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近红外光谱法快速鉴别新鲜和冷冻解冻的鲤鱼()。

Near-Infrared Spectroscopy for Rapid Differentiation of Fresh and Frozen-Thawed Common Carp ().

机构信息

Department of Agricultural Engineering, Faculty of Agriculture, Trakia University, Students Campus, 6000 Stara Zagora, Bulgaria.

Department of Food Quality and Safety, Faculty of Veterinary Medicine, Trakia University, Students Campus, 6000 Stara Zagora, Bulgaria.

出版信息

Sensors (Basel). 2024 Jun 4;24(11):3620. doi: 10.3390/s24113620.

Abstract

This study aimed to investigate near-infrared spectroscopy (NIRS) in combination with classification methods for the discrimination of fresh and once- or twice-freeze-thawed fish. An experiment was carried out with common carp (). From each fish, test pieces were cut from the dorsal and ventral regions and measured from the skin side as fresh, after single freezing at minus 18 °C for 15 ÷ 28 days and 15 ÷ 21 days for the second freezing after the freeze-thawing cycle. NIRS measurements were performed via a NIRQuest 512 spectrometer at the region of 900-1700 nm in Reflection mode. The Pirouette 4.5 software was used for data processing. SIMCA and PLS-DA models were developed for classification, and their performance was estimated using the F1 score and total accuracy. The predictive power of each model was evaluated for fish samples in the fresh, single-freezing, and second-freezing classes. Additionally, aquagrams were calculated. Differences in the spectra between fresh and frozen samples were observed. They might be assigned mainly to the O-H and N-H bands. The aquagrams confirmed changes in water organization in the fish samples due to freezing-thawing. The total accuracy of the SIMCA models for the dorsal samples was 98.23% for the calibration set and 90.55% for the validation set. For the ventral samples, respective values were 99.28 and 79.70%. Similar accuracy was found for the PLS-PA models. The NIR spectroscopy and tested classification methods have a potential for nondestructively discriminating fresh from frozen-thawed fish in as methods to protect against fish meat food fraud.

摘要

本研究旨在探索近红外光谱(NIRS)与分类方法相结合,用于区分新鲜鱼和一次或两次冻融鱼。该实验以鲤鱼()为研究对象。从每条鱼的背部和腹部切取试片,从鱼皮一侧进行测量,分别为新鲜、单次冷冻(-18°C,15-28 天)和冻融循环后第二次冷冻(15-21 天)的状态。采用 NIRQuest 512 光谱仪在反射模式下于 900-1700nm 区域进行 NIRS 测量。使用 Pirouette 4.5 软件进行数据处理。采用 SIMCA 和 PLS-DA 模型进行分类,并使用 F1 评分和总准确率评估模型性能。针对新鲜、单次冷冻和第二次冷冻鱼样本,评估了每个模型的预测能力。此外,还计算了 aquagrams。观察到新鲜和冷冻样本之间光谱的差异,可能主要归因于 O-H 和 N-H 带。水图谱证实了冷冻-解冻过程中鱼样中水组织的变化。对于背部样本,SIMCA 模型的总准确率为校准集的 98.23%和验证集的 90.55%;对于腹部样本,相应的值分别为 99.28%和 79.70%。PLS-PA 模型也具有类似的准确性。NIR 光谱和测试的分类方法具有无损区分新鲜鱼和冻融鱼的潜力,可作为防止鱼肉食品欺诈的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/38a4/11175329/5213bbfb9049/sensors-24-03620-g001.jpg

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