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对食用油成分的歧视性研究是通过斜射反射率差异进行的

Shanzhe Zhang¹, Hao Yang², Yiran Hu¹

¹School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University, Beijing 100048, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|July 30, 2025

概括

分析可食用油的成分,如烯酸,对于消费者安全至关重要. 斜发射反射率差 (OIRD) 与深度学习相结合,特别是时间序列变压器 (TST) 模型,在识别这些脂肪酸方面显示出高精度.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
机器学习机器学习

背景情况:

食用油是必不可少的饮食成分,需要准确的分析,以保护消费者和质量保证.
识别特定的脂肪酸,如酸,油酸和α-酸,对于营养和经济评估至关重要.
传统的分析方法可能需要改进,以快速准确地识别食用油中的成分.

研究的目的:

评估斜发射反射率差异 (OIRD) 的有效性,以识别主要食用油成分.
应用和比较八个深度学习算法来分析OIRD检测数据.
确定最佳的深度学习模型,用于准确的食用油成分分析.

主要方法:

倾斜发射反射率差异 (OIRD) 光谱法用于检测食用油成分.
用八种不同的深度学习算法来处理和解释OIRD数据.
包括精度,回忆和F1分数在内的性能指标被用来评估模型的准确性.

主要成果:

所有测试的深度学习模型都在识别油酸方面表现出高准确度.
时间序列变压器 (TST) 模型在所有分析的脂肪酸中获得了最高的F1分数:酸,油酸和α-酸.
与深度学习相结合,OIRD在识别食用油成分方面被证明是有效的.

结论:

斜发射反射率差异 (OIRD) 显示了食用油成分识别的巨大潜力.
深度学习算法,特别是时间序列变压器 (TST),增强了OIRD的分析能力.
这种综合方法为确保食用油的质量和安全提供了一个有希望的方法.

关键词:

深度学习算法深度学习算法可食用油成分识别标识斜发射反射率差异的发生率.

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