Study on discrimination of white tea and albino tea based on near-infrared spectroscopy and chemometrics

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning

Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L. edodes from very limited sources, and thus could not achieve high accuracy for large samples from more varied sources. Strictly, there is a nonlinear relationship between NIR spectral data and chemical label values, and traditional modeling methods for NIR data analysis have problems such as insufficient feature learning ability and difficulty in training. The deep learning model has excellent nonlinear modeling ability and generalization capacity, which is very suitable for analyzing larger samples. In this study, we constructed a novel framework with deep learning techniques on the NIR analysis of the content of polysaccharide in L. edodes. The siPLS model was established based on the combination of the bands 4797-3995 cm-1 and 6401-5600 cm-1, while the one-dimensional convolutional neural network (1D-CNN) model was established with improved feature in the treatment of the spectral data. The comparative experimental results showed that the 1D-CNN model (R2pre = 95.50%; RMSEP =0.1875) outperformed the siPLS model (R2pre = 87.89%, RMSEP = 0.6221). As such, this work has demonstrated that NIR spectroscopy with the integration of deep learning can provide more accurate quantification of polysaccharide in L. edodes. Such method can be very useful for nutritional grading and quality control of diverse L. edodes in the market.

NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea

Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (R_P = 0.9407, RPD = 3.00), FAA (R_P = 0.9110, RPD = 2.21) and TP/FAA (R_P = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.

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.

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

Comparison of various pharmaceutical properties of clobetasol propionate cream formulations - considering stability of mixture with moisturizer

Background

The clobetasol propionate cream formulations (CLB_Cr) belong to the “strongest” group, and are used widely. In addition, those formulations are often used as a mixture with moisturizer. Recently, we evaluated pharmaceutical properties of the CLB_Cr using near infrared (NIR) spectroscopy, and characteristic NIR spectra depending on the formulation were observed. In the present study, we attempted to evaluate the more diverse pharmaceutical properties of CLB_Cr, including the stability of mixture of CLB_Cr and moisturizer.

Method

Pharmaceutical properties of CLB_Cr were evaluated using from rheological characteristics, microscopic observation, dye permeability observations, electrical conductivity method, thermogravimetry-differential thermal analysis (TG-DTA) and near infrared (NIR) spectroscopy. Stability of mixtures of CLB_Cr and moisturizer were evaluated using from dye method and NIR spectroscopy.

Results

The hardness of Dermovate® (DRM), Glydil® (GDL), and Myalone® (MYA) was greater than that of CLB_Cr. High concentrations of white beeswax were considered the reason for the hardness of DRM and GDL. On the other hand, the hardness of MYA may be due to the presence of macrogol 6000. After storage of the cream formulations discharged from the tube at room temperature, mass reduction and attenuation of the peak reflecting water of NIR spectroscopy occurred in a time-dependent manner, except for GDL and MYA. Only GDL was shown to be a w/o-type formulation by dye and electric conductivity measurements, which suggested that this was the reason for the lack of changes in the mass or NIR spectrum of samples after storage. In the NIR spectrum of MYA, the peak reflecting water slightly increased in a time-dependent manner, suggesting the water absorption of macrogol 6000. TG-DTA provided curves indicating the presence of water in each formulation, except for MYA, which was consistent with water quantification previously reported. Finally, when mixing the CLB_Cr with a moisturizer, in any CLB_Cr, the stability of the mixture with w/o-type moisturizer varies greatly depending on the each CLB_Cr.

Conclusion

Thus, even for cream formulations with the same active pharmaceutical ingredient, pharmaceutical properties and stability of mixture with moisturizer may different significantly.

Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy

The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm⁻¹ range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90–100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry.

Exploration of a Method of Distinguishing Different Nongxiang Tieguanyin Tea Grades Based on Aroma Determined by GC-MS Combined with Chemometrics

An aroma-based method for distinguishing different grades of Nongxiang Tieguanyin was explored by taking special grade (K110) and 1-4 grades (K101, K102, K103, and K104) of this tea as samples. Tea samples were analyzed by gas chromatography-mass spectrometry (GC-MS) combined with chemometrics. Results showed differences in the types and relative contents of aroma components among different grades of Nongxiang Tieguanyin tea. In the principal component analysis (PCA) scoring plot, except for K102 and K103, tea samples of different grades were distributed in different regions. Components satisfying variable important for the projection (VIP) > 1 and peak areas with significant differences (p < 0.05) among different tea grades were screened. Finally, 18 differential variables were screened out from 143 volatiles. The clustering results of these variables were consistent with those of PCA. K102 and K103 were initially clustered into one group and then clustered with K101, K110, and K104 in turn. The clear PCA separation of these samples and uniform hierarchical cluster analysis (HCA) clustering results suggests that GC-MS coupled with chemometrics analysis is a valid and accurate approach for discriminating different grades of Nongxiang Tieguanyin. The screened differential variables could represent a difference in aroma quality among five grades of Nongxiang Tieguanyin tea. Clear rules between peak area and the grade were also observed in some differential variables. 1-Ethylpyrrole and unknown-32 were positively correlated with grade. 2-Methylfuran, 2-ethylfuran, 2-methylidenecyclopentan-1-ol, mesityl oxide, 2-amylfuran, and D-limonene were negatively correlated with grade. The peak areas of methyl acetate, dimethyl sulfide, 6-methylhept-5-en-2-one, and (Z)-β-ocimene initially decreased but then increased with declining grade. The toluene content was especially high in K104 but only a negligible difference was observed among other grades. This study provides a potential method for differentiating Nongxiang Tieguanyin teas of different grades based on aroma. Unknown samples could be classified by comparison of their spatial distribution with those of known standard samples in PCA or HCA, as well as the peak area differences of differential variables between unknown samples and known standard samples.

Mixtures of betamethasone butyrate propionate ointments and heparinoid oil-based cream: Physical stability evaluation

Betamethasone butyrate propionate ointment (BBP_O) is mainly used for adult patients in dermatology and is often prescribed as a mixture containing a base or moisturizing cream for various reasons. However, in the case of a moisturizing cream, since this formulation is composed of various ingredients, a physical change is expected to occur by mixing it with an ointment. Therefore, in the present study, the physical stability of a mixture of four BBP_O formulations and heparinoid oily cream (HP_OC) was examined. Layer separation was observed in all mixtures following centrifugation. The near-infrared (NIR) measurement showed a peak at 5200 cm^-1 on the lower layer side, which strongly suggests the presence of water. The peak at 5200 cm^-1 in the middle layer was hardly observed in the mixtures of two BBP_O generic formulations and HP_OC, thus suggesting that the separation was more advanced in those mixtures than in the others. These two mixtures separated into a semisolid layer (upper side) and a liquid layer (lower side) after 3 h of storage at 37 °C. The NIR measurement of each layer revealed that most of the semisolid layer was oil while the liquid layer was water. Furthermore, backscattered light measurements were conducted to monitor the behavior of the mixture's layer separation. An evaluation using model formulations revealed that the layer separation of the mixtures was due to the propylene glycol (PG) and surfactant content of the two generic BBP_O formulations. Thus, these findings suggest that excipients need to be considered in selecting formulations for mixtures of skin preparations.

Assessment of Polysaccharides from Mycelia of genus Ganoderma by Mid-Infrared and Near-Infrared Spectroscopy

Ganoderma lingzhi (G. lingzhi), G. sinense, G. applanatum, etc. belongs to the Ganoderma genus of polypore mushrooms which contain rich polysaccharides valuable for nutrition and positive medicinal effects. In order to evaluate polysaccharide content in Ganoderma mycelia obtained in the fermentation process quickly and accurately, in this work we employed infrared spectroscopy to examine different Ganoderma stains of samples from diversified sources. Through mid-infrared (mid-IR) spectroscopy, we could identify the most relevant spectral bands required for polysaccharide evaluation, and through near-infrared (NIR) spectroscopy, we could establish the quantification model for making satisfactory prediction of polysaccharide ingredient content. As such, we have achieved an effective and convenient approach to quantitative assessment of the total polysaccharides in Ganoderma mycelia but also demonstrated that infrared spectroscopy can be a powerful tool for quality control of Ganoderma polysaccharides obtained from industrial production.