一种基于现场可编程门阵列的循环神经网络阻抗谱系统，用于检测金枪鱼中的亚硝酸盐。 （注：原文中“YAKE”疑有误，推测为“亚硝酸盐（Nitrite，缩写为NO₂⁻ ，有可能被误写为类似的‘YAKE’）”，按照推测进行了翻译，具体需根据准确原文进一步确认。）
A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.
作者信息
Gadea-Girones Rafael, Monzo Jose M, Colom-Palero Ricardo, Fe Jorge, Castro-Giraldez Marta, Fito Pedro J
机构信息
Instituto de Instrumentación para Imagen Molecular I3M, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain.
Instituto Universitario de Ingeniería de Alimentos FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain.
出版信息
Sci Rep. 2025 Jul 1;15(1):22046. doi: 10.1038/s41598-025-05728-0.
This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that treat the impedance data series at different frequencies as a time series, with the intention of improving the identification of alpha and beta dispersions that are fundamental for the determination of food quality. This proposal in addition to being demonstrated its validity in the detection of YAKE on frozen tuna loins, is fully implemented on a low power FPGA device that allows the classification at the edge by means of a portable equipment that allows its application in food distribution chains with high energy efficiency.
本文评估了阻抗谱与人工智能相结合的应用。这两种技术都已广泛应用于食品分类，并且提出了一种使用递归神经网络来改进分类的方法，该网络将不同频率下的阻抗数据序列视为时间序列，旨在改善对α和β色散的识别，这对于确定食品质量至关重要。该提议除了在冷冻金枪鱼里脊上检测YAKE方面证明了其有效性外，还在低功耗FPGA设备上完全实现，该设备允许通过便携式设备在边缘进行分类，从而使其能够以高能效应用于食品分销链。
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