In this study, a simple and accurate approach is proposed for enhancing the origin identification of raspberry samples using a combination of innovative Raman spectral preprocessing techniques, feature selection, and machine learning algorithms. Window function was creatively introduced and combined with baseline removal technique to preprocess the Raman spectral data, reducing the dimensionality of the raw data and ensuring the quality of the processed data. An optimization process was conducted to determine the optimal parameter for the window function, resulting in a binning window width of 5 that yielded the highest accuracy. After applying three feature selection techniques, it was found that the information gain model had the best performance in extracting discriminative spectral features. Finally, ten different machine learning algorithms were employed to construct predictive models, and the optimal models were selected. Linear Support Vector Classifier (LinearSVC), Multi-Layer Perceptron Classifier (MLPClassifier), and Linear Discriminant Analysis (LDA) achieve accuracy, precision, recall, and F1 values above 0.96, while the Random Vector Functional Link Network Classifier (RVFLClassifier) surpasses 0.93 for these performance metrics. These results demonstrate the effectiveness of the proposed approach in identifying the origin of raspberry samples with high accuracy and robustness, providing a valuable tool for agricultural product authentication and quality control.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

In this study, thirty-four samples of Laggera crispata (Vahl) Hepper & J. R. I. Wood from five main production areas in Yunnan Province, were collected for experimentation. UPLC- PDA was used to generate fingerprints and the common peaks were analysed through R and SIMCA-P. L. crispata from different origins can be distinguished by OPLS-DA and PCA. The VIP values were compared, and 8 characteristic components with great differences were obtained. It was confirmed that the two characteristic components were chrysosplenetin and artemisetin, and the quantitative analysis was performed with these two compounds from L. crispata samples with different origins. Based on the variance analysis results, the most significant difference in the content of chrysosplendin and artemisin was in Lancang and Honghe and Lancang and Simao, respectively. The chrysosplenetin can be used as an important indicator for quality control and to trace the origin of L. crispata.

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

The present study has successfully established a scientific and precise approach for distinguishing the geographical origins of young citrus fruits (Qingpi) from four primary production regions in China, using gas chromatography-mass spectrometry (GC-MS) and flash gas chromatography electronic nose (flash GC e-nose) to analyze the volatile composition and odor characteristics. Through the application of chemometric analysis, a clear differentiation among Qingpi samples was established using GC-MS. Additionally, the application of flash GC e-nose facilitated the extraction of flavor information, which enabled the discrimination of geographical origins. Several flavor components were identified as significant factors for origin certification. Furthermore, two pattern recognition algorithms were employed to achieve high accuracy in regional identification. The results of this investigation demonstrate that the amalgamation of multivariate chemometrics and algorithms can proficiently discern the sources of those young citrus fruits. The findings of this research can provide a reference for the assessment of quality control in food and other agricultural commodities in the times ahead.

In this study, we developed a convenient and easy-to-use origin identification method for antler velvets based on a simple DNA extraction technique and single-stranded tag hybridization chromatographic printed-array strip (STH-PAS). The primer sets used to detect Cervus elaphus, Rangifer tarandus, and 12S rRNA did not engage in non-specific reactions such as primer dimer formation. In both the triplex and singleplex assays, the sensitivity was < 1 ng DNA. Moreover, Cervus elaphus DNA could be detected in OTC crude drug products. Although the detection sensitivity resulting from the simplified extraction was slightly lower than that obtained with extraction by conventional methods, the amount of DNA was sufficient even from a small sample. The choice of a triplex or singleplex assay will depend on the purpose of the test. For example, if it is important to determine whether the antler velvet is derived from Cervus elaphus or Rangifer tarandus, a triplex assay is appropriate. If it is necessary to explore whether antler velvet from Cervus elaphus is included in an OTC crude drug product, a singleplex assay using the Cervus elaphus primer set is informative. If it is necessary to explore whether powdered antler velvet includes counterfeit products (from Rangifer tarandus), a singleplex assay employing the Rangifer tarandus primer is appropriate. The singleplex assay detects minor components even at a 1,000:1 ratio. Our study thus demonstrated the utility of a method combining simple DNA extraction with STH-PAS for efficient identification of the origin of antler velvets.

The disadvantages of the traditional one-dimensional convolution neural network (1D-CNN) model based on honeysuckle near-infrared spectral data (NIRS) include high parameter quantity, low efficiency, and inability to identify unknown categories effectively. In this paper, we propose a one-dimensional very deep convolution neural network (1D-VD-CNN) and design an auto-encoder mechanism for detecting honeysuckle from unexplored habitats. First, the 1D-VD-CNN model uses the efficient very deep (VD) structure to replace the hidden layer structure in the traditional 1D-CNN model. The model can be directly applied to analyze one-dimensional near-infrared spectral data (NIRS). Second, combining the reconstruction error of the auto-encoder, a honeysuckle identification method considering an unknown origin is designed, which can solve the problem of high confidence in convolution neural networks by using an auto-encoder and reconstruction errors of the samples to be tested. Whether the sample is an unknown variety can be determined by comparing the corrected confidence level with the preset threshold value. The results show that the accuracy of the 1D-VD-CNN training set and test set is 100%, and the loss value converges to 0.001. Compared with the traditional 1D-CNN model, the parameters and FLOPs are reduced by nearly 71% and 8%, respectively. At the same time, compared with the NIRS analysis and the PLS-DA method, the 1D-VD-CNN model has higher efficiency and better recognition performance for honeysuckle near-infrared spectral classification. Meanwhile, the accuracy rate of the auto-encoder for the category detection mechanism of honeysuckle from an unknown origin is 98%. The model can quickly and efficiently classify honeysuckle from different habitats and detect honeysuckle from unexplored habitats.

Chitosan is a natural polysaccharide which has been authorized for oenological practices for the treatment of musts and wines. This authorization is limited to chitosan of fungal origin while that of crustacean origin is prohibited. To guarantee its origin, a method based on the measurement of the stable isotope ratios (SIR) of carbon δ13C, nitrogen δ15N, oxygen δ18O and hydrogen δ2H of chitosan has been recently proposed without indicating the threshold authenticity limits of these parameters which, for the first time, were estimated in this paper. In addition, on part of the samples analysed through SIR, Fourier transform infrared spectrometry (FTIR) and thermogravimetric analysis (TGA) were performed as simple and rapid discrimination methods due to limited technological resources. Samples having δ13C values above -14.2‱ and below -125.1‱ can be considered as authentic fungal chitosan without needing to analyse other parameters. If the δ13C value falls between -25.1‱ and -24.9‱, it is necessary to proceed further with the evaluation of the parameter δ15N, which must be above +2.7‱. Samples having δ18O values lower than +25.3‱ can be considered as authentic fungal chitosan. The combination of maximum degradation temperatures (obtained using TGA) and peak areas of Amide I and NH2/Amide II (obtained using FTIR) also allows the discrimination between the two origins of the polysaccharide. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) based on TGA, FTIR and SIR data successfully distributed the tested samples into informative clusters. Therefore, we present the technologies described as part of a robust analytical strategy for the correct identification of chitosan samples from crustaceans or fungi.

BACKGROUND: Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted.
OBJECTIVE: The purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content.
METHODS: The P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level).
RESULTS: Four saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R2 ) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) range is 0.301-1.519.
CONCLUSIONS: This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.

Lonicerae Japonicae Flos(LJF), a bulk medicinal material, has long been used in clinical settings. The main/Dao-di production areas are Shandong, Henan, and Hebei. However, no systematic study on the difference in volatile components of LJF from different areas is available at the moment. In this study, gas chromatography-mass spectrometry(GC-MS) was used to detect the volatile components in 30 batches of LJF from 3 main production areas. Based on the relative odor activity value(ROAV), the key aroma components were analyzed. Multivariate statistical analysis was performed to analyze the differential components and characteristic aroma components in the samples from the 3 areas. Finally, 113 volatiles were identified from the samples, which were mainly alcohols, esters, acids, aldehydes, ketones, and alkenes. Among the common components of the three areas, linalool, myristic acid, and α-linolenic acid methyl ester had high content. A total of 15 key and 9 modifying aroma components in LJF were determined based on ROAV. The 15 differential components can be used for origin identification. Among them,(E, E)-2,4-decadienal and hexanal contributed a lot to the aroma of LJF from Henan and α-nerol was a characteristic aroma component of LJF in Hebei. In addition, lauryl aldehyde was a biomarker of LJF from Shandong. This study can provide a reference for the origin identification and quality evaluation of LJF.