Fast Detection of Heavy Metal Content in Fritillaria thunbergii by Laser-Induced Breakdown Spectroscopy with PSO-BP and SSA-BP Analysis.

Fast detection of heavy metals is important to ensure the quality and safety of herbal medicines. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to detect the heavy metal content (Cd, Cu, and Pb) in Fritillaria thunbergii. Quantitative prediction models were established using a back-propagation neural network (BPNN) optimized using the particle swarm optimization (PSO) algorithm and sparrow search algorithm (SSA), called PSO-BP and SSA-BP, respectively. The results revealed that the BPNN models optimized by PSO and SSA had better accuracy than the BPNN model without optimization. The performance evaluation metrics of the PSO-BP and SSA-BP models were similar. However, the SSA-BP model had two advantages: it was faster and had higher prediction accuracy at low concentrations. For the three heavy metals Cd, Cu and Pb, the prediction correlation coefficient (Rp2) values for the SSA-BP model were 0.972, 0.991 and 0.956; the prediction root mean square error (RMSEP) values were 5.553, 7.810 and 12.906 mg/kg; and the prediction relative percent deviation (RPD) values were 6.04, 10.34 and 4.94, respectively. Therefore, LIBS could be considered a constructive tool for the quantification of Cd, Cu and Pb contents in Fritillaria thunbergii.

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics.

Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in Fritillaria thunbergii. A total of three machine learning algorithms were utilized, including a gradient boosting machine (GBM), partial least squares regression (PLSR) and support vector regression (SVR). Three promising wavelength selection methods were evaluated for comparison, namely, a competitive adaptive reweighted sampling method (CARS), a random frog method (RF), and an uninformative variable elimination method (UVE). Compared to full wavelengths, the selected wavelengths produced excellent results. Overall, RC2, RV2, RP2, RSMEC, RSMEV and RSMEP for the selected variables are as follows: 0.9967, 0.8899, 0.9403, 1.9853 mg kg-1, 11.3934 mg kg-1, 8.5354 mg kg-1; 0.9933, 0.9316, 0.9665, 5.9332 mg kg-1, 18.3779 mg kg-1, 11.9356 mg kg-1; 0.9992, 0.9736, 0.9686, 1.6707 mg kg-1, 10.2323 mg kg-1, 10.1224 mg kg-1 were obtained for Cd Cu and Pb, respectively. Experimental results showed that all three methods could perform variable selection effectively, with GBM-UVE for Cd, SVR-RF for Pb, and GBM-CARS for Cu providing the best results. The results of the study suggest that LIBS coupled with wavelength selection can be used to detect heavy metals rapidly and accurately in Fritillaria by extracting only a few variables that contain useful information and eliminating non-informative variables.

Chemometric Approach Based on Explainable AI for Rapid Assessment of Macronutrients in Different Organic Fertilizers Using Fusion Spectra.

Wet chemical methods are usually employed in the analysis of macronutrients such as Potassium (K) and Phosphorus (P) and followed by traditional sensor techniques, including inductively coupled plasma optical emission spectrometry (ICP OES), flame atomic absorption spectrometry (FAAS), graphite furnace atomic absorption spectrometry (GF AAS), and inductively coupled plasma mass spectrometry (ICP-MS). Although these procedures have been established for many years, they are costly, time-consuming, and challenging to follow. This study studied the combination of laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (Vis-NIR) for the quick detection of PK in different varieties of organic fertilizers. Explainable AI (XAI) through Shapley additive explanation values computation (Shap values) was used to extract the valuable features of both sensors. The characteristic variables from different spectroscopic devices were combined to form the spectra fusion. Then, PK was determined using Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Extremely Randomized Trees (Extratrees) models. The computation of the coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD) showed that FUSION was more efficient in detecting P (R2p = 0.9946, RMSEp = 0.0649% and RPD = 13.26) and K (R2p = 0.9976, RMSEp = 0.0508% and RPD = 20.28) than single-sensor detection. The outcomes indicated that the features extracted by XAI and the data fusion of LIBS and Vis-NIR could improve the prediction of PK in different varieties of organic fertilizers.

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii.

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.

Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review.

Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). A comprehensive overview of recent advances in LIBS is presented, along with its future trends, viewpoints, and challenges. Besides reviewing its applications in both FMPs, it is intended to provide a concise description of the use of LIBS and chemometrics for the detection of FMPs, rather than a detailed description of the fundamentals of the technique, which others have already discussed. Finally, LIBS, like conventional approaches, has some limitations. However, it is a promising technique that may be employed as a routine analysis technique for FMPs when utilized effectively.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques.

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

Potential of Vis-NIR to measure heavy metals in different varieties of organic-fertilizers using Boruta and deep belief network.

The quick identification of heavy metals is of major importance and is beneficial for controlling the fertilizer production process in the fertilizer industries. This work aimed to use visible and near-infrared spectroscopy (Vis-NIR), Boruta, and deep learning to establish rapid heavy metals screening methods. Boruta algorithm was used to extract appropriate wavelengths, and a deep belief network (DBN) was computed to determine the amounts of various heavy metals such as chromium (Cr), cadmium (Cd), lead (Pb), and mercury (Hg) for both the entire and selected wavelengths. To assess the model, coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD) were used to calculate the reliability of the model. The results of the selected wavelengths were excellent and much higher than the full wavelengths with R2p = 0.96, RMSEP = 0.2017 mg kg-1 and RPDpred = 5.0 for Cr; R2p = 0.91, RMSEP = 0.2832 mg kg-1 and RPDpred = 3.4 for Pb; R2p = 0.90, RMSEP = 0.2992 mg kg-1, and RPDpred = 3.3 for Hg. Descent prediction was obtained also for Cd (R2p = 0.87, RMSEP = 0.3435 mg kg-1, and RPDpred = 2.7). To further assess the robustness of the DBN, it was compared with conventional machine learning methods such as support vector machine for regression (SVR), k nearest neighbor (KNN), and partial least squares (PLS). The overall results indicated that the Vis-NIR technique coupled with Boruta and DBN could be reliable and accurate for screening heavy metals in organic fertilizers.

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR.

Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R2) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R2p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R2p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer.