Food Science and Nutrition Research Department, National Fishery and Aquatic Life Research Center (NFALRC), Ethiopian Institute of Agricultural Research (EIAR), Sebeta, Ethiopia.
Department of Biosystems (BIOSYST), Division of Mechatronics, Biostatistics and Sensors (MeBioS), University of Leuven (KU Leuven), Leuven, Belgium.
J Food Sci. 2022 Jul;87(7):2847-2857. doi: 10.1111/1750-3841.16195. Epub 2022 May 31.
Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near-infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS-DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four-class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two-class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein- and water-related changes were found important to distinguish the fillets. Based on these findings, the four-class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two-class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. PRACTICAL APPLICATION: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output.
温度波动在冷链中很常见，会导致冷冻产品完全或部分解冻和再冻结，从而形成多次冻结产品。与单一冻结产品相比，这种温度波动会导致显著的质量下降。本研究旨在根据温度波动水平区分冷冻大西洋三文鱼鱼片。近红外光谱（NIRS）结合化学计量学用于对无波动（NF）、低波动（LF）、高波动（HF）和超高波动（VF）温度下储存的冷冻鱼片进行分类。使用冷冻和解冻状态下获得的光谱曲线，通过偏最小二乘判别分析（PLS-DA）基于温度波动水平对鱼片进行分类。解冻样品在四分类模型中的分类准确率（71%）优于冷冻样品（66%）。考虑到前两组（NF、LF）和后两组（HF、VF）之间的小变化，使用解冻样品建立了一个两分类模型，得到的模型能够正确地对这两个组（[NF、LF]和[HF、VF]）进行分类，分类准确率为 100%。发现蛋白质和水相关的变化对区分鱼片很重要。基于这些发现，四分类预测模型不足以用于无损确定冷冻鱼片的温度历史。然而，使用解冻状态下扫描的鱼片，具有进一步外部验证的两分类预测模型可以应用于确定温度波动水平。实际应用：近红外光谱可以用于评估整个冷冻大西洋三文鱼鱼片物流过程中的温度波动程度，从而评估相关质量损失。研究人员、食品控制当局和零售行业可能是这项研究成果的主要受益者。
