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Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To explore the feasibility of using stable carbon and oxygen isotopes and mineral elements in small-scale regional wine production identification, the fractionation characteristics of stable isotopes of carbon and oxygen in wine of Matheran single wine from different sub-producing areas at the eastern foot of Helan Mountain in Ningxia were studied. Methods: Forty-five samples of Massellan single wine from 5 sub-producing areas of Hongsipu, Qingtongxia, Yongning, Helan, and Zhenbeipu were selected to analyze the mineral element contents and the values of δ13C and δ18O. The fisher linear discriminant analysis method was used to establish a wine region discriminant model based on stable isotopes and mineral elements. Result: Carbon and oxygen stable isotopes exhibited significant fractionation during wine fermentation, with a total δ13C>ethanol δ13C>glycerol δ13C, and the three showed a certain degree of homology. The order of the δ18O size was grape juice δ18O>wine δ18O>water δ18O. As、B、K、Li、Mn、Ni、Pb、Rb、Sb、Sr、Ti and Cs showed significant differences between some production areas (P0.05). The accuracy rate of origin discrimination based on stable carbon and oxygen isotopes was only 40%, while the accuracy rates based on mineral elements and carbon and oxygen stable isotopes combined with mineral elements were both 95.6%. Conclusion: Mineral elements can distinguish wine samples from different sub-producing areas effectively. Stable carbon and oxygen isotopes cannot be used to identify the producing areas of wine from different small-scale regions.

This study examines the acceptance of young Chinese consumers for different "Fuji" apples, focusing on their sensory characteristics and physicochemical foundations. The sensory attributes of the samples were evaluated using a combination of static descriptive analysis (DA) and temporal check‐all‐that‐apply fading (TCATA‐fading) methods. Furthermore, the volatile compounds, soluble sugars, organic acids, and textural parameters of the samples were analyzed. The findings revealed that participants favored apples that were perceived as "sweet," "crunchy," "juicy," and "aromatic." The results from the DA indicated that certain sensory attributes, such as "sweet," "vanilla," "honey," and "pear" positively influenced acceptance, while attributes like "sour," "hard," and "grass" had a negative impact. The findings from both the DA and TCATA‐fading methods were consistent with each other. In terms of dynamic evaluation, sweetness, and sourness were the initial perceptions, followed by a range of other flavors. Notably, our data suggested that sweetness perception could be enhanced by attributes such as "honey" and "banana." Additionally, the sugar‐acid ratio and specific volatile compounds, including hexanal, (E)‐2‐hexenal, β‐damascenone, butyl acetate, and propyl 2‐methylbutyrate, were found to influence the perception of sweetness in apples. Practical Application: This study helps to understand the effect of different origins on the acceptance of "Fuji" apples and to know the sensory and material basis for the emergence of such differences. It is beneficial for growers and marketers to improve 'Fuji' apples. [ABSTRACT FROM AUTHOR]

Objective: To explore the feasibility of using stable carbon and oxygen isotopes and mineral elements in smallscale regional wine production identification, the fractionation characteristics of stable isotopes of carbon and oxygen in wine of Matheran single wine from different sub-producing areas at the eastern foot of Helan Mountain in Ningxia were studied. Methods: Forty-five samples of Massellan single wine from 5 sub-producing areas of Hongsipu, Qingtongxia, Yongning, Helan, and Zhenbeipu were selected to analyze the mineral element contents and the values of δ13C and δ18O. The fisher linear discriminant analysis method was used to establish a wine region discriminant model based on stable isotopes and mineral elements. Result: Carbon and oxygen stable isotopes exhibited significant fractionation during wine fermentation, with a total δ13C>ethanol δ13C>glycerol δ13C, and the three showed a certain degree of homology. The order of the δ18O size was grape juice δ18O>wine δ18O>water δ18O. As, B, K, Li, Mn, Ni, Pb, Rb, Sb, Sr, Ti and Cs showed significant differences between some production areas (P<0.05). There was no significant difference in the distribution of δ13C, δ18O, Ba, Ca and other 13 mineral elements among different regions (P>0.05). The accuracy rate of origin discrimination based on stable carbon and oxygen isotopes was only 40%, while the accuracy rates based on mineral elements and carbon and oxygen stable isotopes combined with mineral elements were both 95.6%. Conclusion: Mineral elements can distinguish wine samples from different sub-producing areas effectively. Stable carbon and oxygen isotopes cannot be used to identify the producing areas of wine from different small-scale regions. [ABSTRACT FROM AUTHOR]

'Black pearl' fresh corns from different regions were analyzed using an electronic nose to capture the aroma profile. Principal component analysis (PCA) and discriminant function analysis (DFA) were used for multivariate statistical analysis of 200 data from two regions. Based on this, the judgment model of samples from Heilongjiang production area was built using a soft independent modeling class analysis (SIMCA) algorithm, and a back propagation neural network model was established by Pytorch software to identify and differentiate samples from different regions. The results illustrated that, although the volatile flavor of 'black pearl' fresh corns from different origins were similar, it also showed obvious origin characteristics. SIMCA model could effectively distinguish whether unknown samples come from Heilongjiang (the accuracy rate was 97%), while BP neural network model could predict and identify the origin of 'black pearl' fresh corns from unknown production areas, and the average accuracy rate was 99.44%. The combination of electronic nose technology and BP neural network model could accurately distinguish and identify the origin of 'black pearl' fresh corns.

Hirudo is a medicinal and edible homologous animal. Its rich polypeptides have been proven to have strong biological activity and effects on human health or disease. However, the quality control of hirudo can still be improved. Based on the traditional scientific understanding of oral hirudo administration, this study adopted artificial gastric‐juice extraction combined with pepsin enzymolysis to simulate the digestion and absorption of gastrointestinal tract after taking hirudo. Ultrahigh‐performance liquid chromatography–mass spectrometry technology was initially used to screen and characterize 52 enzymolysis components of hirudo. The fingerprint of hirudo enzymolysis polypeptide was then established. The method was confirmed to have high accuracy, repeatability, and stability. Using the established hirudo enzymolysis polypeptide fingerprint, we proved that it can effectively identify different origins of hirudo. The established fingerprint revealed that 19 enzymolysis polypeptides derived from hirudo in the Qizhi capsule, indicating that it can promote and improve the quality control of Qizhi capsule. The present study provided a new technology and idea for the origin identification and quality control of hirudo, and standards for Chinese patent medicines containing hirudo. It can also serve as a reference for the quality control of other animal‐origin drugs or foods. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

'Black pearl' fresh corns from different regions were analyzed using an electronic nose to capture the aroma profile. Principal component analysis (PCA) and discriminant function analysis (DFA) were used for multivariate statistical analysis of 200 data from two regions. Based on this, the judgment model of samples from Heilongjiang production area was built using a soft independent modeling class analysis (SIMCA) algorithm, and a back propagation neural network model was established by Pytorch software to identify and differentiate samples from different regions. The results illustrated that, although the volatile flavor of 'black pearl' fresh corns from different origins were similar, it also showed obvious origin characteristics. SIMCA model could effectively distinguish whether unknown samples come from Heilongjiang (the accuracy rate was 97%), while BP neural network model could predict and identify the origin of 'black pearl' fresh corns from unknown production areas, and the average accuracy rate was 99.44%. The combination of electronic nose technology and BP neural network model could accurately distinguish and identify the origin of 'black pearl' fresh corns. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The geographical origin was one of the important factors affecting the quality of hawthorn. To discriminate the geographical origin of hawthorn rapidly and nondestructively, hawthorns from five different provincial production areas were used as samples, and visible-shortwave infrared (410~2500 nm) band hyperspectral data were obtained for the pedicel face (G), side (C), and bottom (D) of each sample by using a near-infrared hyperspectral imaging system. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and random forests (RF) classification models were built by multivariate scattering correction (MSC), first derivative (D1), SG smoothing (Savitzky-Golay, SG), and standard normal transform (SNV) four preprocessing methods. The results showed that the D-D1-SVM model discriminated optimally, with 100% accuracy in both the training and prediction sets. To simplify the model, successive projections algorithm (SPA) and competitive adaptive reweighted sampling algorithm (CARS) were applied to select feature wavelength. The multivariate data analysis revealed that the D-SPA-SVM model had the best performance, with an accuracy of 95.2% and 93% for the training and prediction sets, respectively. This study could provide technical support for rapid and non-destructive identification of hawthorn origin.

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. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traceability of agri-food products refers to the ability to trace the history of a product through records related to identification data, such as the origin of the materials, the history of processing and the distribution and location of the product after delivery. The purpose of this paper is to highlight the importance of traceability systems in the Romanian agri-food sector and provides an overview of the project "Strengthening the capacity of the Ministry of Agriculture and Rural Development to develop specific policies and regulations in order to implement a national strategic system for the traceability and integrity of agri-food products". The results were concluded based on bibliographic study, analysis and interpretation of relevant databases and evaluation of the activities performed during the project. Through the activities carried out within the project, the aim is to achieve the following results: a package of public policy proposals, active fund of the simplified legislation, regulations to reduce the administrative burden, procedures for reducing bureaucracy in citizenpublic institutions interaction, transfer of knowledge and creation of new skills for MARD in order to manage public policies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: 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. Objectives: 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. Materials and 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. Conclusion: This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng. The purpose of this study 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. This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng. [ABSTRACT FROM AUTHOR]

The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis (A. sinensis) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a method for recognizing the origin of Saposhnikovia divaricata using the IResNet model to achieve computer vision-based classification. Firstly, we created a small sample dataset and applied data augmentation techniques to enhance its diversity. After that, we introduced the hierarchical residual connection block in the early stage of the original model to expand the perceptual field of the neural network and enhance the extraction of scale features. Meanwhile, a depth-separable convolution operation was adopted in the later stage of the model to replace the conventional convolution operation and further reduce the time cost of the model. The experimental results demonstrate that the improved network model achieved a 5.03% improvement in accuracy compared to the original model while also significantly reducing the number of parameters required for the model. In our experiments, we compared the accuracy of the proposed model with several classical convolutional neural network models, including ResNet50, Resnest50, Res2net50, RepVggNet_B0, and ConvNext_T. The results showed that our proposed model achieved an accuracy of 93.72%, which outperformed ResNet50 (86.68%), Resnest50 (89.38%), Res2net50 (91.83%), RepVggNet_B0 (88.68%), and ConvNext_T (92.18%). Furthermore, our proposed model achieved the highest accuracy among the compared models, with a transmission frame rate of 158.9 fps and an inference time of only 6.29 ms. The research methodology employed in this study has demonstrated the ability to reduce potential errors caused by manual observation, effectively improving the recognition ability of Saposhnikovia divaricata based on existing data. Furthermore, the findings of this study provide valuable reference and support for future efforts to develop lightweight models in this area. [ABSTRACT FROM AUTHOR]

Tomato cultivation in China is concentrated in Xinjiang, Henan, and Shandong provinces, and the quality of tomatoes produced in different regions shows huge differences. Xinjiang tomatoes have extraordinary nutritional compositions and sensory qualities, and counterfeit Xinjiang tomato products are proliferating in the market. This study aimed to investigate the feasibility of identifying the geographical origin of Chinese tomatoes by using long-wave Fourier-Transform near-infrared spectroscopy (FT-NIR, 10,000–4000 cm−1). First, principal component analysis (PCA) was conducted on the raw spectra, and it was found that the first two PCs effectively identified the geographical origin of the tomatoes. Meanwhile, the results of partial least squares discriminant analysis (PLS-DA) combined with different preprocessing methods showed that the PLS-DA model based on the raw spectra achieved the best performance. The optimal PLS-DA model achieved the correct classification rate (CCR) of 97.8% in an external prediction set, and showed that raw spectra contained sufficient valid information. Besides, six algorithms, grid search (GS), genetic algorithm (GA), particle swarm algorithm (PSO), grey wolf algorithm (GWO), improved grey wolf algorithm (IGWO), and sparrow search algorithm (SSA) were employed to optimize the parameters of support vector machine (SVM). The SSA-SVM model exhibited the best performance with a CCR of 97.8% in the prediction set. Afterward, 40 and 8 spectral features were extracted from the raw full spectra using stacked autoencoder (SAE) and PC loading, respectively. Finally, the qualitative analysis model based on the feature variables was further investigated, and the SAE-SSA-SVM simplified model showed the best performance with a CCR of 98.7%, 95.6%, and 95.6% in the calibration, cross-validation, and prediction set, respectively. The results of this study provide theoretical support for applying long-wave FT-NIRS combined with machine learning and deep learning to tomato origin identification. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Proposed method combines Raman preprocessing, feature selection, ML for origin identification. • Innovative Raman spectral preprocessing techniques improve data quality and reduce dimensionality. • Optimized window function with binning width of 5 achieves highest accuracy in preprocessing. • Information gain feature selection extracts discriminative spectral features effectively. • LinearSVC, MLPClassifier, LDA, and RVFLClassifier provide robust performances. 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. [ABSTRACT FROM AUTHOR]

Origin identification of the earliest cases during the pandemic is crucial in containing the transmission of the disease. The high infectiousness of the disease during its incubation period (no symptom yet) and underlying human interaction pattern make it difficult to capture the entire line of the spread. The hidden spreading period is when the disease is silently spreading, for the “silent spreaders” showing no symptoms yet can transmit the infection. Being uncertain of the hidden spreading period would bring a severe challenge to the contact tracing mission. To find the possible hidden spreading period span, we utilized the SEITR (susceptible–exposed–infected–tested positive–recovered) model on networks where the relation between E state and T state can implicitly model the hidden spreading mechanism. We calibrated the model with real local resurgence epidemic data. Through our study, we found that the hidden spreading period span of the possible earliest case of local resurgence could vary according to the people interaction networks. Our modeling results showed the clustering and shortcuts that exist in the human interaction network significantly affect the results in finding the hidden spreading period span. Our study can be a guide for understanding the pandemic and for contact tracing the origin of local resurgence.

In order to establish an accurate and efficient model for geographical origin identification of oranges, a new model based on L1-norm linear regression classification (L1-LRC) is proposed. The proposed L1-LRC for orange origin identification is based on minimum reconstruction error using the L1-norm regularization learning method, which can combine the feature selection and classifier learning, and can reveal the structure characteristics of spectral information effectively. The experimental results show that the proposed L1-LRC model can achieve higher accuracy rate of 92.35% and perform much better than existing models when using only a few training samples. Thus, this work would lead to a new method for fast and efficient identification of geographical origins with near infrared (NIR) spectroscopy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis (A. sinensis) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis.

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the new era of rapid development of internet, where massive forgery images with updated tampering techniques flood into, traditional algorithms are no longer able to deal with the latest multimedia tampering techniques, especially those caused by Deepfake and deep learning techniques. Thus, a universal framework for image forensics including image pre-processing module, feature extraction module and post-processing module designed for specific classification were proposed creatively. Accordingly, the state-of-the-art algorithms were reviewed，and meanwhile the main strength and limitations of current algorithms were generalized. More importantly, the future studies were also listed for advancing the development of digital image forensics.

Wolfberry, a valuable commodity straddling the realms of medicine and nutrition, faces increasing scrutiny regarding its provenance authenticity. In this investigation, electronic nose, electronic tongue, headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), and solid phase microextraction-gas chromatography-ion mobility spectrometry (SPME-GC-IMS) were employed for a thorough analysis of wolfberry pulp samples sourced from Ningxia (NX), Qinghai (QH), Gansu (GS), and Xinjiang (XJ). The results of the study showed that wolfberry samples from four regions could be effectively distinguished by combining intelligent sensory techniques with principal component analysis (PCA). In addition, smart sensory technology, combined with support vector machines (SVM) and random forest (RF) classifiers, can accurately distinguish samples from different regions (accuracy = 100 %). HS-SPME-GC-MS and SPME-GC-IMS identified 180 and 73 volatile organic compounds (VOCs), respectively. Through a combination of multivariate analysis (VIP > 1.2) and univariate analysis (P < 0.05), eight VOCs, including Nonadecane and alpha-Terpinolene, emerged as pivotal variables for distinguishing wolfberry pulp based on geographical origin. Overall, this study furnishes robust theoretical underpinnings for addressing concerns regarding the provenance authenticity of wolfberry pulp. • Various methods characterized VOCs in Wolfberry pulp. • Significant VOCs variations among pulps suggest potential for origin authentication. • SVM and RF effectively distinguished pulp origins, promising for authentication. • HS-SPME/GC-MS and HS-GC-IMS identified 180 and 73 VOCs, respectively. • Eight crucial VOCs were identified as biomarkers for Wolfberry origin determination. [ABSTRACT FROM AUTHOR]

• Volatiles of natural coals were obtained from five geographical origins. • Supervised machine learning algorithms were optimized to predict the origin. • Significant features were found via random forest model and quantitative analysis. • Integration of HS-GC-IMS and machine learning supports coal larceny litigation. The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials. [ABSTRACT FROM AUTHOR]

The determination of the origin of wolfberry (Lycium barbarum) pulp in China remains a challenge despite its nutritional significance and popularity among consumers. To address this gap, 144 samples from four distinct regions in China were analyzed using nutrient elements, stable isotopes combined with multi-element assessments. Chemometric analyses successfully demonstrated the initial separation of wolfberry pulp samples. Notably, a nutrient element (Glucose) along with 12 mineral elements (Tb, Co, Fe, Cd, Pr, V, Mo, Gd, Al, Mg, As, Zn) emerged as pivotal factors for origin identification. Furthermore, employing a random forest algorithm resulted in the highest classification accuracy of 100 %, surpassing support vector machine (96.43 %) and K-nearest (71.43 %) methods. The study's findings underscore the efficacy of utilizing stable isotopes, mineral elements, and nutritional composition as effective markers for tracing the origin of wolfberry pulp. Moreover, this methodology offers promising insights into the potential identification of origins for other agricultural products. [Display omitted] • Pioneering a novel approach for wolfberry pulp origin identification. • Characterized wolfberry pulp from 4 regions via multi-element analysis. • 12 mineral elements and 1 nutrient element were identified for origin identification. • Used chemometrics to reveal elemental relationships. • RF analysis has achieved an unparalleled 100 % accuracy in origin identification. [ABSTRACT FROM AUTHOR]

The quality and price of rice wines vary greatly under different origins. Therefore, identifying the origins of rice wines is essential to product quality evaluation and market maintenance. The electronic nose (e-nose) has been applied as an effective recognition technology for food origins when used in conjunction with attention mechanisms (AMs). However, the ability of traditional AMs to learn features from the e-nose data is limited, affecting the performance of e-nose system. Motivated by this, a collaborative strategy that combines a self-developed e-nose system and a convolution dot-product AM (CDPAM) is proposed to improve the performance of origin identification for rice wines. First, gas information of rice wine samples for 10 production origins is acquired by the self-developed e-nose system. Second, gas information is processed with the proposed CDPAM module for origin identification, and the ability of feature learning for origin classification models is enhanced using the CDPAM. Finally, compared with the classification results of multiple AMs, multiple models, and ablation studies, the best identification performance for rice wine origins, including an accuracy of 98.47 %, an F 1 -score of 98.53 %, a kappa coefficient of 98.30 %, a precision of 98.69 and a recall of 98.37 %, is achieved using the e-nose, CDPAM, and residual network50 models. In conclusion, effective identification for rice wine origins is achieved by the e-nose and CDPAM. [Display omitted] • An e-nose system is designed to acquire the gas information for rice wines. • A CDPAM is proposed on the data characteristics of the e-nose. • The ability of the feature learning for models is enhanced by the CDPAM. • The best identification of rice wine origins is achieved using the e-nose and CDPAM. [ABSTRACT FROM AUTHOR]

[Display omitted] • The development of a global prediction model for in-situ analysis in Rehmanniae Radix using near-infrared spectrometers. • An analysis was conducted to evaluate the qualitative and quantitative models for different portable near-infrared spectrometers (DLP NIRscan Nano and VIAVI MicroNIR 1700). • The study employed a transfer method based on Improved Principal Component Analysis (IPCA), which converted spectra from different types of NIR spectrometers with different data points or absorbance. As a Chinese herbal medicine with high medical value, Rehmanniae Radix (RR) has a wide variety of geographical origins leading to distinctly diverse quality, and moisture content during storage affects the critical active ingredient content. In this work, two portable near-infrared (NIR) spectrometers, DLP NIRscan Nano (DLP) and VIAVI MicroNIR 1700 (M1700) were used for in-situ spectral acquisition from intact RR. The partial least squares-discriminant analysis (PLS-DA) was employed for three geographical sources (Shandong, Shanxi, Henan). After multiple pre-processing and wavelength selection, the M1700-based PLS-DA model, accuracy (Acc), sensitivity (Sen), and specificity (Spe), achieved 100 % accuracy. In contrast, model predictions using the low-cost instrument (DLP) was not satisfactory. The partial least squares regression (PLSR) was applied to predict the moisture content of RR. The best correlation coefficients in the prediction set (R2 p), Root Mean Square Error of Prediction (RMSEP), and residual prediction deviation (RPD) values (0.98, 0.98, 4.98) were obtained by DLP and were also employed for comparison with the M1700 model (0.99, 0.70, 7.00). Therefore, to further improve the model prediction effect of the low-cost DLP, we employed the improved principal component analysis (IPCA), direct standardization (DS), and piecewise direct standardization (PDS) calibration transfer techniques. Unequivocally, IPCA optimized the origin identification model (Acc, Sen, and Spe were all 1.00), the moisture content prediction model RPD was increased by 23 %, and its RMSEP was reduced by 18 %, leading to the improvement in prediction accuracy of the DLP model. This study provides a portable and low-cost detection method for the in-situ evaluation of the quality of RR quality and a feasible solution for the NIR techniques to be used in the rapid and accurate in-situ analysis of RR. [ABSTRACT FROM AUTHOR]

Introduction: Panax notoginseng is one of the traditional precious and bulk‐traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported. Objectives: We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method. Materials and methods: Pharmacological experiments, HPLC, and ATR‐FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted. Results: The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside Rg1 had the greatest influence on the anticoagulant effect. The results of spectroscopy combined with chemometrics show that the variable selection method could extract a small number of variables containing valid information and improve the performance of the model. The variable importance in projection has the best ability to identify the origins of P. notoginseng; the accuracy of the training set and the test set was 0.975 and 0.984, respectively. Conclusion: This method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins. For the aim of carrying out the correlation of saponins and anticoagulant activities of different origins and rapid origin identification of Panax notoginseng in this text. The results showed that the P. notoginseng saponin (PNS) of Wenshan (WS) was the highest, and the anticoagulant activity was positively correlated with the contents of the three saponins. Therein, the content of ginsenoside Rg1 had the greatest influence on the anticoagulant effect. The variable selection method of variable importance in projection (VIP) has the best ability to identify the different origins. [ABSTRACT FROM AUTHOR]

Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Douglas fir is expected to play an increasingly important role in Swedish forestry under a changing climate. Thus far, seed orchards with clones of phenotypically selected trees (plus trees) have been established to supply the market with highly qualitative reproductive material. Given the high genetic variation of the species, its growth properties are significantly affected by the provenance. Here, we applied microsatellite markers to identify the origin of clones selected within the Swedish breeding programme. Moreover, we analysed the timing of bud burst in open-pollinated families of these clones. In particular, we aimed to explain the provenance effect on phenology by using molecular identification as a proxy. A Bayesian clustering analysis with microsatellite data enabled the assignment of the clones to one of the two varieties and also resolved within-variety origins. The phenological observations indicated an earlier bud burst of the interior variety. Within the coastal variety, the northern provenances exhibited a later bud burst. We found a significant effect of the identified origin on bud burst timing. The results of this study will be used to support further breeding efforts. [ABSTRACT FROM AUTHOR]

Citrus is one of the most important economic crops and is widely distributed across the monsoon region. Citrus fruits are deeply loved by consumers because of their special color, fragrance and high nutritional value. However, their health benefits have not been fully understood, especially the pericarps of citrus fruits which have barely been utilized due to their unknown chemical composition. In the present study, the pericarp and juices of four typical varieties of citrus fruits (lemon, dekopon, sweet orange and pomelo) were analyzed by NMR spectroscopy combined with pattern recognition. A total of 62 components from the citrus juices and 87 components from the citrus pericarps were identified and quantified, respectively. The different varieties of the citrus fruits could be distinguished from the others, and the chemical markers in each citrus juice and pericarp were identified by a combination of univariate and multivariate statistical analyses. The nutritional analysis of citrus juices offers favorable diet recommendations for human consumption and data guidance for their potential medical use, and the nutritional analysis of citrus pericarps provides a data reference for the subsequent comprehensive utilization of citrus fruits. Our results not only provide an important reference for the potential nutritional and medical values of citrus fruits but also provide a feasible platform for the traceability analysis, adulteration identification and chemical composition analysis of other fruits. [ABSTRACT FROM AUTHOR]

Breathomics, a growing field in exposure monitoring and clinical diagnostics, has faced accuracy challenges due to unclear contributing factors. This study aims to enhance the potential of breathomics in various frontiers by categorizing exhaled volatile organic compounds (VOCs) as endogenous or exogenous. Analyzing ambient air and breath samples from 271 volunteers via TD-GC × GC-TOF MS/FID, we identify and quantify 50 common VOCs in exhaled breath. Advanced quantitative structure-property relationships and compartment models are employed to obtain VOCs kinetic parameters. This in-depth approach allows us to accurately determine the alveolar concentration of VOCs and further discern their origins, facilitating personalized application of breathomics in exposure assessment and disease diagnosis. Our findings demonstrate that prolonged external exposure turns humans into secondary pollutant sources. Analysis of endogenous VOCs reveals that internal exposure poses more significant health risks than external. Moreover, by correcting environmental backgrounds, we improve the accuracy of gastrointestinal disease diagnostic models by 15-25%. This advancement in identifying VOC origins via compartmental models promises to elevate the clinical relevance of breathomics, marking a leap forward in exposure assessment and precision medicine.

Accumulation of heavy metals in farmland soil threaten ecological environment and affect human health through food chain. 31 soil samples, 30 crops samples and 22 irrigation water samples were collected from a typical agricultural area, Xiqing district in western Tianjin, China, then the concentrations of 9 heavy metals were investigated. The heavy metals pollution and ecological risk were respectively evaluated by the Geoaccumulation Index and the Potential Ecological Risk Index methods. Multivariate statistical analysis is employed to analyse the correlations among these heavy metals and the potential sources of them. The results showed that the soils in the study area generally had low contents of heavy metals except that Zn and Cr were enriched in the soils near residential areas, highways, railways, or closed livestock farm. Potential ecological risk assessment indicated slight risk of soil pollution by heavy metals. Ordination results in indirect gradient analysis revealed the first three ordination axes re-spectively accounted for 49%, 23%, 12% of the total variance of the samples. Irrigation water significantly influenced the enrichment of As and Pb, while the distribution of Cr and Ni can be attributed to soil parent material. The contents of Pb, Zn, Cu were affected by the emissions of nearby road traffic. V and As had similar distribution and were mainly related to the accumulation of historical pollutions from in-dustrial. [ABSTRACT FROM AUTHOR]

Introduction: Medicinal plants are very important to human health, and ensuring their quality and rapid evaluation are the current research concerns. Deep learning has a strong ability in recognition. This study extended it to the identification of medicinal plants from the perspective of spectrum. Objective: In order to realise the rapid identification and provide a reference for the selection of high‐quality resources of medicinal plants, a combination of deep learning and two‐dimensional correlation spectroscopy (2DCOS) was proposed. Methods: For the first time, Fourier transform mid‐infrared (FT‐MIR) and near‐infrared (NIR) spectroscopy 2DCOS images combined with residual neural network (ResNet) was used for the origin identification of Paris polyphylla var. yunnanensis. In total 1593 samples were collected and 12821 2DCOS images were drawn. The climate of different origins was briefly analysed. Results: The xishuangbanna, puer, lincang, honghe and wenshan are the five regions with more ecological advantages. The synchronous 2DCOS models of FT‐MIR and NIR could realise origin identification with the accuracy of 100%. The synchronous images were suitable for the identification of medicinal plants with complex systems. The full band, feature band and different contour models had no big difference in distinguishing ability, so they were not the key factors affecting the discrimination results. Conclusion: The ResNet models established were stable, reliable, and robust, which not only solved the problem of origin identification, expanded the application field of deep learning, but also provided practical reference for the related research of other medicinal plants. Deep learning combined with two‐dimensional correlation spectroscopy was used to identify the origins of P. polyphylla var. yunnanensis for the first time. The sample size used was up to 1593, which had a wide representative significance. The total number of 2DCOS images drawn was 12,821, which had dual advantages in terms of resource size and identification method. This is not available in the previous evaluation study of P. polyphylla var. yunnanensis. Through the simple analysis of the climate of different sampling sites and the practical application of modern analysis technology, this paper provided a reference for the selection of high‐quality resources, and supplied a practical and efficient method for the evaluation and study of medicinal plants such as P. polyphylla var. yunnanensis. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)