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Aquatic products are easy to be corrupted and deteriorated after fishing. However, due to the large volume, inconvenient movement, and complicated operation, it is difficult to be applied for conventional detection devices. In order to meet the needs of rapid detection of aquatic products, a portable multiquality parameter detection device for aquatic products based on Raman spectroscopy was developed. The hardware system of the device was mainly composed of a light source module, a spectrum acquisition module, a system control processing module, a touch screen, a power module, and a communication module. The Raman spectral data of samples collected by the hardware system of the device, and the collected spectral data was smoothed and background subtracted by standard normal variate transformation, Whittaker smoothing algorithm, adaptive iterative reweighted penalty least squares algorithm. At the same time, according to the national standard method, the standard physicochemical values of pomfret color b*, thiobarbituric acid (TBA) and total volatile base nitrogen (TVBN) were collected, combined with spectral data, partial least squares regression quantitative prediction model for aquatic product quality was established. The results showed that the correlation coefficients of the validation color of b*, TBA and TVBN were 0.907, 0.897 and 0.915, respectively. The root mean square error of the verification set was 1315, 0104mg/(100g) and 2875mg/(100g). The prediction model was implanted into the portable device software system to realize onekey detection of multiquality parameter indicators of aquatic products. Finally, the accuracy of portable device was verified. The correlation coefficients between the predicted values of b*, TBA and TVBN of 18 pomfret samples and standard values were 0.927, 0.883 and 0.904, and the root mean square errors were 1139, 0271mg/(100g) and 1896mg/(100g), respectively. The results showed that the portable device can realize the multiindex realtime nondestructive detection of pomfret.