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Maize (Zea mays) is one of the key crops in the world, taking third place after wheat and rice in terms of cultivated area. This study aimed to demonstrate the potential of non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm to discriminate between and classify maize kernels in three cultivars by using non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm. Three cultivars of maize kernels were exposed to hyperspectral imaging with 20 replications. Predictor variables included 28 intensities of reflection wave for spectral imaging and 4 variables in terms of the weight, length, width, and thickness of a single kernel. The classification was successfully performed through Linear Discriminant Analysis and Artificial Neural Network methods, taking into account 32, 15, and 5 predictor variables. According to the results, Linear Discriminant Analysis with 32 predictor variables is characterized by a high degree of accuracy (95%). The most important predictor variables included the reflection wave intensity of the third peak, the wavelength intensity of 490 nm, the wavelength intensity of 580 nm, and the weight and thickness of a single kernel. [ABSTRACT FROM AUTHOR]

In this study, an electronic nose coupled with artificial neural network (ANN) was used to predict the shelf life of two oil with new production date and oil with old production date over a period of 150 days. According to the American Oil Chemists' Society results, the oils were oxidized after 60 days. Principal component analysis results indicated that all the oil samples were correctly discriminated from each other during their storage times, and samples of oxidized and nonoxidized oils can be properly distinguished from each other. Two main components (PC1, PC2) managed to describe 97% of the data set variance concerning the shelf life of the oil. To develop the ANN models, the data were first divided into three groups: training (60%), validation, and test (40%). To determine the best model, two criteria (R2 and root mean square error) were used. The results revealed that the ANN model can be used as a powerful tool for pattern recognition and determination of the shelf life of oil and its oxidation degree at high precision. Scientific and feasible results can be obtained by matching ANN and the results obtained by metal oxide semiconductor sensors of E‐nose. Practical applications: One of the most important causes of food spoilage is lipid oxidation. The American Oil Chemists' Society (AOCS) has developed a variety of methods for assessing the state of food oxidation. In this study, oil shelf life studied by a combination of artificial senses and chemometrics methods. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the AOCS standard. Principal component analysis and artificial neural neural methods succeeded in classifying the samples based on their storage time with high accuracy. [ABSTRACT FROM AUTHOR]

The presence of weed seeds and other impurities in the wheat grains affect the identification quality of the wheat grains. This study explores the possibility of identifying the wheat in the wheat grain mass and estimates the amount of impurities in the wheat mass based on the video processing combined with the artificial neural network (ANN) integrated by particle swarm optimization (PSO) algorithm. After pre‐processing the video of the mass movement on the conveyor belt, 35 shapes, color, and textural features were extracted from each grain sample in the image in the presence of the MATLAB software and image processing (IP) toolbox. The data obtained from the IP section were categorized into two approaches. The first one, purposes the identification of the wheat grains in the grain mass, and the other one, identifies each components in the wheat mass. Both of them employs the hybrid ANN‐PSO algorithm to achieve the highest classification accuracy and the lowest error value. According to the results, ANN with the architecture of 36‐10‐10‐5 and ANN with the architecture of 36‐8‐8‐5 overtakes the other architectures with the highest accuracy and performance values for training (100 and 100%) and testing phases (100 and 86.66%), respectively for the first and second approaches. Accordingly, based on the results of the hybrid ANN‐PSO algorithm, the highest classification accuracy was 98.62% and 97.77% (for training and testing phases, respectively) and 76.08% and 73.1% (for training and testing phases, respectively) related to the first and the second approaches, respectively. Finally, the video processing using ANN can be considered as a powerful approach for identifying the impurities in the wheat grain mass. Practical applications: One of the major problems in wheat and similar mass products is the presence of impurities inside the total mass, which drastically reduces the quality and the marketability of the product. Conventional methods of detecting the amount of impurities in the grain mass are always accompanied by difficulties such as lack of precision, time consuming, and tedious. The use of new technologies such as machine vision and artificial intelligence have been considered as the important tools in this regard. One of the practical applications of this study is to detect the amount of impurities in wheat grain mass at the inlet of silos and in pricing on grain masses. The system proposed in this study can accurately detect and report impurities in wheat grain mass. [ABSTRACT FROM AUTHOR]

Consumer demands for fresh foods with desired sensory properties. Lack of easy, cheap, and nondestructive methods to control the quality of fruit juices is one of the main challenges in the beverage industry. The present study has examined two types of natural and industrial juices. Machine olfaction with 9 metal oxide semiconductor (MOS) sensors was used for the experiments. Sensor response patterns were analyzed using artificial neural networks (ANN). PCR method was used to evaluate the discrimination power of the sensors. According to the results, the discrimination power of the sensors used in the electronic nose sensor array was determined, three of which were removed due to improper response power. The proposed method can be used to select the minimum and most effective number of MOS gas sensors to construct an electronic nose system to control the olfactory quality of different foods. The use of a minimum number of sensors reduces the cost of constructing an electronic nose system, decreases the volume of the processor data input, and consequently increases the classification accuracy. Therefore, the MOS-based electronic nose combined with the ANN can be an effective and highly efficient tool in the fast and nondestructive classification of pure and industrial fruit juices. [ABSTRACT FROM AUTHOR]

Foodstuff adulteration involves addition of any low‐cost substances to the high‐price materials to reduce the content of the expensive components, and hence decrease the production cost and reach to the maximum profit. An electronic nose was used in this study to detect the adulterations in mixed edible oils. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the official American Oil Chemist Society (AOCS) standard. The results were analyzed by Cluster analysis (CA), principle component analysis (PCA), principal component regression (PCR), linear discriminant analysis (LDA), and artificial neural network (ANN) methods with accuracy of 95, 98, 98, 88, and 97.3%, respectively. According to the results, the ANN method with structure of 8‐7‐5 showed the highest accuracy in classification of oil adulteration. Its correct classification ratio, mean square errors, and correlation (r) were 97.3%,.117211, and.0963, respectively. The results also indicated that the proposed method can be used as an alternative of the official AOCS methods to innovatively detect the edible oil adulteration with high accuracy and speed. Practical applications: Lipid oxidation is one of the major causes of food spoilage especially in those containing oil. AOCS has developed various methods to evaluate the oxidation status of the oil assets. However, these chemical tests are time‐consuming, destructive, and costly and require several glassware and reagents. E‐nose could be used for real‐time monitoring of the volatile components of the food to evaluate different features of the product. Generally, E‐nose evaluates mixture of smells released form a sample and is a reliable, nondestructive, cost‐effective, and portable method with high feasibility and speed as well as simple use. CA, PCA, and ANN methods were also applied for qualitative differentiation of different adulteration percentages in oxidized and nonoxidized oils. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively. [ABSTRACT FROM AUTHOR]

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively. [ABSTRACT FROM AUTHOR]