Transient charge-driven 3D conformal printing via pulsed-plasma impingement.

Seamless integration of microstructures and circuits on three-dimensional (3D) complex surfaces is of significance and is catalyzing the emergence of many innovative 3D curvy electronic devices. However, patterning fine features on arbitrary 3D targets remains challenging. Here, we propose a facile charge-driven electrohydrodynamic 3D microprinting technique that allows micron- and even submicron-scale patterning of functional inks on a couple of 3D-shaped dielectrics via an atmospheric-pressure cold plasma jet. Relying on the transient charging of exposed sites arising from the weakly ionized gas jet, the specified charge is programmably deposited onto the surface as a virtual electrode with spatial and time spans of ~mm in diameter and ~µs in duration to generate a localized electric field accordantly. Therefore, inks with a wide range of viscosities can be directly drawn out from micro-orifices and deposited on both two-dimensional (2D) planar and 3D curved surfaces with a curvature radius down to ~1 mm and even on the inner wall of narrow cavities via localized electrostatic attraction, exhibiting a printing resolution of ~450 nm. In addition, several conformal electronic devices were successfully printed on 3D dielectric objects. Self-aligned 3D microprinting, with stacking layers up to 1400, is also achieved due to the electrified surfaces. This microplasma-induced printing technique exhibits great advantages such as ultrahigh resolution, excellent compatibility of inks and substrates, antigravity droplet dispersion, and omnidirectional printing on 3D freeform surfaces. It could provide a promising solution for intimately fabricating electronic devices on arbitrary 3D surfaces.

Plasma parameters correction method based on plasma image-spectrum fusion for matrix effect elimination in LIBS.

Matrix effect is one of the obstacles that hinders the rapid development of laser-induced breakdown spectroscopy (LIBS), and it is currently a hot, challenging, and focal point in research. To eliminate the matrix effect, this study proposed a plasma parameters correction method based on plasma image-spectrum fusion (PPC-PISF). This method corrects the total number density, plasma temperature, and electron number density variations caused by matrix effect using effective features in plasma images and spectra. To verify the feasibility of this method, experiments were conducted on pressed and metal samples, and the results were compared with those corrected by image-assisted LIBS (IA-LIBS). For the pressed samples, after correction by PPC-PISF, the R2 of the calibration curves all improved to above 0.993, the average root-mean-square error (RMSE) decreased by 41.05%, and the average relative error (ARE) decreased by 59.35% evenly in comparison to IA-LIBS. For the metal samples, after correction by PPC-PISF, the R2 of the calibration curves all increased to above 0.997. Additionally, the RMSE decreased by 29.63% evenly, the average ARE decreased by 38.74% compared to IA-LIBS. The experimental results indicate that this method is an effective method for eliminating the matrix effect, promoting the further development of LIBS in industrial detection.

Individual Micron-Sized Aerosol Qualitative Analysis-Combined Raman Spectroscopy and Laser-Induced Breakdown Spectroscopy by Optical Trapping in Air.

The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laser-induced breakdown spectroscopy (LIBS) in a single levitated micron aerosol. The findings of the spectroscopic imaging indicated that the particle plasma formed by a single particle ablation with a pulsed laser within 7 ns deviates from the trapped particle location. The LIBS acquisition field of view was expanded using the 19-bundle fiber, which also reduces the fluctuation of a single particle signal. In addition, gated Raman was utilized to suppress the fluorescence and increase the Raman signal-to-noise ratio. Based on this, Raman can measure hard-to-ionize substances with LIBS, such as sulfates. The LIBS radical can overcome the restriction that Raman cannot detect ionic chemicals like fluoride and chloride in halogens. To test the capability of directly identifying distinctive feature compounds utilizing spectra, we detected anions using Raman spectroscopy and cations using LIBS. Four typical mineral aerosols are subjected to precise qualitative evaluations (marble, gypsum, baking soda, and activated carbon adsorbed potassium bicarbonate). To further validate the application potential for substances with indistinctive feature discrimination, we employed machine learning algorithms to conduct a qualitative analysis of the coal aerosol from ten different origin regions. Three data fusion methodologies (early fusion, intermediate fusion, and late fusion) for Raman and LIBS are implemented, respectively. The accuracy of the late fusion model prediction using StackingClassifier is higher than that of the LIBS data (66.7%) and Raman data (86.1%) models, with an average accuracy of 90.6%. This research has the potential to provide online single aerosol analysis as well as technical assistance for aerosol monitoring and early warning.

Staging classification of omicron variant SARS-CoV-2 infection based on dual-spectrometer LIBS (DS-LIBS) combined with machine learning.

Effective differentiation of the infection stages of omicron can provide significant assistance in transmission control and treatment strategies. The combination of LIBS serum detection and machine learning methods, as a novel disease auxiliary diagnostic approach, has a high potential for rapid and accurate staging classification of Omicron infection. However, conventional single-spectrometer LIBS serum detection methods focus on detecting the spectra of major elements, while trace elements are more closely related to the progression of COVID-19. Here, we proposed a rapid analytical method with dual-spectrometer LIBS (DS-LIBS) assisted with machine learning to classify different infection stages of omicron. The DS-LIBS, including a broadband spectrometer and a narrowband spectrometer, enables synchronous collection of major and trace elemental spectra in serum, respectively. By employing the RF machine learning models, the classification accuracy using the spectra data collected from DS-LIBS can reach 0.92, compared to 0.84 and 0.73 when using spectra data collected from single-spectrometer LIBS. This significant improvement in classification accuracy highlights the efficacy of the DS-LIBS approach. Then, the performance of four different models, SVM, RF, IGBT, and ETree, is compared. ETree demonstrates the best, with cross-validation and test set accuracies of 0.94 and 0.93, respectively. Additionally, it achieves classification accuracies of 1.00, 0.92, 0.92, and 0.89 for the four stages B1-acute, B1-post, B2, and B3. Overall, the results demonstrate that DS-LIBS combined with the ETree machine learning model enables effective staging classification of omicron infection.

Line intensity calculation of laser-induced breakdown spectroscopy during plasma expansion in nonlocal thermodynamic equilibrium.

We present a new, to the best of our knowledge, simulation method for laser-induced breakdown spectroscopy during the plasma expansion phase in nonlocal thermodynamic equilibrium. Our method uses the particle-in-cell/Monte Carlo collision model to calculate dynamic processes and line intensity of nonequilibrium laser-induced plasma (LIP) in the afterglow phase. The effects of ambient gas pressure and type on LIP evolution are investigated. This simulation provides an added way to understand the nonequilibrium processes in more detail than the current fluid and collision radiation models. Our simulation results are compared with experimental and SimulatedLIBS package results and show good agreement.

Self-absorption correction method for one-point calibration laser-induced breakdown spectroscopy.

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem. This method uses an algorithm to correct the self-absorption and does not require the calculation of the self-absorption coefficient. To verify the effectiveness of this SAC method, Ti, V, and Al elements in two titanium alloys were determined by classical OPC-LIBS and OPC-LIBS with SAC. The average relative errors (AREs) of all elements in the two samples were decreased from 8.78% and 9.28% to 8.07% and 7.56%, respectively. The results demonstrated the effectiveness of this SAC method for OPC-LIBS.

Accuracy improvement of single-sample calibration laser-induced breakdown spectroscopy with self-absorption correction.

The single sample calibration laser-induced breakdown spectroscopy (SSC-LIBS) is quite suitable for the fields where the standard sample is hard to obtain, including space exploration, geology, archaeology, and jewelry identification. But in practice, the self-absorption effect of plasma destroys the linear relationship of spectral intensity and element concentration based on the Lomakin-Scherbe formula which is the guarantee of the high accuracy of the SSC-LIBS. Thus, the self-absorption effect limits the quantitative accuracy of SSC-LIBS greatly. In this work, an improved SSC-LIBS with self-absorption correction (SSC-LIBS with SAC) is proposed for the promotion of quantitative accuracy of SSC-LIBS. The SSC-LIBS with SAC can correct the intensity ratio of spectral lines in the calculation of SSC-LIBS through relative self-absorption coefficient K without complicated preparatory information. The alloy samples and pressed ore samples were used to verify the effect of the SSC-LIBS with SAC. Compared with SSC-LIBS, for alloy samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 0.83 wt.% and 13.75% to 0.40 wt.% and 4.06%, respectively. For the pressed ore samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 4.77 wt.% and 90.48% to 2.34 wt.% and 14.60%. The experimental result indicates that SSC-LIBS with SAC has a great improvement of quantitative accuracy and better universality compared with traditional SSC-LIBS, which is a mighty promotion of the wide application of SSC-LIBS.

Classification of steel using laser-induced breakdown spectroscopy combined with deep belief network.

The identification of steels is a crucial step in the process of recycling and reusing steel waste. Laser-induced breakdown spectroscopy (LIBS) coupled with machine learning is a convenient method to classify the types of materials. LIBS can generate characteristic spectra of various samples as input variable for steel classification in real time. However, the performance of classification model is limited to the complex input due to similar chemical composition in samples and nonlinearity problems between spectral intensities and elemental concentrations. In this study, we developed a method of LIBS coupled with deep belief network (DBN), which is suitable to deal with a nonlinear problem, to classify 13 brands of special steels. The performance of the training and validation sets were used as the standard to optimize the structure of DBN. For different input, such as the intensities of full-spectra signals and characteristic spectra lines, the accuracies of the optimized DBN model in the training, validation, and test set are all over 98%. Moreover, compared with the self-organizing maps, linear discriminant analysis (LDA), k-nearest neighbor (KNN) and back-propagation artificial neural networks (BPANN), the result of the test set showed that the optimized DBN model performed second best (98.46%) in all methods using characteristic spectra lines as input. The test accuracy of the DBN model could reach 100% and the maximum accuracy of other methods ranged from 62.31% to 96.16% using full-spectra signals as input. This study demonstrates that DBN can extract representative feature information from high-dimensional input, and that LIBS coupled with DBN has great potential for steel classification.

Indirect quantitative analysis of soluble solid content in citrus by the leaves using hyperspectral imaging combined with machine learning.

Due to the effect of bagging on fruit growth, non-destructive and in situ soluble solid content (SSC) in citrus detection remains a challenge. In this work, a new method for accurately quantifying SSC in citrus using hyperspectral imaging of citrus leaves was proposed. Sixty-five Ehime Kashi No. 28 citruses with surrounding leaves picked at two different times were picked for the experiment. Using the principal components analysis combined with Gaussian process regression model, the correlation coefficients of prediction-real value by citrus and its leaves in cross-validation were 0.972 and 0.986, respectively. In addition, the relationship between citrus leaves and SSC content was further explored, and the possible relationship between chlorophyll in leaves and SSC of citrus was analyzed. Comparing the quantitative analysis results by citrus and its leaves, the results show that the proposed method is a non-destructive and reliable method for determining the SSC by citrus leaves and has broad application prospects in indirect detection of citrus.

Laser ultrasonic improvement and its application in defect detection based on the composite coating method.

Herein, we studied the increasing tendency of photoacoustic (PA) conversion efficiency of the Au/polydimethylsiloxane (PDMS) composite. The thickness of the Au layer was optimized by modeling the PA process based on the Drude-Lorentz model and finite element analysis method, and corresponding results were verified. The results showed that the optimal Au thickness of the Au/PDMS composite was 35 nm. Finally, the Au/PDMS composites were coated onto the surface of aluminum alloys, which improved the thermoelastic laser ultrasonic (LU) signals to near 100 times. Besides, the defect mapping was performed by thermoelastic LU signals with Au/PDMS coating and ablation LU signals without coating; the Pearson correlation coefficient was higher than 0.95. The application in the defect detection in metal could provide guides for nondestructive detection on metals by laser ultrasound.

Blind-zone formation in laser shockwave nano-cleaning.

Laser shockwave cleaning (LSC) has attracted growing attention due to its advantages in non-contact, site-selective nanoparticle removal on microelectronic/optical devices. However, an uncleaned blind-zone formed directly under the laser-induced plasma kernel severely affects the cleaning effect. Laser shockwave cleaning of 300 nm polystyrene latex nanoparticles on silicon wafers is fully explored to understand the blind-zone formation mechanism. The size of the uncleaned blind-zone quickly increases from 0.84 to 19.50 mm2 associated with a growing fraction of the uncleaned blind-zone area within the whole cleaned area from 0.05 to 0.93 as the plasma-substrate gap distance is increased from 0.5 to 2 mm and the laser fluence is increased from 75 to 150 J/cm2. Besides, the variation of the blind-zone size is more strongly dependent on the plasma-substrate gap distance than the laser fluence. A time-resolved analysis of the laser-induced plasma evolution shows an inseparable relationship between the blind-zone and the geometric location of the plasma kernel. Theoretical analysis of the removal force in LSC based on the rolling mode reveals that the lack of dragging force acting on the nanoparticles in the region right under the plasma kernel impedes their removal and causes the uncleaned blind-zone formation.

FROZEN! Intracellular multi-electrolyte analysis measures millimolar lithium in mammalian cells.

Lithium salts are commonly used as medication for Bipolar Disorder (BD) and depression. However, there are limited methods to quantify intracellular lithium. Most methods to analyze intracellular electrolytes require tedious sample processing, specialized and often expensive machinery, sometimes involving harmful chemicals, and a bulk amount of the sample. In this work, we report a novel method (FROZEN!) based on cell isolation (from the surrounding medium) through rapid de-ionized water cleaning, followed by flash freezing for preservation. SKOV3 cells were cultured in normal medium and a medium containing 1.0 mM lithium. Lithium and other intracellular electrolytes in the isolated and preserved cells were simultaneously analyzed with laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). Key electrolytes such as sodium, potassium, and magnesium, along with lithium, were detectable at the single-cell level. We found that cells cultured in the lithium medium have an intracellular lithium concentration of 0.5 mM. Concurrently, the intracellular concentrations of other positively charged electrolytes (sodium, potassium, and magnesium) were reduced by the presence of lithium. FROZEN! will greatly facilitate research in intracellular electrolyte balance during drug treatment, or other physiological stresses. In particular, the cell isolation and preservation steps can be easily performed by many laboratories worldwide, after which the samples are sent to an analytical laboratory for electrolyte analysis.

Meat species identification accuracy improvement using sample set portioning based on joint x-y distance and laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was suitable for the identification of meat species due to fast and less sample preparation. However, the problem of low accuracy rate of the recognition model caused by improper selection of training set samples by random split has severely restricted the development of LIBS in meat detection. Sample set portioning based on the joint x-y distance (SPXY) method was applied for dividing the meat spectra into a training set and a test set. Then, the five kinds of meat samples (shrimp, chicken, beef, scallop, and pig liver) were classified by the support vector machine (SVM). With the random split method, Kennard-Stone method, and SPXY method, the recognition accuracies of the SVM model were 90.44%, 91.95%, and 94.35%, respectively. The multidimensional scaling method was used to visualize the results of the sample split for the interpretation of the classification. The results showed that the identification performance of the SPXY method combined with the SVM model was best, and the accuracy rates of shrimp, chicken, beef, scallop, and pig liver were 100.00%, 100.00%, 100.00%, 78.57%, and 92.00%, respectively. Moreover, to verify the broad adaptability of the SPXY method, the linear discriminant analysis model, the K-nearest neighbor model, and the ensemble learning model were applied as the meat species identification model. The results demonstrated that the accuracy rate of the classification model can be improved with the SPXY method. In light of the findings, the proposed sample portioning method can improve the accuracy rate of the recognition model using LIBS.

Self-absorption reduction in laser-induced breakdown spectroscopy using laser-stimulated absorption: publisher's note.

This publisher's note contains corrections to Opt. Lett.40, 5224 (2015).OPLEDP0146-959210.1364/OL.40.005224.

Investigation of the mechanism and influence of laser wavelength and energy on laser opto-ultrasonic dual detection.

Laser opto-ultrasonic dual (LOUD) detection, which uses laser irradiation of samples to generate spectral and ultrasonic signals simultaneously, can perform multimodal detection of element composition and structural property. As such, it has been applied to the detection of additive manufacturing (AM) components. Further, optimized parameters lead to better detection results. To the best of our knowledge, however, there is no study on the effect of laser properties on LOUD detection. Therefore, we studied the mechanism and influence of laser wavelength and energy on LOUD detection. In this work, the intensity, signal-to-noise ratio (SNR), and stability evolution of the laser excitation spectrum and ultrasonic signals at different wavelengths and energies were analyzed. It was found in the plasma evolution that high electron number density means a large amount of ablated mass generated, which was favorable for laser ultrasonic excitation and can produce higher SNR and a more stable signal. However, it also led to more atoms of the ground-state, which resulted in the self-absorption effect and reduced spectrum intensity in the spectrum analysis. Therefore, with self-absorption correction, better stability, and higher signal intensity, an SNR of spectral and ultrasonic signals can be obtained using 355 nm laser excitation at optimal energy. As a result, in the quantitative analysis of Cu and Si elements by LOUD detection, the determination coefficients (R2) were higher than 0.995, and the average relative errors were less than 2.5%, the limit of detection could reach the order of 100 ppm. Further, the defect size of 0.55 mm in the wire +arc additive manufacturing sample was detected by LOUD detection, and the average relative error was 5.59% compared with the digital radiography results, which indicate that laser wavelength and laser energy affect the intensity and stability of spectral and ultrasonic signals in LOUD detection, which means selecting appropriate laser parameters is important to obtain a high precision detection.

Stability improvement for dried droplet pretreatment by suppression of coffee ring effect using electrochemical anodized nanoporous tin dioxide substrate.

A novel nanoporous analytical platform is reported to improve the stability of the dried droplet method (DDM). This nanoporous platform was made of tin dioxide (Np SnO2) substrate by electrochemical anodization from tin (Sn) slide. The DDM is a widely used sample pretreatment in analytical chemistry that involves placing a droplet of solution onto the substrate and drying for analytical testing. However, during the droplet drying process, the solutes would converge at the droplet edge and cause inhomogeneous solutes distribution. This is the coffee ring effect (CRE). The Np SnO2 has irregular nanopores, which allows droplet solutions to penetrate into the substrate rather than spreading out, effectively suppressing CRE. Theoretical models were built to explain the formation of CRE on blank tin (Sn) substrate and suppression of CRE on Np SnO2. Better results were obtained in detecting lithium (Li) using the Np SnO2 by laser-induced breakdown spectroscopy (LIBS). The line scanning results indicated that the Li emission line (670.8 nm) intensities on Np SnO2 substrate had lower relative standard deviation (RSD = 3.3%) than those on Sn substrate (RSD = 31.5%), which illustrate suppression of CRE and stability improvement on Np SnO2 substrate. Furthermore, Li calibration curves were built for LIBS with DDM. The curve using Np SnO2 substrate had better linearity (R2 = 0.997), higher precision (RSD = 4.2%), and higher sensitivity (LOD = 0.13 mg/L) than that by Sn substrate (R2 = 0.954, RSD = 17%, and LOD = 1.21 mg/L). All in all, the anodic Np SnO2 substrate can suppress CRE in DDM and hence improve the stability and precision of subsequent analysis. Graphical abstract.

Determination of trace heavy metal elements in aqueous solution using surface-enhanced laser-induced breakdown spectroscopy.

Heavy metal pollution is one of the main problems in water pollution, which is harmful to humans. Surface-enhanced laser-induced breakdown spectroscopy (SENLIBS) has been applied to detect trace amounts of heavy metal elements in aqueous solution; however, it is still a big challenge to explore the relationship between the LIBS detection sensitivity and the substrate's physical properties. In this work, four typical substrates, zinc (Zn), magnesium alloy (Mg), nickel (Ni), and silicon (Si), were compared; and the mechanism of spectral enhancement by different substrates in SENLIBS was investigated. The results indicated that the limit of detection (LoD) of heavy metal elements on different substrates is positively proportional to the boiling of the substrate. That is mainly because a higher plasma excitation temperature and electron density are obtained, leading to more intense collision between particles. The signal enhancement is associated with the lower boiling point of the substrate (corresponding to a lower ablation threshold and higher ablation quantity from the substrate). As a result, the best LoD was 0.0011 mg/L for chromium (Cr) and 0.004 mg/L for lead (Pb) on an optimal Zn substrate, respectively. The LoDs were sufficiently low to meet the drinking water sanitation standard. These results showed that the detection sensitivity of heavy metal elements in aqueous solution can be improved by choosing a substrate with a lower boiling point in SENLIBS.

Investigation of the self-absorption effect using time-resolved laser-induced breakdown spectroscopy.

Self-absorption seriously affects the accuracy and stability of quantitative analysis in laser-induced breakdown spectroscopy (LIBS). To reduce the effect of self-absorption, we investigated the temporal evolution of the self-absorption effect by establishing exponential calibration curves. Meanwhile, the temporal evolution mechanism of the self-absorption effect was also investigated. The results indicated that self-absorption was weak at the early stage of plasma expansion. For determination of manganese (Mn) in steel, as an example, the concentration of upper bound of linearity (Cint) was 2.000 wt. % at the early stage of plasma expansion (in a time window of 0.2-0.4 µs)-much higher than 0.363 wt. % at a traditional optimization time window (2-3 µs). The accuracy and stability of quantitative analysis at the time window of 0.2-0.4 µs was also much better than at the time window of 2-3 µs. This work provides a simple method for improving quantitative analysis performance and avoiding the self-absorption effect in LIBS.

Multivariate quantitative analysis of metal elements in steel using laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS), an element-detection technology with the advantages of no sample preparation and in situ detection of metal samples, is suitable for the quantitative analysis of metal samples. However, severe spectral interference in the detection of metal samples makes the quantitative analysis difficult. Three quantitative analysis methods, including single-variable calibration, partial least squares regression (PLSR), and support vector regression (SVR), are used to conduct the quantitative analysis of four common metal elements (Manganese (Mn), Chromium (Cr), Vanadium (V), and Titanium (Ti)). The PLSR model adds interference spectrum lines to the model for linear modeling, while the SVR model adds interference spectrum lines to the model for nonlinear modeling. The quantitative analysis results of the nonlinear SVR model are the best. The R square (R2) values of Mn, Cr, V, and Ti are 0.993, 0.995, 0.990, and 0.992, respectively. The root-mean-squared errors of the prediction set of Mn, Cr, V, and Ti are 0.044, 0.045, 0.011, and 0.014, respectively. Therefore, the results of PLSR and SVR are better than the calibration curves of the spectral intensity and concentration due to the influence of multivariate factors. SVR has almost no element bias, while PLSR and the single-variable calibration model have different quantitative results due to the different degrees of influence on spectral lines. These results demonstrate that the combined influence of the spectral interference, background noise, and self-absorption can be suppressed by the nonlinear quantitative analysis model in the steel field using LIBS.

Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system.

BACKGROUND: Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-1000 nm) was developed in the present study. A Savitsky-Golay first derivative (SG1) transform was utilized to eliminate the constant spectral baseline offset. Then, the adulterated levels of rice samples were quantified by partial least squares regression (PLSR). RESULTS: A SG1-PLSR model based on full-wavelength was attained with a coefficient of determination of prediction set (RP ) of 0.9909, root-mean-square error of prediction set (RMSEP ) of 0.0447 g kg-1 and residual predictive deviation (RPDP ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (BW ) and a simplified model (PLSR-15) was established with RP of 0.9769, RMSEP of 0.0708 g kg-1 and RPDP of 3.49. Finally, two visualization maps produced by applying the optimal models (SG1-PLSR and PLSR-15) were used to visualize the adulterated levels of rice. CONCLUSION: These results demonstrate that VNIR hyperspectral imaging system is an effective tool for rapidly quantifying and visualizing the adulterated levels of rice. © 2019 Society of Chemical Industry.