Variety identification and age prediction of Pu-erh tea using graphene oxide and porphyrin complex based mid-infrared spectroscopy coupled with chemometrics

Prediction of multi-task physicochemical indices based on hyperspectral imaging and analysis of the relationship between physicochemical composition and sensory quality of tea

Tea is highly valued by consumers worldwide for its distinctive flavor and rich nutritional profile. Efficient and accurate assessment of tea quality is essential for both producers and consumers. This study focuses on Yongchuan Xiuya green tea and utilizes hyperspectral imaging (HSI) technology integrated with a multi-task regression (MTR) model to simultaneously predict 12 physicochemical indices (WE, SSC, FAA, TP, CAF, EGCG, EGC, EC, ECG, GA, C, GC). To develop this model, the relationship between sensory attributes and physicochemical components was first analyzed, identifying key quality indicators. The original spectral data were preprocessed using the SNV-SG method to enhance data quality. The predictive performance of various models, including partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost), was evaluated, with XGBoost identified as the most effective. Subsequently, the Newton-Raphson-Based Optimization (NRBO) algorithm was employed to optimize the parameters of XGBoost, forming the foundation of the MTR model. By incorporating feature enhancement and correlation analysis, the MTR model effectively predicted multiple quality indices. The model exhibited high predictive accuracy, as indicated by an average RP2 of 0.9774 and an average RMSEP of 0.1097, demonstrating its robustness and reliability in assessing tea quality.

Discriminating the adulteration of varieties and misrepresentation of vintages of Pu'er tea based on Fourier transform near infrared diffuse reflectance spectroscopy

In the Pu'er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu'er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu'er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu'er tea quality inspection, and offering a robust method for combating the pervasive issues within the market.

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates

Identifying the aging time of Liupao Tea (LPT) presents a persistent challenge. We utilized an AI-Multimodal fusion method combining FTIR, E-nose, and E-tongue to discern LPT's aging years. Compared to single-source and two-source fusion methods, the three-source fusion significantly enhanced identifying accuracy across all four machine learning algorithms (Decision tree, Random forest, K-nearest neighbor, and Partial least squares Discriminant Analysis), achieving optimal accuracy of 98-100 %. Physicochemical analysis revealed monotonic variations in tea polysaccharide (TPS) conjugates with aging, observed through SEM imaging as a transition from lamellar to granular TPS conjugate structures. These quality changes were reflected in FTIR spectral characteristics. Two-dimensional correlation spectroscopy (2D-COS) identified sensitive wavelength regions of FTIR from LPT and TPS conjugates, indicating a high similarity in spectral changes between TPS conjugates and LPT with aging years, highlighting the significant role of TPS conjugates variation in LPT quality. Additionally, we established an index for evaluating quality of aging, which is sum of three fingerprint peaks (1029 cm-1, 1635 cm-1, 2920 cm-1) intensities. The index could effectively signify the changes in aging years on macro-scale (R2 = 0.94) and micro-scale (R2 = 0.88). These findings demonstrate FTIR's effectiveness in identifying aging time, providing robust evidence for quality assessment.

Covalent organic frameworks (COFs) and metal-organic frameworks (MOFs) as electrochemical sensors for the efficient detection of pharmaceutical residues

Pharmaceutical residues are the undecomposed remains from drugs used in the medical and food industries. Due to their potential adverse effects on human health and natural ecosystems, they are of increasing worldwide concern. The acute detection of pharmaceutical residues can give a rapid examination of their quantity and then prevent them from further contamination. Herein, this study summarizes and discusses the most recent porous covalent-organic frameworks (COFs) and metal-organic frameworks (MOFs) for the electrochemical detection of various pharmaceutical residues. The review first introduces a brief overview of drug toxicity and its effects on living organisms. Subsequently, different porous materials and drug detection techniques are discussed with materials' properties and applications. Then the development of COFs and MOFs has been addressed with their structural properties and sensing applications. Further, the stability, reusability, and sustainability of MOFs/COFs are reviewed and discussed. Besides, COFs and MOFs' detection limits, linear ranges, the role of functionalities, and immobilized nanoparticles are analyzed and discussed. Lastly, this review summarized and discussed the MOF@COF composite as sensors, the fabrication strategies to enhance detection potential, and the current challenges in this area.

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.

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.