1. Introduction
Panax notoginseng (
P. notoginseng) is a valuable medicinal plant in great demand. Its root, acknowledged as a medicinal part, is beneficial for blood circulation, blood stasis alleviation, detumescence, and pain alleviation in clinical practice [
1]. Modern pharmaceutical research hypothesizes that
P. notoginseng can also be used for the treatment of cardiovascular diseases, hypertension, and hyperlipidemia.
P. notoginseng is available as a dietary food supplement and healthcare product due to its bioactive compounds, such as saponins and flavonoids [
2]. The efficacy of
P. notoginseng is affected by toxic metal contamination, a topic of increasing interest because of the abundance of mineral resources in
P. notoginseng-cultivated soil. Cadmium (Cd) absorption and enrichment in
P. notoginseng is relatively strong, and Cd is more easily transferred to the ground [
3]. Cd contamination not only reduces the yield of
P. notoginseng and diminishes the accumulation of bioactive compounds [
4], but it also poses risks to environmental pollution and human health [
5]. Consequently, excessive Cd pollution has emerged as a major concern, highlighting the critical importance of Cd detection for ensuring the quality and safety of
P. notoginseng [
6,
7]. The regulated detection methods include atomic absorption spectrometry (AAS) [
8,
9] and inductively coupled plasma–mass spectrometry (ICP-MS) [
10,
11,
12]. These methods are widely recognized due to their good repeatability, low detection limit, and high accuracy. However, samples need to undergo processes such as digestion and dilution to meet the requirements of these instruments. Their shortcomings are also apparent, with complex pretreatments, high time costs, and the requirement of professional operators, meaning they cannot ensure intelligent and rapid detection with a short response time.
Laser-induced breakdown spectroscopy (LIBS), an atomic emission spectroscopy depending on plasma formation, has the advantages of no or minimal pretreatment, a rapid detection process, and a wide analytical range, and it can be used in multiple elements, long-distance transmission, and online detection. The emission lines of LIBS that characterize the substance’s features can be applied for qualitative and quantitative analysis in coal production [
13], agriculture [
14,
15], soil [
16], and so on. Cd detection using LIBS has been researched for use in lots of plants, such as cabbage [
17], herbs [
18,
19], lettuce [
20], and rice [
21]. The bottleneck that causes a relatively lower measurement precision and accuracy of LIBS toxic metal quantification is signal uncertainty, which hinders further development [
22]. The factors influencing uncertainty come from various aspects, including the matrix effect, the LIBS system, and the surrounding environment.
Research on reducing signal uncertainty comprises sample preparation, system setting, and data processing. Yang et al. [
23] proposed a solid–liquid–solid transformation method, with rice samples prepared by means of ultrasound-assisted extraction for Cd and Pb determination using LIBS. Wang et al. [
24] optimized the laser energy and delay time to obtain spectra and then built a multiple linear regression model for Pb and Cu detection in
Ligusticum wallichii with limits of detection of 15.7 and 6.3 μg/g, respectively. From the point of view of data processing, normalization [
25,
26] (based on the specific element [
27], background [
28], and peak area [
29], etc.), calibration-free LIBS (CF-LIBS) [
30,
31], and multivariate analysis [
32] have been used and have obtained improved results. Zhao et al. [
33] detected five metal elements in lily bulbs using partial least squares regression (PLSR) by combining various data preprocessing and selection methods to build the best-fitting model. In comparison, Su et al. [
34] adopted a framework that removed noise and low-intensity variables and then combined it with PLSR to simultaneously and quantitatively measure several toxic metals in
Sargassum fusiforme. Nonetheless, the preparation-modified method requires more operations, and the data-driven model, based on multiple spectra information, fails to achieve a reduction in signal uncertainty, because the multivariate model is only partially able to compensate for signal uncertainty. Matrix effects refer to differences in the physical (particle size and distribution) and chemical (composition of elements) properties of the samples, which affect the generation and evolution of plasma [
35]. Borduchi [
36] and Lei [
37] tried to reduce the matrix effects for soybean leaf and milk powder, respectively. CF-LIBS was employed in their studies, and this approach did not require the building of models when needed to meet specific conditions. The ablated crater can also reflect the laser ablation status and help researchers to understand the ablation process, so it is regarded as a plasma parameter, besides the line intensity and temperature [
38]. Regarding the research on the relationship between signal intensity and crater morphology [
39], energy optimization based on craters [
40] and LIBS signal enhancement interpretation [
41] has been carried out. Sun et al. [
42] corrected the LIBS signal with the ablation crater volume to improve the signal’s repeatability. The above attempts demonstrate the vital roles craters play in LIBS analysis. However, existing studies on crater analysis have typically been carried out under different system parameters or utilized standard metal samples, which enlarge crater discrepancies and minimize sample variance.
In our study,
P. notoginseng, sourced from various origins with diverse compositions, was used as the experimental material. The differences observed in the ablation craters prompted us to investigate the potential of combining LIBS raw signals and crater morphology to predict Cd concentrations. Following this, a simple framework known as the characteristic peak ratio correction (CPRC) method was proposed to refine the LIBS signals. Additionally, the crater–spectrum feature fusion method was employed to further enhance the analysis. As depicted in
Figure 1, we aimed to develop a more robust model by addressing signal uncertainties from two perspectives: ablation crater morphology compensation and signal ratio correction.
2. Materials and Methods
2.1. Sample Preparation
Six brands of
P. notoginseng powders were purchased from different sources at markets. Detailed information is shown in
Table 1. Implementation standards reflect the difference in processing methods before the products entered the markets for sale. The quality of
P. notoginseng powders varies due to differences in habitat, maturity, and processing methods, even though they are all derived from
P. notoginseng. This variation results in a matrix effect during analysis. We first prepared a 0.01 mol/L cadmium nitrate solution by dissolving cadmium nitrate tetrahydrate (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China), and then added different volumes to mix with 4 g of dried
P. notoginseng powder, creating Cd-contaminated samples. To ensure homogeneity in the mixture, we added deionized water to create a suspension of
P. notoginseng. After thorough stirring with a glass rod, the mixture was placed in an oven at 80 °C for 48 h to remove moisture.
The contaminated P. notoginseng powders were prepared at concentrations of 0, 0.5, 1, 10, 20, 30, 40, 50, 60, and 70 μg/g, resulting in ten processing levels. Then, 0.2 g contaminated powders were pressed into tablets with a diameter of 13 mm and thickness of 1 mm using a tablet machine at a pressure of 20 MPa for twenty seconds. Four samples were prepared for each concentration. Finally, 40 samples for each brand were collected. We randomly divided the 40 samples of each brand into four groups, labeled Group1, Group2, Group3, and Group4. Group1, with six brands, made up Dataset1. Dataset2, Dataset3, and Dataset4 were composed in the same way. Dataset1, Dataset2, and Dataset3 were used as the calibration set, and Dataset4 was used as the prediction set.
2.2. The Determination of Cd Reference Value
Considering the Cd concentration of the sample itself and the manipulation error, the Cd reference value was measured by means of ICP-MS (Agilent 7800ICPMS, Santa Clara, CA, USA) [
43]. Samples needed to be pretreated before testing. Here, 0.1 g of contaminated powder from each sample was weighed and put into a digestion tubes; 5 mL of 65% nitric acid was added into each digestion tube. Then, the digestion tubes were placed in the graphite digestion furnace at 110 °C. After the sample had been nearly digested, the lids of the digestion tubes were opened to add the acid. When the above operations were completed, the digestion solution was placed in a volumetric flask and diluted with deionized water to 25 mL. Then, 5 mL of filter solution was used for the determination of the Cd concentration. Similar steps were described by Geng et al. [
44]. The Cd reference value is shown in
Table 2.
2.3. Experimental Instruments
2.3.1. LIBS Instrumentation
A self-assembled LIBS system was employed for LIBS data acquisition. As shown in
Figure 2, a Q-switched Nd: YAG pulsed laser (Vlite-200, Beamtech Optronics, Beijing, China) generated the 532 nm laser with a pulse duration of 8 ns. The laser was focused on the samples using an optical reflection system. The electromagnetic signal of plasma radiation that was generated after the samples had been ablated was captured through a signal collector. The monochromator (SR-500i-A-R, Andor, Belfast, UK) was used to disperse light, and then an ICCD detector (iStar DH334T-18F-03, Andor, Belfast, UK) converted the optical signal into an electrical signal, presented in the computer. In this experiment, the LIBS spectra were collected in the range of 210-231 nm. A digital delay generator (DG645, Stanford Research Systems, Sunnyvale, CA, USA) was adopted to control the timings of lasers and ICCD detectors, and the laser frequency was set to 1 Hz. The LIBS system was detected via single-shot scanning. The sample was positioned on the X-Y-Z motorized stage (Zolix, Beijing, China), which was utilized to collect spectra at different sites. Before the experiment, several parameters were optimized, including a delay time of 2 μs, a gate width of 8 μs, and a laser pulse energy of 40 mJ. The laser ablation path was configured as a 2 × 3 array of craters. Each position underwent nine ablations, and the resulting spectra were averaged to minimize laser-induced point-to-point fluctuations. The laser beam was focused 2 mm below the sample surface to ensure stable signal acquisition [
45].
2.3.2. Ablation Crater Measurement
The shape measurement laser microscopy system, mainly consisting of a controller (VK-X1000, Keyence, Osaka, Japan) and measurement module (VK-X1050, Keyence, Osaka, Japan) and a base (VK-D1, Keyence, Osaka, Japan), was used to obtain the morphologies and parameters of craters. The VK-X1050 was equipped with a red semiconductor laser at a wavelength of 661 nm. The optical receiving elements included a 16-bit induction photomultiplier and an ultra-high fine color complementary metal oxide semiconductor (CMOS). The instrument found the samples’ focal lengths for each point by way of progressive scanning and pinhole conjugate focusing. The morphologies of different heights were obtained by physically moving the objective lens. The 3D morphology of the sample was recorded by means of longitudinal splicing.
2.4. Characteristic Peak Ratio Correction
We proposed a signal correction method that aimed at reducing the fluctuation of target emission lines, named the characteristic peak ratio correction method (CPRC). The specific steps of CPRC are as follows, and Cd was the target element.
(i) We obtained
n LIBS spectra matrices
X = [
x1, …,
xp]
n∗p (
p is the number of wavelengths, regarded as
p variables) and
n Cd reference value matrices
Y =
Cn. The characteristic peaks were Cd emission lines employed for analysis, referring to the National Institute of Standards and Technology (NIST) atomic spectral database. The
jth (
j = 1, 2, 3, …,
p) wavelength position was selected successively from
X, and the corresponding signal intensity was marked as
zj (
zj ∈
X). We then calculated the ratio of the characteristic peak intensity to
zj:
where
xN is the intensity of the characteristic peak, and
zj is each variable of the spectrum.
(ii) We then calculated the linear correlation coefficient (
r-value) between n
Bj and
Cn:
where
is the corrected intensity based on the
jth wavelength position of the
ith (
i = 1, 2, 3, …,
n) collection site;
yi is the Cd reference value of
ith collection site;
and
are the average values of signal intensity and Cd reference value, respectively.
The R-value was used as an evaluation index to find the highly correlated corrected intensity (
) [
46]. The higher the
r-value, the more effective
is. Each variable (
zj) that composes the LIBS spectrum participates in the trial (Equation (1)). The wavelength range of LIBS spectrum is composed of
p wavelength numbers; thus,
p calculated
r values are obtained and arranged in descending order. The variables corresponding to the top
r values are chosen as those that have a positive effect on the signal correction. The first
m (
m ≤ 10) variables of combination [
zi]
n are selected, and we here define these variables as matrix-related variables, as we thought these variables contributed to reducing the matrix effect, recorded as
Z = [
z1, …,
zm]
n. The mean of matrix-related variables is calculated as follows:
where
m is the number of matrix-related variables selected.
(iii) Output the corrected spectral matrix. The corrected spectral matrix is the ratio of
X to
:
where
x1, …,
xp are raw intensity values in the LIBS spectrum.
The approach to confirming
m was as follows. We introduced variables one by one in order of
r value from high to low. PLSR was performed based on the full spectrum that had been corrected. The RMSE of cross-validation is set as the evaluation index [
47]. A combination of variables corresponding to the minimum RMSE was selected.
Corrected characteristic peaks can be selected and extracted from the new spectral matrix according to the wavelengths that they lie in, which are used for Cd concentration analysis.
2.5. Performance Evaluation and Software
We had to use evaluation indexes to check the model’s feasibility. In this study, the calibration results were evaluated with the determination coefficient (
R2) and the root-mean-square error (RMSE). As the following formulas show, the closer
R2 is to 1, the better the model performs. A lower RMSE indicates a smaller deviation between the predicted and reference values.
Here,
and
are the predicted values and the reference value of the
i-th observation, respectively.
is the average of the true value of the
i-th observation.
n is the total number of observations.
The ablation crater’s profile was measured using MultiFileAnalyzer 2.2.0.93 (Keyence, Osaka, Japan). Data analysis in this study was performed in MATLAB 2019b (The MathWorks, Natick, MA, USA).
4. Conclusions
To tackle the challenge of signal uncertainty, especially in plant samples with complex compositions, we undertook a study to investigate the effectiveness of crater morphology compensation and signal intensity correction. Firstly, the crater parameters that characterize the states were carefully selected. By incorporating these crater parameters as input variables in MLR analysis, the RMSEP was reduced from 7.0233 μg/g to 5.4043 μg/g. This result proves that the crater morphology compensation method was conducive to constructing a model suitable for various categories of P. notoginseng. Secondly, the characteristic peak correction employed in data pretreatment was demonstrated to be effective for detecting Cd concentrations in P. notoginseng. The CPRC pretreatment exhibited an improved linear relationship between the reference value and the predicted value. Prediction results were obtained using the calibration curve model with an RMSEP of 3.4980 μg/g and using the PLSR model with RMSEP of 3.1889 μg/g. Thirdly, by integrating crater morphology compensation and the CPRC method, a crater–spectrum feature fusion method was proposed, which yielded satisfactory results in our study. This fusion method was applied to both linear and non-linear models, showing good practicality. The best result was derived when combining crater–spectrum feature fusion and the LSSVM model, with the lowest RMSEP of 2.8556 μg/g.
The proposed approaches are anticipated to advance the application of LIBS for toxic metal detection in plant samples. Crater morphology compensation, which accounts for variations in sample states, can help reduce deviations to a certain extent. The CPRC method is expected to gain widespread acceptance as a technique for preprocessing LIBS spectra in order to minimize signal variations, proving effective in both univariate and multivariate analyses. The crater–spectrum feature fusion method, being relatively straightforward and more precise, is suitable for use with portable LIBS instruments. Certainly, it is important to note that further improvements beyond our current study are necessary. Exploring the ability of the CPRC method to extend to multi-element synchronous correction is essential. The integration of crater morphology parameter calculation and the LIBS signal acquisition system could enhance the collection of more useful information for analysis. With ongoing developments in LIBS systems and data processing, the rapid and accurate detection of toxic metals in plant samples holds significant promise, ensuring the quality and safety of agricultural production.