基于气流与多点激光技术的牛肉新鲜度检测装置研发

为了提高气流-激光技术在冷鲜肉品质检测方面的预测性能，该研究根据牛肉黏弹性与其品质的相关性，基于气流与多点激光技术研发了牛肉新鲜度检测装置。该装置硬件系统主要包括气流控制模块、位移信息采集模块、载物台升降模块和气室。基于该检测装置获取不同新鲜度牛肉样本的黏弹性信息，采用S-G卷积平滑（Savitzky-Golay smooth, S-G）、一阶导数处理（First derivative, FD）、一阶导数处理结合S-G卷积平滑（Savitzky-Golay smoothing after the first derivative pre-processing, FD+S-G）对样品黏弹性信息进行预处理，并测定牛肉新鲜度指标挥发性盐基总氮（Total Volatile Basic Nitrogen, TVB-N）含量, 建立了牛肉TVB-N含量的较佳预测模型。模型验证集的相关系数与均方根误差分别为0.859和1.337 mg/100 g。基于QT应用程序开发框架设计完成了检测装置控制软件，并将预测模型植入软件内部，实现了该检测装置的一键式操作。为验证检测装置稳定性进行外部验证试验，结果表明该检测装置预测值与国标测量参考值间相关系数为0.887，均方根误差为1.385 mg/100 g。该检测装置基于黏弹性原理实现了牛肉新鲜度的无损检测，预测性能较好，可以为肉品新鲜度检测提供参考。

Development of beef freshness detection device based on air flow and multi-point laser technique

Abstract: The quality and texture of meat are closely related to the viscoelasticity changes during storage. The current viscoelastic model can also be widely applied to assess the physical properties and chemical components in the meat. Therefore, the viscoelasticity and meat freshness can be indirectly related, where the internal properties of meat caused by spoilage can be expressed by viscoelastic characteristics. It is very necessary to detect the viscoelastic information for a better prediction of freshness. However, the current laser technique has been limited in the food industry, due to the airflow diffusion. In this study, an innovative detection device of beef freshness was developed using airflow and multi-point laser technique. The hardware system of the device mainly included an airflow control, a displacement information acquisition, a stage lifting, and an air chamber. Some design strategies were selected to obtain a stable air flow, including the air chamber and the nozzle with the contraction curve, while the specific circuits to control the solenoid and electro-pneumatic proportional valve. Some key parameters were also selected for the displacement information acquisition and stage lifting module. The viscoelasticity of beef samples with different freshness was first represented by the displacement of the beef under airflow. Then, the data set of displacement was preprocessed via the Savitzky-Golay smooth (SG), the First Derivative processing (FD), and the FD+SG. After that, a prediction model was established using the Total Volatile Basic Nitrogen (TVB-N) content. Finally, a systematic evaluation was also made using the Partial Least Squares Regression (PLSR) and Principle Component Regression (PCR). The results showed that the preprocessing was greatly contributed to the accuracy of the model, where the accuracy of the PLSR model was much higher than that of the PCR model. The best PLSR prediction model was also achieved, when the viscoelasticity information was pretreated by FD+SG with the correlation coefficients in the calibration and validation set of 0.891 and 0.859, respectively, and the root mean squared errors in the calibration and prediction set of 1.071 and 1.337 mg/100 g, respectively. It indicated that the accuracy and stability of the model were improved significantly, compared with the traditional. Particularly, the multi-point laser technique was superior to the traditional single-point one. In addition, the control software of the device was designed to implement using the QT application development framework. Subsequently, the prediction model was implanted in the software to realize the one-click operation of the device. Furthermore, an external prediction test was performed on the 13 beef samples, in order to verify the stability of the device. It was found that the correlation coefficient between the prediction and measurement value was 0.887, where the root mean square error was 1.385 mg/100 g. Consequently, an excellent performance of the device was achieved for the non-destructive detection of beef freshness. Furthermore, the new technique can be widely expected to comprehensively represent the deformation of the sample in the future. The finding can also provide a strong reference for the freshness detection of meat products.