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Detection of Artificially Ripened Mango Using Spectrometric Analysis

机译：光谱分析法检测人工成熟的芒果

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摘要

Hyperspectral sensing has been proven to be useful to determine the quality of food in general. It has also been used to distinguish naturally and artificially ripened mangoes by analyzing the spectral signature. However the focus has been on improving the accuracy of classification after performing dimensionality reduction, optimum feature selection and using suitable learning algorithm on the complete visible and NIR spectrum range data, namely 350nm to 1050nm. In this paper we focus on, (ⅰ) the use of low wavelength resolution and low cost multispectral sensor to reliably identify artificially ripened mango by selectively using the spectral information so that classification accuracy is not hampered at the cost of low resolution spectral data and (ⅱ) use of visible spectrum i.e. 390nm to 700 nm data to accurately discriminate artificially ripened mangoes. Our results show that on a low resolution spectral data, the use of logistic regression produces an accuracy of 98.83% and outperforms other methods like classification tree, random forest significantly. And this is achieved by analyzing only 36 spectral reflectance data points instead of the complete 216 data points available in visual and NIR range. Another interesting experimental observation is that we are able to achieve more than 98% classification accuracy by selecting only 15 irradiance values in the visible spectrum. Even the number of data needs to be collected using hyper-spectral or multi-spectral sensor can be reduced by a factor of 24 for classification with high degree of confidence.
机译：高光谱传感已被证明对确定总体食品质量很有用。通过分析光谱特征，它也已被用于区分天然和人工成熟的芒果。但是，重点是在执行降维，优化特征选择并在完整的可见光和NIR光谱范围数据（即350nm至1050nm）上使用合适的学习算法后，提高分类的准确性。在本文中，我们着重于（ⅰ）使用低波长分辨率和低成本多光谱传感器，通过有选择地使用光谱信息来可靠地识别人工成熟的芒果，从而不会以低分辨率光谱数据为代价而影响分类精度，并且（ ⅱ）使用可见光谱，即390nm至700nm的数据来准确地区分人工成熟的芒果。我们的结果表明，在低分辨率光谱数据上，使用逻辑回归可以产生98.83％的准确度，并且明显优于其他方法，例如分类树，随机森林。这是通过仅分析36个光谱反射率数据点而不是在视觉和NIR范围内分析全部216个数据点来实现的。另一个有趣的实验观察结果是，通过在可见光谱中仅选择15个辐照度值，我们能够达到98％以上的分类精度。甚至需要使用高光谱或多光谱传感器收集的数据数量也可以减少24倍，以实现高置信度的分类。

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