Electronic tongue as innovative instrument for detection of crocin concentration in saffron (Crocus sativus L.).

Electronic tongue is a new approach for simple and fast detection, classification, and quantification of the solved compounds. Crocin is the main source of color of saffron (Crocus sativus L.). An electronic tongue system was used to predict the concentration of saffron crocin in the present study. The measurement system included an electrochemical sensor array based on voltammetry electrodes, a three-electrode cell, a potentiostat, a personal computer. Aqueous analyte were provided by blending pure crocin and different saffron samples from Iran and Spain with distilled water. Output signals of the electronic tongue system were analyzed by principal component analysis and artificial neural networks. Based on principal component analysis, the total variance among pure crocin was 99% and that of saffron samples was 100%. The accuracy of artificial neural network model was 98.80%. The results indicated that the developed electronic tongue system and artificial neural network model can successfully predict crocin concentration in saffron.

Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning.

Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with ginger in powder form and sold in the market. Demand for non-destructive methods of measuring food quality, such as machine vision and the growing need for food and spices, were the main motives to conduct this study. This study classified ginger powder images to detect fraud by improving convolutional neural networks (CNN) through a gated pooling function. The main approach to improving CNN is to use a pooling function that combines average pooling and max pooling. The Batch normalization (BN) technique is used in CNN to improve classification results. We show empirically that the combining operation used increases the accuracy of ginger powder classification compared to the baseline pooling method. For this purpose, 3360 image samples of ginger powder were prepared in 7 categories (pure ginger powder, chickpea powder, 10%, 20%, 30%, 40%, and 50% fraud in ginger powder). Moreover, MLP, Fuzzy, SVM, GBT, and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that using batch normalization based on gated pooling, the proposed CNN was able to grade the images of ginger powder with 99.70% accuracy compared to other classifiers. Therefore, it can be said that the CNN method and image processing technique effectively increase marketability, prevent ginger powder fraud, and promote traditional methods of ginger powder fraud detection.

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder.

Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into consideration by researchers. Due to the high nutritional value of turmeric among the spices as well as the fraudulent motives to gain economic profit from the selling of this product, its quality assessment is very important. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with turmeric in powder form and sold in the market. In this study, an improved convolutional neural network (CNN) was used to classify turmeric powder images to detect fraud. CNN was improved through the use of gated pooling functions. We also show with a combined approach based on the integration of average pooling and max pooling that the accuracy and performance of the proposed CNN has increased. In this study, 6240 image samples were prepared in 13 categories (pure turmeric powder, chickpea powder, chickpea powder mixed with food coloring, 10, 20, 30, 40 and 50% fraud in turmeric). In the preprocessing step, unwanted parts of the image were removed. The data augmentation (DA) was used to reduce the overfitting problem on CNN. Also in this research, MLP, Fuzzy, SVM, GBT and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that prevention of the overfitting problem using gated pooling, the proposed CNN was able to grade the images of turmeric powder with 99.36% accuracy compared to other classifiers. The results of this study also showed that computer vision, especially when used with deep learning (DL), can be a valuable method in evaluating the quality and detecting fraud in turmeric powder.

Acoustic analysis of a single-cylinder diesel engine using magnetized biodiesel-diesel fuel blends.

Fuels have important effects on the quality parameters of engines such as noise pollution and vibration. Diesel fuel are used in wide range of applications especially in compression ignition engines. The objective of the present research is evaluation of the noise emitted from a single-cylinder diesel engine using magnetized biodiesel-diesel fuel blends. In general, samples were provided with different percentages of the biodiesel-diesel blends as diesel (100, 95, 90, and 80%) and biodiesel (0, 5, 10, and 20%) by applying magnetic field (0, 5300 and 7000 G) to fuel line known as DxByMz. The measurements were done on a power tiller engine at a 10 cm distance from driver ear with three replications. The statistical approach in time domain and signal processing in frequency domain were applied for data analysis. The results of the variance analysis approved significant differences between the studied fuel blends and magnetic levels at 1% probability level. The highest and lowest average value of sound pressure level was corresponded to D100B0M0 and D80B20M5300, respectively. The results in frequency domain showed that the maximum sound pressure level values were in the frequency range of 31.5-200 Hz for all fuel blends and magnetic levels. The frequencies related to the maximum sound values varied by changing biodiesel percent and magnetic level.

Evaluation of engineering properties for waste control of tomato during harvesting and postharvesting.

In Iran, more than 30% of agricultural products turn into waste at different stages from harvesting to consumption. Thus, main factors for performing of this present study are including of: (a) the importance of tomato as an agricultural product and (b) lack of information about reducing waste during tomato processing. In this study, some physical, nutritional, mechanical, and hydrodynamic properties of tomato were measured under standard conditions. Physical properties included the length, width, thickness, mean diameter (geometric and arithmetic), mass, volume, density, sphericity, surface area, and aspect ratio. Also, nutritional properties, moisture, dry matter, pH, total soluble solid (TSS), and titration acidity (TA) of tomato were evaluated. The mechanical properties of tomato (compression and shear) were measured using Instron instrument. The hydrodynamic properties were measured with water in transportation, separation, and sorting of tomatoes. The physical properties were including of length, width, thickness, mass, volume, and geometric and arithmetic mean diameters showed a direct relationship with the size of tomatoes. Also, volumetric mass (density) had an inverse relation with tomato size. Yield point and shear force were obtained 51.27 and 22.20 N, respectively. The nutritional properties such as pH value, TSS, and TA were equal to 4.22, 22.23οBrix, and 2%, respectively. The hydrodynamic properties of tomatoes such as the terminal velocity, the tomatoes' rise time in the water column, the buoyancy force, and the drag force were obtained to be equal to 0.05 m/s, 10.11 S, 0.52 N, and 0.17 N, respectively.