Abstract：
The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.
摘要：
三个无损传感器的性能，基于不同的原则，生物电阻抗分析(BIA)近红外光谱(NIR)和时域反射(TDR)，进行了研究，以区分未冷冻和冻融的鱼。选择大眼金枪鱼（Thunnusobesus）作为评估这些技术的模型。添加水和添加剂在鱼类行业是常见的，因此，为了有广泛的可能的商业条件，一些样品被注入不同的水溶液(基于不同的盐浓度，多磷酸盐和蛋白质水解物溶液)。三种不同的模型，基于偏最小二乘判别分析(PLS-DA)，是为每种技术开发的。这是一种线性分类方法，将偏最小二乘（PLS）回归的属性与判别技术的分类能力相结合。在测试集的评估中获得的结果对于所有传感器都是令人满意的，为NIR提供最佳性能（精度=0.91，错误率=0.10）。然而，用BIA和TDR数据完成的分类结果也令人满意，几乎同样好，精度分别为0.88和0.86，错误率分别为0.14和0.15。这项工作为区分具有不同非破坏性替代品的未冷冻和冻融鱼类样品开辟了新的可能性，不管他们是否添加了水。
