Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
Department of Biosystems Engineering, Ilam University, Ilam, Iran.
Comput Biol Med. 2021 Sep;136:104764. doi: 10.1016/j.compbiomed.2021.104764. Epub 2021 Aug 13.
Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. 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 ginger in powder form and sold in the market. Demand for non-destructive methods of measuring food quality, such as machine vision and the growing need for food and spices, were the main motives to conduct this study. This study classified ginger powder images to detect fraud by improving convolutional neural networks (CNN) through a gated pooling function. The main approach to improving CNN is to use a pooling function that combines average pooling and max pooling. The Batch normalization (BN) technique is used in CNN to improve classification results. We show empirically that the combining operation used increases the accuracy of ginger powder classification compared to the baseline pooling method. For this purpose, 3360 image samples of ginger powder were prepared in 7 categories (pure ginger powder, chickpea powder, 10%, 20%, 30%, 40%, and 50% fraud in ginger powder). Moreover, MLP, Fuzzy, SVM, GBT, and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that using batch normalization based on gated pooling, the proposed CNN was able to grade the images of ginger powder with 99.70% accuracy compared to other classifiers. Therefore, it can be said that the CNN method and image processing technique effectively increase marketability, prevent ginger powder fraud, and promote traditional methods of ginger powder fraud detection.
生姜是食品和制药行业广为人知的产品。生姜是为了经济利益而掺假的香料之一。由于 3 级鹰嘴豆（小而碎的鹰嘴豆）的市场销售性差且价格非常低，因此将它们与粉末状的生姜混合并在市场上出售成为了一种很好的选择。对无损测量食品质量的方法（例如机器视觉）的需求以及对食品和香料的不断增长的需求是进行这项研究的主要动机。本研究通过改进门控池化功能的卷积神经网络（CNN）对生姜粉图像进行分类，以检测欺诈行为。改进 CNN 的主要方法是使用结合平均池化和最大池化的池化功能。批量归一化（BN）技术用于 CNN 以提高分类结果。我们通过实验表明，与基线池化方法相比，所使用的组合操作提高了生姜粉分类的准确性。为此，我们准备了 7 类（纯生姜粉、鹰嘴豆粉、10%、20%、30%、40%和 50%生姜粉掺假）的 3360 个生姜粉图像样本。此外，还使用了 MLP、Fuzzy、SVM、GBT 和 EDT 算法将提出的 CNN 结果与其他分类器进行比较。结果表明，与其他分类器相比，使用基于门控池化的批量归一化，所提出的 CNN 能够以 99.70%的准确度对生姜粉图像进行分级。因此，可以说 CNN 方法和图像处理技术有效地提高了市场销售性，防止了生姜粉欺诈，并促进了传统的生姜粉欺诈检测方法。
