A near‐infrared spectroscopy method for the detection of texture profile analysis of Litopeneo vannamei based on partial least squares regression

Abstract

AbstractAmong aquatic products freshness testing methods, spectral detection method is the most commonly used rapid nondestructive testing method, and the quality structure index can be used as the judgment index of aquatic products freshness, so studying the near‐infrared (NIR) spectroscopy detection method of aquatic products based on texture index is helpful to improve the detection accuracy of freshness of products. In order to find a method for NIR spectroscopy based on texture indicators, we took shrimp as the research target, collected total 216 NIR spectral data of 680–2600 nm and texture index of sample for 8 consecutive days, and established a quantitative prediction model. In this study, we compared the effects of six spectral pretreatments on partial least squares regression (PLSR) models, including multiple scattering correction, standard normal variate transformation, mean centering, Savitzky–Golay (S‐G) smoothing, moving average method, and normalization. The results show that the PLSR model based on S‐G smoothing has the best model accuracy in prediction after the S‐G smoothing algorithm has preprocessed the spectral data. In addition, principal component analysis and the Mahalanobis distance algorithm were used in the study to remove abnormal samples to improve the accuracy of the model. Finally, the quantitative prediction model for qualitative construct indicators constructed based on the S‐G smoothing algorithm and the PLSR algorithm had better prediction results than the other models algorithm we considered, with a correlation coefficient (R) of 0.868 for the cohesive validation set and 0.781 for the elastic validation set of qualitative construct indicators. Finally, based on the above study, this study developed a prediction graphical user interface system for qualitative metrics of texture.Practical ApplicationsMost of the existing aquatic meat freshness detection methods are offline detection based on physical and chemical tests, and some use spectral technology to detect the chemical parameters of aquatic meat freshness. This project innovatively applies NIR spectroscopy technology to aquatic products. The physical parameters of meat freshness can be quickly detected online. By preprocessing the data samples and selecting the response characteristic wavelengths, a NIR spectroscopy prediction model of texture indicators with self‐learning ability and high precision is constructed, and the texture is established based on this. Indicator NIR spectroscopy prediction system. This whole process provides new research methods and analytical tools for rapid nondestructive testing of aquatic product freshness.