Untargeted detection and quantitative analysis of poplar balata (PB) in Chinese propolis by FT-NIR spectroscopy and chemometrics

Tea authentication and determination of chemical constituents using digital image-based fingerprint signatures and chemometrics

Tea (Camellia sinensis) fraud has been frequently identified and involves tampering with the labelling of inferior products or without geographical origin certification and even mixing them with superior quality teas to mask an adulteration. Consequently, economic losses and health damage to consumers are observed. Thus, a Chemometrics-assisted Color Histogram-based Analytical System (CACHAS) was employed a simple, cost-effective, reliable, and green analytical tool to screen the quality of teas. Data-Driven Soft Independent Modeling of Class Analogy was used to authenticate their geographical origin and category simultaneously, recognizing correctly all Argentinean and Sri Lankan black teas and Argentinean green teas. For the determination of moisture, total polyphenols, and caffeine, Partial Least Squares obtained satisfactory predictive abilities, with values of root mean squared error of prediction (RMSEP) of 0.50, 0.788, and 0.25 mg kg^-1, r_pred of 0.81, 0.902, and 0.81, and relative error of prediction (REP) of 6.38, 9.031, and 14.58%., respectively. CACHAS proved to be a good alternative tool for environmentally-friendly non-destructive chemical analysis.

Extraction, purification, structural character and biological properties of propolis flavonoids: A review

Propolis is an aromatic substance which is collected by bees and mixed with bee saliva. The plant sources of propolis are mainly consisted with plant exudates from bark, buds and etc. Flavonoids are secondary metabolites widely found in natural plants, which have a variety of health care functions and are the main active ingredients of propolis. This article summarized the types, active ingredients, pharmacological effects, extraction methods and applications of propolis flavonoids, the aim was to provide the theoretical basis for further research and development of propolis flavonoids.

Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: The example of non-protein nitrogen

The melamine scandal indicates that traditional targeted detection methods only detect the specifically listed forms of contamination, which leads to the failure to identify new adulterants in time. In order to deal with continually changing forms of adulterations in food and feed and make up for the inadequacy of targeted detection methods, an untargeted detection method based on local anomaly detection (LAD) using near infrared (NIR) imaging was examined in this study. In the LAD method, with a particular size of window filter and at a 99% level of confidence, a specific value of Global H (GH, modified Mahalanobis distance) can be used as a threshold for anomalous spectra detection and quantitative analysis. The results showed an acceptable performance for the detection of contaminations with the advantage of no need of building a 'clean' library. And, a high coefficient of determination (R2LAD = 0.9984 and R2PLS-DA = 0.9978) for the quantitative analysis of melamine with a limit of detection lower than 0.01% was obtained. This indicates that the new strategy of untargeted detection has the potential to move from passive to active for food and feed safety control.

A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0-3 kilometers, 7.52-88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0-0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

The phytochemistry of the honeybee

Honeybees rely on plants for everything they need to keep the colony running; plant nectar and pollen are their only carbohydrate and protein food sources. By foraging to satisfy their basic nutritional demand, honeybees inevitably gather specialized plant metabolites as part of the nectar and pollen. In general, these compounds possess biological activity which may become relevant in fighting pests and pathogens in the hive. The third plant derived bee product, besides honey and bee pollen, is propolis (bee glue), which comes from plant resins. It is not a food; it is used as a building material and a defensive substance. Thus, the beehive is rich in specialized plant metabolites, produced by many different plant species and the expression "Phytochemistry of honeybees" is not inappropriate. However, it is virtually impossible to perform a detailed overview of the phytochemical features of honey and pollen in a review article of this nature, for reasons of space. The present review deals with propolis, because it is the bee product with highest concentration of specialized plant metabolites and has valuable pharmacological activities. The most recent developments concerning plant sources of propolis, bees' preferences to particular plants, the application of metabolomic approaches and chemometrics to propolis research and the problems concerning standardization of propolis are summarized. The overview covers the literature published in the last decade, after 2007.

Flavonoid and Antioxidant Capacity of Propolis Prediction Using Near Infrared Spectroscopy

The use of propolis as a dietary supplement or as an ingredient in different food products is increasing, due to its antioxidant and bactericidal properties. These nutritional properties directly depend on its phenolic composition. For this reason, this study analysed the total contents of flavones and flavonols, flavanones and dihydroflavonols, and the antioxidant capacity by using the methods of ABTS and linoleic acid/β-carotene in 99 samples of propolis from Spain and Chile. A rapid method was developed for quantifying these parameters in raw propolis using near infrared (NIR) spectroscopy with a remote reflectance fibre-optic probe applied directly to the ground-up sample. The models developed allow for the determination of the total flavones and flavonols (0–183 mg quercetin/g propolis and 0–72 mg rutin/g propolis), of the total flavanones and dihydroflavonols (9–109 mg pinocembrin/g propolis extract), and of its antioxidant capacity by the ABTS method based on the reduction of the 2.2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation(0–3212.6 nmol Trolox/mg of propolis) and of linoleic acid/β-carotene (22–86% inhibition). The NIR spectroscopy models were applied in external validation to different samples of the calibration group, which led to the conclusion that the methods developed provide significantly identical data to the initial chemical data of reference.

Pesticide residues in propolis from Spain and Chile. An approach using near infrared spectroscopy

This study analyses the presence of pesticides in 106 samples of raw propolis from Spain and Chile. The components detected and quantified using gas chromatography mass spectrometry were as follows: triamidefon present in 70.1% of the samples with concentrations between 0.35 and 42.17mg/kg, and dicofol detected in 7.5% of the samples with concentrations between 0.39 and 2.83mg/kg. Diclorofluanid, procymidone, folpet, propham and metazachlor were also detected but only in a small number of samples. Near infrared spectroscopy with a remote reflectance fibre-optic probe was used to detect samples contaminated with triadimefon via partial least squares discriminant, with a R² value of 0.71. In addition, the presence of the pesticide triadimefon in propolis was quantified using the modified partial least squares (MPLS) regression method. The calibration results showed a coefficient of determination (R²) of 0.81, a root mean square error (RMSE) of 0.36, and a ratio of performance to deviation (RPD) of 2.5.

Determination of the Mineral Composition and Toxic Element Contents of Propolis by Near Infrared Spectroscopy

The potential of near infrared spectroscopy (NIR) with remote reflectance fiber-optic probes for determining the mineral composition of propolis was evaluated. This technology allows direct measurements without prior sample treatment. Ninety one samples of propolis were collected in Chile (Bio-Bio region) and Spain (Castilla-León and Galicia regions). The minerals measured were aluminum, calcium, iron, potassium, magnesium, phosphorus, and some potentially toxic trace elements such as zinc, chromium, nickel, copper and lead. The modified partial least squares (MPLS) regression method was used to develop the NIR calibration model. The determination coefficient (R²) and root mean square error of prediction (RMSEP) obtained for aluminum (0.79, 53), calcium (0.83, 94), iron (0.69, 134) potassium (0.95, 117), magnesium (0.70, 99), phosphorus (0.94, 24) zinc (0.87, 10) chromium (0.48, 0.6) nickel (0.52, 0.7) copper (0.64, 0.9) and lead (0.70, 2) in ppm. The results demonstrated that the capacity for prediction can be considered good for wide ranges of potassium, phosphorus and zinc concentrations, and acceptable for aluminum, calcium, magnesium, iron and lead. This indicated that the NIR method is comparable to chemical methods. The method is of interest in the rapid prediction of potentially toxic elements in propolis before consumption.