Recent advances and applications of laser-based imaging techniques in food crops and products: a critical review.

To meet the growing demand for food quality and safety, there is a pressing need for fast and visible techniques to monitor the food crop and product production processing, and to understand the chemical changes that occur during these processes. Herein, the fundamental principles, instruments, and characteristics of three major laser-based imaging techniques (LBITs), namely, laser-induced breakdown spectroscopy, Raman spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry, are introduced. Additionally, the advances, challenges, and prospects for the application of LBITs in food crops and products are discussed. In recent years, LBITs have played a crucial role in mapping primary metabolites, secondary metabolites, nanoparticles, toxic metals, and mineral elements in food crops, as well as visualizing food adulteration, composition changes, pesticide residue, microbial contamination, and elements in food products. However, LBITs are still facing challenges in achieving accurate and sensitive quantification of compositions due to the complex sample matrix and minimal laser sampling quantity. Thus, further research is required to develop comprehensive data processing strategies and signal enhancement methods. With the continued development of imaging methods and equipment, LBITs have the potential to further explore chemical distribution mechanisms and ensure the safety and quality of food crops and products.

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.

Design and Optimization of Lower Limb Rehabilitation Exoskeleton with a Multiaxial Knee Joint.

To facilitate rehabilitation training for patients, we proposed the implementation of an anthropomorphic exoskeleton structure that incorporates a variable instantaneous center of rotation (ICR). This design considers the variability in knee ICR among individuals, resulting from the irregular form of the human knee joint, and leverages a double-degrees-of-freedom (2DOF) five-bar mechanism to adapt to these differences. The walking gait of the human lower limb and the corresponding knee ICR were measured and calculated using an optical 3D motion capture system. The optimal dimension parameters of the five-bar mechanism were then obtained through the optimization of human movement position inputs and rod length constraints to minimize the error in knee ICR, gait angle, and ankle trajectory between the human and the exoskeleton. Finally, we established an exoskeleton prototype to conduct relevant experimental tests. The experiment results showed that the average errors of knee ICR trajectory, hip angle, knee angle, and ankle trajectory were 5.52 × 10-4 m, 0.010 rad, 0.014 rad, and 1.57 × 10-3 m, respectively. The experimental results demonstrated that the exoskeleton's movement trajectory was close to the human's, reducing the human-mechanism interaction force and improving patient comfort during rehabilitation training.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy.

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches.

The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.

Agarose Film-Based Liquid-Solid Conversion for Heavy Metal Detection of Water Samples by Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) shows promising applications in the analysis of environmental heavy metals. However, direct analysis in water by LIBS faces the problems of droplet splashing and laser energy decay. In this study, a novel liquid-solid conversion method based on agarose films is proposed to provide an easy-to-operate and sensitive detection of heavy metals. First, the water samples were converted into semi-solid hydrogels with the aid of agarose and then dried into agarose films to make the signal intensities stronger. The calibration curves of Cd, Pb and Cr were constructed. The proposed method was validated by standard heavy metal solutions and real water samples. The results showed that the values of R2 were 0.990, 0.989 and 0.975, and the values of the LOD were 0.011, 0.122 and 0.118 mg L-1 for Cd (I) 228.80, Pb (I) 405.78 and Cr (I) 427.48 nm, respectively. The RMSEs of validation were 0.068 (Cd), 0.107 (Pb) and 0.112 mg·L-1 (Cr), and the recovery values were in the range of 91.2-107.9%. The agarose film-based liquid-solid conversion method achieved the desired ease of operation and sensitivity of LIBS in heavy-metal detection, thereby, showing good application prospects in heavy metal monitoring of water.

Chromium in soil detection using adaptive weighted normalization and linear weighted network framework for LIBS matrix effect reduction.

Rapid and accurate detection of agricultural soil chromium (Cr) is of great significance for soil pollution assessment. Laser-induced breakdown spectroscopy (LIBS) could serve as a rapid and chemical-free method for hazardous metal analysis compared with conventional chemical methods. However, the detection of LIBS is interfered by uncertainty and matrix effect. In this study, an average strategy combined with linear weighted network (LWNet) was proposed to reduce the uncertainty. Adaptive weighted normalization-LWNet (AWN-LWNet) framework was proposed to reduce the matrix effect in two soil types. The results indicated that LWNet outperformed traditional machine learning and achieved the average relative error (ARE) of 2.08 % and 3.03 % for yellow brown soil and lateritic red soil, respectively. Moreover, LWNet could effectively mine Cr feature peaks even under the low spectral resolution. AWN-LWNet was the optimal model compared with commonly used models to reduce matrix effect (ARE=4.12 %). Besides, AWN-LWNet greatly reduced the number (from 22016 to 72) of spectral variables for model input. By extracting Cr peaks from models, the difference of Cr peaks intensity could be intuitively observed, which served as spectral interpretation for matrix effect reduction. The two methods have the potential to realize the detection of hazardous metals in soil by LIBS.

Fast detection of minerals in rice leaves under chromium stress based on laser-induced breakdown spectroscopy.

Minerals in rice leaves is a crucial indicator of plant health, and their concentrations can be used to guide plant management. It is important to predict mineral content in contaminated rice rapidly. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to quantify minerals (Ca, Cu, Fe, K, Mg, Mn, and Na) in rice leaves under chromium (Cr) stress. Two feature extraction methods, including principal component analysis (PCA) and extreme gradient boosting (XGBoost), were compared to identify important variables that related to mineral concentrations. Results showed that partial least square regression (PLSR) achieved good performance in Ca, Fe Mg, K, Mn, and Na, with correlation coefficient of 0.9782, 0.8712, 0.8933, 0.9206, 0.9856, and 0.9865, root mean square error of 219.25, 14.78, 1192.47, 385.12, 9.56, and 124.32 mg/kg, respectively. In addition, the correlation between different spectral lines were further analyzed. Cr exhibited a positive correlation with Ca, Mg, and Na, and a negative correlation with Mn, Cu, and K. The proposed method provides a high-accuracy and fast approach for minerals prediction in rice leaves under Cr stress, which is important for environmental protection and food safety.

Research on Dynamic Measurement Method of Flow Rate in Tea Processing.

Tea flow rate is a key indicator in tea production and processing. Due to the small real-time flow of tea leaves on the production line, the noise caused by the transmission system is greater than or close to the real signal of tea leaves. This issue may affect the dynamic measurement accuracy of tea flow. Therefore, a variational mode decomposition combined with a wavelet threshold (VMD-WT) denoising method is proposed to improve the accuracy of tea flow measurement. The denoising method of the tea flow signal based on VMD-WT is established, and the results are compared with WT, VMD, empirical mode decomposition (EMD), and empirical mode decomposition combined with wavelet threshold (EMD-WT). In addition, the dynamic measurement of different tea flow in tea processing is carried out. The result shows that the main noise of tea flow measurement comes from mechanical vibration. The VMD-WT method can effectively remove the noise in the tea dynamic weighing signal, and the denoising performance is better than WT, VMD, EMD, and EMD-WT methods. The average cumulative measurement accuracy of the tea flow signal based on the VMD-WT algorithm is 0.88%, which is 55% higher than that before denoising. This study provides an effective method for dynamic and accurate measurement of tea flow and offers technical support for digital control of the tea processing.

Natural P-gp inhibitor EGCG improves the acteoside absorption in Caco-2 cell monolayers and increases the oral bioavailability of acteoside in rats.

Acteoside is one of the most widespread phenylethanoid glycosides with pharmacological activities, including antioxidant, neuroprotective property, etc. However, its bioavailability is poor due to the low absorption and P-gp efflux. This study aimed to select food derived P-gp inhibitors for promoting the acteoside absorption and investigate whether the inhibitors could increase the bioavailability and stability of acteoside. Results showed that EGCG and quercetin significantly decreased the BL-to-AP efflux and promoted the AP-to-BL influx of acteoside across Caco-2 monolayers with optimum concentrations of 320 µM EGCG or 240 µM quercetin adding to 320 µM acteoside. EGCG increased the bioavailability of acteoside to 1.43-fold, but quercetin had no such effect. Further study showed that EGCG and quercetin had no effects on the storage and digestion stability of acteoside. This work revealed that EGCG could improve the acteoside absorption across the Caco-2 monolayers and enhance the bioavailability of acteoside in rats.

Fast Classification of Geographical Origins of Honey Based on Laser-Induced Breakdown Spectroscopy and Multivariate Analysis.

Traceability of honey is highly required by consumers and food administration with the consideration of food safety and quality. In this study, a technique named laser-induced breakdown spectroscopy (LIBS) was used to fast trace geographical origins of acacia honey and multi-floral honey. LIBS emissions from elements of Mg, Ca, Na, and K had significant differences among different geographical origins. The clusters of honey from different geographical origins were visualized with principal component analysis. In addition, support vector machine (SVM) and linear discrimination analysis (LDA) were used to quantitively classify the origins. The results indicated that SVM performed better than LDA, and the discriminant results of multi-floral honey were better than acacia honey. The accuracy and mean average precision for multi-floral honey were 99.7% and 99.7%, respectively. This study provided a fast approach for geographical origin classification, and might be helpful for food traceability.

Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods.

Honey adulteration is a major issue in food production, which may reduce the effective components in honey and have a detrimental effect on human health. Herein, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two common types of adulteration, including mixing acacia honey with high fructose corn syrup (HFCS) and rape honey, were quantified with univariate analysis and partial least squares regression (PLSR). In addition, the variable importance was tested with univariable analysis and feature selection methods (genetic algorithm (GA), variable importance in projection (VIP), selectivity ratio (SR)). The results indicated that emissions from Mg II 279.58, 280.30 nm, Mg I 285.25 nm, Ca II 393.37, 396.89 nm, Ca I 422.70 nm, Na I 589.03, 589.64 nm, and K I 766.57, 769.97 nm had compact relationship with adulterant content. Best models for detecting the adulteration ratio of HFCS 55, HFCS 90, and rape honey were achieved by SR-PLSR, VIP-PLSR, and VIP-PLSR, with root-mean-square error (RMSE) of 8.9%, 8.2%, and 4.8%, respectively. This study provided a fast and simple approach for detecting honey adulteration.

Fast visualization of distribution of chromium in rice leaves by re-heating dual-pulse laser-induced breakdown spectroscopy and chemometric methods.

Knowledge of distribution of toxic metal in crop is essential for studying toxic metal uptake, transportation and bioaccumulation, and it is important for environmental pollution monitoring. In this study, the macro spatial distribution of chromium in rice leaves was visualized by re-heating dual-pulse laser-induced breakdown spectroscopy (DPLIBS) and chemometric methods. After the optimization of two important parameters (delay time and energy ratio) in DPLIBS, chromium prediction model was established based on global spectra. The global model achieved acceptable performance while slight overfitting for model was found because of numerous irrelevant variables. Feature variables including emissions from chromium and other elements were successfully selected by the values of regression coefficient in partial least square regression model. Best performance was achieved by using the feature variables and support vector machine, with correlation coefficient of prediction of 0.959, root mean square error of prediction of 13.4 mg/kg and residual predictive deviation of 3.6. Finally, the distribution of chromium in rice leaves was visualized with the best prediction model. The distribution image showed that chromium distributed approximately symmetrically along the vein and was likely to be accumulated in leaf apex. The preliminary results provide an approach for investigating the macro spatial distribution of elements in crops, which is important for environmental protection and food safety.

High-accuracy and fast determination of chromium content in rice leaves based on collinear dual-pulse laser-induced breakdown spectroscopy and chemometric methods.

Dual-pulse laser-induced breakdown spectroscopy (DPLIBS) and chemometric methods were used to predict chromium content in rice leaves, along with the purpose for increasing the detection sensitivity and accuracy. The influence of important parameters in DPLIBS were investigated and optimized. Then, partial least square (PLS) was used to establish chromium content prediction models, and the value of regression coefficient based on PLS was applied to determine feature variables. In addition, multivariate and univariate analysis were used to verify the modeling performance of selected feature variables. The results indicated that support vector machine model based on feature variables achieved the best performance, with correlation coefficient of 0.9946, root mean square error of 4.85 mg/kg and residual predictive deviation of 9.70 in prediction set. The proposed method provides a high-accuracy and fast approach for chromium content prediction in rice leaves, which could potentially be used for toxic and nutrient elements detection in food.

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.

High-Sensitivity Determination of Nutrient Elements in Panax notoginseng by Laser-induced Breakdown Spectroscopy and Chemometric Methods.

High-accuracy and fast detection of nutritive elements in traditional Chinese medicine Panax notoginseng (PN) is beneficial for providing useful assessment of the healthy alimentation and pharmaceutical value of PN herbs. Laser-induced breakdown spectroscopy (LIBS) was applied for high-accuracy and fast quantitative detection of six nutritive elements in PN samples from eight producing areas. More than 20,000 LIBS spectral variables were obtained to show elemental differences in PN samples. Univariate and multivariate calibrations were used to analyze the quantitative relationship between spectral variables and elements. Multivariate calibration based on full spectra and selected variables by the least absolute shrinkage and selection operator (Lasso) weights was used to compare the prediction ability of the partial least-squares regression (PLS), least-squares support vector machines (LS-SVM), and Lasso models. More than 90 emission lines for elements in PN were found and located. Univariate analysis was negatively interfered by matrix effects. For potassium, calcium, magnesium, zinc, and boron, LS-SVM models based on the selected variables obtained the best prediction performance with Rp values of 0.9546, 0.9176, 0.9412, 0.9665, and 0.9569 and root mean squared error of prediction (RMSEP) of 0.7704 mg/g, 0.0712 mg/g, 0.1000 mg/g, 0.0012 mg/g, and 0.0008 mg/g, respectively. For iron, the Lasso model based on full spectra obtained the best result with an Rp value of 0.9348 and RMSEP of 0.0726 mg/g. The results indicated that the LIBS technique coupled with proper multivariate chemometrics could be an accurate and fast method in the determination of PN nutritive elements for traditional Chinese medicine management and pharmaceutical analysis.

Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil.

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

Rapid Determination of Cadmium Contamination in Lettuce Using Laser-Induced Breakdown Spectroscopy.

Quick access to cadmium (Cd) contamination in lettuce is important to supervise the leafy vegetable growth environment and market. This study aims to apply laser-induced breakdown spectroscopy (LIBS) technology for fast determination of Cd content and diagnosis of the Cd contamination degree in lettuce. Emission lines Cd II 214.44 nm, Cd II 226.50 nm, and Cd I 228.80 nm were selected to establish the univariate analysis model. Multivariate analysis including partial least squares (PLS) regression, was used to establish Cd content calibration models, and PLS model based on 22 variables selected by genetic algorithm (GA) obtained the best performance with correlation coefficient in the prediction set Rp² = 0.9716, limit of detection (LOD) = 1.7 mg/kg. K-Nearest Neighbors (KNN) and random forest (RF) were used to analyze Cd contamination degree, and RF model obtained the correct classification rate of 100% in prediction set. The preliminary results indicate LIBS coupled with chemometrics could be used as a fast, efficient and low-cost method to assess Cd contamination in the vegetable industry.

Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions.

Rapid detection of Cd content in soil is beneficial to the prevention of soil heavy metal pollution. In this study, we aimed at exploring the rapid quantitative detection ability of laser- induced breakdown spectroscopy (LIBS) under the conditions of air and Ar for Cd in soil, and finding a fast and accurate method for quantitative detection of heavy metal elements in soil. Spectral intensity of Cd and system performance under air and Ar conditions were analyzed and compared. The univariate model and multivariate models of partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM) of Cd under the air and Ar conditions were built, and the LS-SVM model under the Ar condition obtained the best performance. In addition, the principle of influence of Ar on LIBS detection was investigated by analyzing the three-dimensional profile of the ablation crater. The overall results indicated that LIBS combined with LS-SVM under the Ar condition could be a useful tool for the accurate quantitative detection of Cd in soil and could provide reference for environmental monitoring.

Quantitative Analysis of Cadmium in Tobacco Roots Using Laser-Induced Breakdown Spectroscopy With Variable Index and Chemometrics.

The study investigated some new developed variable indices and chemometrics for the fast detection of cadmium (Cd) in tobacco root samples by laser-induced breakdown spectroscopy. The variables selection methods of interval partial least squares (iPLS), backward interval partial least squares (BiPLS), and successive projections algorithm (SPA) were used to locate the optimal Cd emission line for univariate analysis and to select the maximal relevant variables for multivariate analysis. iPLS and BiPLS located 10 Cd emission lines to establish univariate analysis models. Univariate analysis model based on Cd I (508.58 nm) performed best with the coefficient of determination of prediction (Rp 2) of 0.9426 and root mean square error of prediction (RMSEP) of 1.060 mg g-1. We developed two new variable indices to remove negative effects for Cd content prediction, including Index1 = (I 508.58 + I 361.05)/2 × I 466.23 and Index2 = I 508.58/I 466.23 based on Cd emission lines at 508.58, 361.05, and 466.23 nm. Univariate model based on Index2 obtained better result (Rp 2 of 0.9502 and RMSEP of 0.988 mg g-1) than univariate analysis based on the best Cd emission line at 508.58 nm. PLS and support vector machines (SVM) were adopted and compared for multivariate analysis. The results of multivariate analysis outperformed univariate analysis and the best quantitative model was achieved by the iPLS-SVM model (Rc 2 of 0.9820, RMSECV of 0.214 mg g-1, Rp 2 of 0.9759, and RMSEP of 0.712 mg g-1) using the maximal relevant variables in the range of 474-526 nm. The results indicated that LIBS coupled with new developed variable index and chemometrics could provide a feasible, effective, and economical approach for fast detecting Cd in tobacco roots.