Lashgari Majid, Imanmehr Abdullah, Tavakoli Hamed
Department of Mechanical Engineering of Biosystems, Arak University, Arak, 38156-8-8349 Iran.
J Food Sci Technol. 2020 Jun;57(6):2233-2240. doi: 10.1007/s13197-020-04259-y. Epub 2020 Jan 24.
Mealiness in apple fruit can occur during storage or because of harvesting in an inappropriate time; it degrades the quality of the fruit and has a considerable role in the fruit industry. In this paper, a novel non-destructive approach for detection of mealiness in Red Delicious apple using acoustic and deep learning techniques was proposed. A confined compression test was performed to assign labels of mealy and non-mealy to the apple samples. The criteria for the assignment were hardness and juiciness of the samples. For the acoustic measurements, a plastic ball pendulum was used as the impact device, and a microphone was installed near the sample to record the impact response. The recorded acoustic signals were converted to images. Two famous pre-trained convolutional neural networks, AlexNet and VGGNet were fine-tuned and employed as classifiers. According to the result obtained, the accuracy of AlexNet and VGGNet for classifying the apples to the two categories of mealy and non-mealy apples was 91.11% and 86.94%, respectively. In addition, the training and classification speed of AlexNet was higher. The results indicated that the suggested method provides an effective and promising tool for assessment of mealiness in apple fruit non-destructively and inexpensively.
苹果果实的粉绵质地可能在储存期间出现，或者是由于在不恰当的时间采收所致；它会降低果实品质，在水果行业中具有相当大的影响。本文提出了一种利用声学和深度学习技术检测红富士苹果粉绵质地的新型无损方法。进行了受限压缩试验，以便为苹果样本赋予粉绵质地和非粉绵质地的标签。赋值的标准是样本的硬度和多汁性。对于声学测量，使用塑料球摆作为冲击装置，并在样本附近安装一个麦克风来记录冲击响应。记录的声学信号被转换为图像。对两个著名的预训练卷积神经网络AlexNet和VGGNet进行了微调，并用作分类器。根据所得结果，AlexNet和VGGNet将苹果分为粉绵质地苹果和非粉绵质地苹果两类的准确率分别为91.11%和86.94%。此外，AlexNet的训练和分类速度更高。结果表明，所提出的方法为无损且低成本地评估苹果果实的粉绵质地提供了一种有效且有前景的工具。
