation of pigments, the amount of SPF parameter and the survival of colonies on the culture medium in the presence of ultraviolet rays were measured.

Both types of fungi can withstand 350 nm for about 20 minutes, but the resistance of the fungi to light is 290 nm is much lower. Also, the SPF of Aspergillus flavus is higher than that of Aspergillus oryzae. Thus, it can be concluded that the pigments of Aspergillus oryza and Aspergillus flavus have shown good protection against ultraviolet rays. Therefore, these natural pigments can be used as alternatives to chemicals in industry.

the pigments of Aspergillus fungi studied have shown good protection against ultraviolet rays and have good potential for use in industry.

Among metal nanoparticles, elemental selenium nanoparticles (SeNPs) are considered for their low toxicity and vitality in the human body. Yarrowia lipolytica yeast, due to its reductase enzymes, has the ability to accumulate and detoxify heavy metals and reduce metal salts to nanoparticles with a narrow size and less dispersion. The aim of the present study was to use Y. lipolytica strain MP10 as a biological safe source for the biological synthesis of SeNP.

Agar dilution method was used for determining the resistance pattern of Y. lipolytica strain MP10 to selenite oxyanion. Synthesis of SeNPs was investigated under resting cell strategy. The effects of selenite oxyanion concentrations and also time incubation on the production rate, size, and dispersity of SeNPs were investigated in the bioconversion reaction. SeNPs produced with Y. lipolytica strain MP10 were defined by images obtained from Scanning Electron Microscopy (SEM) analysis, energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR).

The obtained results demonstrated that Y. lipolytica strain MP10 showed tolerance to selenite oxyanion up to 10 mm. The yeast strain, at the optimal condition of 4 mM sodium selenite, and after 24 hours of incubation, was able to produce spherical SeNPs with an average size of 70 nm. As the incubation time increased, the dispersion of spherical SeNPs reduced and the average size of the nanoparticles increased to 117 nm after 72 h incubation. 

Discussion and Conclusion:

The present study is a report on the capability of native yeast Yarrowia lipolytica for the successful synthesis of SeNP under resting cell strategy and the improvement in the dispersion and particle size of the nanoparticles in the optimal incubation time.

Walnut is an important economic and commercial product all over the world. The reflected light spectrum can be one of the key factors in determining the ripening time of the fruit and it depends on the content of the chemical compounds of the fruit and its skin. Crunchyness and easy peeling are the main features that affect the level of satisfaction of walnut consumers. The complexity of the reflectance spectrum of food makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, sensory analysis by experts is a costly process and requires trained people who can only work for a relatively short period of time. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties.The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented so far for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive way, and only some of them have been able to meet the above conditions and have been justified in terms of technical and industrial aspects. To be meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality.With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now necessary. Because manual methods do not have automaticcontrol, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, the ripening time of walnuts was investigated using spectrometry and chemometrics methods.

In each treatment period (in total 5 periods were considered and the intervals of periods were determined as one week), unripe walnut samples in addition to ripe samples (in the last period) were taken from one of the orchards around Ardabil (located in Shahrivar village) was prepared, tested and data collected.A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer with the purpose of displaying and storing the acquired spectra in the Spectra Wiz software through the USB port. With the aim of creating optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented in order to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process, which is shown in Figure 1.

The average absorption spectra of Vis/NIR absorption spectra for different treatments in the range of 680-970 nm are presented in Figure 1.Environmental factors (light and heat) as well as the spectrometer's expression quality cause disturbances in the initial and final wavelengths of the spectra, so some of these wavelengths are removed from the data set. And as it is clear in Figure 1, the samples had an almost similar trend; this may be affected by the colour of the samples. According to Figure 1, there are two distinct peaks for the spectra and it is that the peaks appeared around the wavelength of 680 and 970 nm. It can also be seen in Figure 1 that the amount of absorption of ripe walnuts is higher compared to other periods, which can be due to the difference in the content and texture of the product.Based on the PCA analysis results presented in Figure 2, the first principal component (PC-1) describes 94% and the second principal component (PC-2) describes 5% of the variance of the tested samples. As a result, the first two principal components together express 99% of the data. Considering that it is possible that the degree of correlation between the properties of different samples during the tests, due to various reasons such as technical problems of the equipment, data collection, incorrect sampling, etc., in some samples, inappropriate or so-called outliers.The values of R2 and RMSE for calibration and validation sets of different regression models (PLS) with raw and processed data are presented in Figure 3, which is equal to 0.98. The results show that the spectra are able to detect the ripening time of walnuts with high accuracy. Khodabakhshian et al investigated the potential of visible and infrared spectroscopy to classify the ripening stage and predict the quality traits of pomegranate varieties including SSC and TA. Among the methods of centring, Savitzky-Golay smoothing, median filter, standard normal variable, incremental spread correction (MSC) and differentiation with first derivative and second derivative, the use of incremental spread correction (MSC) has the highest accuracy in identifying pomegranate quality parameters. followed Zhang and colleagues (Zhang et al, 2018) in estimating the SSC of red Fuji apple using near-infrared spectroscopy to reduce noises using the functions of additive scatter correction (MSC) and standard normal distribution (SNV) and reported that the additive scatter correction method (MSC) compared to the standard normal distribution (SNV) will result in a more accurate estimate of the SSC value. Kim et al. estimated the SSC of oriental melon using near-infrared spectroscopy among different pre-processing methods including Savitzky-Golay smoothing, normalization with maximum and minimum, stable normalization, standardization, stable normal variable, distribution Standard normal (SNV) and incremental spread correction (MSC) reported that the best result was obtained with a standard normal distribution (SNV). Although considering the different nature of the samples, measurement methods and equipment, and other conditions affecting the spectral properties of the product, it is better not to compare the data obtained from different researches with each other.

Sweet potato grows as a strong plant all over the world and is a product compatible with drought, temperature, and low fertile soils. Potatoes are high in starch, vitamins, minerals, and non-mineral salts such as calcium, phosphorus, iron and low in calories. This product is widely consumed fresh, boiled, etc. due to its functions for various reasons, such as improving immunity and preventing cancer, and its consumption is due to the abundance of nutrients such as carbohydrates, dietary fiber, minerals and other health-promoting compounds such as beta-carotene, vitamin C, phenolic acids, etc. are on the rise.Conventional evaluation methods for the internal quality of potatoes are mostly destructive and inefficient. In the practical production of potatoes, the quality evaluation system must have good accuracy, high speed, and low cost. Such goals can be achieved using modern techniques such as spectroscopy and electronic nose, as they do not require sample preparation, are non-destructive, efficient, fast, accurate, pollution-free, and inexpensive.Organic acids (OAs) are organic acidic compounds containing carboxyl groups that are widely present in organisms. Organic acids in fruits mainly include citric acid, malic acid, tartaric acid, and succinic acid. The traditional method for detecting OA concentrations is ion chromatography in the laboratory. Ion chromatographic testing requires standard solutions as a reference, also requires the use of chemical reagents, and organic acids must be measured separately. This is a tedious operation that wastes a lot of time. Therefore, a rapid detection technology is needed and preferred as an alternative.Near-infrared spectroscopy is a type of rapid detection technology that extracts spectral information from a sample through the difference between radiated light and reflected light. NIR technology has the advantages of fast performance, no use of chemical reagents and is also able to detect multiple components simultaneously. Spectral signals can be further amplified by the combined use of the Fourier transform technique. Fourier transform near-infrared spectroscopy has been widely used in the fields of food science, agricultural informatics, environmental monitoring, biomedicine, and pharmacy.Based on the simplicity of PLS regression, nonlinear methods are investigated to improve the PLS algorithm by embedding nonlinear core functions. This method plots the data before PLS scoring in a high-dimensional feature space, and the data converted in the new space characterize the samples. In this study, a neural network as a core function is designed to optimize PLS in the quantitative NIR analysis of OA concentrations in potato samples. A three-layer lattice with an adjustable number of neural nodes is designed to extract spectral feature variables to optimize the PLS core model.

Potato samples were harvested and 248 of healthy size and almost the same size were selected. The samples were transferred to the laboratory 24 hours after picking and stored at room temperature for 2 days. In the next 5 days, about 50 glands per day were selected and their OA concentration and FT-NIR spectrum were identified. Each potato sample was divided into two parts, half of which were used to detect the OA concentration and the other half to measure the NIR spectrum. The FT-NIR spectrum was measured using a PS-100 spectroradiometer (Apogee Instruments, INC., Logan, UT, USA) made in the USA. Temperature and humidity were kept constant at 25 ° C and 47% during the spectrum study.PLS kernel is an improved PLS method to deal with the nonlinear problem of spectral data. Raw data is mapped by a special nonlinear core function in high-resolution image space, so the original PLS linear algorithm can be used to discover the relationship between feature data and sample analysis. In short, this method can be done in two consecutive steps of mapping and regression.In modern studies, a neural network is a good tool for operating dynamic data, as it is flexibly taught by automatically fitting its link weights to the data-based model. A three-layer neural network was constructed in this study as a new nucleus for PLS output in the quantitative NIR analysis of potato OA concentrations.All 248 potato samples were divided into three parts for calibration, validation, and testing. The calibration section is used to create models and teach the model structure as well as the main algorithmic parameters. The validation section is used to check the model and optimize the parameter values. And the test section to evaluate the model.All 248 potato samples were divided into three parts for calibration, validation, and testing. The calibration section is used to create models and teach the model structure as well as the main algorithmic parameters. The validation section is used to check the model and optimize the parameter values. And the test section to evaluate the model.

Core PLS regression was applied to create FT-NIR calibration models to quantify OA concentrations in potato samples. The proposed network architecture was used as a new kernel conversion function to select attribute variables. The network was created connected with an input layer, a hidden layer, and an output layer.All 3114 wave number variables were transferred to the input layer. The same number of input nodes were generated to accept the data, and then perceptron units were applied, converting the data into a hidden layer. In the case of using a data-driven learning mechanism, the number of hidden nodes varies from 10 to 200 with step 10. Each Nh value was tested to screen for the best latent structure. Perceptron calculations converted the hidden data into an output layer, and a total of 20 output neurons were generated in the output layer to reduce the dimensions. These output variables were mostly used for PLS regression.In general, neural perceptron units were adjusted with their link weights, which automatically matched the data. 20 output variables were delivered to the softmax MLR predictor. Predictive errors were used for 50 rounds of error-feedback repetition optimization on link weights. Figure 3 shows that the RMSEV gradually shrinks with more repetitions and gradually decreases for each Nh number. This phenomenon means that the initial feedback and error replication mechanism can optimize machine learning for the network kernel. Duplicate optimized network link weights were used to serve the network architecture as a core evaluation function to optimize PLS regression. . The most optimal network structure was constructed with 130 hidden nodes and 20 output nodes.Then, the optimal network structure constructed with 130 hidden nodes and 20 output nodes is used as the core function for PLS regression. Hidden PLS variables were selected by network search mode. We tested PLS regression models with f = 1, 2… 20 based on the optimal network core. The results of model training for validation samples are shown in Figure 4. The optimal number of latent variables was determined as f = 8. The results of the network core model prediction and common cores are listed in Table 2.According to the principle of sample division introduced, PLS core models were quantified for FT-NIR analysis of potato OA concentration based on calibration samples and optimized by validation samples. The PLS model of the selected optimal network core should then be evaluated by 64 experimental samples that were unique to the model training process. Spectral data of the experimental samples were entered into the core of the optimal network with 130 hidden nodes and 20 output nodes.Table 2 shows that for PLS kernel regression, the proposed network kernel performs better than conventional kernels, regardless of the model training process or in the model evaluation process. Therefore, using neural network architecture to optimize the PLS regression kernel is a practical idea. FT-NIR calibration models have clearly improved compatibility by the adjustable network core.

This study investigated the potential of aquatic bacteria for their ability as a biocatalyst to synthesized Fe2O3 nanoparticles using iron precursor, FeCl3. A total of 25 aquatic bacterial strains were isolated in trypticase soy agar plus 10 mM FeCl3 with selective enrichment technique. Among the bacterial strains evaluated, NV06 was the only strain able to synthesize Fe2O3 nanoparticles extracellularly. The strain NV06 was identified as Alcaligenes sp., on the basis of phenotypic and molecular characteristics. Extracellular synthesis of Fe2O3 nanoparticles by this strain was investigated under the optimal conditions. The biosynthesized Fe2O3 nanoparticles were characterized using UV–visible spectrophotometry (UV-Vis), Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared (FTIR) spectroscopy. The results showed that cell-free extract (CFE) of the bacterium strain can produce the rod-shaped Fe2O3 nanoparticles with mean edge lengths of 80.2 nm and mean diameters of 25.5 nm, after being exposed to FeCl3 solution (10 mM), at an optimum pH of 6 and an optimum temperature of 28 °C, after 96 hours of incubation at 150 rpm. This is the first report on the extracellular biosynthesis of Fe2O3 nanoparticles using the genus of Alcaligenes under the CFE strategy. It could be speculated that the results of the study can hopefully introduce the inherent capabilities of aquatic microbes as safe, simple, and effective biocatalysts in the production of Fe2O3 nanoparticles.

Spectral properties and thermal stability of AS1411 G-quadruplex AS1411 is an anticancer four-stranded deoxyoligonucleotide with high affinity and specificity to a putative surface biomarker, nucleolin, which is an overexpressed protein on numerous cancer cells. AS1411 has valuable functional potential for the targeted delivery of nanoparticles, oligonucleotides, peptides and small drug molecules to cancer cells. Considering that understanding interaction of drug with target molecule is important and necessary for pharmaceutical studies, in the present study, the interaction of a porphyrin photosensitizer called TMPYP4 was evaluated with aptamer AS1411. Absorption and fluorescence spectroscopy and circular dichroism technique were used to identify how and where TMPYP4 binds to AS1411 and the resulting structural changes. The results showed that binding of the porphyrin to AS1411 caused 13 nm red shift and 56% hypochromicity in the absorption spectrum; in addition, due to this binding, the emission spectrum of porphyrin is changed and its emission intensity is reduced. The results of structural studies showed that the binding of TMPYP4 does not significantly change the shape of the AS1411 circular dichroism spectrum, but at high concentrations leads to an intense decrease in the intensity of the spectrum. These changes in the spectra indicate that TMPYP4 binds to the aptamer through intercalation between tetrad planes and end-staking and causes to opening of the aptamer structure. As a conclusion, it can be proposed that AS1411 aptamer has appropriate potential for delivery of porphyrin compounds and their photosensitizer types and can be used in photodynamic therapy of cancer cells.

Potato is an important vegetable that grows all over the world and is considered an important product in developing and developed countries for the human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and after wheat, rice and corn, it is the fourth product in the food basket of human societies. According to the statistics of the Food and Agriculture Organization of the United Nations, the area under cultivation of this crop in Iran in 2017 was 161 thousand hectares and the crop harvested from this area is about 5.1 million tons. As expectations for food products with high quality and safety standards increase, it is necessary to determine their characteristics accurately, quickly, and purposefully. In the potato crop, quality evaluation, after harvest and isolation, is very important to provide a reliable and uniform product to the market, because the potato, like many other crops, has the non-uniform quality and care during the harvest stage. While the quality of raw potatoes is primarily determined by the size, shape, color, and attractiveness of the tuber, the quality of potatoes is generally determined by examining the quality of the final product. The quality of processed products is examined in terms of color, flavor, and texture. The quality of most processed products stems from the quality of raw potatoes. Uniformity in size, shape, and composition is essential for optimal quality. During storage, processing, or cooking, potatoes are exposed to a variety of phenomena that affect the final quality of the product. For consumers, the main quality characteristics of potatoes are color, size, and texture. However, quality assessment for industrial potato processing includes various parameters such as dry matter content, starch content and characteristics, shelf life after storage (storage), and after processing. The type of cultivar, physical and chemical composition, and post-harvest storage are important factors that can affect the cooking characteristics of potatoes and potato crops. Quality assessment of agricultural products includes two main methods, quality rating systems based on the apparent properties of agricultural products and quality rating systems based on internal quality assessment, which has gained prominence in recent years. In the meantime, several methods have been developed so far for non-destructive quality classification of agricultural products based on the evaluation of their internal properties, only some of which have been able to meet the above conditions and are technically and industrially justified. To be. In the meantime, spectroscopy can have high efficiency in determining the quality of figures. Spectroscopy is a system that has a different structure and approach from other methods (image processing, neural network, etc.) and can classify and determine the quality of the digit.

First, 3 different potato cultivars were prepared from Ardabil Agricultural Research Center. After preparing the data, data were collected to determine the amount of sugar (SSC) and at the same time, the samples were tested with a spectrometer to determine the wavelengths of the samples. The glucose level of each sample was measured in 18 replications using an SBR-62T ocular refractometer. To do this, the water of the samples was placed on a refractometer at ambient temperature and its sugar level was read in terms of Brix. A PS-100 spectroradiometer (Apogee Instruments, INC., Logan, UT, USA) made in the USA was used to obtain the spectrum of the samples. This ultra-small, lightweight, portable spectrophotometer has a 1nm sprayer-type single-diffuser and a linear silicon CCD array detector with 2048 pixels, which has a range of 250-150 nm (Vis / NIR) cover. There is also the ability to connect fiber optics to the PS-100 spectroradiometer and transfer data to a computer for the purpose of displaying and storing the acquired spectra in the Spectra Wiz software via the USB port. The data obtained from spectral imaging may be affected by the scattering of light by the detector by changing the sample, changing the sample size, surface roughness in the sample, noise caused by the temperature of the device and many other factors, and unwanted information Affect the accuracy of calibration models. Therefore, data processing is required to achieve stable, accurate, and reliable calibration models. The application of non-destructive methods based on spectroscopy in the full range of wavelengths requires a lot of time and money, which makes the practical application of this method almost impossible; therefore, one should look for a way to find the optimal wavelengths and limit the wavelengths to the minimum possible. The partial least squares (PLS) regression method seems ideal in this regard. In this study, in order to build the models, the data were randomly divided into two parts: 80% of the samples were used for cross-training and cross-validation and the rest of the data were used for independent validation.

Mean absorption spectra Vis / NIR absorption spectra for different treatments in the range of 1000-500 nm are shown in Figure 1. Environmental factors (light and heat) as well as the quality of spectrometer expression cause perturbations in the initial and final wavelengths of the spectra, so part of these wavelengths are removed from the data set and as shown in Figure 1, the samples had a roughly similar pattern; This may be due to the color of the samples. According to Figure 1, there are two well-defined peaks for the spectra, and it appears that for the Colombo and Sante cultivars the peaks appeared at around 480 and 1000 nm and for the Milwa cultivar at around 540 and 950 nm. Figure 1 also shows that the absorption rate of the Milwa cultivar is higher than the other two cultivars, which can be due to differences in the number of different substances such as sugar or SSC. Based on the analysis (PCA) results presented in Figure 2, the first principal component (PC-1) describes 67% and the second principal component (PC-3) describes 27% of the variance of the samples tested. As a result, the first two principal components together represent 94% of the data. Due to the fact that the relationship between the properties of different samples during the tests, for various reasons such as technical problems of equipment, data collection, incorrect sampling, etc. in some samples is inappropriate or to correct. To be out. The values of R2 and RMSE for the calibration and validation sets of different regression models (PLS) with raw and processed data are presented in Figure 3, which is equal to 1.

Copper nanoparticles (CuNPs) are used in various industries, including pharmaceutical and biomedical sciences, agricultural and food chemistry, electronics and environmental applications, due to their novel optical, chemical, photoelectrochemical, and electronic properties as well as antimicrobial activities. The aim of the present study was to evaluate the ability of endophytic bacteria to synthesize CuNPs.

Eighteen endophytic bacteria (CN1-CN18) were isolated from the root and crown rot of the bean. The agar dilution method was used to determe the intrinsic tolerance of endophytic bacterial isolates to copper ions. Characterization of CuNPs was performed using different methods including ultraviolet–visible spectroscopy (UV–Vis), field emission scanning electron microscope (FE-SEM), Fourier transform infrared (FTIR), X-ray energy diffraction spectroscopy (EDS), and powder X-ray diffraction (XRD). The antimicrobial activity of the copper nanoparticles was investigated by the agar well diffusion method.

The results of the study indicated that isolate CN10 was able to reduce CuCl2 to CuNPs. Based on the obtained phenotypic data and 16s rDNA sequence analysis (similarity above 99%), the isolate was identified as Pseudomonas grimontii. Additionally, spherical CuNPs with an average size of 24.8 nm were synthesized by resting cells of strain CN10 under 3 mM of copper chloride, pH 7.0, agitation 100 rpm, and 96 hours of incubation. The results of antimicrobial tests showed that the produced CuNPs exhibited the highest inhibitory effects on Xanthomonas campestris (19.2 mm) and the lowest inhibitory effects on Staphylococcus aureus (9.4 mm).

The current study is the first report on the synthesis of CuNPs using a resting cell of Pseudomonas grimontii. The results suggested the synthesis of CuNPs using endophytic bacteria as a promising tool for the production of CuNPs in a controlled shape and size.

In this study, the localized surface plasmon resonance of Copper and Cobalt quantum dots embedded in diamond-like Carbon films, fabricated through radiofrequency plasma-enhanced chemical vapor deposition, were investigated. In addition, the Cobalt and Copper nanoparticles were fabricated through radiofrequency and direct current sputtering methods via co-deposition in a simultaneous sputtering process. Then, the ultravioletvisible-near-infrared spectroscopy, energy dispersive x-ray spectroscopy, atomic force microscopy and field emission scanning electron microscopy were used to analyze the samples and the effect of changes in sputtering conditions on plasmon resonance of Copper and Cobalt quantum dots were also studied. The results showed that the localized surface plasmon resonance of Copper and Cobalt quantum dots embedded in diamond-like Carbon occurred at wavelengths of about 600 and 230 nanometers, respectively. Furthermore, the simultaneous sputtering of elements led to the formation of two types of spectra from plasmon resonance of the samples.

The biosynthesis of nanoparticles (NPs) has been proposed due to its fast, clean, safe, and cost-effective production and being efficient alternative to conventional physicochemical methods. This study aimed to isolate and identify aquatic yeast strains for their potential to form Zinc oxide nanoparticles (ZnONPs). A yeast strain, NS02, with high tolerance against zinc ion (5.25 mM) was isolated using the enrichment technique and was selected as efficient candidate for the biosynthesis of ZnONPs under cell-free extract (CFE) strategy. The preliminary evaluation on the formation of ZnONPs was performed by visual observation and UV-visible absorption spectra of the biosynthesized ZnONPs. The morphology, size and elemental distribution of the nanoparticles were determined by Field emission scanning electron microscopy (FESEM) equipped with energy-dispersive X-ray (EDX). X-ray diffractometer (XRD) was used to identify the crystalline phase of the ZnONPs. Antibacterial activity of ZnONPs against pathogenic bacteria isolated from the clinical specimens was investigated using agar well diffusion method. The isolate NS02 was characterized based on their morphological properties and amplification the ITS-5.8S-ITS2 rDNA regions. The present study pioneered the capabilities of the native aquatic strain Rhodotorula pacifica for the extracellular synthesis of ZnONPs with CFE strategy. The biosynthesized ZnONPs had a growth inhibitory effect all tested clinical isolates due to their nanometric size and well-defined dispersity. This investigation is attempted to indicate the novel microbial sources of aquatic yeasts as biological plant in the synthesis of ZnONPs with antimicrobial activity under cell-free extract strategy.

Potato tubers are one of the most important sources of nutrition in most countries. This product is the world's fourth important food crop after wheat, rice and maize because of its higher yield potential along with high nutritive value.Soluble solids content (SSC) is one of the quality parameters of potatoes and it play an important role in quality and commercial value of potato. The amount of SSC in potatoes varies greatly depending on the cultivars, growing environment and storage conditions.Today, the development of fast, non-destructive, accurate and online technique identification methods to determine product quality is strongly felt. One method for this purpose is Near-infrared spectroscopy. The high speed and accuracy of near-infrared spectroscopy to determine of vegetables and fruits quality in high tonnage, has led to the use of this method in many grading and quality control systems.Shao, et al., Using reflection Near-infrared spectroscopy for investigating the qualitative properties of tomatoes, including firmness, SSC and treatable acidity, and predicted these properties to be non-destructive with a high correlation coefficient. A study was conducted by Saad, et al. to evaluate the non-destructive quality of stored tomatoes using vis/NIR absorption spectroscopy in wavelength range from 350 to 1050 nm. In another study, the main compounds of potatoes were determined by near-infrared spectroscopy by Ainara Lopez. The sugar content of potato tubers was determined using visible and near infrared spectroscopy by Ji Yu Chen et al.Khodabakhshian et al., using reflection Near-infrared spectroscopy for classify the maturity stage and to predict the quality attributes of pomegranate, Principal component analysis was used to distinguish among different maturities and several preprocessing methods were utilized including centering, smoothing (Savitzky–Golay algorithm, median filter), normalization (multiplicative scatter correction and standard normal variate) and differentiation (first derivative and second derivative). The results of this study concluded that different preprocessing techniques had effects on the classification performance of the model using the principal component analysis method.The aim of this study was to investigate the possibility of using near-infrared spectroscopy (vis/NIR) in estimating changes of SSC in three potato cultivars during storage under different storage conditions.

Three potato cultivars, including SANTE, MARABEL and GRANA, which were different in terms of yield, crop quality and shelf life, were prepared from a farm near the University of Onsekiz Mart Çanakkale in Turkey and were stored under different storage conditions. Required values of each cultivar was stored under three different conditions including 4 ̊C temperature with relative humidity of 90%, 7 ̊C temperature with relative humidity of 80-90% and 22-28 ̊C variable temperature with relative humidity of 70-90%, for 65 days.Spectroscopy was performed using a multi-purpose analyzer (MPA) FT-NIR spectrometer in the range of 800-2500 nm.To measure the SSC in the samples, 7 samples from each potato cultivar were randomly selected and kept in the laboratory for 2 hours at 22 ̊C before analysis. After peeling the potatoes, the juice of the samples was extracted by using a common juicer and then filtered through filter paper to remove any remaining pulps. The amount of SSC in the samples was measured by using a digital refractometer at 25 ̊C as a ̊Brix.In order to remove the outlier data, principal component analysis (PCA) was used before any processing on the data. Moving averages, Multiple Gaussian Fitting Regressions, Median filter, Savitzky-Golay smoothing, normalization, Multiplicative Scatter Correction (MSC) and Standard Normal Variety Transformation (SNV) were applied to the data and compared.Partial least squares regression (PLS) models for all Pre-processed data were extracted and the statistical indicators include correlation coefficient (R2) and Root Mean Square Error (RMSE) were used to find the best model. To extract the models, the data were randomly divided into two parts: 80% of the data was used for training and cross-validation and the rest of the data was used for independent validation.To select the effective wavelengths and reduce the wavelengths to a limited number, the regression coefficients of the best calibration model obtained from (PLS) model were used.In order to find the best fitting model for the relationship between effective wavelengths and SSC changes of potatoes during storage, Partial least squares regression (PLS), Principal Component Regression (PCR), Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were employed.This research were performed with factorial design on a completely randomized design (CRD) base include cultivar in three levels (SANTE, MARABEL and GRANA), storage temperature in three levels (4, 7 and 20 ̊C) and storage time in four levels (20, 34 , 46 and 62 days) with five replications. The statistical analysis was performed using the Unscrambler X 10.4 statistical software.

According to results, the amount of SSC was increases during storage. This increasing trend during storage can be attributed to the increase in sugar content during the ripening process and the hydrolysis of starch to maintain the physiological activities of the crop in the postharvest period.The results also show that the raw and all Pre-processed spectral data are able to predict SSC with acceptable accuracy. The best results were obtained from the Median filter Pre-processed model with RMSEC = 0.410, R2c = 0.875, RMSECV = 0.420, R2CV = 0.867.Based on the regression coefficients of best the (PLS) model, 12 wavelengths in the range of 1400 to 2500 nm were identified as effective wavelengths. Based on distribution of main overtone bond, the discriminated of potato samples according to SSC quality index can be attributed second overtones of C-H, CH2 and CH3 in the wavelength range from 1400 to 1500 nm, the first overtones of C-H, CH2 and CH3 in the wavelength range from 1500 to 2100 nm and composition overton of these bonds in the wavelength range from 2200 to 2500 nm which all this overtone are present in the structure of hydrocarbons such monomeric sugars (glucose and xylose) and oligosaccharides (starch).According to the results, all models are able to predict SSC of potatoes during storage based on effective wavelengths with acceptable accuracy but among these models, the Artificial Neural Network (ANN) model with RMSEC = 0.215, R2c = 0.974, RMSECV = 0.927, R2CV = 0.280 has the highest accuracy to prediction of SSC of potatoes during storage.

Human chorionic gonadotropin (hCG) is a glycoprotein produced during pregnancy by trophoblast and placental syncytiotrophoblast cells. The produced hCG hormone affects the corpus luteum and prevents it from being destroyed. Studies show that diamond nanoparticles (NDs) interact with biological systems such as proteins, enzymes, and antibodies. In other words, NDs are capable of being absorbed by biomolecules and can also be used as a carrier for the transport of biologically active substances. This study aimed to investigate the effect of NDs on hCG structure and on hCG level in Wistar rats.

The effect of 10 nm NDs on the structure of hCG was investigated using spectroscopic techniques. Rats were randomly divided into five groups: Control, sham, and experimental groups (received NDs solution at doses of 25, 75, and 100 mg/ml). Injections were carried out twice a week. Then, serum biochemical parameters were calculated. Data were analyzed utilizing one-way ANOVA followed by Tukey's multiple comparisons test.

The results showed that NDs could form a complex with hCG. The zeta potential and DLS results showed that NDs cause a decrease in the surface charge and size of hCG. Administration of NDs in concentrations of 100 mg/ml significantly decreased hCG levels compared with that of control samples (P<0.05),

Based on the investigation NDs could change the tertiary structure of hCG. NDs had toxic effects on hCG function. More studies to explore the toxic effects of NDs in various doses and times of exposure seem to be necessary.