Classification of Green and Black Teas by PCA and SVM Analysis of Cyclic Voltammetric Signals from Metallic Oxide-Modified Electrode

Application and Progress of Chemometrics in Voltammetric Biosensing

The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea

The fermentation stage is vital during the black tea manufacturing process to produce the best-quality tea. The oxidation of tea biochemical compounds results in the appearance of characteristic smell peaks during the fermentation stage. These subtle changes in tea aroma are hard to detect unless one is a trained personnel. Here for the first time, we applied e-nose to monitor the fermentation process of Sri Lankan low-country tea. In this study, detection of smell peaks during fermentation was conducted by a custom-made e-nose (Digi-Nose) with four gas sensors. Singular value decomposition (SVD) is applied to eliminate the noise and dimensionality reduction in the sensor responses observed. The prediction of the time of appearance of smell peaks was conducted with a support-vector machine (SVM). Finally, theaflavin content with time was compared to validate the optimum fermentation times observed with an e-nose.

Tea and tea drinking: China's outstanding contributions to the mankind

Background

Tea trees originated in southwest China 60 million or 70 million years ago. Written records show that Chinese ancestors had begun drinking tea over 3000 years ago. Nowadays, with the aging of populations worldwide and more people suffering from non-communicable diseases or poor health, tea beverages have become an inexpensive and fine complementary and alternative medicine (CAM) therapy. At present, there are 3 billion people who like to drink tea in the world, but few of them actually understand tea, especially on its development process and the spiritual and cultural connotations.

Methods

We searched PubMed, Google Scholar, Web of Science, CNKI, and other relevant platforms with the key word “tea”, and reviewed and analyzed tea-related literatures and pictures in the past 40 years about tea’s history, culture, customs, experimental studies, and markets.

Results

China is the hometown of tea, tea trees, tea drinking, and tea culture. China has the oldest wild and planted tea trees in the world, fossil of a tea leaf from 35,400,000 years ago, and abundant tea-related literatures and art works. Moreover, tea may be the first Chinese herbal medicine (CHM) used by Chinese people in ancient times. Tea drinking has many benefits to our physical health via its antioxidant, anti-inflammatory, immuno-regulatory, anticancer, cardiovascular-protective, anti-diabetic, and anti-obesity activities. At the moment, COVID-19 is wreaking havoc across the globe and causing severe damages to people’s health and lives. Tea has anti-COVID-19 functions via the enhancement of the innate immune response and inhibition of viral growth. Besides, drinking tea can allow people to acquire a peaceful, relaxed, refreshed and cheerful enjoyment, and even longevity. According to the meridian theory of traditional Chinese medicine, different kinds of tea can activate different meridian systems in the human body. At present, black tea (fermented tea) and green tea (non-fermented tea) are the most popular in the world. Black tea accounts for over 90% of all teas sold in western countries. The world’s top-grade black teas include Qi Men black in China, Darjeeling and Assam black tea in India, and Uva black tea in Sri Lanka. However, all top ten famous green teas in the world are produced in China, and Xi Hu Long Jing tea is the most famous among all green teas. More than 700 different kinds of components and 27 mineral elements can be found in tea. Tea polyphenols and theaflavin/thearubigins are considered to be the major bioactive components of black tea and green tea, respectively. Overly strong or overheated tea liquid should be avoided when drinking tea.

Conclusions

Today, CAM provides an array of treatment modalities for the health promotion in both developed and developing countries all over the world. Tea drinking, a simple herb-based CAM therapy, has become a popular man-made non-alcoholic beverage widely consumed worldwide, and it can improve the growth of economy as well. Tea can improve our physical and mental health and promote the harmonious development of society through its chemical and cultural elements.

Emerging techniques for determining the quality and safety of tea products: A review

Spectroscopic techniques, electrochemical methods, nanozymes, computer vision, and modified chromatographic techniques are the emerging techniques for determining the quality and safety parameters (e.g., physical, chemical, microbiological, and classified parameters, as well as inorganic and organic contaminants) of tea products (such as fresh tea leaves, commercial tea, tea beverage, tea powder, and tea bakery products) effectively. By simplifying the sample preparation, speeding up the detection process, reducing the interference of other substances contained in the sample, and improving the sensitivity and accuracy of the current standard techniques, the abovementioned emerging techniques achieve rapid, cost-effective, and nondestructive or slightly destructive determination of tea products, with some of them providing real-time detection results. Applying these emerging techniques in the whole industry of tea product processing, right from the picking of fresh tea leaves, fermentation of tea leaves, to the sensory evaluation of commercial tea, as well as developing portable devices for real-time and on-site determination of classified and safety parameters (e.g., the geographical origin, grade, and content of contaminants) will not only eliminate the strong dependence on professionals but also help mechanize the production of tea products, which deserves further research. Conducting a review on the application of spectroscopic techniques, electrochemical methods, nanozymes, computer vision, and modifications of chromatographic techniques for quality and safety determination of tea products may serve as guide for other types of foods and beverages, offering potential techniques for their detection and evaluation, which would promote the development of the food industry.

Analysis of chemical compounds in beverages - Guidance for establishing a compositional analysis

In recent years, much analysis has been carried out regarding the chemical compositions of nonalcoholic and alcoholic beverages. However, no study has ever considered the compositional pitfalls inherent in such studies, which may lead to arbitrary results. Two approaches were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses, to demonstrate (1) how aging affects beer and (2) how the results vary depending on the applied approach. Exemplarily, chemical compounds of 43 beer samples were analyzed. Our work has led us to obtain different results when the correlation analysis and principal component analysis were conducted on the unmodified-, log-transformed-, or closed-data in contrary to the analyses of log-ratio coordinates. The outcome of the latter provided better separation between aged and fresh beer samples and also easier interpretability. These results lead us to advise the usage of CoDa methods for the analyses of chemical compositions of beverages.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry

Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides/pesticides, industrial compounds, and heavy metals. A key barrier to the widespread application of CSWV for chemical identification is the necessity of a high performance, generalizable classification algorithm. Here, machine and deep learning models were developed for classifying samples based on voltammograms alone. The highest performing models were Long Short-Term Memory (LSTM) and Fully Convolutional Networks (FCNs), depending on the dataset against which performance was assessed. When compared to other algorithms, previously used for classification of CSWV and other similar data, our LSTM and FCN-based neural networks achieve higher sensitivity and specificity with the area under the curve values from receiver operating characteristic (ROC) analyses greater than 0.99 for several datasets. Class activation maps were paired with CSWV scans to assist in understanding the decision-making process of the networks, and their ability to utilize this information was examined. The best-performing models were then successfully applied to new or holdout experimental data. An automated method for processing CSWV data, training machine learning models, and evaluating their prediction performance is described, and the tools generated provide support for the identification of compounds using CSWV from samples in the field.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Paraquat-Melanin Redox-Cycling: Evidence from Electrochemical Reverse Engineering

Parkinson's disease is a neurodegenerative disorder associated with oxidative stress and the death of melanin-containing neurons of the substantia nigra. Epidemiological evidence links exposure to the pesticide paraquat (PQ) to Parkinson's disease, and this link has been explained by a redox cycling mechanism that induces oxidative stress. Here, we used a novel electrochemistry-based reverse engineering methodology to test the hypothesis that PQ can undergo reductive redox cycling with melanin. In this method, (i) an insoluble natural melanin (from Sepia melanin) and a synthetic model melanin (having a cysteinyldopamine-melanin core and dopamine-melanin shell) were entrapped in a nonconducting hydrogel film adjacent to an electrode, (ii) the film-coated electrode was immersed in solutions containing PQ (putative redox cycling reductant) and a redox cycling oxidant (ferrocene dimethanol), (iii) sequences of input potentials (i.e., voltages) were imposed to the underlying electrode to systematically engage reductive and oxidative redox cycling, and (iv) output response currents were analyzed for signatures of redox cycling. The response characteristics of the PQ-melanin systems to various input potential sequences support the hypothesis that PQ can directly donate electrons to melanin. This observation of PQ-melanin redox interactions demonstrates an association between two components that have been individually linked to oxidative stress and Parkinson's disease. Potentially, melanin's redox activity could be an important component in understanding the etiology of neurological disorders such as Parkinson's disease.

Application of gas chromatography-mass spectrometry and chemometrics methods for assessing volatile profiles of Pu-erh tea with different processing methods and ageing years

A method was developed based on head-space solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) combined with multivariate statistical methods to assess volatile profiles in different types of Pu-erh teas.