Department of Physics, School of Science, Jimei University, Xiamen, Fujian Province, China.
College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, Fujian Province, China.
J Food Sci. 2024 Jul;89(7):4359-4371. doi: 10.1111/1750-3841.17159. Epub 2024 Jun 7.
Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real-time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone-based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre-training and fine-tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin-Tiny (Swin-T) achieved the highest trade-off performance, with a Top-1 accuracy of 0.997, a detection speed of 3.2 ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin-T model was also capable of distinguishing between different types of hydrocolloids with a Top-1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real-time quantitative information about meat adulteration anytime, anywhere. PRACTICAL APPLICATION: This research provides a practical solution for regulators and consumers to non-destructively and quantitatively detect the content and type of hydrocolloids in beef in real-time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments.
水胶体广泛应用于肉类产品中，作为常见的食品添加剂。然而，研究表明，过量摄入这些水胶体可能对健康产生潜在影响。目前，消费者主要依靠感官评价来识别肉类产品中水胶体的掺假情况。尽管许多关于生化方法在实验室环境下水胶体定量检测的研究已经开展，但目前缺乏消费者和监管机构获取水胶体掺假实时、可靠信息的有效工具。为了解决这一挑战，本工作开发了一种基于智能手机的计算机视觉方法，用于定量检测牛肉中的卡拉胶掺假。具体来说，使用 Swin Transformer 模型以及预训练和微调技术，成功地实现了对牛肉中九个不同卡拉胶掺假水平（0%至 20%）的自动分类。在测试的模型中，Swin-Tiny（Swin-T）模型实现了最高的权衡性能，其 Top-1 准确率为 0.997，检测速度为 3.2ms，模型大小为 103.45Mb。与计算机视觉相比，电化学阻抗谱的准确率较低，为 0.792，并且需要恒温环境和大约 30 分钟的数据稳定等待时间。此外，Swin-T 模型还能够区分不同类型的水胶体，其 Top-1 准确率为 0.975。本研究为消费者和监管机构提供了一种有价值的工具，使他们能够随时随地获得关于肉类掺假的实时定量信息。实际应用：本研究为监管机构和消费者提供了一种实用的解决方案，可使用智能手机对牛肉中水胶体的含量和类型进行非破坏性、实时、定量检测。这项创新有可能显著降低与肉类质量检测相关的成本，例如化学试剂和昂贵仪器的使用。
