Prediction of Oil and Oleic Acid Concentrations in Individual Corn (Zea Mays L.) Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Multivariate Analysis

Abstract

Due to their heterogeneous structure and variability in form, individual corn (Zea mays L.) kernels present an optical challenge for nondestructive spectroscopic determination of their chemical composition. Increasing demand in agricultural science for knowledge of specific traits in kernels is driving the need to find high-throughput methods of examination. In this study macroscopic near-infrared (NIR) reflectance hyperspectral imaging was used to measure small sets of kernels in the spectroscopic range of 950 nm to 1700 nm. Image analysis and principal component analysis (PCA) were used to determine kernel germ from endosperm regions as well as to define individual kernels as objects out of sets of kernels. Partial least squares (PLS) analysis was used to predict oil or oleic acid concentrations derived from germ or full kernel spectra. The relative precision of the minimum cross-validated root mean square error (RMSECV) and root mean square error of prediction (RMSEP) for oil and oleic acid concentration were compared for two sets of two hundred kernels. An optimal statistical prediction method was determined using a limited set of wavelengths selected by a genetic algorithm. Given these parameters, oil content was predicted with an RMSEP of 0.7% and oleic acid content with an RMSEP of 14% for a given corn kernel.

Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels

“…This preprocessing method is known to reduce the scattering effects smoothing the noise of the signal. It has led to improvement in the results of several studies of single seed applications using NIRS because it removed differences due to sample positioning (Weinstock et al, 2006).…”

“…However, NIRS has been used in several applications for single seed analysis with notable success. Quantitative applications in single corn kernels are mainly targeting oil content (Cogdill et al, 2004;Janni et al, 2008;Jiang et al, 2007;Orman and Schumann, 1992;Spielbauer et al, 2009;Weinstock et al, 2006), protein levels (Spielbauer et al, 2009), moisture calibrations (Armstrong, 2006;Finney and Norris, 1978), and starch (Spielbauer et al, 2009). The predictive ability of those calibrations indicated that NIRS is a suitable technology for screening organic compounds in single corn kernels.…”

Selection of Haploid Maize Kernels from Hybrid Kernels for Plant Breeding Using Near-Infrared Spectroscopy and SIMCA Analysis

“…The reported reflection spectra extend deeper into the NIR range than the transmission spectra, usually reaching 1700 nm. 4,6,7,9,10 Reflection, however, interrogates only the surface region of the kernel and only the region struck by the NIR beam, while diffuse transmission examines most of the bulk of the kernel. Some of the reflection-based studies have partly compensated for this by tumbling or dropping the kernels during analysis so as to view a larger portion of the kernel.…”

Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review

“…With a combination of microscopy and spectroscopy, FTIR could be used to quantify the chemical composition of microscopic samples [38]. Both FTIR and NIR imaging techniques that provide spatially resolved chemical information have been applied in pharmaceutical [39,40] and food industry [41]. The imaging techniques are helpful for better understanding biomass structure.…”