A new study published in Nature by Whatley et al. found that Fourier transforms mid-infrared (FT-MIR) spectroscopy combined with modeling techniques is helpful for multivariate chemical analysis in agricultural research. Still, a drawback of this method is the time-consuming sample preparation requirement. Therefore, this study investigates the effect of fine grinding on model performance using leaf tissue from various crop species.
The study obtained dried leaf samples (N = 300) from various environmental conditions with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques, with scanning repeated after fine grinding for 2, 5, and 10 minutes. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations.
Results showed that all analytes except boron, iron, and zinc were well-modeled (average R2 > 0.7), with higher R2 values on ATR spectra. The 5-minute level of fine grinding was found to be most optimal considering overall model performance and sample preparation time.
The study’s findings suggest that the time and cost of analysis can be dramatically reduced by optimizing the level of sample grinding for FT-MIR spectroscopy. This is particularly important for agricultural research involving large sample sets, where efficient analysis is crucial for making informed decisions.
Fourier transforms mid-infrared spectroscopy combined with modeling techniques have the potential to revolutionize agricultural analysis, offering an efficient and cost-effective approach for multivariate chemical analysis. The study’s findings highlight the importance of considering sample preparation in agricultural research and offer a promising new approach for improving the efficiency and accuracy of the analysis.
Further research in this area could lead to new advancements in agricultural analysis, potentially improving crop yields and addressing issues related to food security and sustainability. The findings of this study demonstrate the power of scientific research in addressing real-world challenges and offer a promising new approach to improving agricultural efficiency and productivity.
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