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.

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.

Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy.

Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near-infrared (Vis-NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis-NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis-NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis-NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real-time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes.

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.