Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
Department of Biosystems Engineering, Ilam University, Ilam, Iran.
Comput Biol Med. 2021 Sep;136:104728. doi: 10.1016/j.compbiomed.2021.104728. Epub 2021 Aug 3.
Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into consideration by researchers. Due to the high nutritional value of turmeric among the spices as well as the fraudulent motives to gain economic profit from the selling of this product, its quality assessment is very important. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with turmeric in powder form and sold in the market. In this study, an improved convolutional neural network (CNN) was used to classify turmeric powder images to detect fraud. CNN was improved through the use of gated pooling functions. We also show with a combined approach based on the integration of average pooling and max pooling that the accuracy and performance of the proposed CNN has increased. In this study, 6240 image samples were prepared in 13 categories (pure turmeric powder, chickpea powder, chickpea powder mixed with food coloring, 10, 20, 30, 40 and 50% fraud in turmeric). In the preprocessing step, unwanted parts of the image were removed. The data augmentation (DA) was used to reduce the overfitting problem on CNN. Also in this research, MLP, Fuzzy, SVM, GBT and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that prevention of the overfitting problem using gated pooling, the proposed CNN was able to grade the images of turmeric powder with 99.36% accuracy compared to other classifiers. The results of this study also showed that computer vision, especially when used with deep learning (DL), can be a valuable method in evaluating the quality and detecting fraud in turmeric powder.
评估食品和香料的质量对于确保人类的适当营养尤为重要。研究人员一直考虑将计算机视觉方法作为一种无损技术来测量食品和香料的质量。由于姜黄在香料中的营养价值很高，而且存在欺诈动机，即从销售这种产品中获利，因此对其质量评估非常重要。3 级鹰嘴豆（小而碎的鹰嘴豆）的市场可销售性差，价格非常低，这使得它们成为与姜黄粉混合并在市场上销售的好选择。在这项研究中，使用改进的卷积神经网络（CNN）对姜黄粉图像进行分类，以检测欺诈行为。通过使用门控池化函数对 CNN 进行了改进。我们还展示了一种基于平均池化和最大池化集成的组合方法，表明所提出的 CNN 的准确性和性能得到了提高。在这项研究中，我们准备了 6240 个图像样本，分为 13 类（纯姜黄粉、鹰嘴豆粉、混合有食用色素的鹰嘴豆粉、10%、20%、30%、40%和 50%的姜黄粉掺假）。在预处理步骤中，去除了图像的不相关部分。数据增强（DA）用于减少 CNN 上的过拟合问题。在这项研究中，还使用了 MLP、Fuzzy、SVM、GBT 和 EDT 算法将所提出的 CNN 结果与其他分类器进行比较。结果表明，通过门控池化防止过拟合问题，所提出的 CNN 能够以 99.36%的准确率对姜黄粉图像进行分级，优于其他分类器。这项研究的结果还表明，计算机视觉，特别是与深度学习（DL）结合使用，可以成为评估姜黄粉质量和检测欺诈行为的一种有价值的方法。
