加强近红外光谱在谷物真菌毒素检测中的应用:一种跨污染物和谷物的迁移学习方法的探索

IF 9.8 1区 农林科学 Q1 CHEMISTRY, APPLIED
Jihong Deng , Congli Mei , Hui Jiang
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引用次数: 0

摘要

谷物是人类的主要食物来源。监测、控制和预防谷物中的真菌毒素对于确保谷物及其衍生产品的安全至关重要。本研究将迁移学习策略引入化学计量学,以改进应用于不同谷物或毒素光谱数据的深度学习模型。探讨了三种迁移学习方法在谷物真菌毒素定量检测中的潜力。通过在不同仪器上预测小麦玉米赤霉烯酮(ZEN)和花生黄曲霉毒素B1 (AFB1)样品集,验证了迁移学习的可行性。结果表明,二次传递法是一种有效的毒素检测方法。在FT-NIR光谱分析中,传递模型对小麦ZEN的预测R2为0.9356,相对预测偏差(RPD)为3.9497;对花生AFB1的预测R2为0.9419,相对预测偏差(RPD)为4.1551。采用近红外光谱法对花生AFB1进行有效检测,预测集的R2为0.9386,RPD为4.0434。这些结果表明,所提出的迁移学习方法可以成功地将源领域模型更新为适合目标领域任务的模型。本研究为单一来源模型适应性差的问题提供了可行的解决方案,为谷物真菌毒素的光谱检测提供了一种更为普遍适用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the application of near-infrared spectroscopy in grain mycotoxin detection: An exploration of a transfer learning approach across contaminants and grains
Cereals are a primary source of sustenance for humanity. Monitoring, controlling, and preventing mycotoxins in cereals are vital for ensuring the safety of the cereals and their derived products. This study introduces transfer learning strategies into chemometrics to improve deep learning models applied to spectral data from different grains or toxins. Three transfer learning methods were explored for their potential to quantitatively detect fungal toxins in cereals. The feasibility of transfer learning was demonstrated by predicting wheat zearalenone (ZEN) and peanut aflatoxin B1 (AFB1) sample sets on different instruments. The results indicated that the second transfer method is effective in detecting toxins. For FT-NIR spectrometry, the transfer model achieved an R2 of 0.9356, a relative prediction deviation (RPD) of 3.9497 for wheat ZEN prediction, and an R2 of 0.9419 with an RPD of 4.1551 for peanut AFB1 detection. With NIR spectrometry, effective peanut AFB1 detection was also achieved, yielding an R2 of 0.9386 and an RPD of 4.0434 in the prediction set. These results suggest that the proposed transfer learning approach can successfully update a source domain model into one that is suitable for tasks in the target domain. This study provides a viable solution to the problem of poor adaptability of single-source models, presenting a more universally applicable method for spectral detection of fungal toxins in cereals.
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来源期刊
Food Chemistry
Food Chemistry 工程技术-食品科技
CiteScore
16.30
自引率
10.20%
发文量
3130
审稿时长
122 days
期刊介绍: Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.
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