Evaluation and selection of thin-layer models for drying kinetics of apricot (cv. NASIRY)

E. Mirzaee, S. Rafiee, A. Keyhani(Agricultural Machinery Engineering Department, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran) Abstract: This paper presents the thin layer drying behavior of apricot (cv. NASIRY) at the air temperatures of 40ºC, 50ºC, 60ºC, 70ºC and air velocity of 1m/s and 2 m/s.  In order to select a suitable form of the drying curve, 12 different thin layer drying models were fitted to experimental data.  Fick’s second law was used as a major equation to calculate the moisture diffusivity with some simplification.  The high values of coefficient of determination and the low values of reduced chi-square and root mean square error indicated that the Logarithmic model and the Midilli et al. model could satisfactorily describe the drying curve of apricot for drying air velocity of 1m/s and 2 m/s, respectively.  According to the research results the calculated value of effective moisture diffusivity varied from 1.78×10-10–5.11×10-10 m2/s and the value of activation energy varied from a minimum of 24.01 kJ/mol to a maximum of 25.00 kJ/mol.Keywords: apricot, thin layer drying, effective moisture diffusivity, activation energy Citation: E. Mirzaee, S. Rafiee, A. Keyhani.  Evaluation and selection of thin-layer models for drying kinetics of apricot (cv. NASIRY).  Agric Eng Int: CIGR Journal, 2010, 12(2): 111-116.  &nbsp

Detecting the adulteration in apple vinegar using olfactory machine coupled PCA and ANN methods

Nowadays, the number of food adulteration cases is increasing sharply for reasons such as population growth, increasing demand and profitability of suppliers. Mixing apple vinegar with white vinegar and acetic acid are the most common methods of cheating on the market in Iran. In this study, an electrical olfactory system was used to detect pure apple vinegar from acetic acid and white vinegar. The data obtained from the sensors were analyzed by PCA and ANN methods after preprocessing. Based on the results, TGS822 and MQ136 sensors showed the highest response to odor of samples of vinegar mixed with acetic acid and white vinegar, respectively. Also, the confusion matrix obtained from ANN analysis for different levels of adulteration with acetic acid and white vinegar showed correct classification rate of 93.3% and 94.7%, respectively

Shear Bond Strength of Composite to Dentin following Light Curing with Light Emitting Diode and Quartz Tungsten Halogen Light Curing Units

Background and Aim: The use of light emitting diode (LED) light curing units has recently increased due to optimal properties such as longer durability, no need for filter and less heat generation compared to quartz tungsten halogen (QTH) devices. The aim of this study was to assess the shear bond strength of composite to dentin following lightcuring with QTH and LED light curing units for different time periods. Materials and Methods: In this experimental study, 60 sound extracted human molar and premolar teeth with no decay or restorations were collected. The buccal surface of the teeth was ground to expose adequate amount of dentin. The teeth were randomly divided into six groups. After acid etching and bonding, composite was packed in plastic cylindrical molds and placed on dentin surface. Groups one, two and three were cured by a LED unit (Wood Pecker) with ramp method for 20, 30 and 40 seconds, respectively. Groups four, five and six were cured with a QTH unit (Optilux 501) with a light intensity of 500 mW/cm2 for 20, 30 and 40 seconds, respectively. After keeping the samples for two weeks in distilled water at room temperature, shear bond strength was measured by a universal testing machine at a crosshead speed of 1mm/minute. The data were analyzed by Two-way ANOVA, one-way ANOVA and Tukey’s HSD test. Results: One-way ANOVA showed a significant difference among the groups and the mean shear bond strength was the highest following light curing by LED device for 40 seconds (18.63 MPa). Pairwise comparisons by Tukey’s test showed significant differences in shear bond strength of groups cured with LED unit (P= 0.059 for the difference between 20 seconds and 40 seconds and P=0.004 for the difference between 30 seconds and 40 seconds). Conclusion: Use of LED units (ramp method) yielded superior results in terms of shear bond strength compared to QTH. Also, 40 seconds of curing is recommended in use of LED devices

Rapid Detection of Urea Fertilizer Effects on VOC Emissions from Cucumber Fruits Using a MOS E-Nose Sensor Array

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization

Investigation the effect of outlet air flow and chamber temperature on some bio-char properties of wheat straw in a fixed-bed oxidative pyrolysis

Soil plays an important role in the sustainability of ecosystems. In recent years, the increasing growth in the degradation of soil resources has drawn attention to management strategies for maintaining the soil quality. Researchers have recently studied the impact of using biochar on physical and chemical properties of soil. It has been found that adding biochar improves the soil quality. Some factors such as pyrolysis chamber conditions, pyrolysis peak temperature and air flow rate affect the physical and chemical properties of biochar including the density, pH, ash content, and so on. In this study, the effect of changes in the air flow rate and chamber temperature in the fixed-bed oxidative pyrolysis on the biochar yield, ash content, density and pH were investigated. For this purpose, a fixed-bed biochar production apparatus with varying chamber temperature and flow rate of outlet air was designed and manufactured. The experiments were performed at four air flow rates of 20, 25, 30 and 35 L min-1 and four temperatures of 350, 400, 450 and 500 °C for wheat straw. The results showed that increasing the temperature and flow rate of the outlet air from the chamber increased the ash content and pH. However, increasing these parameters decreased the biochar bulk density and yield

Prediction of Residual NPK Levels in Crop Fruits by Electronic-Nose VOC Analysis following Application of Multiple Fertilizer Rates

The excessive application of nitrogen in cucumber cultivation may lead to nitrate accumulation in fruits with potential toxicity to humans. Harvested fruits of agricultural crops should be evaluated for residual nitrogen, phosphorus, and potassium (NPK) nutrient levels. This is necessary to avoid nutrient toxicity from the consumption of fresh produce with excessive nutrient levels. Electronic noses are instruments well-suited for the nondestructive detection of fruit and vegetable quality based on volatile organic compound (VOC) emissions. This proof-of-concept study was designed to test the efficacy of using an electronic nose with statistical regression models to indirectly predict excessive fertilizer application based on VOC emissions from cucumber fruits grown under controlled greenhouse conditions to simulate field conditions but eliminate most environmental variables affecting plant volatile emissions. To identify excess nitrogen in cucumber plants, five different levels of urea fertilizer application rates were tested on cucumbers (control without fertilizer, 100, 200, 300, and 400 kg/ha). Chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models to predict nitrogen (N), phosphorus (P), and potassium (K) levels in cucumber fruits following application of different fertilizer rates to greenhouse soils. The correlation coefficients for the MLR model (based on the optimal parameters of PCR and PLSR) were 0.905 and 0.905 for the calibration sets and 0.900 and 0.900 for the validation sets, respectively. The nitrogen prediction model for fruit nitrates was more accurate than other nutrient models. The proposed method could potentially be used to indirectly detect excessive use of fertilizers in cucumber field crops

Electronic Nose Analysis and Statistical Methods for Investigating Volatile Organic Compounds and Yield of Mint Essential Oils Obtained by Hydrodistillation

A major problem associated with the development of medicinal plant products is the lack of quick, easy, and inexpensive methods to assess and monitor product quality. Essential oils are natural plant-derived volatile substances used worldwide for numerous applications. The important uses of these valuable products often induce producers to create fraudulent or lower quality products. As a result, consumers place a high value on authentic and certified products. Mint is valued for essential oil used in the food, pharmaceutical, cosmetic, and health industries. This study investigated the use of an experimental electronic nose (e-nose) for the detection of steam-distilled essential oils. The e-nose was used to evaluate and analyze VOC emissions from essential oil (EO) and distilled water extracts (DWEs) obtained from mint plants of different ages and for leaves dried in the shade or in the sun prior to hydrodistillation. Principal component analysis (PCA), linear discriminant analysis (LDA), and artificial neural networks (ANN) were performed on electrical signals generated from electronic nose sensors for the classification of VOC emissions. More accurate discriminations were obtained for DWEs sample VOCs than for EO VOCs. The electronic nose proved to be a reliable and fast tool for identifying plant EO. The age of plants had no statistically significant effect on the EO concentration extracted from mint leaves

Rapid Detection of Urea Fertilizer Effects on VOC Emissions from Cucumber Fruits Using a MOS E-Nose Sensor Array

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization

Prediction of Residual NPK Levels in Crop Fruits by Electronic-Nose VOC Analysis following Application of Multiple Fertilizer Rates

The excessive application of nitrogen in cucumber cultivation may lead to nitrate accumulation in fruits with potential toxicity to humans. Harvested fruits of agricultural crops should be evaluated for residual nitrogen, phosphorus, and potassium (NPK) nutrient levels. This is necessary to avoid nutrient toxicity from the consumption of fresh produce with excessive nutrient levels. Electronic noses are instruments well-suited for the nondestructive detection of fruit and vegetable quality based on volatile organic compound (VOC) emissions. This proof-of-concept study was designed to test the efficacy of using an electronic nose with statistical regression models to indirectly predict excessive fertilizer application based on VOC emissions from cucumber fruits grown under controlled greenhouse conditions to simulate field conditions but eliminate most environmental variables affecting plant volatile emissions. To identify excess nitrogen in cucumber plants, five different levels of urea fertilizer application rates were tested on cucumbers (control without fertilizer, 100, 200, 300, and 400 kg/ha). Chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models to predict nitrogen (N), phosphorus (P), and potassium (K) levels in cucumber fruits following application of different fertilizer rates to greenhouse soils. The correlation coefficients for the MLR model (based on the optimal parameters of PCR and PLSR) were 0.905 and 0.905 for the calibration sets and 0.900 and 0.900 for the validation sets, respectively. The nitrogen prediction model for fruit nitrates was more accurate than other nutrient models. The proposed method could potentially be used to indirectly detect excessive use of fertilizers in cucumber field crops