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Geographical authenticity evaluation of Mentha haplocalyx by LIBS coupled with multivariate analyses
摘要： Mentha haplocalyx (mint) is a significant traditional Chinese medicine (TCM) listed in the Catalogue of “Medicinal and Food Homology”, therefore, its geographical origins (GOs) are critical to the medicinal and food value. Laser-induced breakdown spectroscopy (LIBS) is an advanced analytical technique for GOs certification, due to the fast multi-elemental analysis requiring minimal sample pretreatment. In this study, LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyses. The spectral data was analyzed by two chemometric algorithms, i.e. principal component analysis (PCA) and least squares support vector machines (LS-SVM). Specifically, the performance of LS-SVM with linear kernel and radial basis function (RBF) kernel was explored in sensitivity and robustness tests. Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance (a little inferior) in robustness test. Generally, LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel. The result indicated the potential for future applications in herbs and food, especially for in situ GOs applications of TCM authenticity rapidly.
关键词： laser-induced breakdown spectroscopy (LIBS),geographical origin,herb authenticity,least squares support vector machines,Mentha haplocalyx
更新于2025-09-23 15:19:57
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Fast Classification of Geographical Origins of Honey Based on Laser-Induced Breakdown Spectroscopy and Multivariate Analysis
摘要： 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.
关键词： geographical origin,classification,honey,laser-induced breakdown spectroscopy,multivariate analysis
更新于2025-09-23 15:19:57
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Tracing the Geographical Origin of Lentils (Lens culinaris Medik.) by Infrared Spectroscopy and Chemometrics
摘要： The feasibility of applying the infrared spectroscopy for the geographical origin traceability of lentils from two different countries (Italy and Canada) was investigated. In particular, lentil samples were analyzed by Fourier transform near- and mid-infrared (FT-NIR and FT-MIR) spectroscopy and then discriminated by applying supervised models, i.e., linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA). To avoid LDA overfitting, two variable strategies were adopted, i.e., a variable reduction by principal component analysis and a variable compression by wavelet packet transform algorithm. FT-MIR models were more discriminating compared to FT-NIR ones with prediction abilities ranging from 98 to 100% and from 91 to 100% for cross- and external validation, respectively. The combination of the FT-MIR and FT-NIR data did not improve the model performances. These findings demonstrated the suitability of the FT-MIR spectroscopy, in combination with supervised pattern recognition techniques, to successfully classify lentils according to their geographical origin.
关键词： Lentils,FT-NIR spectroscopy,FT-MIR spectroscopy,Partial least squares discriminant analysis,Geographical origin,Linear discriminant analysis
更新于2025-09-04 15:30:14