College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Research Center of Information Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China.
Biosensors (Basel). 2022 Jul 20;12(7):544. doi: 10.3390/bios12070544.
This study presents a novel composite thin film based on rhodamine B encapsulated into MOF-5 (Metal Organic Frameworks) as a fluorescence sensor for the real-time detection of the freshness of chilled pork. The composite film can adsorb and respond to the volatile amines produced by the quality deterioration of pork during storage at 4 °C, with the fluorescence intensity of RhB decreasing over time. The quantitative model used for predicting the freshness indicator (total volatile base nitrogen) of pork was built using the fluorescence spectra (excited at 340 nm) of the RhB@MOF-5 composite film combined with the partial least squares (PLS) algorithm, providing R and R values of 0.908 and 0.821 and RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction) values of 3.435 mg/100 g and 3.647 mg/100 g, respectively. The qualitative model established by the partial least squares discriminant analysis (PLS-DA) algorithm was able to accurately classify pork samples as fresh, acceptable or spoiled, and the accuracy was 86.67%.
本研究提出了一种基于罗丹明 B 封装在 MOF-5（金属有机骨架）中的新型复合薄膜，作为荧光传感器，用于实时检测冷藏猪肉的新鲜度。复合膜可以吸附并响应猪肉在 4°C 储存过程中因质量下降而产生的挥发性胺，随着时间的推移，RhB 的荧光强度逐渐降低。使用荧光光谱（在 340nm 激发）和偏最小二乘（PLS）算法相结合，构建了用于预测猪肉新鲜度指标（总挥发性碱基氮）的定量模型，提供了 0.908 和 0.821 的 R 和 R 值，以及 3.435mg/100g 和 3.647mg/100g 的 RMSEC（校准均方根误差）和 RMSEP（预测均方根误差）值。偏最小二乘判别分析（PLS-DA）算法建立的定性模型能够准确地将猪肉样品分类为新鲜、可接受或变质，准确率为 86.67%。
