Mechanical Engineering of Biosystems Department, Ilam University, Ilam, Iran.
Sci Rep. 2024 Oct 25;14(1):25417. doi: 10.1038/s41598-024-76617-1.
Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics' values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics' values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper.
可见成像技术是一种快速、廉价且准确的食品质量和安全评估技术。本研究采用该技术检测黑胡椒和红胡椒中的海沫掺假水平。欺诈水平包括 0、5、15、30 和 50%。本研究进行了样品制备、图像采集和预处理以及特征工程（特征提取、选择和分类）。使用人工神经网络和支持向量机方法对有效特征进行分类。使用特异性、敏感性、精度和准确性指标评估分类器。人工神经网络在分类黑胡椒中不同掺假水平方面的结果优于支持向量机方法，其指标值分别为 98.89、95.67、95.56 和 98.22%。相反，支持向量机方法在红胡椒方面具有更高的指标值（99.46、98.00、97.78 和 99.11%）。结果表明，可见成像和机器学习方法能够检测黑胡椒和红胡椒中的欺诈水平。
