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Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution
Food Control ( IF 6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.foodcont.2020.107454
Antoine Laborde , Francesc Puig-Castellví , Delphine Jouan-Rimbaud Bouveresse , Luc Eveleigh , Christophe Cordella , Benoît Jaillais

Abstract This study aims to detect peanut flour adulteration in chocolate powder using near-infrared (NIR) hyperspectral imaging. Fifteen samples were prepared by mixing both food products in different proportions (0%, 0.1%, 1%, 10% and 100% of peanut) and measured using the hyperspectral camera. A preliminary Principal Component Analysis (PCA) was performed to investigate the structure of the data. Next, the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometric method combined with a selectivity constraint in the concentration matrix was applied to untangle the spectral data into a set of components representative of the main constituents found in the samples. Moreover, a detection algorithm based on the calculation of the Mahalanobis distance for every pixel to the model distribution of chocolate powder was implemented. This analysis revealed the complexity of the unmixing problem, allegedly due to the spectral signature overlap in the pixel field of view and because the pure products presented similar spectral signatures. MCR-ALS results were improved after the application of a selectivity constraint, which resulted in a higher performance of the detection algorithm. MCR-ALS detected from 0% to 2.2% of adulterated pixels in mixed samples. On the other hand, the selectivity-constrained MCR-ALS method provided detections from 0.03% to 17.0% in those samples. This pipeline showed that peanut adulteration can be detected even for the lowest concentration level tested (0.1% of peanut). This work highlights the potential of NIR hyperspectral imaging combined with chemometrics for detection purposes.

中文翻译:

使用近红外高光谱成像和多元曲线分辨率检测巧克力粉掺假花生

摘要 本研究旨在利用近红外 (NIR) 高光谱成像检测巧克力粉中掺假的花生粉。通过以不同比例(0%、0.1%、1%、10% 和 100% 的花生)混合两种食品制备 15 个样品,并使用高光谱相机进行测量。进行了初步的主成分分析 (PCA) 以研究数据的结构。接下来,应用多元曲线分辨率 - 交替最小二乘法 (MCR-ALS) 化学计量方法与浓度矩阵中的选择性约束相结合,将光谱数据解开为一组代表样品中发现的主要成分的成分。此外,实现了一种基于计算每个像素的马哈拉诺比斯距离到巧克力粉模型分布的检测算法。该分析揭示了分离问题的复杂性,据称是由于像素视场中的光谱特征重叠以及纯产物呈现相似的光谱特征。应用选择性约束后,MCR-ALS 结果得到改善,从而提高了检测算法的性能。MCR-ALS 在混合样本中检测到 0% 到 2.2% 的掺假像素。另一方面,选择性约束 MCR-ALS 方法在这些样品中提供了 0.03% 到 17.0% 的检测率。该管道表明,即使在测试的最低浓度水平(0.1% 的花生)下也能检测到花生掺假。这项工作突出了 NIR 高光谱成像与化学计量学相结合用于检测目的的潜力。据称是由于像素视场中的光谱特征重叠,并且因为纯产品呈现相似的光谱特征。应用选择性约束后,MCR-ALS 结果得到改善,从而提高了检测算法的性能。MCR-ALS 在混合样本中检测到 0% 到 2.2% 的掺假像素。另一方面,选择性约束 MCR-ALS 方法在这些样品中提供了 0.03% 到 17.0% 的检测率。该管道表明,即使在测试的最低浓度水平(0.1% 的花生)下也能检测到花生掺假。这项工作突出了 NIR 高光谱成像与化学计量学相结合用于检测目的的潜力。据称是由于像素视场中的光谱特征重叠,并且因为纯产品呈现相似的光谱特征。应用选择性约束后,MCR-ALS 结果得到改善,从而提高了检测算法的性能。MCR-ALS 在混合样本中检测到 0% 到 2.2% 的掺假像素。另一方面,选择性约束 MCR-ALS 方法在这些样品中提供了 0.03% 到 17.0% 的检测率。该管道表明,即使在测试的最低浓度水平(0.1% 的花生)下也能检测到花生掺假。这项工作突出了 NIR 高光谱成像与化学计量学相结合用于检测目的的潜力。
更新日期:2021-01-01
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