牛肉富含丰富的蛋白质、脂肪、维生素和矿物质，可为人类提供必需氨基酸、矿物质和维生素等必需营养素，近年来牛肉在我国的生产和消费率逐年增长，在我国肉类消费中占据重要地位，且冷鲜肉在消费比例中占据主导地位。然而，牛肉在生产、运输和销售各环节极易遭到微生物污染，导致新鲜度下降。利用牛肉理化品质多指标综合评价，实现牛肉新鲜度等级（新鲜、次新鲜和腐败）准确地评定是目前牛肉品质研究中的重要目标。因此，本研究以4℃贮藏的牛肉为研究对象，对其进行感官评价和理化品质测定，筛选出表征牛肉新鲜度变化的标志性理化指标，结合Fisher判别模型和BP神经网络模型，通过结构优化和参数优化得到具有最佳性能的模型，以此准确地评定生鲜牛肉新鲜度等级。在此基础上，以吸附-热脱附-气质联用法（ATD-GC-MS）和固相微萃取-气质联用法（SPME-GC-MS）为检测方法，鉴定不同新鲜度等级下牛肉的关键挥发性气味物质。该研究为生鲜牛肉的新鲜度等级评定提供了科学的方法，主要的研究结果如下：
1.4℃贮藏下生鲜牛肉新鲜度品质关键性指标的研究
本章开展4℃贮藏生鲜牛肉的感官评价和理化品质指标测定试验，结合感官评分和理化指标进行分析，筛选出表征新鲜度变化的标志性理化指标。生鲜牛肉在冷藏过程中的新鲜度品质不断下降，主要体现在色泽、组织形态、粘弹性和气味上，并依据这四个方面划分生鲜牛肉新鲜度等级（新鲜、次新鲜和腐败）。根据感官评价与理化指标结果之间的相关性分析，TVB-N、TBARS和TCA可溶性肽含量三个指标与感官评价具有强相关性和稳定性，可以显著表征生鲜牛肉的新鲜度等级变化。
2.生鲜牛肉新鲜度等级评定模型的构建与优化
本章以Fisher判别分析和BP神经网络模型为建模方法，进行结构优化和参数优化。Fisher判别模型的输入参数为TVB-N含量和TBARS值时，模型具有最优评定新鲜度等级性能，建模组分类效果为85.2%，验证组分类效果为87.8%。BP神经网络模型当输入参数为TVB-N值和TBARS值，训练函数为trainlm，传递函数组合为logsig和purelin，学习率为0.9，隐藏层神经元个数为10时，新鲜度等级评定模型具有最优性能。
3.吸附-热脱附-GC-MS和SPME-GC-MS法分析不同新鲜度牛肉关键挥发性气味物质
本章以新鲜、次新鲜和腐败牛肉为研究对象，利用吸附-热脱附-气质联用法（ATD-GC-MS）和固相微萃取-气质联用法（SPME-GC-MS）为检测方法，鉴定出牛肉中关键挥发性气味物质。吸附-热脱附-GC-MS法检测结果分析，正己烷和2-丁酮为新鲜牛肉组关键挥发性气味物质，2,3-丁二醇和2,3-丁二酮为次新鲜牛肉组关键挥发性气味物质，乙酸为腐败组的关键性气味物质。SPME--GC-MS法检测结果分析，苯甲醚和十五醛为新鲜牛肉关键性挥发性气味物质，9-十八烯酸甲酯和十六醛为次新鲜牛肉关键性气味物质，十六酸甲酯和十四酸甲酯为腐败牛肉关键性气味物质。

Beef is rich in proteins, fats, vitamins, and minerals, providing humans with essential nutrients such as amino acids, minerals, and vitamins. However, beef is highly susceptible to microbial contamination during production, transportation, and retail, leading to a decline in freshness. A comprehensive evaluation of beef’s physicochemical properties to accurately assess its freshness grade (fresh, sub-fresh, or spoiled) remains a critical objective in beef quality research. This study focuses on beef stored at 4, conducting sensory evaluations and physicochemical analyses to identify key indicators of freshness changes. By integrating Fisher discriminant analysis and a BP neural network model, optimized through structural and parameter adjustments, we developed a high-performance model for precise freshness grading. Furthermore, adsorption-thermal desorption-gas chromatography-mass spectrometry (ATD-GC-MS) and solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) were employed to identify critical volatile organic compounds (VOCs) associated with different freshness levels. This research establishes a scientific methodology for freshness assessment of raw beef, with key findings as follows:
1. Study on key indicators of fresh beef quality during 4℃ storage
This chapter investigated the sensory evaluation and physicochemical properties of fresh beef stored at 4℃. By analyzing the correlation between sensory scores and physicochemical parameters, we identified key indicators that effectively characterize freshness deterioration. During refrigerated storage, the freshness quality of beef progressively declines, primarily manifested in color, texture, viscoelasticity, and odor. Based on these attributes, beef freshness was classified into three grades: fresh, sub-fresh, and spoiled. Correlation analysis revealed that TVB-N, TBARS, and TCA-soluble peptide content exhibited strong and stable associations with sensory evaluation. These three indicators serve as robust markers for distinguishing freshness levels in beef.

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