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.
Lyme disease (LD), one of the most prevalent tick-borne diseases in the United States (US), is caused by Borreliella burgdorferi sensu stricto (Bb). To date, in the US, LD diagnostics is primarily based on validated two-tiered serological testing, which overall exhibits low sensitivity among other drawbacks. In the present study, a potential of Raman spectroscopy (RS) to detect Bb infection in mice has been explored. For that, C3H mice were infected with wild-type Bb strains, 297, B31, or B31-derived mutant, ΔvlsE. Blood samples taken prior to and post Bb infection were subjected to RS. The data demonstrated that RS did not directly detect Bb spirochetes in blood, but rather sensed biochemical changes associated with Bb infection. Despite Bb infection-associated blood changes detectable by RS were very limited, the partial least square discriminant analysis showed that the average true positive rates were 86% for 297 and 89% for B31 and ΔvlsE.
The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations. The glyphosate-resistant populations used in this study were 11-, 32-, and 36-fold more resistant compared to the susceptible standard. The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number for these resistant populations ranged from 86 to 116. We found that Raman spectroscopy could be used to differentiate herbicide-treated and non-treated susceptible populations based on changes in the intensity of vibrational bands at 1156, 1186, and 1525 cm–1 that originate from carotenoids. The partial least squares discriminant analysis (PLS-DA) model indicated that within 1 day of glyphosate treatment (D1), the average accuracy of detecting herbicide-treated and non-treated susceptible populations was 90 and 73.3%, respectively. We also found that glyphosate-resistant and -susceptible populations of Palmer amaranth can be easily detected with an accuracy of 84.7 and 71.9%, respectively, as early as D1. There were relative differences in the concentration of carotenoids in plants with different resistance levels, but these changes were not significant. The results of the study illustrate the utility of Raman spectra for evaluation of herbicide resistance and stress response in plants under field conditions.
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