首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh
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Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh

机译:可见光和近红外高光谱成像技术在非侵入式测量鲑鱼肉持水量分布中的应用

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摘要

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.
机译:持水量(WHC)是鲑鱼肉的主要品质决定因素。无法直接测量鲑鱼品质等级的WHC的限制因素之一是当前的WHC测量具有破坏性,耗时且效率低下。在这项研究中,将两个在可见光和短波近红外范围(400-1000 nm)和长波近红外范围(897-1753 nm)中操作的高光谱图像系统用于四个WHC指数的非侵入性测定,即鲑鱼肉的失水百分比(PLL),失水百分比(PWL),脂肪损失百分比(PFL)和剩余水分百分比(PWR)。分别应用了偏最小二乘回归(PLSR)和最小二乘支持向量机(LS-SVM)的两种校准方法,基于鲑鱼肉的光谱特征以及这两种性能,建立了WHC指数的校准模型。比较了方法以确定最佳的光谱校准策略。当考虑全范围光谱时,还比较了两个高光谱图像系统之间的性能。通过使用竞争性自适应重加权采样(CARS)算法减少高光谱的冗余度和共线性,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。图片。 CARS-PLSR组合被确定为校准用于WHC测定的预测模型的最佳方法,因此,PLL,PWL,PFL和PLL的预测相关系数(〜(rP))分别为0.941、0.937、0.815和0.970。分别进行PWR分析。根据CARS-PLSR模型的回归系数获得了CARS-PLSR方程,并将其转移到图像中的每个像素,以可视化鲑鱼片各部分的WHC指数。总体结果表明,高光谱成像可以代替费力,费时且具有破坏性的传统技术,从而可以快速,无创地测量鲑鱼肉中WHC的分布。

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