Mr. Milton Mondal Profile

Milton Mondal

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Proceedings Article | 1 May 2017 Presentation + Paper

Detection of artificially ripened mango using spectrometric analysis

Mithun B.S., Milton Mondal, Harsh Vishwakarma, Sujit Shinde, Sanjay Kimbahune

Proceedings Volume 10217, 102170A (2017) https://doi.org/10.1117/12.2262457

KEYWORDS: Chemical analysis, Contamination, Data analysis, Near infrared, Visible radiation, Spectral resolution, Spectroscopy, Sensors, Hyperspectral sensing, Feature selection, Visual analytics

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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

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