Tracing the Geographical Origins of Dendrobe (Dendrobium spp.) by Near-Infrared Spectroscopy Sensor Combined with Porphyrin and Chemometrics

Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas ('non-Zhejiang'). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R² reaching 0.9979, while that of the second derivative was the lowest with an R² of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

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.

Application of Time of Flight Mass Spectrometry in the Identification of Dendrobium devonianum Paxt and Dendrobium officinale Kimura et Migo Grown in Longling Area of Yunnan, China

In this study, in order to protect the characteristic Dendrobium devonianum Paxt industry in the Longling area, and promote the healthy development of its characteristic Chinese herbal medicines in Yunnan Province, China, the identification of Dendrobium devonianum Paxt and Dendrobium officinale Kimura et Migo from Longling county was discussed using time of flight mass spectrometry. The data of 13 Dendrobium devonianum and 7 Dendrobium officinale in the Longling area were collected by TOF MS-IDA-15 MS/MS mode, and the collected data were analyzed by PCA and T-test using MarkerView software, and the difference markers were searched using the database to confirm their compound structures. In positive and negative ion modes, 3645 and 2344 peaks were detected, respectively; 64 positive ion compounds and 60 negative ion compounds, for a total of 124 compounds were identified, mainly including organic acids, polyphenols, alkaloids, amino acids and their derivatives, benzene and its derivatives, and other compounds. The score plot and loading plot analyzed by PCA show that Dendrobium devonianum and Dendrobium officinale collected in the Longling area can be effectively identified and differentiated by high-resolution mass spectrometry with the 15 different markers in positive ion mode and 17 markers in negative ion mode, respectively. The successful identification of Dendrobium devonianum and Dendrobium officinale fully demonstrates that TOF MS can be effectively used in the identification of Dendrobium and related Chinese herbal medicines with broadly application foreground.

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.

The Regulatory Effects and the Signaling Pathways of Natural Bioactive Compounds on Ferroptosis

Natural bioactive compounds abundantly presented in foods and medicinal plants have recently received a remarkable attention because of their various biological activities and minimal toxicity. In recent years, many natural compounds appear to offer significant effects in the regulation of ferroptosis. Ferroptosis is the forefront of international scientific research which has been exponential growth since the term was coined. This type of regulated cell death is driven by iron-dependent phospholipid peroxidation. Recent studies have shown that numerous organ injuries and pathophysiological processes of many diseases are driven by ferroptosis, such as cancer, arteriosclerosis, neurodegenerative disease, diabetes, ischemia-reperfusion injury and acute renal failure. It is reported that the initiation and inhibition of ferroptosis plays a pivotal role in lipid peroxidation, organ damage, neurodegeneration and cancer growth and progression. Recently, many natural phytochemicals extracted from edible plants have been demonstrated to be novel ferroptosis regulators and have the potential to treat ferroptosis-related diseases. This review provides an updated overview on the role of natural bioactive compounds and the potential signaling pathways in the regulation of ferroptosis.

Classification of organic and ordinary kiwifruit by chemometrics analysis of elemental fingerprint and stable isotopic ratios

Elemental fingerprint, stable isotopic analysis, and chemometrics were combined to identify organic kiwifruit from ordinarily cultivated kiwifruit. Samples of organic (n1 = 78) and ordinary kiwifruit (n2 = 85) were collected from neighboring areas. For elemental fingerprint, the contents of 15 elements in fresh fruits, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, and K, were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Three stable isotopes, including δ¹³ C, δ¹⁵ N, and δ¹⁸ O, were analyzed using an isotope-ratio mass spectrometer (IRMS). Different classification methods including soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLSDA), and least squares support vector machines (LS-SVM), were used to discriminate the organic and ordinary kiwifruits by fusion of elemental and stable isotopic. As a result, the sensitivity, specificity, and overall accuracy of SIMCA model were 0.885, 0.857, and 0.864, respectively. PLSDA and LS-SVM obtained 0.950 and 0.983 classification accuracy of organic and ordinary kiwifruits, respectively. It was demonstrated that elemental fingerprint and stable isotopic analysis would provide useful chemical information for the identification of organic fruits, and the capacity of these methods could be enhanced by chemometrics. PRACTICAL APPLICATION: The classification of kiwifruit usually relies on the label assigned by the merchant, which is prone to deceive consumers. This research has developed an accurate and effective classification method based on stable isotopes and mineral elements for the identification of ordinary kiwifruit and organic kiwifruit, providing a tool for the quality monitoring of organic food.