Rapid detection of drought stress in plants using femtosecond laser-induced breakdown spectroscopy

LIBS assisted PCA analysis of multiple rare-earth elements (La, Ce, Nd, Sm, and Yb) in phosphorite deposits

In the present study, the immediate detection of rare-earth elements (REEs) in phosphorite deposits has been reported using laser-induced breakdown spectroscopy (LIBS). Numerous emission lines corresponding to the REEs such as lanthanum (La), cerium (Ce), neodymium (Nd), samarium (Sm), and ytterbium (Yb), have been detected in the emission spectra of phosphorite-induced plasma plume. For the quantitative analysis, we employed the calibration-free LIBS (CF-LIBS), and Energy Dispersive X-ray (EDX) spectroscopy techniques. The results obtained using the CF-LIBS technique show excellent agreement with that obtained by EDX. Besides principal component analysis (PCA) was employed by incorporating the LIBS spectral data of rare earth phosphorite rocks samples containing La, Ce, Nd, Sm, and Yb emission lines. The first three PCs were observed using LIBS spectral data set showing a covariance (interpretation rate) up to 76.3%. This study suggests that LIBS yields a quick and very reliable qualitative and quantitative analysis of REEs in any geological ore sample.

Accuracy improvement in plastics classification by laser-induced breakdown spectroscopy based on a residual network

The whole ecosystem is suffering from serious plastic pollution. Automatic and accurate classification is an essential process in plastic effective recycle. In this work, we proposed an accurate approach for plastics classification using a residual network based on laser-induced breakdown spectroscopy (LIBS). To increasing efficiency, the LIBS spectral data were compressed by peak searching algorithm based on continuous wavelet, then were transformed to characteristic images for training and validation of the residual network. Acrylonitrile butadiene styrene (ABS), polyamide (PA), polymethyl methacrylate (PMMA), and polyvinyl chloride (PVC) from 13 manufacturers were used. The accuracy of the proposed method in few-shot learning was evaluated. The results show that when the number of training image data was 1, the verification accuracy of classification by plastic type under residual network still kept 100%, which was much higher than conventional classification algorithms (BP, kNN and SVM). Furthermore, the training and testing data were separated from different manufacturers to evaluate the anti-interference properties of the proposed method from various additives in plastics, where 73.34% accuracy was obtained. To demonstrate the superiority of classification accuracy in the proposed method, all the evaluations were also implemented by using conventional classification algorithm (kNN, BP, SVM algorithm). The results confirmed that the residual network has a significantly higher accuracy in plastics classification and shows great potential in plastic recycle industries for pollution mitigation.

Detection of the metabolic response to drought stress using hyperspectral reflectance

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

Morpho-Physiological and Biochemical Changes in Syzygium cumini and Populus deltoides: A Case Study on Young Saplings under Water Stress

Drought is one of the most devastating climate factors in terms of its spatial extent and intensity. Therefore, a study was conducted to evaluate the water stress tolerance in young saplings of Syzygium cumini (L.) Skeels and Populus deltoides Marchall that are cultivated in the rain fed areas of Pakistan. Plants were subjected to three levels of moisture regimes: well-watered (WW, 90% of field capacity), mild stress (MS, 60% field capacity), and severe stress (SS, 30% of field capacity). Results showed that dry biomass production (leaf, stem, and root), chlorophyll a, b and carotenoid contents decreased significantly while osmolyte accumulation increased in both species, with the highest increase was evidenced in Populus deltoides saplings. A significant decrease was evidenced in CO2 assimilation rate and stomatal conductance that resulted in a significant increase in intrinsic water use efficiency in both species under MS and SS. In both the species, along with a significant increase in the production of hydrogen peroxide and superoxide radical, the antioxidants enzyme activities of superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase also increased significantly in both species under MS and SS with highest activity evidenced in Syzygium cumini. The results suggest that Syzygium cumini saplings showed better a tolerance mechanism to water stress.

Continuous background correction using effective points selected in third-order minima segments in low-cost laser-induced breakdown spectroscopy without intensified CCD

This work presents a method that can automatically estimate and remove varying continuous background emission for low-cost laser-induced breakdown spectroscopy (LIBS) without intensified CCD. The algorithm finds all third-order minima points in spectra and uses these points to partition the spectra into multiple subintervals. The mean value is then used as a threshold to select the effective points for the second-order minima in each subinterval. Finally, a linear interpolation method is used to realize extension of these effective points and complete fitting of the background using polynomials. Using simulated and real LIBS spectra with different complexities examine the validity of proposed algorithm. Additionally, five elements of five standard cast iron alloy samples are calibrated and improved very well after background removal. The results successfully prove the validity of the background correction algorithm.

Accuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy

An efficient method has been developed to identify meat species by using laser-induced breakdown spectroscopy (LIBS). To improve the accuracy and stability of meat species identification, multiplicative scatter correction (MSC) was adopted to first pretreat the spectrum for correction of spectrum scatter. Then the corrected spectra were identified by using the K-nearest neighbor (KNN) model. The results showed that the identification rate improved from 94.17% to 100% and the prediction coefficient of variance (CV) decreased from 5.16% to 0.56%. This means that the accuracy and stability of meat species identification using MSC and LIBS simultaneously improved. In light of the findings, the proposed method can be a valuable tool for meat species identification using LIBS.