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

Xue Wang, Tiemin Ma, Tao Yang, Ping Song, Zhengguang Chen, Huan Xie

Abstract


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 (R2), 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 R2 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.
Keywords: moisture content monitoring, maize, growth stage, near-infrared spectroscopy (NIRS), small sample set, model screening, optimal factor number, Bootstrap-SPXY-PLS
DOI: 10.25165/j.ijabe.20191202.4708

Citation: Wang X, Ma T M, Yang T, Song P, Chen Z G, Xie H. Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size. Int J Agric & Biol Eng, 2019; 12(2): 132–140.

Keywords


moisture content monitoring, maize, growth stage, near-infrared spectroscopy (NIRS), small sample set, model screening, optimal factor number, Bootstrap-SPXY-PLS

Full Text:

PDF

References


Cai Z W, Wang K R, Guo Y Q, Xie R Z, Li L L, Ming B. Current status of maize mechanical grain harvesting and its relationship with grain moisture content. China Journal of Scientia Agricultura Sinica, 2017; 50(11): 2036–2043. (in Chinese)

Li L L, Xie R Z, Lei X P, Wang K R, Hou P, Zhang F L. Analysis of Influential Factors on Mechanical Grain Harvest Quality of Summer Maize. China Journal of Scientia Agricultura Sinica, 2017; 50(11): 2044–2051. (in Chinese)

Liu F H , Wang K R, Li J, Wang X M, Sun Y L, Chen Y S. Factors affecting corn mechanically harvesting grain quality. China Journal of Crops, 2013; 4: 116–119. (in Chinese)

Ghorchiani M, Etesami H, Alikhani H A. Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agriculture, Ecosystems & Environment, 2018; 25(8): 59–70.

Wang W Y. Research on Problems and Countermeasures of Maize Breeding in the New Period. China Journal of Seed Science & Technology 2019; 2: 30–31. (in Chinese)

Liu S Q, Zhong X M, Li F H, Zhu M, Wang H W, Lu X L. Comparisons of grain filling and dehydration rates in 4representative maize varieties in northeast provinces. China Journal of Seed, 2015; 34(12): 69–72. (in Chinese)

Ercioglu E, Velioglu H M, Boyaci I H. Determination of terpenoid contents of aromatic plants using NIRS. Talanta, 2018; 178: 716–721.

Jiang H, Lu J. Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn. Spectrochim Acta A Mol Biomol Spectrosc, 2018; 196: 131–140.

Perissinato A G, Garcia J S, Trevisan M G. Determination of β-galactosidase in tablets by infrared spectroscopy. Chemical Papers, 2016; 71(1): 171–176.

Rodionova O Y, Balyklova K S, Titova A V, Pomerantsev A L. Application of NIR spectroscopy and chemometrics for revealing of the ‘high quality fakes’ among the medicines. Forensic Chemistry, 2018; 8: 82–89.

Stefano M, Giuseppe P, Luigi R, Roberta R. High-throughput prediction of AKB48 in emerging illicit products by NIR spectroscopy and chemometrics. Microchemical Journal, 2017; 134: 277–283 .

Revilla I, Vivar-Quintana A M, González-Martín I, Escuredo O, Seijo C. The potential of near infrared spectroscopy for determining the phenolic, antioxidant, color and bactericide characteristics of raw propolis. Microchemical Journal, 2017; 134: 211–217.

Cui Y, Xu L, An D, Liu Z, Gu J, Li S. Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics. International Journal of Agricultural and Biological Engineering, 2018; 11(2): 177–183.

Stockl A, Lichti F. Near-infrared spectroscopy (NIRS) for a real time monitoring of the biogas process. Bioresour Technol, 2018; 247: 1249–1252.

Druckenmuller K, Gunther K, Elbers G. Near-infrared spectroscopy (NIRS) as a tool to monitor exhaust air from poultry operations. Sci Total Environ, 2018; 630: 536–543.

Shan C, Wang B, Hu B, Liu W, Tang Y. Smart yolk-shell type luminescent nanocomposites based on rare-earth complex for NIR–NIR monitor of drug release in chemotherapy. Journal of Photochemistry and Photobiology A: Chemistry, 2018; 355: 233–241.

Shinzawa H, Mizukado J. Near-infrared (NIR) monitoring of Nylon 6 during quenching studied by projection two-dimensional (2D) correlation spectroscopy. Journal of Molecular Structure, 2016; 1124: 188–191.

Salgó A, Gergely S. Analysis of wheat grain development using NIR spectroscopy. Journal of Cereal Science, 2012; 56(1): 31–38.

Qian W, Hong S, Minzan L, Wei Y. Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings. Int J Agric & Biol Eng, 2014;7(2): 138–145.

Dai X, Hang S, Wen L, Yao S, Gang W. On-line UV-NIR spectroscopy as a process analytical technology (PAT) tool for on-line and real-time monitoring of the extraction process of Coptis Rhizome. Rsc Advances, 2016; 6(12): 10078–10085.

Lopes L C, Brandão I V, Sánchez O C, Franceschi E, Borges G, Dariva C. Horseradish peroxidase biocatalytic reaction monitoring using Near-Infrared (NIR) Spectroscopy. Process Biochemistry, 2018. DOI: 10.1016/j.procbio.2018.05.024.

Ringsted T, Siesler H W, Engelsen S B. Monitoring the staling of wheat bread using 2D MIR-NIR correlation spectroscopy. Journal of Cereal Science, 2017; 75: 92–99.

Genisheva Z, Quintelas C, Mesquita D P, Ferreira E C, Oliveira J M, Amaral A L. New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR). Food Chem., 2018; 246: 172–178.

Jiang H, Mei C, Li K, Huang Y, Chen Q. Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR spectroscopy and L1-PLS regression. Spectrochim Acta A Mol Biomol Spectrosc, 2018; 204: 73–80.

Villar A, Vadillo J, Santos J I, Gorritxategi E, Mabe J, Arnaiz A, et al. Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics. Food Chem., 2017; 221: 100–106.

Kim D Y, Cho B K. Rapid monitoring of the fermentation process for Korean traditional rice wine ‘Makgeolli’ using FT-NIR spectroscopy. Infrared Physics & Technology, 2015; 73: 95–102.

Li W, Han H, Cheng Z, Zhang Y, Liu S, Qu H. A feasibility research on the monitoring of traditional Chinese medicine production process using NIR-based multivariate process trajectories. Sensors and Actuators B: Chemical, 2016; 231: 313–323.

Verstraeten M, Van Hauwermeiren D, Hellings M, Hermans E, Geens J, Vervaet C. Model-based NIR spectroscopy implementation for in-line assay monitoring during a pharmaceutical suspension manufacturing process. Int J Pharm, 2018; 546: 247–254.

Nee K, Bryan S A, Levitskaia T G, Kuo J W J, Nilsson M. Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models. Analytica Chimica Acta, 2018; 1006: 10–21.

Goodarzi M, Sharma S, Ramon H, Saeys W. Multivariate calibration of NIR spectroscopic sensors for continuous glucose monitoring. TrAC Trends in Analytical Chemistry, 2015; 67: 147–158.

Li M, Ebel B, Chauchard F, Guédon E, Marc A. Parallel comparison of in situ Raman and NIR spectroscopies to simultaneously measure multiple variables toward real-time monitoring of CHO cell bioreactor cultures. Biochemical Engineering Journal, 2018,2018(137): 205–213.

Zhu L W, Ma W G, Hu J, Zheng J H,Tian Y X, Guan Y J. Advances of NIR Spectroscopy Technology Applied in Seed Quality Detection. Spectroscopy and Spectral Analysis, 2015; 35(2): 346–349. (in Chinese)

Wang M, Kong L, Li Z, Zhang L J. Covariance estimators for generalized estimating equations (GEE) in longitudinal analysis with small samples. Statistics in medicine, 2016; 35(10): 1706–1721.

Jennison, Christopher, Turnbull, W. Adaptive sample size modification in clinical trials: start small then ask for more? Statistics in medicine, 2015; 34(29): 3793–3810.

Dwivedi A K, Mallawaarachchi I, Alvarado L A. Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method. Statistic Medicine, 2017; 36(14): 2187–2205.

Krebsbach C M. Bootstrapping with small samples in structural equation modeling: goodness of fit and confidence intervals: University of Rhode Island, Rhode Island, 2014.

Amalnerkar E, Lee T H, Lim W. Bootstrap Guided Information Criterion for Reliability Analysis Using Small Sample Size Information. World Congress of Structural and Multidisciplinary Optimisation, 2017; pp.326–333.

Wang Y, Zhou W, Dong D, Wang Z. Estimation of random vibration signals with small samples using bootstrap maximum entropy method. Measurement, 2017; 105(7): 45–55.

Wang X, Ma T, Yang T, Song P, Xie Q J, Chen Z G. Moisture quantitative analysis with small sample set of maize grain in filling stage based on near infrared spectroscopy. Transactions of the CSAE, 2018; 34(13): 203–310. (in Chinese)

Li J B, Guo Z M, Huang W Q. Near-infrared spectra combining with CARS and SPA algorithms to screen the variables and samples for quantitatively determining the soluble solids content in strawberry. Spectroscopy & Spectral Analysis, 2015; 35(2): 372–378. (in Chinese)

Xie Y, Li F, Fan X J, Hu S J, Xiao X, Wang J F. Components Analysis of Biochar Based on Near Infrared Spectroscopy Technology. Chinese Journal of Analytical Chemistry, 2018; 46(4): 609–615. (in Chinese)




Copyright (c) 2019 International Journal of Agricultural and Biological Engineering



2023-2026 Copyright IJABE Editing and Publishing Office