通过里埃转换近红外光谱学对饼干组成进行分析,并与化学分析相结合
Cristina Quintelas1, Cláudia Rodrigues2, Clara Sousa3
1CEB - Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal.
Food chemistry
|October 1, 2023
概括
福里埃变换近红外光谱 (FT-NIR) 为分析饼干组成提供了一种快速可靠的方法. 这种非破坏性技术可以准确预测营养含量,识别饼干类型和谷物,确保产品质量.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 饼干消费的增加,特别是在COVID-19大流行期间,需要强有力的质量和安全控制措施.
- 福里埃变换近红外 (FT-NIR) 光谱技术提供了快速的,非破坏性的分析,样品的准备时间最小.
- 化学测量技术增强了对复杂食品矩阵的光谱数据解释.
研究的目的:
- 评估FT-NIR光谱法在预测饼干中的营养参数 (脂质,碳水化合物,纤维,蛋白质,盐,能量) 的有效性.
- 评估FT-NIR识别饼干类型和用于其配方的初级谷物的能力.
- 为了比较用于cookie分析的各种化学测量模型的性能.
主要方法:
- 收购120个商业饼干样本.
- 应用FT-NIR光谱法用于光谱数据收集.
- 预测模型的开发和比较:普通最小平方 (OLS),部分最小平方 (PLS),PLS-差异分析 (PLS-DA),PLS-k-最近邻居 (PLS-kNN) 和PLS-朴素贝叶斯 (PLS-NB).
主要成果:
- 营养参数的高预测准确度,相关系数超过0.9.
- 使用PLS-kNN模型成功识别了所有5种主要谷物 (小麦,全麦,麦,玉米,大米) 和14种饼干类型.
- 已证明能够准确确定cookie组成的能力.
结论:
- 与化学测量相结合的FT-NIR光谱学,为 Cookie 质量评估提供了传统分析方法的快速,可靠和环保的替代方案.
- 开发的方法允许准确预测营养含量,准确识别饼干类型和谷物来源.
- 这种方法支持在不断增长的饼干行业中加强质量控制和产品安全.
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