Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra
Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra
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DOI：
10.1016/j.aca.2005.02.075
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发表时间：
2005-07-15
影响因子：
6.2
通讯作者：
Ulrici, A
中科院分区：
文献类型：
--
作者：
Cocchi, M;Corbellini, M;Ulrici, A
在意大利，面包用小麦粉在商业上基于质量综合指数（Indice Sintetico di Qualit，ISQ）被分类为不同的质量类别，该质量综合指数通过特定的参数来定义，即，百泌重、降落值、蛋白质含量、肺泡指数（W、P/L）和粉质稳定性。确定这些参数所涉及的分析费用昂贵、耗时，并需要专业人员，因此，当需要在很短的时间内对产品进行定性时，人们关注的是开发可在商业交易期间应用的替代方法。因此，一种快速的技术，如基于小麦粉样品的近红外光谱的自动分类，是一种非常有用的工具。在这项工作中，属于四种不同ISQ类的各种小麦粉样品已经通过近红外光谱进行了分析，并且所获得的光谱已经通过SIMCA对经过不同预处理方法的信号进行了分类，并通过使用基于小波的特征选择/分类算法，称为WPTER。由于两个中间质量类别的高度重叠，不可能对所有数据集信号进行分类。然而，当只考虑两个极端的类别，可以得到一个可接受的程度的类分离后，特征选择的WPTER。此外，这种方法使我们能够定位的近红外光谱区域，主要参与分配的小麦粉样品，这两个质量类别。(c)2005 Elsevier B. V.保留所有权利。
In the Italian context, bread wheat flour is commercially classified in different quality categories on the basis of a Synthetic Index of Quality (Indice Sintetico di Qualit, ISQ), which is defined by means of specific parameters, i.e., hectolitric weight, falling number, protein content, alveographic indexes (W, P/L) and farinograph stability. The analyses involved in the determination of these parameters are expensive, time consuming and require specialized personnel, thus there is concern to develop alternative methods to be applied during the commercial transactions, when the products need to be characterized in very short times. For this reason, a fast technique such as an automated classification on the basis of NIR spectra acquired on the wheat flour samples could be a very useful tool.In this work, various wheat flour samples belonging to four different ISQ classes have been analysed by means of NIR spectroscopy, and the obtained spectra have been classified both by SIMCA applied to the signals subjected to different pretreatment methods, and by using a wavelet-based feature selection/classification algorithm, called WPTER. Due to the high overlap of the two intermediate quality classes, it was not possible to classify all the data set signals. However, when considering only the two extreme categories, an acceptable degree of class separation can be gained after feature selection by WPTER. Moreover, this approach allowed us to locate the NIR spectral regions that are mainly involved in the assignment of the wheat flour samples to these two quality categories. (c) 2005 Elsevier B.V. All rights reserved.