نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم و مهندسى صنایع غذایی، دانشکده فنى و منابع طبیعى تویسرکان، دانشگاه بوعلى سینا، همدان، ایران
2 گروه مهندسی مکانیک بیوسیستم، دانشگاه جیرفت، جیرفت، ایران
3 گروه مهندسی انرژی، دانشگاه ملی آموزش از راه دور، مادرید، اسپانیا.
4 گروه کشاورزی و گیاهان دارویی، مجتمع آموزش عالی فاطمیه نهاوند، دانشگاه بوعلی سینا، همدان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Persian cumin plays a significant role in both exports and domestic industries of Iran. Today, due to the widespread availability of counterfeit cumin in the market, identifying authentic Persian cumin from its counterfeit counterparts has become increasingly essential. Among the various criteria for identification, color and texture indices are particularly notable. Traditional methods, such as manual and visual inspections, are not only time-consuming but also prone to a high degree of human error. In this study, in order to propose a new, precise, and rapid method, machine vision technology was utilized to extract the color and texture features of cumin from its images. Subsequently, a multi-layer perceptron artificial neural network with a backpropagation algorithm and a hidden layer was employed, evaluating different neurons in this layer to perform the process of distinguishing authentic Persian cumin from counterfeit varieties in the market. In this study, five samples of authentic Persian cumin and four samples of counterfeit cumin, were evaluated. The results showed that the highest average classification and identification accuracy of authentic cumin from counterfeit cumin, using a neural network with one hidden layer and employing a sigmoid transfer function in this layer and a linear function in the output layer with the Levenberg-Marquardt learning algorithm, were 93.51% for color features, 95.86% for texture features, and 95.59% for the combination of these two features (color-texture). However, the findings showed that machine vision technology and artificial neural networks have a high capability in accurately identifying authentic Persian cumin from counterfeit samples.
کلیدواژه‌ها [English]
EXTENDED ABSTRACT
Medicinal plants have gained considerable attention as valuable natural resources in scientific and industrial communities. Iran, with its high diversity of medicinal plants, particularly Bunium persicum (Persian cumin), holds a significant position in this field. Bunium persicum, a herbaceous plant, grows in various regions of Asia and Europe. One of the main challenges in the cumin market is fraud, where authentic cumin is often mixed with lower-value species. Therefore, there is an increasing need for precise methods to distinguish genuine cumin from counterfeit ones. Image processing, as a non-destructive and innovative method, has been widely used in the food industry for quality evaluation and product differentiation. Color and texture features play a crucial role in food quality assessment, and artificial neural networks (ANNs) are suitable tools for classifying these complex features due to their capacity for data analysis.
In this study, nine cumin samples were collected from the local market of Kerman, consisting of five authentic Bunium persicum samples and four counterfeit samples (including European black cumin, artificially colored green cumin, and two types of low-quality mixed cumin). The images of the samples were captured using a digital camera, fluorescent light source, computer, and image processing software in a controlled environment. After image acquisition, the samples were converted into different color spaces (RGB, HSI, HSV), and their color and texture features were extracted. The color features included the mean, variance, standard deviation, and range of color components. The texture features were extracted using the Gray-Level Co-occurrence Matrix (GLCM) and included contrast, homogeneity, correlation, energy, and entropy. A total of 36 color features and 108 texture features were considered for analysis. To reduce complexity and enhance model accuracy, important features were selected using factor analysis. Then, a Multilayer Perceptron (MLP) neural network with the Levenberg-Marquardt backpropagation method in MATLAB software was used for classification and recognition. The data were divided into three subsets: training (60%), validation (20%), and testing (20%).
The classification accuracy using color features varied. The highest accuracy (93.51%) was achieved using the sigmoid transfer function in the hidden layer and a linear output function. The reduced color features, selected using factor analysis, resulted in improved classification accuracy. For texture features, the highest classification accuracy (95.86%) was achieved with a network using 108 texture features. The network with the sigmoid transfer function in the hidden layer and a linear output function showed the best performance. Texture features outperformed color features in classification accuracy. The combination of color and texture features resulted in better performance, with the classification accuracy reaching 95.95%. The reduced combined features, obtained through factor analysis, provided greater accuracy than using color or texture features alone. Factor analysis was employed at all stages to reduce the number of features and eliminate redundant features, which not only reduced computational complexity but also improved model accuracy.
This study evaluated the use of a combination of image processing and artificial neural networks (ANNs) for the identification and classification of authentic and counterfeit Bunium persicum. The results demonstrated that texture features provided the highest accuracy in distinguishing the two types of cumin, with overall classification accuracies of 93.51%, 95.86%, and 95.59% for color, texture, and combined features, respectively. Additionally, after feature selection using factor analysis, the best average classification accuracy with color, texture, and combined features was 93.81%, 95.65%, and 94.67%, respectively. The classification accuracy increased with the reduction of color features, while a decrease in texture and combined features led to a reduction in classification accuracy. These findings highlight the significant potential of image processing and ANNs in identifying and classifying genuine cumin from counterfeit ones. The application of these methods in the design of intelligent systems for identifying authentic and counterfeit cumin in the food industry could be a valuable step towards improving food quality and safety.
Conceptualization, M.D., I.G. A.N.; methodology, M.D. and A.N.; software, I.G. and A.M.; validation, A.N., M.D., I.G. and A.M.; formal analysis, I.G.; investigation, A.N. and M.D.; resources, M.D. and A.M.; data curation, M.D. and A.M.; writing-original draft preparation, A.M. and M.D; writing-review and editing, A.M. and M.D.; visualization, A.M. and I.G..; supervision, M.D.; project administration, M.D.
All authors have read and agreed to the published version of the manuscript.” All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.
All data generated or analyzed during this study are available from the corresponding author on request
The authors would like to extend their sincere appreciations for financial support provided by the University of Jiroft. The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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