Acrylamide is a carcinogenic chemical compound found in carbohydrate rich foods when fried and baked at high temperatures, like potato chips. Identification of such toxic substances in food items is of tremendous significance. Conventional identification approaches like liquid chromatography-mass spectrometry (LC–MS) are time-consuming, destructive and require trained manpower. Traditional machine learning methods involve the extraction of handcrafted features that needs to be judiciously selected. To overcome such shortcomings of the existing researches, an alternate method incorporating deep convolutional neural network (DCNN) for acrylamide identification has been proposed. The novelty of the proposed research work provides an opportunity to explore and distinguish between traditional machine learning and deep learning techniques. Also, the novel contribution in the proposed research work remarkably improves computation complexity which thereby, increases its system accuracy. Deep learning models, pre-trained on ImageNet dataset, showed a remarkable performance in comparison to existing methods. Simulation results demonstrate that MobileNetv2 out-performed AlexNet, ResNet-34, ResNet-101, VGG-16 and VGG-19 models. Therefore, the vitality of algorithm used, validates the advantages of the proposed research work, which could be used as an efficient and effective tool for food-quality evaluation in real-time applications.

丙烯酰胺是富含碳水化合物的食物，在高温下油炸和烘烤后会发现的致癌化学化合物，例如薯片。食品中此类有毒物质的鉴定具有重要意义。诸如液相色谱-质谱（LC-MS）之类的常规识别方法既耗时，破坏性强，又需要经过培训的人员。传统的机器学习方法需要手工选择需要手工选择的特征。为了克服现有研究的这些缺点，已经提出了一种结合深度卷积神经网络（DCNN）的丙烯酰胺识别方法。拟议的研究工作的新颖性为探索和区分传统机器学习和深度学习技术提供了机会。也，在提出的研究工作中的新颖贡献显着提高了计算复杂度，从而提高了系统精度。与现有方法相比，在ImageNet数据集上进行了预训练的深度学习模型表现出了卓越的性能。仿真结果表明，MobileNetv2的性能优于AlexNet，ResNet-34，ResNet-101，VGG-16和VGG-19模型。因此，所用算法的生命力验证了所提出的研究工作的优势，可以作为实时应用中食品质量评估的有效工具。仿真结果表明，MobileNetv2的性能优于AlexNet，ResNet-34，ResNet-101，VGG-16和VGG-19模型。因此，所用算法的生命力验证了所提出的研究工作的优势，可以作为实时应用中食品质量评估的有效工具。仿真结果表明，MobileNetv2的性能优于AlexNet，ResNet-34，ResNet-101，VGG-16和VGG-19模型。因此，所用算法的生命力验证了所提出的研究工作的优势，可以作为实时应用中食品质量评估的有效工具。