论文标题

使用荧光光谱和机器学习算法在粒状杏仁中的光学检测B中的光学检测

Optical detection of Aflatoxins B in grained almonds using fluorescence spectroscopy and machine learning algorithms

论文作者

Bertani, F. R., Businaro, L., Gambacorta, L., Mencattin, A., Brenda, D., Di Giuseppe, D., De Ninno, A., Solfrizzo, M., Martinelli, E., Gerardino, A.

论文摘要

黄曲霉毒素是真菌代谢物,由许多不同的真菌物种广泛产生,可能污染各种农业食品。由于与各种慢性疾病和急性疾病有关,特别是免疫抑制和癌症,因此对它们进行了广泛的研究,并且在全球范围内严格监测并受到监测。黄曲霉毒素的检测和测量主要取决于通常基于色谱方法的化学方法,并且最近开发了具有优势但也有局限性的基于免疫化学的测定,因为这些测定是昂贵且具有破坏性的技术。最近正在开发非破坏性的光学方法,以以成本和时间有效的方式评估污染的存在,从而保持可接受的准确性和可重复性。在本文中,介绍了使用简单的便携式设备获得的结果,用于无损地检测杏仁中的黄曲霉毒素。提出的方法是基于在375 nm波长激发下对藻类杏仁的荧光光谱的分析。用HPLC/FLD确定的杏仁在2.7-320.2 ng/g总黄霉素B(AFB1 + AFB2)范围内进行实验。应用预处理步骤后,通过基于SVM算法的二进制分类模型进行了光谱分析。然后对分类结果进行多数投票程序。通过这种方式,我们可以将分类精度达到94%(和虚假的负率为5%),而阈值设置为6.4 ng/g。这些结果说明了这种方法在黄曲霉毒素检测到食物和饲料安全性的巨大挑战中的可行性。

Aflatoxins are fungal metabolites extensively produced by many different fungal species that may contaminate a wide range of agricultural food products. They have been studied extensively because of being associated with various chronic and acute diseases especially immunosuppression and cancer and their presence in food is strictly monitored and regulated worldwide. Aflatoxin detection and measurement relies mainly on chemical methods usually based on chromatography approaches, and recently developed immunochemical based assays that have advantages but also limitations, since these are expensive and destructive techniques. Nondestructive, optical approaches are recently being developed to assess presence of contamination in a cost and time effective way, maintaining acceptable accuracy and reproducibility. In this paper are presented the results obtained with a simple portable device for nondestructive detection of aflatoxins in almonds. The presented approach is based on the analysis of fluorescence spectra of slurried almonds under 375 nm wavelength excitation. Experiments were conducted with almonds contaminated in the range of 2.7-320.2 ng/g total aflatoxins B (AFB1 + AFB2) as determined by HPLC/FLD. After applying pre-processing steps, spectral analysis was carried out by a binary classification model based on SVM algorithm. A majority vote procedure was then performed on the classification results. In this way we could achieve, as best result, a classification accuracy of 94% (and false negative rate 5%) with a threshold set at 6.4 ng/g. These results illustrate the feasibility of such an approach in the great challenge of aflatoxin detection for food and feed safety.

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