Abstract：
A novel smart film MP/BNC/ACN for real-time monitoring of fish freshness was developed using myofibrillar protein (MP) and bacterial nanocellulose (BNC) as film raw materials and anthocyanin (Lycium ruthenicum, ACN) as an indicator. Firstly, the film containing 1 % ACN (MP/BNC/ACN1) was found to have a moderate thickness (0.44 ± 0.01 mm) and superior mechanical properties (tensile strength (TS) = 8.53 ± 0.11 MPa; elongation at break (EB) = 24.85 ± 1.38 %) by determining the physical structure. The covalent, electrostatic, and hydrogen bonding interactions between anthocyanin and the film matrix were identified and confirmed by FT-IR spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscope (SEM) analysis. A comprehensive evaluation concluded that MP/BNC/ACN1 exhibited excellent trimethylamine (TMA) sensitivity (total color difference (ΔE), ΔETMA0-1000 = 4.47-31.05; limit of detection (LOD), LOD = 1.03) and UV stability (ΔE96h = 4.16 ± 0.13). The performance of the films in assessing fish freshness was evaluated, principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that MP/BNC/ACN1 (ΔE2-10d = 16.84-32.05) could clearly distinguish between fresh (0-2 d), sub-fresh (4-6 d), and spoiled (8-10 d) stages of fish, which corresponded to the film colors of red, light red, and gray-black. In conclusion, this study addresses the limitation that intelligent films cannot visually discern real-time freshness during fish storage and provides a promising approach for real-time fish freshness monitoring.
摘要：
以肌原纤维蛋白(MP)和细菌纳米纤维素(BNC)为薄膜原料和花青素(枸杞,ACN)作为指标。首先,通过确定物理结构，发现含有1％ACN（MP/BNC/ACN1）的薄膜具有中等厚度（0.44±0.01mm）和优异的机械性能（拉伸强度（TS）=8.53±0.11MPa；断裂伸长率（EB）=24.85±1.38％）。共价的，静电,通过FT-IR光谱(FTIR)鉴定和证实了花色苷与薄膜基质之间的氢键相互作用,X射线衍射(XRD)扫描电镜(SEM)分析。综合评估得出的结论是，MP/BNC/ACN1表现出优异的三甲胺（TMA）敏感性（总色差（ΔE），ΔETMA0-1000=4.47-31.05；检测限（LOD），LOD=1.03）和UV稳定性（ΔE96h=4.16±0.13）。评估了薄膜在评估鱼新鲜度方面的性能，主成分分析（PCA）和层次聚类分析（HCA）表明，MP/BNC/ACN1（ΔE2-10d=16.84-32.05）可以清楚地区分新鲜（0-2d），次新鲜（4-6d），和被宠坏的（8-10d）阶段的鱼，对应于红色的胶片颜色，浅红色,和灰黑色。总之,这项研究解决了智能薄膜在鱼类储存过程中无法直观识别实时新鲜度的局限性，并为实时鱼类新鲜度监测提供了一种有前途的方法。
