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Updated: Aug 19, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
[The algorithm of eliminating the similar samples in the process of calibration and prediction]
Yong-jun Lu1, Yan-ling Qu, Ren-guan Piao
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130022, China.
Abstract:
During the manufacture and debugging of the NIR instruments it is compulsory to make final calibration analysis to verify whether the instruments operate correctly. However, the conventional method of NIR calibration needs to make calibration analysis for the instruments with all the samples at hand. In fact in order to cover the samples that will be encountered in the future the amount of the samples set and the labor that the personnel need to offer are enormous. In this paper a new algorithm is presented which can be used to effectively eliminate the similar samples in the original sample set. By using this algorithm we have chosen 94 optimal samples in the original 178 sample set successfully. After performing calibration experiment we found that the sample set chosen by this algorithm are equally representative to the original sample set and obtained almost the same precision compared to the original sample set when the two sample sets were individually calibrated. This algorithm brings great relief to the labor of the workers, presents the possibility of performing more experiments and greatly improves the efficiency of performing calibration experiment. As a result, the amount of calibration sets and the labor of the personnel are reduced remarkably.
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