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

Research on improving the accuracy of laser-induced breakdown spectroscopy analysis by considering plasma attenuation rate characteristics

BACKGROUND

Laser-induced breakdown spectroscopy (LIBS) is widely applied in various fields, but accuracy issues limit its further development. Signal uncertainty is the main reason that affects the accuracy of LIBS measurements, but the signal uncertainty caused by different plasmas exhibiting different radiation attenuation rates during the integration time is often neglected. There is a need for a method to correct LIBS signals by quantifying the radiation attenuation rate.

RESULTS

In order to reduce the uncertainty due to different plasma attenuation rates, the attenuation rates of the energy level radiation emitted by plasma are described as attenuation coefficients, which are obtained by linearly fitting the logarithm of the time series of line intensities. The calibration curve was corrected by attenuation coefficients for 4 major elements in 7 standard samples. The results showed that the line intensities corrected by attenuation coefficients showed better linearity with elemental concentrations.

SIGNIFICANCE

This study is important for improving the accuracy of LIBS measurements, and is also significant for modeling the plasma radiative attenuation of laser-induced plasma, and is expected to be applied to spectrometers that can obtain time series spectra of the same plasma to improve the accuracy of in-situ fast LIBS analysis.

Visualization of laser-induced breakdown spectroscopy data of mouse organs based on the feature extraction method

At present, there is no comprehensive and systematic research on laser-induced breakdown spectroscopy (LIBS) data visualization. In particular, the LIBS spectra of biological samples have large noise and weak signals, which seriously affect the feature visualization. Here, three commonly used sample visualization methods were compared, and a new method was applied for tissue sample visualization. We used the LIBS mapping technique to obtain LIBS spectra of different organ slice samples from mice. LIBS spectral distribution was visualized after extracting the region of interest. The three spectral visualization methods are compared, and the performance of visualization algorithms is quantitatively analyzed. The potential of heat-diffusion for the affinity-based transition embedding (PHATE) method highlights the details of the LIBS spectral distribution while maintaining the overall structure of the data. The correlation coefficient between dimensionality reduction data and raw data is 0.97, and the average distance between samples of different categories is 0.64, which are superior to those of traditional principal component analysis (PCA), multidimensional scaling (MDS), and t-distributed stochastic neighbor embedding (t-SNE). The results show that the PHATE method can serve as a very promising LIBS spectral visualization tool.

Effects of water stress and fertilizer stress on maize growth and spectral identification of different stresses

Water stress and fertilizer stress have a significant impact on the growth and yield of maize. In order to improve the timeliness and accuracy of irrigation and fertilizer application, it is crucial to monitor water stress and fertilizer stress rapidly and accurately. This would help in conserving water and fertilizer resources and ensuring a stable maize yield. To this end, pot experiments were set up to explore the growth differences and photosynthetic properties of maize under water stress and fertilizer stress. The hyperspectral technology was used to construct the spectral indexes that can distinguish stress types, and the classification algorithm was combined to identify stress types. The research has shown that the plant height, basal diameter, leaf area, and photosynthetic properties of maize decreased with an increase in drought stress. However, rewatering could compensate for drought stress. Furthermore, fertilizer stress also affected water uptake by plants, and high nitrogen stress had a significant negative effect on the growth of maize plants. We employed a combination of spectral indexes and the support vector machine (SVM) classification algorithm in a stepwise manner to identify stress types. Using the training dataset, we constructed six classifiers for distinguishing stress types, including the SVM classifier, K-nearest neighbor (KNN) classifier, naive Bayes (NB) classifier, decision tree (DT) classifier, random forest (RF) classifier, and AdaBoost classifier. Our results showed that the RF and AdaBoost classifiers obtained stable results in stress type differentiation, achieving accurate identification of unstressed, water stressed, and fertilizer stressed maize plants. This is expected to provide a solid basis and reference for monitoring crop stress types in agricultural fields.

Identification of growth years for Puerariae Thomsonii Radix based on hyperspectral imaging technology and deep learning algorithm

Puerariae Thomsonii Radix (PTR) is not only widely used in disease prevention and treatment but is also an important raw material as a source of starch and other food. The growth years of PTR are closely related to its quality. The rapid and nondestructive identification of growth year is essential for the quality control of PTR and other traditional Chinese medicines. In this study, we proposed a convolutional neural network (CNN)-based classification framework in conjunction with hyperspectral imaging (HSI) technology for the rapid identification of the growth years of PTRs. Traditional treatment methods (i.e., multiplicative scatter correction, standard normal variate, and Savitzky-Golay smoothing) combined with machine learning algorithms (i.e., random forest, logistic regression, naive Bayes, and eXtreme gradient boost) were used as baseline models. Among them, the F1-score of CNN-based models based on PTRs' outer surfaces was over 90%, outperforming all the other baseline models. These results showed that it was feasible to use a deep learning algorithm in conjunction with HSI technology to identify the growth years of PTR. This method provides a fast, nondestructive, and simple method of identifying the growth years of PTR. It can be easily applied to other scenarios, such as for the identification of the locality or years of growth for other traditional Chinese herbs.

In-situ and fast classification of origins of Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy

In this Letter, a rapid origin classification device and method for Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy (LIBS) is proposed. The enhancement of spectral signal intensity and stability through auto-focus was investigated, as were different preprocessing methods, with area normalization (AN) achieving the best results-increasing by 7.74%-but unable to replace the improved spectral signal quality provided by auto-focus. A residual neural network (ResNet) was used as both a classifier and feature extractor, achieving higher classification accuracy than traditional machine learning methods. The effectiveness of auto-focus was elucidated by extracting LIBS features from the last pooling layer output using uniform manifold approximation and projection (UMAP). Our approach demonstrated that auto-focus could efficiently optimize the LIBS signal, providing broad prospects for rapid origin classification of traditional Chinese medicines.

Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging

This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (r _P) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSE_P) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato.

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.

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.

Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy

Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92-100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Application of Laser-Induced Breakdown Spectroscopy Coupled With Spectral Matrix and Convolutional Neural Network for Identifying Geographical Origins of Gentiana rigescens Franch

Accurate geographical origin identification is of great significance to ensure the quality of traditional Chinese medicine (TCM). Laser-induced breakdown spectroscopy (LIBS) was applied to achieve the fast geographical origin identification of wild Gentiana rigescens Franch (G. rigescens Franch). However, LIBS spectra with too many variables could increase the training time of models and reduce the discrimination accuracy. In order to solve the problems, we proposed two methods. One was reducing the number of variables through two consecutive variable selections. The other was transforming the spectrum into spectral matrix by spectrum segmentation and recombination. Combined with convolutional neural network (CNN), both methods could improve the accuracy of discrimination. For the underground parts of G. rigescens Franch, the optimal accuracy in the prediction set for the two methods was 92.19 and 94.01%, respectively. For the aerial parts, the two corresponding accuracies were the same with the value of 94.01%. Saliency map was used to explain the rationality of discriminant analysis by CNN combined with spectral matrix. The first method could provide some support for LIBS portable instrument development. The second method could offer some reference for the discriminant analysis of LIBS spectra with too many variables by the end-to-end learning of CNN. The present results demonstrated that LIBS combined with CNN was an effective tool to quickly identify the geographical origin of G. rigescens Franch.

Identification of Graves' ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

Diagnosis of the Graves' ophthalmology remains a significant challenge. We identified between Graves' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves' ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves' ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves' ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves' ophthalmology.

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.

Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices

It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (R_p) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10⁻⁶, and the limit of detection (LOD) is 2.3610 × 10⁻⁵ g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.

Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis

In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 h of germination were continuously collected at one-hour intervals. The optimal band was selected for the original and pre-processed spectra which were treated by multiple scatter correction (MSC) and the Savitzky-Golay first derivative (SG 1st). Five characteristic wavelength methods were compared: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable important in projection (VIP), and random frog (RF). Partial least square-discriminant analysis (PLS-DA) and K-nearest neighbor (KNN) built the vitality estimation model based on different data sets, and GA + PLS-DA constructed the optimal model with the highest accuracy. According to the weight coefficient and reflectance of the characteristic band extracted by the GA, the reflectance curves of different levels over time were plotted. The data of 0 h was employed to establish the vitality forecast model. The forecast model had a high recognition rate, with PLS-DA exceeding 99% and KNN exceeding 85%. This indicated that hyperspectral imaging of seed germination processes could achieve non-destructive estimation of Q. variabilis seed vitality, and accurate prediction in a shorter time is feasible.

Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

Half-life determination of inorganic-organic hybrid nanomaterials in mice using laser-induced breakdown spectroscopy

Inorganic or inorganic-organic hybrid nanomaterials have great potential for applications in the biomedical fields. Biological half-life is an essential pharmacokinetic parameter for these materials to function in vivo. Compared to inductively coupled plasma mass spectrometry (ICP-MS), which is the gold standard, laser-induced breakdown spectroscopy (LIBS) is a faster and more efficient elemental detection method. We investigated an efficient way to quantify the metabolic rate using LIBS. Nanoparticle platforms, such as manganese dioxide-bovine serum albumin (MnO2-BSA) or boehmite-bovine serum albumin (AlO(OH)-BSA) were injected into mice through intravenous administration for LIBS spectrum acquisition. First, the spectral background was corrected using the polynomial fitting method; The spectral interference was eliminated by Lorentz fitting for each LIBS spectrum simultaneously. The support vector regression (SVR) was then used for LIBS quantitative analyses. Finally, the LIBS results were compared with the ICP-MS ones. The half-lives of MnO2-BSA calculated by LIBS and ICP-MS were 2.49 and 2.42 h, respectively. For AlO(OH)-BSA, the half-lives detected by LIBS and ICP-MS were 3.46 and 3.57 h, respectively. The relative error of LIBS is within 5% compared to ICP-MS. The results demonstrate that LIBS is a valuable tool for quantifying the metabolic rates with a high degree of accuracy.

Comparative review and the recent progress in detection technologies of meat product adulteration

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

Current analytical methods for porcine identification in meat and meat products

Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.

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 (R_P ) of 0.9909, root-mean-square error of prediction set (RMSE_P ) of 0.0447 g kg^-1 and residual predictive deviation (RPD_P ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (B_W ) and a simplified model (PLSR-15) was established with R_P of 0.9769, RMSE_P of 0.0708 g kg^-1 and RPD_P 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.

Rapid Identification of Kudzu Powder of Different Origins Using Laser-Induced Breakdown Spectroscopy

The rapid identification of kudzu powder of different origins is of great significance for studying the authenticity identification of Chinese medicine. The feasibility of rapidly identifying kudzu powder origin was investigated based on laser-induced breakdown spectroscopy (LIBS) technology combined with chemometrics methods. The discriminant models based on the full spectrum include extreme learning machine (ELM), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN) and random forest (RF), and the accuracy of models was more than 99.00%. The prediction results of KNN and RF models were best: the accuracy of calibration and prediction sets of kudzu powder from different producing areas both reached 100%. The characteristic wavelengths were selected using principal component analysis (PCA) loadings. The accuracy of calibration set and the prediction set of discrimination models, based on characteristic wavelengths, is all higher than 98.00%. Random forest and KNN have the same excellent identification results, and the accuracy of calibration and prediction sets of kudzu powder from different producing areas reached 100%. Compared with the full spectrum discriminant analysis model, the discriminant analysis model based on the characteristic wavelength had almost the same discriminant effects, and the input variables were reduced by 99.92%. The results of this research show that the characteristic wavelength can be used instead of the LIBS full spectrum to quickly identify kudzu powder from different producing areas, which had the advantages of reducing input, simplifying the model, increasing the speed and improving the model effect. Therefore, LIBS technology is an effective method for rapid identification of kudzu powder from different habitats. This study provides a basis for LIBS to be applied in the genuineness and authenticity identification of Chinese medicine.

Spreading a water droplet through filter paper on the metal substrate for surface-enhanced laser-induced breakdown spectroscopy

To improve the quantitative analysis accuracy of an aqueous solution using surface-enhanced laser-induced breakdown spectroscopy (SENLIBS), the filter paper was used as a transmission medium by placing it onto the surface of a metallic substrate to make the microdroplet spreading more uniform in a fixed region of the substrate surface. The trace elements (Cu, Pb, Cd, and Cr) in an aqueous solution were detected successfully using this method. The results showed that the sample preparation repeatability of SENLIBS was noticeably improved with the aid of filter paper. Moreover, the limit of detection (LoD) values was similar to those without filter paper. Furthermore, the R² values were improved from 0.6192~0.9321 to 0.9481~0.9766, the RMSECV values were decreased from 0.53~1.95 μg/mL to 0.33~1.06 μg/mL, and the average relative error (ARE) values were decreased from 8.96~22.31% to 4.28~14.37% with the aid of filter paper. This demonstrated that the use of filter paper could improve the quantitative analysis accuracy of SENLIBS by increasing the sample preparation repeatability.

Continuous-wavelet-transform-based automatic curve fitting method for laser-induced breakdown spectroscopy

In this work, an automatic curve fitting method based on a continuous-wavelet transform (CWT) is proposed to resolve overlapped peaks and to adaptively extract the major peaks in laser-induced breakdown spectroscopy (LIBS). From the local minimum of the second derivative of the LIBS spectrum calculated with CWT, the number of individual peaks is determined, and corresponding peak positions are estimated. The full width at half-maximums (FWHMs) of individual peaks are estimated from the separation of two maxima siding the minimum. A threshold is introduced to eliminate the small peaks and therefore reduce the number of fitting parameters and adaptively extract the major peaks with different spectral intensities. The Trust-Region algorithm is used for parameter optimization. The proposed method is used to analyze both simulated LIBS spectra and experimental overlapped peaks. Both simulated and experimental results show that the proposed method can resolve overlapped peaks even with a low separation degree, although the minimum resolvable separation degree depends on the FWHM ratio and strength ratio of individual peaks and the wavelet scale. In a LIBS calibration experiment of N₂/SF₆ gasses mixture, after resolving the overlapped peaks with the proposed method, better linear correlations between the concentration and intensity of F (with an adjusted R-squared value 0.9972), as well as between the concentration ratio and intensity ratio of nitrogen to fluorine (with adjusted R-squared values >0.98 and 0.99) are obtained.