A novel fluorescence sensing strategy based on nanoparticles combined with spectral splicing and chemometrics for the recognition of Citrus reticulata 'Chachi' and its storage year

Evaluation of chemical components and quality in Xinhui Chenpi (Citrus reticulata 'Chachi') with two different storage times by GC-MS and UPLC

Xinhui Chenpi (XHCP) is a well-known type of Chenpi (CP) widely used as both a Chinese herb and a food ingredient. While previous studies have explored how the quality of CP changes over time, there has been limited research specifically on XHCP. This study aims to assess the chemical components and quality of XHCP based on total flavonoid content (TF), antioxidant activity (AA), and color value (CV) at two stages: freshly harvested (XHCP-0Y) and after 3 years of storage (XHCP-3Y). Thirty-eight common volatile compounds were identified, and the content of 17 compounds among them, nine nonvolatile compounds, which included one alkaloid (synephrine), three phenolic acids (PA, protocatechuic acid, vanillic acid, and ferulic acid), and five flavonoids (narirutin, hesperidin, sinensetin, nobiletin, and tangeretin), were firstly detected by the newly developed gas chromatograph-mass spectrometer (GC-MS) and ultra-performance liquid chromatography (UPLC) methods. Compared to XHCP-0Y, the content of 17 volatile compounds and synephrine decreased in XHCP-3Y to varying degrees, while the content of PA, five flavonoids, TF, AA, and CV increased. The reduction of dryness caused by volatile compounds and the enhancement of efficacy related to PA, flavonoids, and AA suggested improved quality of XHCP after 3 years of storage. The methods developed in this study show promise for evaluating the quality of XHCP during the aging process.

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.

Nano effect fluorescence visual sensor based on Au-AgNCs: A novel strategy to identify the origin and growth year of Lilium bulbs

In this study, we developed a cost-effective fluorescence visual sensor strategy based on gold and silver nanocluster (Au-AgNCs) for the rapid identification of the origins and growth years of Lilium bulbs (LB). Au-AgNCs combined with catechins in LB produce aggregation-induced emission (AIE). The catechin content in LB of different origins and growth years varied, resulting in different fluorescence color responses of the sensor system. Furthermore, the RGB values of the fluorescent color were extracted, and the discriminant effect of visual visualisation was verified using the data-driven soft independent modelling of class analogy (DD-SIMCA) and partial least squares discriminant analysis (PLSDA) models. The results showed that the accuracy of DD-SIMCA for identifying LB origins and PLSDA for growth year identification was 100%. These results indicated that the established strategy could accurately identify the quality of LB, which has great potential for application in the rapid and visual identification of other foods.

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.

Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method

Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

The geographical origin and the important nutrient contents greatly affect the quality of red raspberry (RRB, Rubus idaeus L.), a popular fruit with various health benefits. In this study, a chemometrics-assisted hyperspectral imaging (HSI) method was developed for predicting the nutrient contents, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF) and total phenolics (TP), and identifying the geographical origin of RRB fruits. The results showed that these nutrient contents in RRB fruits had significant differences between regions (P < 0.05) and could be well predicted based on the HSI full or effective wavelengths selected through competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA). The best prediction results of PPS, RS, TF, and TP contents were achieved with the highest residual predictive deviation (RPD) values of 3.66, 3.95, 2.85, and 4.85, respectively. The RRB fruits from multi-regions in China were effectively distinguished by using the first derivative-partial least squares discriminant analysis (DER-PLSDA) model, with an accuracy of above 97%. Meanwhile, the fruits from three protected geographical indication (PGI) regions were successfully classified by using the orthogonal partial least squares discrimination analysis (OPLSDA) model, with an accuracy of above 98%. The study results indicate that HSI assisted with chemometrics is a promising method for predicting the important nutrient contents and identifying the geographical origin of red raspberry fruits.

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

The feasibility of identifying geographical origin and storage age of tangerine peel was explored by using a handheld near-infrared (NIR) spectrometer combined with machine learning. A handheld NIR spectrometer (900-1700 nm) was used to scan the outer surface of tangerine peel and collect the corresponding NIR diffuse reflectance spectra. Principal component analysis (PCA) combined with Mahalanobis distance were used to detect outliers. The accuracies of all models in the anomaly set were much lower than that in calibration set and test set, indicating that the outliers were effectively identified. After removing the outliers, in order to initially explore the clustering characteristics of tangerine peels, PCA was performed on tangerine peels from different origins and the same origin with different storage ages. The results showed that the tangerine peels from the same origin or the same storage age had the potential to cluster, indicating that the spectral data of the same origin or the same storage age had a certain similarity, which laid the foundation for subsequent modeling and identification. However, there were quite a few samples with different origins or different storage ages overlapped and could not be distinguished from each other. In order to achieve qualitative identification of origin and storage age, Savitzky-Golay convolution smoothing with first derivative (SGFD) and standard normal variate (SNV) were used to preprocess the raw spectra. Random forest (RF), K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to establish the discriminant model. The results showed that SGFD-LDA could accurately distinguish the origin and storage age of tangerine peel at the same time. The origin identification accuracy was 96.99%. The storage age identification accuracy was 100% for Guangdong tangerine peel and 97.15% for Sichuan tangerine peel. This indicated that the near-infrared spectroscopy (NIRS) combine with machine learning can simultaneously and rapidly identify the origin and storage age of tangerine peel on site.

Accurate identification of the geographical origins of lily using near-infrared spectroscopy combined with carbon dot-tetramethoxyporphyrin nanocomposite and chemometrics

Near-infrared spectroscopy technique is a prevailing tool for quality control of foods and traditional Chinese medicines. However, it usually faced the problems of severe peak overlap, low classification accuracy and poor specificity. In this work, the potential of carbon dot-tetramethoxyporphyrin nanocomposite-based nano-effect near-infrared spectroscopy sensor combined with chemometric method was investigated for the accurate identification lily from different geographical origins. Partial least squares-discriminant analysis (PLS-DA) was used for differentiating geographical origins of lily based on the collected traditional and nano-effect near-infrared spectroscopy. Compared with traditional near-infrared spectroscopy, the nano-effect near-infrared spectroscopy obtains superior classification performance with 100% accuracy on the training and test set. The results showed that the proposed method based on near-infrared spectroscopy combined with nanocomposites and chemometrics could be considered as a promising tool for rapid discrimination of the authenticity of food and traditional Chinese medicine in the future.

High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS

Environment stress can promote the synthesis and accumulation of a series of secondary metabolites, which are important quality factors in medicinal plants. However, the data related to metabolites is often too large, making it difficult to screen quickly, accurately and comprehensively various differential compounds. In this study, a high-throughput screening method for differential secondary metabolites produced by medicinal plants under environmental stress has been developed based on ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) and automatic data analysis strategy. This work uses Sorbus pohuashanensis cells with biotic stress (Harpin protein) and abiotic stress (Cd²⁺ ) as potential environmental stress factors. The results showed that S. pohuashanensis cells could rapidly respond to both Harpin protein and Cd²⁺ within 24 h, and a significant positive correlation was observed between their concentration (within a certain range) and induction time. The proposed screening method can automatically screen the bulk UHPLC-QTOF metabolic data for differential compounds with high-throughput, and also perform preliminary identification of their possible structures. The screening results indicated that the stress response of S. pohuashanensis cells to Cd²⁺ was significantly higher than that of Harpin protein, and all of them could produce a series of biphenyls, terpenes, and other phytoalexins with stress-resistance and physiological functional properties. Overall, the screening method provides an efficient and powerful tool to study the response mechanisms of plants to environmental stress, to improve the resistance of medicinal plants and also to select and breed high-quality Chinese medicinal plants.