Laser-induced breakdown spectroscopy (LIBS) for rapid analysis of ash, potassium and magnesium in gluten free flours

Gluten-containing flours and gluten-free flours as a source of calcium, magnesium, iron and zinc

Wheat flour is widely used in Poland for the preparation of bread, pasta and other foods. Due to the increasing number of people diagnosed with diet-related diseases, consumer awareness of health-promoting issues and interest in gluten-free products (GFP). There is a dynamic development of the market for these foods with high quality and nutritional value and minerals that benefit human health and prevent deficiencies in patients on a gluten-free diet. The aim of this study was to determine the content of minerals: Ca, Fe, Mg and Zn in flours using the ICP-OES method. The mineral composition of selected GF flours available on the Polish market was analysed. It was tested how they supplement the mineral requirements compared to gluten-containing flours. It was found that these products can be a valuable source of essential minerals, which are often in short supply, especially in patients with gastrointestinal disorders. As our study has shown, flours from the GFP group are a good source of essential minerals, especially in the case of chia and flax flours, as well as buckwheat, amaranth, quinoa, lupin or almonds flours.

Laser-induced breakdown spectroscopy (LIBS) for the detection of exogenous contamination of metal elements in lily bulbs

Natural products with the underground edible part have the risk of excessive heavy metals due to the influence of the growing environment. In this study, the content of five metal elements in lily bulbs was detected by laser-induced breakdown spectroscopy (LIBS). In view of the mutual interference among elements, multivariable analysis models were established to effectively eliminate the interference. The partial least squares regression (PLSR) multivariate analysis model was evaluated by combining different data preprocessing with variable selection methods to achieve the best fit. The results show that the best regression model for Cu, Pb, Zn, Al, and Mg content achieved the coefficients determination of prediction (R_p²) values of 0.9920, 0.9737, 0.9835, 0.9723 and 0.9939, respectively, and root mean square error of prediction (RMSEP) values of 3.2386 mg/kg, 5.8559 mg/kg, 4.6334 mg/kg, 6.0073 mg/kg and 2.8103 mg/kg, respectively. Comprehensively comparing the accuracy, robustness, and number of variables of each model, it can be found that the PLSR model on the least absolute shrinkage and selection operator (LASSO) achieved good results in the quantitative prediction model of three kinds of metal elements. This indicates the superiority of the LASSO-PLSR algorithm framework and confirms the feasibility of LIBS technology for the detection of various metal elements in natural products.

Analysis of corn and sorghum flour mixtures using laser-induced breakdown spectroscopy

BACKGROUND

In a world constantly challenged by climate change, corn and sorghum are two important grains because of their high productivity and adaptability, and their multifunctional use for different purposes such as human food, animal feed, and feedstock for many industrial products and biofuels. Corn and sorghum can be utilized interchangeably in certain applications; one grain may be preferred over the other for several reasons. The determination of the composition corn and sorghum flour mixtures may be necessary for economic, regulatory, environmental, functional, or nutritional reasons.

RESULTS

Laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics, was used for the classification of flour samples based on the LIBS spectra of flour types and mixtures using partial least squares discriminant analysis (PLS-DA) and the determination of the sorghum ratio in sorghum / corn flour mixture based on their elemental composition using partial least squares (PLS) regression. Laser-induced breakdown spectroscopy with PLS-DA successfully identified the samples as either pure corn, pure sorghum, or corn-sorghum mixtures. Moreover, the addition of various levels of sorghum flour to mixtures of corn-sorghum flour were used for PLS analysis. The coefficient of determination values of calibration and validation PLS models are 0.979 and 0.965, respectively. The limit of detection of the PLS models is 4.36%.

CONCLUSION

This study offers a rapid method for the determination of the sorghum level in corn-sorghum flour mixtures and the classification of flour samples with high accuracy, a short analysis time, and no requirement for time-consuming sample preparation procedures. © 2020 Society of Chemical Industry.

Ash analyses of bio-coal briquettes produced using blended binder

The behaviour of ash of fuel affects its thermal efficiency when in use. The ash analyses of bio-coal briquettes developed from lean grade coal and torrefied woody biomass have received limited intensive study. Therefore, the present study aims at analysing the ashes of briquette made from lean grade coal and torrefied woody biomass using blended coal tar pitch and molasses as the binder. Bio-coal briquettes were produced from coal and torrefied biomass in various hybrid ratios. Ashing of various briquettes was done in a muffle furnace at 850 °C for 3 h. Mineral phases of the ash were identified using an X-ray Diffractometer (XRD), while the mineral oxides were obtained using an X-ray Fluorescence Spectrometer. The AFT700 Furnace was used with its AFT700 software to evaluate the ash fusion temperatures of the ashes. The XRD patterns look similar, and quartz was found to be the dominant mineral phase present in the raw coal and bio-coal briquettes. The SiO₂ (57-58%), Al₂O₃ (19-21%), and Fe₂O₃ (8-9%) were the major oxides observed in the ashes. The final fusion temperatures of the ashes range from 1300-1350 °C. The compositions of the ashes of the bio-coal briquettes are classified as detrital minerals. It was concluded that the addition of torrefied biomass (≤ [Formula: see text]and blended binder ([Formula: see text] 15%) to coal gave a negligible impact on the ashes of the resultant bio-coal briquettes.

Elemental analysis of fish feed by laser-induced breakdown spectroscopy

In this study, the potential of laser-induced breakdown spectroscopy (LIBS) as an efficient multi-elemental quantification tool for fish feed is determined. A particular focus of this paper is total chromium, an essential element that has the potential to be toxic and carcinogenic. In total six elements, four macro-elements (Ca, Fe, K and Mg) and two micro-elements (Cr and Rb), were modelled using LIBS spectra of aquafeed samples. Reference analysis was conducted via inductively coupled plasma mass spectrometry (ICP-MS) and showed good agreement with LIBS predictions. These results provide evidence that LIBS has the potential to be utilized in the field as a real-time screening tool for establishing the elemental composition of a range of fish feeds.

Quantitative multiple-element simultaneous analysis of seaweed fertilizer by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy, coupled with advanced chemometric methods, was used to quantitate multiple elements in a seaweed-based fertilizer. The influence of important parameters was determined using partial least squares regression (PLSR), support vector regression (SVR) and random forest (RF) optimizations. Optimal results for Mg, K and P were obtained using PLSR, whereas RF yielded the best results for Mn, Cu, Sr and Ca. The best predictions for Ba levels were obtained with SVR. The lowest root mean square errors in the prediction sets for Mn, Cu, Sr, Ba, Mg, K, P and Ca were 48.27 µg/g, 36.90 µg/g, 0.37 mg/g, 40.32 µg/g, 1.99 mg/g, 2.03 mg/g, 4.81 mg/g and 14.08 mg/g, respectively, with average relative standard deviations of 13.65%, 2.68%, 19.80%, 5.17%, 3.32%, 2.98%, 1.82% and 5.81%. The results showed that the optimal multivariate model depended on the specific element being analyzed. The proposed method provides a rapid means of determining multielement concentrations in seaweed-based fertilizers.

Investigation into the Effect of Increasing Target Temperature and the Size of Cavity Confinements on Laser-Induced Plasmas

In this work, the effect of the sample temperature on the magnesium (Mg) and titanium (Ti) plasmas generated by a Q-switched Neodymium-doped Yttrium Aluminum Garnet (Nd:YAG) laser operating at its fundamental wavelength of 1064 nm has been investigated. We observed that increasing the sample temperature significantly enhanced the emission intensities of the plasmas. Comparing the emission peak intensities of the case of 100 °C to the case of 300 °C, we recorded a substantial enhancement of the peak intensities of the latter compared to the former. From these results it can be observed that increasing the sample temperature has a significant effect on the emission intensities of the plasmas. We also studied the plasma dynamics and found that increasing the sample temperature also decreases the air density around the Mg sample surface. The reduction in the air density resulted in a decrease in the radiation process and lowers collision probability. Furthermore, as the plasma expands, the plasma pressure also decreases. In addition, we also employed circular and square cavities to confine the titanium plasma, and investigated the effect of the sizes of the circular and square cavities on the titanium plasma. We observed a general improvement in the emission intensities with both the circular and square cavities and attributed this improvement to the plasma compression effect of the shock waves produced by the plasma within the cavities.

Quantification of Ash and Moisture in Wheat Flour by Raman Spectroscopy

Wheat flour is widely used on an industrial scale in baked goods, pasta, food concentrates, and confectionaries. Ash content and moisture can serve as important indicators of the wheat flour’s quality and use, but the routinely applied assessment methods are laborious. Partial least squares regression models, obtained using Raman spectra of flour samples and the results of reference gravimetric analysis, allow for fast and reliable determination of ash and moisture in wheat flour, with relative standard errors of prediction of the order of 2%. Analogous calibration models that enable quantification of carbon, oxygen, sulfur, and nitrogen, and hence protein, in the analyzed flours, with relative standard errors of prediction equal to 0.1, 0.3, 3.3, and 1.4%, respectively, were built combining the results of elemental analysis and Raman spectra.

Accurate quantification of alkalinity of sintered ore by random forest model based on PCA and variable importance (PCA-VI-RF)

The alkalinity of sintered ore has an important impact on the quality, output, and energy consumption of blast furnace smelting, and there is an urgent need for a method for accurate quantifying of the alkalinity of sintered ore. The present work explores the combination of the laser-induced breakdown spectroscopy (LIBS) technique and random forest (RF) based on principal component analysis (PCA) and variable importance for the quantitative analysis of the alkalinity of sintered ore. Sixteen sintered ore samples were used in this study, and the characteristic lines of LIBS spectra for sintered ore samples can be identified based on the National Institute of Standards and Technology (NIST) database. At first, abnormal spectra are identified and rejected by PCA coupled with Mahalanobis distance (MD). Then, the input variable for the RF calibration model is optimized according to the variable importance threshold obtained by the RF model, and two RF model parameters of ${{n}_{\rm tree}}$n_tree and ${{m}_{\rm try}}$m_try are determined by out-of-bag estimate. Finally, the PCA-VI-RF model is built under the optimal model parameters. In order to verify the predictive ability of the quantitative model, the PCA-VI-RF model prediction results were compared with the RF model, partial least-squares model, and least-squares support vector machine model. The result demonstrated that PCA-VI-RF shows better analytical performance than other methods. Compared with the RF model with the original spectrum as input, the averaged relative errors of test results decreased from 5.82% to 3.94%, coefficients of determination (${R^2}$R²) of the test set increased from 0.8957 to 0.9814, and the root mean square error decreased from 0.1502% to 0.0860%. The speed of modeling and prediction has also been greatly improved, and the modeling time was reduced from 4675.56 to 16.86 s. The stability of the PCA-VI-RF model was verified by the relative standard deviation (RSD) of the test data prediction results, and the RSD reached below 4.74%. This study shows LIBS combining PCA-VI-RF is an effective method for accurate quantification of the alkalinity of sintered ore. It has great significance for the potential application of real-time online analysis of the alkalinity of sintered ore.

Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model

In the quantitative analysis of spectral data, small sample size and high dimensionality of spectral variables often lead to poor accuracy of a calibration model. We proposed two methods, namely sample consensus and unsupervised variable consensus models, in order to solve the problem of poor accuracy. Three public near-infrared (NIR) or infrared (IR) spectroscopy data from corn, wine, and soil were used to build the partial least squares regression (PLSR) model. Then, Monte Carlo sampling and unsupervised variable clustering methods of a self-organizing map were coupled with the consensus modeling strategy to establish the multiple sub-models. Finally, sample consensus and unsupervised variable consensus models were obtained by assigning the weights to each PLSR sub-model. The calculated results show that both sample consensus and unsupervised variable consensus models can significantly improve the accuracy of the calibration model compared to the single PLSR model. The effectiveness of these two methods points out a new approach to achieve a further accurate result, which can take full advantage of the sample information and valid variable information.

Extending the spectral database of laser-induced breakdown spectroscopy with generative adversarial nets

As a famous spectroscopy method for substance detection and classification, laser-induced breakdown spectroscopy (LIBS) is not a nondestructive detection method. Considering the precious samples and the experimental environment, sometimes it is difficult to get enough spectra to build the classification model, which is important for qualitative analysis. In this paper, a spectral generation method for extending the spectral database of LIBS is proposed based on generative adversarial nets (GAN). After enough interactive training, the generated spectra looked very similar to the experimental spectra. Evaluated with unsupervised clustering methods PCA and K-means, the generated spectra could not be distinguished from the real spectra. For each type of sample, most of the simulated spectra and experimental spectra were clustered into the same class, which meant the proposed method was effective to extend the spectral database. Using the spectral database extended by this method as training set data to build the SVM model, the results showed that when there were only a few experimental spectra, the combination of the generated spectra and the experimental spectra for building the classification model could achieve better identification results.