Bootstrap Guided Information Criterion for Reliability Analysis Using Small Sample Size Information

Amalnerkar, Eshan; Lee, Tae Hee; Lim, Woochul

doi:10.1007/978-3-319-67988-4_25

Cited by 1 publication

(1 citation statement)

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“…Commonly, the sample size is increased using certain methods to improve the results. The bootstrap algorithm [35] is a common resampling method for small sample set in chemometrics and its reliability has been demonstrated by many researchers [36][37][38] . We have previously investigated the feasibility of using the bootstrap method for the quantitative analysis of the maize moisture content at the grain-filling stage [39] and a similar study was conducted by other researchers [35] .…”

Section: Introductionmentioning

confidence: 99%

Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size

Wang¹,

Yang²,

Song³

et al. 2019

International Journal of Agricultural and Biological Engineering

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The change in the maize moisture content during different growth stages is an important indicator to evaluate the growth status of maize. In particular, the moisture content during the grain-filling stage reflects the grain quality and maturity and it can also be used as an important indicator for breeding and seed selection. At present, the drying method is usually used to calculate the moisture content and the dehydration rate at the grain-filling stage, however, it requires large sample size and long test time. In order to monitor the change in the moisture content at the maize grain-filling stage using small sample set, the Bootstrap re-sampling strategy-sample set partitioning based on joint x-y distances-partial least squares (Bootstrap-SPXY-PLS) moisture content monitoring model and near-infrared spectroscopy for small sample sizes of 10, 20, and 50 were used. To improve the prediction accuracy of the model, the optimal number of factors of the model was determined and the comprehensive evaluation thresholds RVP (coefficient of determination (R 2), the root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP)) was proposed for sub-model screening. The model exhibited a good performance for predicting the moisture content of the maize grain at the filling stage for small sample set. For the sample sizes of 20 and 50, the R 2 values were greater than 0.99. The average deviations of the predicted and reference values of the model were 0.1078%, 0.057%, and 0.0918%, respectively. Therefore, the model was effective for monitoring the moisture content at the grain-filling stage for a small sample size. The method is also suitable for the quantitative analysis of different concentrations using near-infrared spectroscopy and small sample size.

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