用拉曼光谱学和近红外光谱学检测和量化牛肉肉丸中的猪肉改
Zaqlul Iqbal1, Nils Kristian Afseth2, Annelies Postelmans3
1KU Leuven, Department of Biosystems, MeBioS-Biophotonics, B-3001 Leuven, Belgium; Department of Biosystems Engineering, Faculty of Agricultural Technology, Universitas Brawijaya, 65145 Malang, Indonesia.
概括
拉曼光谱法 (RS) 和近红外光谱法 (NIRS) 显示了在肉球中快速,非破坏性选猪肉改的潜力. 虽然这两种方法都是有效的,但RS为哈拉尔认证提供了更好的肉类成分洞察力.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 哈拉尔认证要求食品不含猪肉物质.
- 目前的方法 (DNA,蛋白质,脂肪分析) 是可靠的,但对于选大样本量而言产量不高.
- 振动光谱,包括拉曼光谱 (RS) 和近红外光谱 (NIRS),提供快速,非破坏性的分析.
研究的目的:
- 评估拉曼光谱法 (RS) 和近红外光谱法 (NIRS) 对选肉丸中的猪肉改的潜力.
- 为了比较RS和NIRS的性能,用于识别伪造的肉球样本.
主要方法:
- 准备好的牛肉肉丸有不同程度的猪肉改 (3-100% w/w).
- 使用雷曼光谱 (RS) 在反射散射和近红外光谱 (NIRS) 在反射模式下扫描样本,无论是完整的还是横截面的形式.
- 使用部分最小平方差异分析 (PLS-DA) 来建模分类性能.
主要成果:
- 与完整样本相比,横截面样本分析为RS和NIRS提供了更稳定的分类性能.
- 部分最小方程差异分析 (PLS-DA) 模型的准确度在RS中达到52.50%至85.00%,在NIRS中达到58.97%至75.00%.
- 与NIRS相比,拉曼光谱对主要的肉成分提供了更好的洞察力.
结论:
- RS和NIRS都是可行的工具,用于快速选肉丸中的猪肉改,支持清真认证流程.
- 专注于提高拉曼信号质量的进一步研究可以提高模型性能,以实现更准确的检测.
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