Rapid and Noninvasive Typing and Assessment of Nutrient Content of Maize Kernels Using a Handheld Raman Spectrometer

Abstract

To thrive as a global civilization, food production must meet the demands of our ever-growing population. There are more than a billion people on the planet suffering from malnutrition through poor quality or lack of food. Nutrient content of food can be determined by a variety of methods, which have issues such as slow analysis or sample destruction. Near-infrared (NIR) spectroscopy is a long-standing alternative to these methods. In this work, we demonstrated that Raman spectroscopy (RS), another spectroscopic method, can also be used to assess the nutrient content of maize (Zea mays), one of the most widely cultivated grains in the world. Using a handheld Raman spectrometer, we predicted the content of carbohydrates, fibers, carotenoids, and proteins in six different varieties of maize. This analysis requires only a single maize kernel and is fast (1s), portable, noninvasive, and nondestructive. Moreover, we showed that RS in combination with chemometric methods can be used for highly accurate (approximately 90%) spectroscopic typing of maize, which is important for plant breeders and farmers. Finally, we demonstrate that Raman-based approach is as accurate as NIR analysis. These findings suggest that portable Raman systems can be used on combines and grain elevators for autonomous control of grain quality.

Raman Spectroscopy Enables Non-Invasive Identification of Peanut Genotypes and Value-Added Traits

“…Previously, we showed that overall spectral intensity directly depends on the color of the sample. For instance, in our study on the nutrient content of corn kernels, spectra collected from dark colored corn kernels had much lower intensity comparing to the Raman spectra collected from lighter color kernels 20 . Therefore, to exclude the influence of seed color on the intensities of vibrational bands, spectra were normalized to the total spectral area, as this method is the least biased.…”

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

“…Direct comparison of Raman spectra can be a challenging task, primarily because the overall spectral intensity can vary with coloration of the analyzed specimen. For instance, Krimmer and co-workers found that dark maize kernels absorbed more and consequently scattered less light relative to the yellow or pale color kernels (Krimmer et al, 2019). Since RS is based on inelastic light scattering, the researchers concluded that dark color maize varieties would produce less intense Raman spectra (under the same experimental conditions) compared to the light color maize varieties.…”

“…At the same time, there are several vibrational bands that originate from aliphatic (CH 2 ) vibrations, such as 1,440 and 1,458 cm −1 . Normalization of spectra on these vibrational bands can be used for an unbiased comparison of Raman spectra collected from both leaves and seeds (Farber et al, 2019b(Farber et al, , 2020cKrimmer et al, 2019;Sanchez et al, 2019bSanchez et al, ,c, 2020b). Such normalization allows for avoiding artificial differences in spectra associated with different coloration of analyzed plant material.…”

“…Raman spectroscopy allows for non-invasive and non-destructive detection and identification of biotic (Egging et al, 2018;Farber and Kurouski, 2018;Sanchez et al, 2019aSanchez et al, , 2019c and abiotic (Altangerel et al, 2017;Sanchez et al, 2020b) stresses. RS can be used for accurate and rapid plant phenotyping and the assessment of the nutritional content of grains (Krimmer et al, 2019;Farber et al, 2020c). RS is based on a phenomenon of inelastic light scattering by molecules that are being excited to higher vibrational or rotational states.…”

“…The fast results of RS allow farmers to take advantage of the information and make quick adjustments to cease the development of a certain biotic or abiotic stress. The non-labor-intensive and non-destructive nature of Raman also allows for rapid assessment of the plant phenotype directly in the field, eliminating the need of a wet-laboratory analysis of plants (Krimmer et al, 2019;Farber et al, 2020c).…”