基于高光谱成像技术的多宝鱼肉冷藏时间的可视化研究

提出了一种应用可见-近红外高光谱成像技术快速无损检测多宝鱼肉冷藏时间并实现其可视化的新方法。采集8种不同冷藏时间的共160个鱼肉样本的高光谱图像，并提取样本感兴趣区域(ROI)的平均光谱。取120个建模集样本的光谱数据与其相应的冷藏时间建立偏最小二乘回归(PLSR)模型，对40个预测集样本的冷藏时间进行预测，预测决定系数(R2)为0.966 2，预测均方根误差(RMSEP)为0.679 9 d，获得了满意的预测精度。最后，用所建模型对预测集图像上每个像素点的冷藏时间加以预测，采用IDL图像编程技术将不同的时间用不同的颜色表示，最终以伪彩图的形式实现多宝鱼肉冷藏时间的可视化。结果表明，高光谱成像技术与化学计量学结合可以准确预测鱼肉的冷藏时间，与图像处理方法结合可以实现预测时间的可视化，能形象、直观地展示出鱼肉的新鲜度状态和分布情况，为实现水产品加工的自动化奠定了基础。

Visualization of the Chilling Storage Time for Turbot Flesh Based on Hyperspectral Imaging Technique

This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROI) inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.