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Sensors 2016, 16(1), 89; doi:10.3390/s16010089

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor

1
College of Computer and Information Technology, Three Gorges University, Yichang 443002, China
2
Beijing Research Center for Agricultural Standards and Testing, Beijing 100097, China
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 24 November 2015 / Revised: 27 December 2015 / Accepted: 7 January 2016 / Published: 11 January 2016
(This article belongs to the Special Issue Sensors for Agriculture)
View Full-Text   |   Download PDF [1655 KB, uploaded 11 January 2016]   |  

Abstract

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy. View Full-Text
Keywords: near infrared sensors; information processing; spectroscopy; variable selection; successive projections algorithm near infrared sensors; information processing; spectroscopy; variable selection; successive projections algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Qu, F.; Ren, D.; Wang, J.; Zhang, Z.; Lu, N.; Meng, L. An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor. Sensors 2016, 16, 89.

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