Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy

Quality evaluation of Citri Reticulatae Pericarpium by supercritical fluid chromatography with chemical pattern recognition

An efficient and rapid supercritical fluid chromatography (SFC) method was established for the analysis of flavonoids in Citri Reticulatae Pericarpium (CRP), the best conditions were obtained by optimizing the parameters of the SFC method. The peak area obtained by SFC analysis of CRP samples, combined with chemical pattern recognition, was used to establish a model to evaluate the quality of Guang Chenpi (GCP) and Chenpi (CP) in CRP. Partial least squares discriminant analysis (PLS-DA) proved to be an excellent method that can accurately distinguish GCP from CP. The PLS-DA model was established by selecting the training set, and the characteristic components (Didymin and Hesperidin) were screened out to further establish the model, and the testing sets were brought into the model for verification, indicating that the model established based on the peak areas is reliable, and two quality markers (Narirutin and Neohesperidin) were found to distinguish between Xinhui and Non-Xinhui in GCP based on the content determination. In conclusion, the combination of SFC and chemical pattern recognition for the quality evaluation of CRP has great potential.

Geographical Origin Traceability of Navel Oranges Based on Near-Infrared Spectroscopy Combined with Deep Learning

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000-10,000 cm-1 were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky-Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products.

Geographic traceability of Gastrodia elata Blum based on combination of NIRS and Chemometrics

The content of the active ingredient in G. elata Bl. is affected by the soil and climate of different regions, so geographical traceability is essential to ensure its quality, commercial value. This study used a combination of NIRS and various chemometric methods to establish an effective geotraceability method for G. elata Bl.. Firstly, a traditional machine learning model was built based on the SF dataset NIRS, and a ResNet model was built based on NIRS generated 2DCOS images and 3DCOS images. Secondly, the model performance was validated using the ZT dataset. The results show that the 3DCOS-ResNet model performs the best with 100.00 % and 95.45 % test set and EV accuracy, respectively. This study provides a theoretical basis for regulators to quickly ensure the authenticity of G. elata Bl. sources. However, more data and in-depth studies are needed in the future to validate and improve the applicability of the model.

FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration

Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2 P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR.

Accurate Discrimination of Mold-Damaged Citri Reticulatae Pericarpium Using Partial Least-Squares Discriminant Analysis and Selected Wavelengths

Unscrupulous merchants sell the mold-damaged Citri Reticulatae Pericarpium (CRP) after removing the mold. In this study, an accurate and non-destructive strategy was developed for the discrimination of mold-damaged CRPs using portable near-infrared (NIR) spectroscopy and chemometrics. The outer surface and inner surface spectra were obtained without destroying CRPs. The discrimination models were established using partial least squares-discriminant analysis (PLS-DA) and wavelength selection strategy was used to further improve the discrimination ability. The predictive ability of models was assessed using the test set and an independent test set obtained one month later. The results demonstrate that the models of the outer surface outperform those of the inner surface. With multiplicative scatter correction (MSC)-PLS-DA, 100% accuracies were obtained in test and independent test sets. Furthermore, the wavelength selection strategy simplified the models with 100% discrimination accuracy. In addition, the randomization test (RT)-PLS-DA model developed in this study combines both the benefits of high accuracy and robustness, which can be applied for the accurate discrimination of mold-damaged CRPs.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and "segmentation-extraction-regression" deep learning assisted quantification of flavonoids

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3',5,5'-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a "segmentation-extraction-regression" deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 × 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multi-ingredient quantitative analysis of flavonoids.

Accurate and visualiable discrimination of Chenpi age using 2D-CNN and Grad-CAM++ based on infrared spectral images

Dried tangerine peel ("Chenpi"), has numerous clinical and nutritional benefits, with its quality being significantly influenced by its storage age, referred to as "Chen Jiu Zhe Liang" in Chinese. Concequently, the rapid and accurate identification of Chenpi's age is important for consumers. In this study, Fourier transform infrared spectroscopy (FTIR) was employed to capture spectral images of Chenpi. These FTIR images were then analyzed using a two-dimensional convolutional neural networks (2D-CNN) model, achieving a discrimination accuracy of 97.92%. To address the "black box" nature of the 2D-CNN, Gradient-weighted Class Activation Mapping Plus Plus (Grad-CAM++) was utilized to highlight the important regions contributing to the model's performance. Additionally, six other machine learning models were developped using features identified by the 2D-CNN to validate their effectiveness. The results demonstrated that the combination of FTIR spectral images and 2D-CNN provides a highly effective method for accurately determining the age of Chenpi.

Nondestructive rapid identification of wild Cordyceps sinensis with portable instrument

INTRODUCTION

Cordyceps sinensis (CS) is a precious medicinal fungus. Wild CS (WCS) and artificial CS (ACS) are destroyed for their identification using traditional methods, which are time consuming and labor-intensive. Therefore, it is crucial to establish a nondestructive identification method to rapidly screen WCS.

OBJECTIVE

The aim of this study was to provide technical support for rapid screening of CS and evaluation of its quality. The applicability of the model was improved through model transfer.

METHODS

In this study, continuous wavelet transform was used to analyze the differences in moisture content and active components between WCS and ACS from the perspective of characteristic molecular groups. A portable instrument and a laboratory benchtop instrument were used to determine CS spectra. Partial least squares discrimination analysis was conducted for the identification of WCS and ACS while preserving the original shape of CS. Moreover, improved principal component analysis was utilized to transfer the model between the two types of near-infrared spectroscopy (NIRS) instruments.

RESULTS

The results demonstrated that three peaks, at 1443, 1941, and 2183 nm, were characteristic absorption peaks. The model based on NIRS could initially provide rapid differentiation between WCS and ACS. At the same time, the accuracy of the external test set was further improved to over 95% through forward transfer.

CONCLUSION

Therefore, this method could be used for rapid screening of WCS and provides technical support for the nondestructive identification of CS and initial assessment of CS quality.

The Ability of Different Tea Tree Germplasm Resources in South China to Aggregate Rhizosphere Soil Characteristic Fungi Affects Tea Quality

It is generally recognized that the quality differences in plant germplasm resources are genetically determined, and that only a good "pedigree" can have good quality. Ecological memory of plants and rhizosphere soil fungi provides a new perspective to understand this phenomenon. Here, we selected 45 tea tree germplasm resources and analyzed the rhizosphere soil fungi, nutrient content and tea quality. We found that the ecological memory of tea trees for soil fungi led to the recruitment and aggregation of dominant fungal populations that were similar across tea tree varieties, differing only in the number of fungi. We performed continuous simulation and validation to identify four characteristic fungal genera that determined the quality differences. Further analysis showed that the greater the recruitment and aggregation of Saitozyma and Archaeorhizomyces by tea trees, the greater the rejection of Chaetomium and Trechispora, the higher the available nutrient content in the soil and the better the tea quality. In summary, our study presents a new perspective, showing that ecological memory between tea trees and rhizosphere soil fungi leads to differences in plants' ability to recruit and aggregate characteristic fungi, which is one of the most important determinants of tea quality. The artificial inoculation of rhizosphere fungi may reconstruct the ecological memory of tea trees and substantially improve their quality.

Study on the quality of Corydalis Rhizoma in Zhejiang based on multidimensional evaluation method

ETHNOPHARMACOLOGICAL RELEVANCE

The quality requirements of Corydalis Rhizoma (CR) in different producing areas are uniform, resulting in uneven efficacy. As a genuine producing area, the effective quality control of CR in Zhejiang Province (ZJ) could provide a theoretical basis for the rational application of medicinal materials.

AIM OF THE STUDY

The purpose of this study was to effectively distinguish the CR inside and outside ZJ, and provided a theoretical basis for the quality control and material basis research of ZJ CR.

MATERIALS AND METHODS

The core components of ZJ CR could be identified by HPLC combined with chemometrics screening, and the quality of CR from different producing areas was evaluated by a genetic algorithm-back propagation (GA-BP) neural network. Chromaticity and near-infrared (NIR) spectroscopy were used to identify CR inside and outside ZJ, and rapid content prediction was realized. The analgesic effect of CR in different regions was compared by a zebrafish analgesic experiment. Analgesic experiments in rats and analysis of the research status of quality components were used to screen the quality control components of ZJ CR.

RESULTS

The contents of palmatine hydrochloride (YSBMT), dehydrocorydaline (TQZJJ), tetrahydropalmatine (YHSYS), tetrahydroberberine (SQXBJ), corydaline (YHSJS), stylopine (SQHLJ), and isoimperatorin (YOQHS) in ZJ CR were higher than those in CR from outside ZJ, but the content of protopine (YAPJ) and berberine hydrochloride (YSXBJ) was lower than that in CR from outside ZJ. YHSJS and SQHLJ could be used as the core components to identify ZJ CR. The GA-BP neural network showed that the relative importance of ZJ CR was the strongest. Chroma-content correlation analysis and the NIR qualitative model could effectively distinguish CR from inside and outside of ZJ, and the NIR quantitative model could quickly predict the content of CR from inside and outside of ZJ. Zebrafish experiments showed that ZJ, Shaanxi (SX), Henan (HN), and Sichuan (SC) CR had significant analgesic effects, while Hebei (HB) CR had no significant analgesic effect. Overall comparison, the analgesic effect of ZJ CR was better than that of CR outside ZJ. The comprehensive score of the grey correlation degree between YAPJ, YSBMT, YSXBJ, TQZJJ, YHSYS, YHSJS, SQXBJ, and SQHLJ were higher than 0.9, and the research frequency were extremely high.

CONCLUSIONS

The relative importance of the content and origin of most components of ZJ CR was higher than that of CR outside ZJ. The holistic analgesic effect of ZJ CR was better than that of CR outside ZJ, but slightly lower than that of SX CR. YHSJS and SQHLJ could be used as the core components to identify ZJ CR. YAPJ, YSBMT, YSXBJ, TQZJJ, YHSYS, SQXBJ, YHSJS, and SQHLJ could be used as the quality control components of ZJ CR. The multidimensional evaluation method used in this study provided a reference for the quality control and material basis research of ZJ CR.

Recruitment and Aggregation Capacity of Tea Trees to Rhizosphere Soil Characteristic Bacteria Affects the Quality of Tea Leaves

There are obvious differences in quality between different varieties of the same plant, and it is not clear whether they can be effectively distinguished from each other from a bacterial point of view. In this study, 44 tea tree varieties (Camellia sinensis) were used to analyze the rhizosphere soil bacterial community using high-throughput sequencing technology, and five types of machine deep learning were used for modeling to obtain characteristic microorganisms that can effectively differentiate different varieties, and validation was performed. The relationship between characteristic microorganisms, soil nutrient transformation, and tea quality formation was further analyzed. It was found that 44 tea tree varieties were classified into two groups (group A and group B) and the characteristic bacteria that distinguished them came from 23 genera. Secondly, the content of rhizosphere soil available nutrients (available nitrogen, available phosphorus, and available potassium) and tea quality indexes (tea polyphenols, theanine, and caffeine) was significantly higher in group A than in group B. The classification result based on both was consistent with the above bacteria. This study provides a new insight and research methodology into the main reasons for the formation of quality differences among different varieties of the same plant.

Recent advances in the authentication (geographical origins, varieties and aging time) of tangerine peel (Citri reticulatae pericarpium): A review

The consumption of tangerine peel (Citri reticulatae pericarpium, CRP) has been steadily increasing worldwide due to its proven health benefits and sensory characteristics. However, the price of CRP varies widely based on its origin, variety, and aging time, which has led many manufacturers to offer inferior products by exploiting the sensory similarity of CRP, seriously undermining consumers' interests. Therefore, it is essential to identify the authenticity of the CRP. In this study, the research progress on the authenticity of CRP from different origins, years and varieties over the past 10 years and the application and prospects of the main technologies and techniques were reviewed. The advantages and disadvantages of the commonly used methods were also summarized and compared. Mass spectrometry-based and spectroscopy-based techniques are the most commonly used methods for analyzing CRP authenticity. However, designing fast, non-destructive and green methods for identifying CRP authenticity would be the future trend.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.

Exploring the roles of microorganisms and metabolites in the 30-year aging process of the dried pericarps of Citrus reticulata 'Chachi' based on high-throughput sequencing and comparative metabolomics

GuangChenpi (GCP), the dried pericarps of Citrus reticulata 'Chachi', has been consumed daily as a food and dietary supplement in China for centuries. Its health benefits are generally recognized to be dependent on storage time. However, the specific roles of microorganisms and metabolites during long-term storage are still unclear. In this study, comparative metabolomics and high-throughput sequencing techniques were used to investigate the effects of co-existing microorganisms on the metabolites in GCP stored from 1 to 30 years. In total, 386 metabolites were identified and characterized. Most compounds were flavonoids (37%), followed by phenolic acids (20%). Seventeen differentially upregulated metabolites were identified as potential key metabolites in GCP, and 8 of them were screened out as key active ingredients by Venn diagram comparative analyses and verified by network pharmacology and molecular docking. In addition, long-term storage could promote the accumulation of secondary metabolites. Regarding the GCP microbiota, Xeromyces dominated the whole 30-year aging process.Moreover, Spearman correlation analysis indicated that Bacillus thuringiensis and Xeromyces bisporus, the dominant bacterial and fungal species, were strongly associated with the key active metabolites. Our results suggested that the change of active ingredients caused by the dominant microbial is one of the mechanisms affecting the GCP aging process. Our study provides novel functional insights and research perspectives on microorganism-associated metabolite changes that may improve the GCP aging process.

Review of recent advances on health benefits, microbial transformations, and authenticity identification of Citri reticulatae Pericarpium bioactive compounds

The extensive health-promoting effects of Citri Reticulatae Pericarpium (CRP) have attracted researchers' interest. The difference in storage time, varieties and origin of CRP are closely related to the content of bioactive compounds they contain. The consitituent transformation mediated by environmental microorganisms (bacteria and fungi) and the production of new bioactive components during the storage process may be the main reason for 'the older, the better' of CRP. In addition, the gap in price between different varieties can be as large as 8 times, while the difference due to age can even reach 20 times, making the 'marketing young-CRP as old-CRP and counterfeiting origin' flood the entire market, seriously harming consumers' interests. However, so far, the research on CRP is relatively decentralized. In particular, a summary of the microbial transformation and authenticity identification of CRP has not been reported. Therefore, this review systematically summarized the recent advances on the main bioactive compounds, the major biological activities, the microbial transformation process, the structure, and content changes of the active substances during the transformation process, and authenticity identification of CRP. Furthermore, challenges and perspectives concerning the future research on CRP were proposed.

Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks

The geographical indication of pericarpium citri reticulatae (PCR) is very important in grading the quality and price of PCRs. Therefore, terahertz time-domain spectroscopy (THz-TDS) technology combined with convolutional neural networks (CNN) was proposed to distinguish PCRs of different origins without damage in this study. The one-dimensional CNN (1D-CNN) model with an accuracy of 82.99% based on spectral data processed with SNV was established. The two-dimensional image features were transformed from unprocessed spectral data using the gramian angular field (GAF), the Markov transition field (MTF) and the recurrence plot (RP), which were used to build a two-dimensional CNN (2D-CNN) model with an accuracy of 78.33%. Further, the CNN models with different fusion methods were developed for fusing spectra data and image data. In addition, the adding spectra and images based on the CNN (Add-CNN) model with an accuracy of 86.17% performed better. Eventually, the Add-CNN model based on ten frequencies extracted using permutation importance (PI) achieved the identification of PCRs from different origins. Overall, the current study would provide a new method for identifying PCRs of different origins, which was expected to be used for the traceability of PCRs products.

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

SPME-GC-MS combined with chemometrics to assess the impact of fermentation time on the components, flavor, and function of Laoxianghuang

Laoxianghuang, fermented from Citrus medica L. var. Sarcodactylis Swingle of the Rutaceae family, is a medicinal food. The volatiles of Laoxianghuang fermented in different years were obtained by solid-phase microextraction combined with gas chromatography–mass spectrometry (SPME-GC–MS). Meanwhile, the evolution of its component-flavor function during the fermentation process was explored in depth by combining chemometrics and performance analyses. To extract the volatile compounds from Laoxianghuang, the fiber coating, extraction time, and desorption temperature were optimized in terms of the number and area of peaks. A polydimethylsiloxane/divinylbenzene (PDMS/DVB) with a thickness of 65 μm fiber, extraction time of 30 min, and desorption temperature of 200 °C were shown to be the optimal conditions. There were 42, 44, 52, 53, 53, and 52 volatiles identified in the 3rd, 5th, 8th, 10th, 15th, and 20th years of fermentation of Laoxianghuang, respectively. The relative contents were 97.87%, 98.50%, 98.77%, 98.85%, 99.08%, and 98.36%, respectively. Terpenes (mainly limonene, γ-terpinene and cymene) displayed the highest relative content and were positively correlated with the year of fermentation, followed by alcohols (mainly α-terpineol, β-terpinenol, and γ-terpineol), ketones (mainly cyclohexanone, D(+)-carvone and β-ionone), aldehydes (2-furaldehyde, 5-methylfurfural, and 1-nonanal), phenols (thymol, chlorothymol, and eugenol), esters (bornyl formate, citronellyl acetate, and neryl acetate), and ethers (n-octyl ether and anethole). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed a closer relationship between the composition of Laoxianghuang with similar fermentation years of the same gradient (3rd-5th, 8th-10th, and 15th-20th). Partial least squares discriminant analysis (PLS-DA) VIP scores and PCA-biplot showed that α-terpineol, γ-terpinene, cymene, and limonene were the differential candidate biomarkers. Flavor analysis revealed that Laoxianghuang exhibited wood odor from the 3rd to the 10th year of fermentation, while herb odor appeared in the 15th and the 20th year. This study analyzed the changing pattern of the flavor and function of Laoxianghuang through the evolution of the composition, which provided a theoretical basis for further research on subsequent fermentation.

Reliability evaluation of traditional Chinese medicine fingerprints combined with qualitative and quantitative analysis and antioxidant activity to comprehensively evaluate the quality of Citri Reticulatae Pericarpium

Comprehensive evaluation of Citri Reticulatae Pericapium quality by HPLC, UV and antioxidant activity.