Post-harvest technologies
M. Roshan Moghadam; R. Amiri Chayjan; N. Aghili Nategh
Abstract
In this research, the amount of vitamin C, aromatic compounds, and color change of orange powder was measured using chemical methods, an olfactory machine, and a scanner in four dryers at 45℃. These dryer apparatuses included normal atmospheric vacuum, atmospheric control vacuum, convective, and convective-infrared. ...
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In this research, the amount of vitamin C, aromatic compounds, and color change of orange powder was measured using chemical methods, an olfactory machine, and a scanner in four dryers at 45℃. These dryer apparatuses included normal atmospheric vacuum, atmospheric control vacuum, convective, and convective-infrared. The highest response of sensors to aromatic compounds in convective and lowest response in control vacuum and normal vacuum dryers was observed. The two main components of principal component analysis (PCA) explained 88% of the data variance. The structure of the artificial neural network (ANN) was 8-5-4. Further, based on loading diagrams of partial least squares (PLS) and principal component regression (PCR) models, the MQ3 and MQ6 sensors were the best to predict the amount of vitamin C and the color change of orange powder. MQ135 sensor can also be removed from the set of electronic nose sensors due to their low accuracy and cost reduction. The multiple linear regression (MLR), compared to PCR and PLS models, proved to be more accurate (i.e., R2= 0.83 and RMSE= 0.144 for vitamin C prediction and R2= 0.94 and RMSE= 0.68 for predicting color change). The highest and lowest values of measured color change was observed in convective dryer and atmospheric control vacuum dryer, respectively. Also, the highest and lowest measured vitamin C was observed in convective-infrared dryer and atmospheric control vacuum dryer, respectively. The best dryer to maintain the quality of the orange powder is the convective-infrared dryer. The results of this article showed that the data obtained from the olfactory machine is able to predict the color change and vitamin C of orange powder. Also, the olfactory machine can be used to identify and classify the type of dryer used to prepare orange powders with the least time and cost, without distorting the sample, and to determine the best dryer for preparing orange powder.
Post-harvest technologies
H. Zaki Dizaji; M. Mahmoodi Surestani; N. Aghili Nategh; A. Boveiri Dehsheikh
Abstract
In botanical terms, the classification of plants reveals a multitude of species derived from different sources. The first step for quality control of herbal medicines is to identify their different species and genotypes. The present study investigated the classification of ten different mint genotypes ...
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In botanical terms, the classification of plants reveals a multitude of species derived from different sources. The first step for quality control of herbal medicines is to identify their different species and genotypes. The present study investigated the classification of ten different mint genotypes using Gas Chromatography-mass Spectrometry (GC-MS) and an electronic nose (e-nose) system utilizing Metal Oxide Semiconductor (MOS) sensors. Leaf samples were harvested from various mint genotypes, and subsequently, the system sensors' responses to each of these samples were recorded. The classification of plants was performed using biplot diagrams based on GC and GC-MS data, with clustering facilitated by the Ward method. The responses of all e-nose sensors were further analysed through various approaches, including Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Artificial Neural Network (ANN). The results from the qualitative analysis of essential oils via GC-MS demonstrate that more than 99% of the identified compounds belong to four chemical groups: hydrocarbon and oxygenated monoterpenes, as well as hydrocarbon and oxygenated sesquiterpenes. Also, based on biplot analysis, different mint populations could be generally divided into 8 groups. The results of principal component analysis showed that the first two main components can cover a total of 97% of the data variance. The classification accuracy achieved through e-nose data for LDA, QDA, and ANN was 98.9%, 99.9%, and 96%, respectively. Proper classification of mint genotypes by e-nose system could be used as a sensitive, reliable, and low-cost alternative to traditional methods.
Post-harvest technologies
Z. Zangene Wandi; H. Javadikia; N. Aghili Nategh; L. Naderloo
Abstract
IntroductionThe use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, ...
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IntroductionThe use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, corn oil can be a good alternative to other oils high in saturated fatty acids, as it reduces blood cholesterol levels.This study employed an electrical nasal system to detect the amount of palm oil present in corn oil. The properties extracted from the signals obtained by the device were processed using principal component analysis, artificial neural networks, infusion, and response surface methods. The results were then compared to find the best method for detecting palm oil levels in corn oil.Materials and Methods The required palm oil was obtained from the Nazgol Oil Agro-industrial Plant, while the corn oil was obtained from natural lubrication centers. To prepare samples with different percentages of palm oil, 75 grams of palm oil and corn oil with the specified percentages were mixed and stored in special containers.In the electrical nose system, ten metal oxide semiconductor sensors (MOS) were used to collect output data. Pre-processing operations were performed on this data using RSM, ANFIS, PCA, and ANN methods to estimate the percentage of palm oil in corn oil. The Unscrambler V.9 software, Design Expert 8.07.1, and MATLAB R2013a were used to analyze the results.Results and DiscussionBased on the Score plot, PC-1 and PC-2 explain 53% and 25%, respectively, describing the variance between samples for a total of 78 data points. The analysis indicates that sensors 7 and 8 have minimal impact on the detection process and can be removed from the sensor array. When reducing the cost of the olfactory system's sensor array, sensor 6 plays a more significant role than other sensors in detecting corn oil with palm composition.According to the loading diagram of palm percentage in corn oil, the MQ6 sensor had the least effect in classifying different percentages of palm in corn oil and pattern identification. Out of all functional parameters (accuracy, sensitivity, and specificity), the RSM method is deemed more appropriate for determining the percentage of palm in corn oil.Regarding the separation of corn oil and palm oil by ANFIS, RSM, and ANN, the results in Table 3-1 indicate that the RSM method is better suited for classifying corn and palm oil.Conclusion In this study, we used an electronic multi-sensor system based on metal oxide sensors to analyze various aromatic compounds in different oil and palm samples and to detect the presence of palm. The system provided comparable information for classifying different samples of palm oils. Using PCA, ANN, ANFIS, and RSM methods, we evaluated the system's performance in differentiating and classifying various oil and palm samples.The results obtained from the loading diagrams for the detection of palm in corn oil indicated that the MQ6 sensor had the least impact on the detection process. Therefore, this sensor can be removed from the sensor array.Additionally, our analysis showed that using the RSM method is more effective in detecting different percentages of palm in corn oil. Overall, our study demonstrates the efficacy of the electronic multi-sensor system in analyzing different oil and palm samples and detecting the presence of palm.
