Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland.
Talanta. 2013 Nov 15;116:266-76. doi: 10.1016/j.talanta.2013.05.030. Epub 2013 May 20.
Water-holding capacity (WHC) is a primary quality determinant of salmon flesh. One of the limiting factors for not having a direct measurement of WHC for salmon quality grading is that current WHC measurements are destructive, time-consuming, and inefficient. In this study, two hyperspectral image systems operated in the visible and short-wave near infrared range (400-1000 nm) and the long-wave near infrared range (897-1753 nm) were applied for non-invasive determination of four WHC indices, namely percentage liquid loss (PLL), percentage water loss (PWL), percentage fat loss (PFL), and percentage water remained (PWR) of salmon flesh. Two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) were applied, respectively, to establish calibration models of WHC indices based on the spectral signatures of salmon flesh, and the performances of these two methods were compared to determine the optimal spectral calibration strategy. The performances were also compared between two hyperspectral image systems, when full range spectra were considered. Out of 121 wavelength variables, only thirteen (PLL), twelve (PWL), nine (PFL), and twelve variables (PWR) were selected as important variables by using competitive adaptive reweighted sampling (CARS) algorithm to reduce redundancy and collinearity of hyperspectral images. The CARS-PLSR combination was identified as the optimal method to calibrate the prediction models for WHC determination, resulting in good correlation coefficient of prediction (rP) of 0.941, 0.937, 0.815, and 0.970 for PLL, PWL, PFL, and PWR analysis, respectively. CARS-PLSR equations were obtained according to the regression coefficients of the CARS-PLSR models and were transferred to each pixel in the image for visualizing WHC indices in all portions of the salmon fillet. The overall results show that the laborious, time-consuming, and destructive traditional techniques could be replaced by hyperspectral imaging to provide a rapid and non-invasive measurement of WHC distribution in salmon flesh.
持水力（WHC）是鱼肉主要的质量决定因素之一。目前，限制对三文鱼品质分级进行直接 WHC 测量的一个因素是，现有的 WHC 测量方法具有破坏性、耗时且效率低下。在这项研究中，应用了两个工作在可见和短波近红外范围（400-1000nm）和长波近红外范围（897-1753nm）的高光谱图像系统，对三文鱼鱼肉的四个 WHC 指标（液体损失百分比 PLL、水分损失百分比 PWL、脂肪损失百分比 PFL 和水分保留百分比 PWR）进行非侵入式测定。分别应用偏最小二乘回归（PLSR）和最小二乘支持向量机（LS-SVM）两种校准方法，基于三文鱼鱼肉的光谱特征，建立 WHC 指标的校准模型，并比较这两种方法的性能，以确定最佳的光谱校准策略。当考虑全谱范围时，还比较了这两个高光谱图像系统之间的性能。在 121 个波长变量中，仅选择了 13 个（PLL）、12 个（PWL）、9 个（PFL）和 12 个变量（PWR）作为重要变量，使用竞争自适应重加权采样（CARS）算法减少高光谱图像的冗余和共线性。CARS-PLSR 组合被确定为校准 WHC 测定预测模型的最佳方法，对于 PLL、PWL、PFL 和 PWR 分析，预测模型的相关系数 rP 分别为 0.941、0.937、0.815 和 0.970。根据 CARS-PLSR 模型的回归系数，获得了 CARS-PLSR 方程，并将其转换为图像中的每个像素，以可视化三文鱼鱼片各部分的 WHC 指标。总体结果表明，费力、耗时且具有破坏性的传统技术可以被高光谱成像所取代，为三文鱼鱼肉的 WHC 分布提供快速、非侵入式的测量。
