Zarezadeh Mohammad Reza, Aboonajmi Mohammad, Ghasemi Varnamkhasti Mahdi
Department of Agro-technology College of Aburaihan University of Tehran Tehran Iran.
Department of Mechanical Engineering of Biosystem Shahrekord University Shahrekord Iran.
Food Sci Nutr. 2020 Nov 4;9(1):180-189. doi: 10.1002/fsn3.1980. eCollection 2021 Jan.
Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks.
如今，食品安全被视为人类最重要的优先事项之一，因此已实施有效且新的政策来改进和发展食品行业有效法律的地位。特级初榨橄榄油（EVOO）对人体健康有许多惊人的益处。由于EVOO的营养价值和高价格，其中存在大量欺诈行为。超声方法在食品研究中有许多优点，并且对质量评估快速且无损。在本研究中，为了检测EVOO的掺假情况，提取了五个掺假水平（5%、10%、20%、35%和50%）下油的四种超声特性。在DOI 1,000 STARMANS诊断超声设备的“探头固定机构”中使用了2MHz超声探头。提取的四个超声特征如下：“振幅降低百分比、飞行时间（TOF）、时域中第一和第二最大振幅之间的差值（在时间-振幅图中）以及第一和第二最大振幅的比值”。使用包括“朴素贝叶斯、支持向量机、梯度提升分类器、K近邻、人工神经网络、逻辑回归和AdaBoost”在内的七种分类算法对预处理数据进行分类。结果表明，朴素贝叶斯算法以90.2%的准确率在其他算法中提供了最高的准确率，支持向量机和梯度提升分类器以88.2%的准确率位居其次。
