Abstract:The food industry requires automatic methods to establish authenticity of food products. In this work, we address the problem of the certification of suckling lamb meat with respect to the rearing system. We evaluate the performance of neural network classifiers as well as different dimensionality reduction techniques, with the aim of categorizing lamb fat by means of spectroscopy and analyzing the features with more discrimination power. Assessing the stability of feature ranking algorithms also becomes parti… Show more
“…The development of fast and efficient tools to be implemented in the industry of food attracted great interest in the last decade ( [11]). Modern techniques, including electronic noses, computer vision, spectroscopy and spectral imaging, and so on, have been widely used to detect meat attributes.…”
“…The development of fast and efficient tools to be implemented in the industry of food attracted great interest in the last decade ( [11]). Modern techniques, including electronic noses, computer vision, spectroscopy and spectral imaging, and so on, have been widely used to detect meat attributes.…”
“…They are also widely used methods in agriculture [ 17 , 18 , 19 ], such as for high throughput phenotyping [ 20 ] or predicting plant diseases [ 21 ]. In animals, the ML algorithms have been used for monitoring the health status [ 22 , 23 ], product quality [ 24 , 25 ], and prediction of diseases [ 26 , 27 , 28 , 29 , 30 ]. Selection of the ML algorithms for studies on farm animals depends on the traits and data, and their performance also varies among the studies [ 17 , 28 , 31 ].…”
American mink (Neogale vison) is one of the major sources of fur for the fur industries worldwide, whereas Aleutian disease (AD) is causing severe financial losses to the mink industry. A counterimmunoelectrophoresis (CIEP) method is commonly employed in a test-and-remove strategy and has been considered a gold standard for AD tests. Although machine learning is widely used in livestock species, little has been implemented in the mink industry. Therefore, predicting AD without using CIEP records will be important for controlling AD in mink farms. This research presented the assessments of the CIEP classification using machine learning algorithms. The Aleutian disease was tested on 1157 individuals using CIEP in an AD-positive mink farm (Nova Scotia, Canada). The comprehensive data collection of 33 different features was used for the classification of AD-infected mink. The specificity, sensitivity, accuracy, and F1 measure of nine machine learning algorithms were evaluated for the classification of AD-infected mink. The nine models were artificial neural networks, decision tree, extreme gradient boosting, gradient boosting method, K-nearest neighbors, linear discriminant analysis, support vector machines, naive bayes, and random forest. Among the 33 tested features, the Aleutian mink disease virus capsid protein-based enzyme-linked immunosorbent assay was found to be the most important feature for classifying AD-infected mink. Overall, random forest was the best-performing algorithm for the current dataset with a mean sensitivity of 0.938 ± 0.003, specificity of 0.986 ± 0.005, accuracy of 0.962 ± 0.002, and F1 value of 0.961 ± 0.088, and across tenfold of the cross-validation. Our work demonstrated that it is possible to use the random forest algorithm to classify AD-infected mink accurately. It is recommended that further model tests in other farms need to be performed and the genomic information needs to be used to optimize the model for implementing machine learning methods for AD detection.
“…On the other hand, GA has been utilized for finding the optimum parameters in food whereas NN has been occupied to predict the final fouling rate in food processing [ 84 ]. ML has shown to be advantageous in predicting the food insecurity in the UK [ 85 ]. Apart from that, ML has also proven to have predicted the trend of sales in the food industry [ 86 ] In addition to that, ML was also able to predict the food waste generated and give an insight to the production system [ 87 ].…”
Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.
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