Determination of zinc and iron in wheat using laser-induced breakdown spectroscopy

Abstract

The potential of laser-induced breakdown spectroscopy (LIBS) has been used to determine the micronutrients (iron and zinc) in wheat, which is a staple food and the most widely cultivated crop in Pakistan. Wheat samples consisted of commercial and candidate varieties and a fortified wheat flour (FWF). The LIBS spectrum were recorded from wheat flour pellets using a Q-switched Nd:YAG laser focused on the surface of the wheat sample. Twenty-five single shot spectra were recorded at different sites of the same pellet. The data were normalized to exclude shot-to-shot laser energy variations and then the averaged emission spectrum was used for further analysis. According to the NIST atomic data base and the related literature, the most intense emission lines of zinc at 202.54, 206.20 and 213.85 nm and iron at 238.20, 259.93, 275.57, 344.06 & 373.48/373.71 nm have been detected in the LIBS spectra of wheat genotypes, which has been used to determine their relative intensities. Significant variation among the genotypes was identified for these minerals. In addition, principal component analysis (PCA) has been performed to statistically classify the wheat varieties based on their iron and zinc emission intensities. The PCA grouped the data into clusters based on the similarities in their micronutrient. The highest zinc was identified in NR-488, Zincol-2006 and NARC-2011. Likewise, the strongest iron intensities were observed in FWF, Borlaug-2016 and NR-486. These findings may assist breeders in the selection of genotypes with high micronutrients coupled with high grain yield, and other desirable traits essential for variety release. The present study has validated LIBS as a characterization tool for inferring micronutrients in the staple food.

Detection of oil–water interface based on laser-induced breakdown spectroscopy

“…x = 2a cos θ 3 , θ = arccos DNAρ down 3nAρ up − 3DNAρ down − 3kmρ up (23) where 0 < cos θ 3 < 1 2 . When the semi-ellipsoid only contains the upper-layer liquid:…”

“…This method has been widely used in materials [15], chemistry [16,17], medicine [18], biology [19] and geology [20]. Further, it has the advantages of allowing for real-time, online, and rapid analysis, not requiring any sample pretreatment, and resulting in low sample loss [21][22][23]. At the same time, LIBS technology has also some application in the measurement of thickness and depth, for example, Mateo et al analysed the penetration depth of the nanoparticles employed in biocidal treatments of historical buildings by using LIBS technology for the first time and they proved that the effect depth is between 130 µm and 230 µm [24].…”

Industrial applications of laser-induced breakdown spectroscopy: a review

“…The challenges for application of LIBS in agricultural studies were discussed in a recent review by Peng et al 183 Eum et al 184 discussed the combined use of LIBS and near-infrared spectroscopy for the identication of the geographical origin of vegetable samples. Atta et al used LIBS for the quantication of micro-nutrients (zinc, iron) in wheat, 185 noticing that LIBS analysis can be useful for growers in selecting the wheat genotypes richer in these micronutrients. Sezer et al 186 proposed LIBS as a rapid, reliable, and environmentally friendly alternative to standard protein assay methods; the authors successfully correlated the nitrogen concentration with the protein content in wheat our and whole meal.…”

Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods

“…Han et al used LIBS to determine calcium concentrations in different internal tissues of corn seeds, and the detection limit reached 1.05 PPM [10]. Atta et al identified the emission lines of zinc and iron in wheat by LIBS and performed a qualitative analysis by PCA to identify wheat varieties based on the selected emission lines [11]. Luo et al used LIBS in the range of 210-480 nm and three pattern recognition algorithms to realize the discrimination of 11 rice species [12].…”

“…On the other hand, deep learning, which represents a wide class of machine learning methods mostly based on artificial neural networks, has become the most popular topic in the artificial intelligence field. For LIBS data analysis to date, attention has mostly been paid to the implementation of traditional machine learning algorithms [7,11,12], while only a few studies have searched for a way to interpret LIBS data by deep learning approach [15]. To our knowledge, no attempt has been made to evaluate the feasibility of deep learning for discriminating grape seeds.…”