•Saffron as a high value spice is prone to mislabeling of origin.•Stable isotopes and bioelements are used to discriminate Iranian and Chinese saffron.•Chemometric models show good country and ...regional discrimination of saffron.
Origin verification of high-value saffron is essential for fair trade and to protect consumers' interests and rights. A traceability method using elemental content (% C and % N) and stable isotopes (δ13C, δ2H, δ18O, and δ15N) combined with chemometrics was developed to discriminate saffron from Iran and China and classify major domestic production areas in China. Results showed that Iranian samples had lower % C and % N contents but higher δ13C values than Chinese origin saffron, with δ13C acting as an important variable for origin discrimination. Moreover, δ2H and δ13C isotopes were found to be important variables to classify Chinese regional saffron origin. Two supervised pattern recognition models (PLS-DA) developed to classify Iranian and Chinese saffron, and regional Chinese saffron had a discrimination accuracy of 85.0 % and 80.2 %, respectively. These models provide the basis for a new regulatory inspection procedure to verify saffron origin and label claims, minimizing fraudulent mislabeling and adding value to saffron from specific regions.
•Rapidly evolving field, opportunities and challenges for the analytical scientist.•Analytical science leads the field providing plausible solutions.•A scientometric evaluation provides outlook and ...potential.•Core of research activity comes from South Europe while China makes a big step forward.•Αnalytical breakthroughs – novel techniques along their applications.
Food authentication is a rapidly growing field due to increasing public awareness concerning food quality and safety. This review presents critically the analytical techniques which are used for authenticity assessment, explaining how and why they give plausible solutions. Classification of different methodologies is based on authenticity indicators providing insight into future developments. Analytical breakthroughs and novel techniques that emerged recently are discussed, along with their applications on food authentication. We have discussed current limits and gaps, related to informatics needs for data analysis of large quantities. Reporting standards and reference database are elaborated indicating urgent needs for the progress of this field. A scientometric evaluation highlighted the research trends and emerging approaches of this evolving field. Popular analytical techniques are commented, while the potential of the field is depicted in the temporal evolution of the research output focusing on geographical distribution of research activity and preferred journals used for dissemination.
To apportion regional PM2.5 (atmospheric particles with aerodynamic diameter<2.5μm) source types and their geographic pattern in North China, 120 daily PM2.5 samples on Beihuangcheng Island (BH, a ...regional background site in North China) were collected from August 20th, 2014 to September 15th, 2015 showing one-year period. After the chemical analyses on carbonaceous species, water-soluble ions and inorganic elements, various approaches, such as Mann-Kendall test, chemical mass closure, ISORROPIA II model, Positive Matrix Factorization (PMF) linked with Potential Source Contribution Function (PSCF), were used to explore the PM2.5 speciation, sources, and source regions. Consequently, distinct seasonal variations of PM2.5 and its main species were found and could be explained by varying emission source characteristics. Based on PMF model, seven source factors for PM2.5 were identified, which were coal combustion + biomass burning, vehicle emission, mineral dust, ship emission, sea salt, industry source, refined chrome industry with the contribution of 48.21%, 30.33%, 7.24%, 6.63%, 3.51%, 3.2%, and 0.88%, respectively. In addition, PSCF analysis using the daily contribution of each factor from PMF result suggested that Shandong peninsula and Hebei province were identified as the high potential region for coal combustion + biomass burning; Beijing-Tianjin-Hebei (BTH) region was the main source region for industry source; Bohai Sea and East China Sea were found to be of high source potential for ship emission; Geographical region located northwest of BH Island was possessed of high probability for sea salt; Mineral dust presumably came from the region of Mongolia; Refined chrome industry mostly came from Liaoning, Jilin province; The vehicle emission was primarily of BTH region origin, centring on metropolises, such as Beijing and Tianjin. These results provided precious implications for PM2.5 control strategies in North China.
•120 PM2.5 samples were collected at a regional background site in North China.•PMF combined PSCF were adopted to explore the source and origin regions for PM2.5.•Coal combustion+biomass burning and vehicle emission were the dominant sources.•Shandong and BTH were the main source areas for PM2.5 in North China.
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•EEMs fluorescence method was proposed for identification of the origin of Gastrodia elata.•Discriminant model for identification was built by N-PLS-DA, U-PLS-DA and kNN.•All models ...successfully identified the geographical origin of Gastrodia elata.•kNN was superior to other models in the geographical origin identification of Gastrodia elata.
Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.
Since Indonesia is the world's largest producer of coconuts, it is necessary to classify them according to their chemical profiles. The examination of coconut endosperm from 13 districts in Aceh's ...coastal plantations was done using the NIRS. Chemometric analysis of the NIRS spectra revealed that smoothing, the 1st derivative, and SNV preprocessing of the PCA produced improved visualization outcomes. The discrimination study revealed that the PCA-LDA and PCA-SVM produced accurate results that were satisfactory, with 100% accuracy for each. The effectiveness of combinations of PCA-LDA and PCA-SVM was examined in this article as being helpful for enhancing classification accuracy.
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•Hyperspectral imaging was used, for the first time, to trace the origin of Hangbaiju.•BAGCT-RBFN, compared with CT and RBFN, were used to identify the origin of Hangbaiju.•With fewer ...variables, BAGCT-RBFN compared favorably with RBFN and CT.•The proposed strategy is fast, non-destructive and simple.
Hangbaiju is highly appreciated flower tea for its health benefits, and its quality and price are affected by geographical origin. Fast and accurate identification of the geographical origin of Hangbaiju is very significant for producers, consumers and market regulators. In this work, hyperspectral imaging combined with chemometrics, was used, for the first time, to explore and implement the geographical origin classification of Hangbaiju. The hyperspectral images in the spectral range of 410–2500 nm for 75 samples of five different origins were collected. As a versatile chemometrics tool, bagging classification tree-radial basis function (BAGCT-RBFN), compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins. The results showed that BAGCT-RBFN based on optimal wavelengths yielded superior classification performances to CT and RBFN with full wavelengths. The recognition rates (RR) of the training and prediction sets by BAGCT-RBFN were 96.0 % and 92.0 %, respectively. Hyperspectral imaging combined with chemometric can be considered as a powerful, feasible and convenient tool for the classification of Hangbaiju samples from different origins. It promises to be a potential way for origin discriminant analysis and quality monitor in food fields.
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•FT-NIR spectroscopy was used to rapidly identify geographical origin of rice.•The classification models developed using LDA, PLS-DA, C-SVC, PC-NN and KNN tools presented high ...classification results.•Results did not show overfitting during k-cross validation and optimal hyperparameter fine tuning procedure by GridSearchCV.•The extremely randomized trees (Extra trees) was recommended for use due to the smaller number of featured wavelengths.
The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 – 4,000 cm−1 (800–2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.
This study investigates the feasibility of using UV–Vis spectroscopy coupled with machine learning methods to authenticate tea samples based on their geographical origins in a narrow longitudinal ...strip (200 km). Several preprocessing methods, such as standard normal variate (SNV), auto-scaling, multiplicative scatter correction (MSC), mean centring (MC), first derivative, and their combinations, were applied to eliminate the noninformative information. The partial least squares-linear discriminant analysis (PLS-LDA) model using first derivative spectra represented the following results, including 98.0% sensitivity, 99.5% specificity, and a mean accuracy of 98.0%. The support vector machine (PLS-SVM) classifier using first derivative spectra represented 94.0% sensitivity, 98.6% specificity, and a mean accuracy of 94.0%. The satisfactory results of the models depicted that the chemical components of tea, such as polyphenols, chlorogenic and fatty acids that absorb UV radiation are the chemical markers that can discriminate tea samples based on their geographical origin. Therefore, UV–Vis spectral fingerprinting combined with machine learning methods could be a practical, feasible, and simple method for classifying tea based on their geographical origins in a narrow longitudinal strip.
•Accurate classification models (PCA-LDA and PLS-LDA) identify the origin of the samples.•The results show discrimination amongst samples of different region with similar climate.•The proposed tool can quickly authenticate tea origin.•The metabolic profile of the samples is latent in UV–Vis spectral data.
The aim of this work was to undertake a detailed analysis on chemical constituents of brown propolis, originating from four different states (Bahia, Minas Gerais, Paraná and Sergipe) of Brazil. The ...volatile profile was determined by using HS-SPME-GC–MS along with the determination of total phenolic compounds content, flavonoids and antioxidant activity. A total of 315 volatile compounds were identified, however, several of them have not been reported so far in the Brazilian brown propolis. The terpenes represented the major class with 40.92–84.66% of the total area in the chromatograms. PCA analysis of the majority of compounds successfully indicated the volatile profile of each propolis sample according to their geographical origin. The analysis of volatile compounds and its characterization also varied significantly and confirmed that these depended on the geographical area of collection of propolis. The data generated in this work may help in establishing criteria for quality control and tracking the specific region of propolis production in different states of Brazil.
•The method identified 315 compounds in brown própolis from four different locations of Brazil.•Most compounds have not been described until now in the Brazilian brown própolis.•The terpenes are the predominant category of compounds for all samples of propolis analyzed.•The raw brown propolis of each state has a characteristic profile of major compounds, allowing them to differentiate them.•The method can help in establishing criteria for the quality control of Brazilian propolis samples.
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•Volatile organic compounds in salmonids were analyzed via HS-GC-IMS & electronic nose.•Salmonid taste was analyzed via electronic tongue and amino acid detection.•HS-GC-IMS is ...reliable for salmonid geographical origin and species identification.•Flavor compounds can distinguish salmonid geographical origin and species.•The combination of four methods can effectively distinguish the flavor of salmonid.
The flavor of salmonids is affected by species and origin. Sources of salmonid fish fillets are complex and difficult to identify and label fraud occasionally occurs in the market. In this study, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), electronic nose, electronic tongue and amino acid detection technologies were used to analyze flavor compounds in two salmonid species from different geographical origins. Fingerprints of volatile compounds of salmonid were constructed using HS-GC-IMS technology. Free amino acid (FAA) content differed in salmonids from different geographical origins. Regarding salmonid odor, HS-GC-IMS analysis results were basically consistent with those of the electronic nose. Regarding taste, the conclusions drawn from the electronic tongue were consistent with the amino acid test results. Therefore, our results demonstrate that flavor compounds can be used to distinguish salmonids from different geographical origins, providing a new dimension to food safety and authenticity. Furthermore, HS-GC-IMS, electronic nose and tongue can be used as tools in the market to identify food fraud.
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