利用红外光谱技术预测可可含量:使用Kohonen神经网络和多变量建模的双重方法
Clara Mariana Gonçalves Lima1, Paula Giarolla Silveira2, Renata Ferreira Santana3
1Department of Food Science, Federal University of Lavras, Lavras, MG 37203-202, Brazil; Regional University of Cariri, Crato, CE 63105-000, Brazil.
概括
近红外 (NIR) 光谱学可以准确地确定巧克力中的可可含量. 这种方法与主要组件分析/回归 (PCA/PCR) 和Kohonen神经网络 (KNN) 相结合,为质量控制和真实性验证提供了有价值的工具.
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:
- 从可可豆中提取的巧克力因其味道,香气和潜在的健康益处而受欢迎.
- 它的多样化化学成分包括咖啡因和神胺等兴奋剂,以及有益的抗氧化剂和黄类.
- 这些化合物与改善心血管健康,血液循环,警觉性和注意力有关.
研究的目的:
- 调查近红外 (NIR) 光谱法在商业巧克力样本中量化可可百分比的有效性.
- 探索NIR光谱数据与可可含量之间的相关性.
- 评估先进的分析技术,以提高可可百分比的确定.
主要方法:
- 在900-1600纳米范围内对NIR光谱进行探索性分析.
- 主要组件分析 (PCA) 的应用用于样本歧视.
- 利用Kohonen神经网络 (KNN) 进行模式识别和主要组件回归 (PCR) 进行预测建模.
主要成果:
- 在900-1400 nm NIR光谱范围内,可可百分比和吸收率之间观察到强烈的相关性.
- 根据可可含量,PCA有效地区分了样品.
- 主要成分回归 (PCR) 实现了可可百分比的0.84的预测R平方值.
- 通过将NIR光谱与PCA/PCR和KNN集成,成功确定了可可百分比.
结论:
- 当与PCA/PCR和KNN相结合时,NIR光谱是量化巧克力中可可百分比的可行和有效方法.
- 这种综合方法为确保巧克力质量控制和真实性提供了有价值的工具.
- 该研究强调了光谱技术在食品工业中对产品分析和验证的潜力.
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