Potential use of electronic noses, electronic tongues and biosensors, as multisensor systems for spoilage examination in foods

Development and use of reliable and precise detecting systems in the food supply chain must be taken into account to ensure the maximum level of food safety and quality for consumers. Spoilage is a challenging concern in food safety considerations as it is a threat to public health and is seriously considered in food hygiene issues accordingly. Although some procedures and detection methods are already available for the determination ofspoilage in food products, these traditional methods have some limitations and drawbacks as they are time-consuming,labour intensive and relatively expensive. Therefore, there is an urgent need for the development of rapid, reliable, precise and non-expensive systems to be used in the food supply and production chain as monitoring devices to detect metabolic alterations in foodstuff. Attention to instrumental detection systems such as electronic noses, electronic tongues and biosensors coupled with chemometric approaches has greatly increased because they have been demonstrated as a promising alternative for the purpose of detecting and monitoring food spoilage. This paper mainly focuses on the recent developments and the application of such multisensor systems in the food industry. Furthermore, the most traditionally methods for food spoilage detection are introduced in this context as well. The challenges and future trends of the potential use of the systems are also discussed. Based on the published literature, encouraging reports demonstrate that such systems are indeed the most promising candidates for the detection and monitoring of spoilage microorganisms in different foodstuff

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Mass and Surface Area Modeling of Bergamot (Citrus medica) Fruit with Some Physical Attributes

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Mass and Surface Area Modeling of Bergamot (Citrus medica) Fruit with Some Physical Attributes. Manuscript FP 07 029. Vol. IX. October, 2007

Investigation of Energy Indices and Energy Consumption Optimization for Peach Production- Case Study: Saman Region in Chaharmahal va Bakhtiari Province

As one of the most important conditions in sustainable agriculture, optimization of energy consumption in agriculture is necessary in order to reduce the production cost and saving non renewable resources as well as reduction of air pollutants. In this regard, this study was conducted in Saman region, Chaharmahal va Bakhtiari province. A linear programming based on Data Envelopment Analysis (DEA) was used for optimization of energy consumption in peach production in order to increase the technical efficiency. By performing a linear regression analysis, some inputs including animal fertilizer, pesticide, human labor and machinery had no significant influence on product yield, while some other inputs including fuel, electricity, water and chemical fertilizer showed a significant effect on the product yield. Therefore, the latter inputs and the product yield were considered as the inputs and output, respectively. Selecting the BCC model (efficiency to variable scale model of input nature) and using DEA Solver software, efficient and inefficient farmers were determined. The efficient farmers had the technical efficiency of unit (one) and the inefficient farmers had this value within 0.47-0.94. Also, the technical efficiency of inefficient farmers was computed as 0.74. This means that using 74% of the inputs and keeping the current yield, the inefficient farmers can approach to the efficiency limit. Total technical efficiency of all farmers was found to be 0.82. Based on the results, the maximum value of inefficiency belonged to electricity energy with 65.32%

Estimation of the Best Classification Algorithm and Fraud Detection of Olive Oil by Olfaction Machine

IntroductionExtra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc.Materials and MethodsThere are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive methods. Control sample (EVOO) was applied from "DANZEH food industry", Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in "Rahpooyan e danesh koolak Lab." Tehran, Iran; according to "Institute of standards and industrial research of Iran" ISIRI number: 4091 and INSO 13126-2. Eight semi-conductor gas sensors "FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822" applied in used olfaction machine. In this study there were 6 treatments: 1- Pure EVOO, 2- EVOO with 5% adulteration, 3- EVOO with 10% adulteration, 4- EVOO with 20% adulteration, 5- EVOO with 35% adulteration and 6- EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a non-destructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1- Sensor output (mV) in start of odor pulse (refer to fig. 3) 2- Sensor output at the end of odor pulse 3- Average of sensor output during odor pulse and 4- Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported.Results and DiscussionHistogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time.ConclusionsDue to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration)

Design, development and evaluation of an automatic metering system for bare root seedlings of onion

Introduction In recent years due to lack of water resources in our country, planting of bare root seedlings of onion has been welcomed by farmers. Considering the desired high dense planting of Iranian farmers, lack of proper transplanting machine has appeared as the main problem. To overcome this problem, some researchers tested a few methods, but none of them reached to complete successfully. As the one of last efforts, Taki and Asadi (2012) developed a semi-automatic transplanting machine with 9 planting units. This machine requires to 9 men to separate and single out a bunch of seedlings. Usage of this machine is very time-consuming and labor intensive. In Iran, transplanting of bare root seedlings is practically performed by hand with a density of 700-800 thousand plants at hectare. The main purpose of this study was designed, manufacture, and evaluation of an automatic metering device that with the separation and singulars of bare root seedlings of onion could get a high density planting. Materials and Methods Fig. 1 shows the main employed idea of this research for separation and single out a bunch of seedlings. As shown in Fig. 1, the metering device consisted of two carrying and separating belts with different teeth forms. Placing seedling bunches between the two belts, the belts move at different speeds in opposite directions and separate seedlings from their bunch. For proper design of metering device system, measurement of some physical properties were necessary. The obtained information was used to select two belts form. A belt with flexible plastic teethes with a height of 6 mm and the distance of 4mm was selected as separator while for carrier, two types of belts were selected: the first was the same as a separator and the second was made of metal teethes. Based on the average thickness of seedling bunch and some pre-tests, the horizontal angle of separator belt determined as α=20 degrees. Theoretical calculations were done to computatingof the needed force of the system. In this section, seedlings were modeled as some solid cylinders with a length of 200 and a diameter of 10 mm. In the mentioned system, it was necessary that the speed of separator belt is more than the speed of carrier belt. Thus, ratio of two linear velocities ( ) of 1.67 and 2.32 were considered for evaluation of the system. For evaluation of manufactured metering device, the effects of three factors, i.e., carrier belt type, ratio of linear velocities of the belts, and number of seedlings in a bunch (n = 30 and n =60), on qualitative planting parameters were studied in a factorial experiment based on completely randomized design with three replications. The studied qualitative planting parameters were miss index, consumed seedlings, miss length, quality of feed index, multiple index, mean, and damaged seedlings. Results and Discussion The results of analysis of variance showed that, except of belt type, effects of the two studied factors and all interactions are statistically non-significant on consumed seedlings and miss length indexes. The results indicated significant differences between miss index (P<0.01), multiple index (P<0.05), and mean (P<0.05) as affected by belt type. None of the studied variable had a significant effect on damaged seedlings. Interactions of belt type and ratio of linear velocities significantly affected the quality of feed index (P < 0.01). An increase in ratio of linear velocities in plastic toothed belt lead to decrease of mean and miss indexes, whereas in case of metal toothed belt there is no significant effect on this two indexes. The results also showed that increase of linear velocities for the two types of carrier belt lead to increase of consumed seedlings and decrease of miss length. At the two ratios of linear velocities, miss length in metal toothed is less than plastic toothed belt. Conclusions Commercial transplanting machines are not suitable for dense planting of onion. In this research an automatic metering device for separation and singularize of bare root seedlings of onion was manufactured and evaluated. The results indicated that the carrier belt with long and rigid teeth, having an angle of attack, could separate seedlings more efficiently. The results also showed a 80 percent increased in uniformity of plant seedlings distances is reachable using the metering system

The Effects of Drop Height and Padding Surface on Bruising of Exportable Apple

Unfortunately despite the great ranking of Iran for apple production around the world, the export potential is not suitable. It seems that one of the major causes of poor quality for Iranian apple varieties is bruising damage of this product. Therefore, in this study, some factors affecting the apple bruising were addressed. For this purpose, factorial experiment in a completely randomized design with 72 treatments, including variety factor in three levels (Golden Delicious, Red Delicious and Granny Smith), type of padding surface in four levels (Cardboard on plastic, wood, Rubber on steel and apple) and the drop height in six levels (5, 15, 25, 35, 45 and 55 cm) with four replications were considered. Moreover, the maximum allowable drop heights of apples along with bruising volume estimation models were studied. Analysis of variance (ANOVA) showed that bruising area and volume were significantly affected by all experimental parameters at the 1% level. The comparison test revealed that Granny Smith has tougher tissues and is less prone to vulnerability. Based on the results of this study, the maximum allowable drop heights for the Red Delicious, Golden Delicious and Granny Smith varieties were found to be 12, 15 and 20 cm, respectively. In addition, the effect of apple variety on the dependent parameters was significant. Based on the findings of this study, the bruising due to the impact of apple and apple was lower for the moving apples compared with the stationary apples