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首页> 外文期刊>Journal of food engineering >An evaluation of hyperspectral imaging for characterising milk powders
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An evaluation of hyperspectral imaging for characterising milk powders

机译：表征奶粉的高光谱成像评估

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The ability to quantify and qualify subtle differences between milk powders is very advantageous to industrial manufacturers. Hyperspectral imaging (HSI) combines the spatial attributes of image processing with the chemical diagnostic attributes of spectroscopy, and was evaluated to determine if it could be used to discriminate between milk powders produced in various factories, and of differing functional qualities, such as dispersibility. The results showed that HSI can achieve these aims when multivariate analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression are applied. The PCA results showed that the most obvious differences were in the first and second principal components. Strategies to pre-process hyperspectral data, and to optimally automatically detect and remove artefacts in the images were also established. The PLS results showed that the information from HSI can be used to predict with reasonable accuracy the key functional property of dispersibility, and is the first step in a 'real-time quality' initiative to establish correlations between hyperspectral images and key quality attributes of milk powder either on, or at-line in close to real-time. (C) 2017 Elsevier Ltd. All rights reserved.
机译：量化和鉴定奶粉之间细微差异的能力对工业制造商而言非常有利。高光谱成像（HSI）将图像处理的空间属性与光谱学的化学诊断属性结合在一起，并进行了评估，以确定是否可以将其用于区分各家工厂生产的奶粉以及不同的功能质量（例如分散性）。结果表明，采用多元分析技术，例如主成分分析（PCA）和偏最小二乘（PLS）回归，HSI可以实现这些目标。 PCA结果表明，最明显的区别在于第一和第二主成分。还建立了预处理高光谱数据以及最佳地自动检测和去除图像中伪像的策略。 PLS结果表明，来自HSI的信息可用于以合理的准确性预测分散性的关键功能特性，并且是“实时质量”计划中建立高光谱图像与牛奶关键质量属性之间相关性的第一步。粉末可以实时在线或在线实时显示。 （C）2017 Elsevier Ltd.保留所有权利。

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