scholarly journals A Quick and Efficient Non-Targeted Screening Test for Saffron Authentication: Application of Chemometrics to Gas-Chromatographic Data

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2602 ◽  
Author(s):  
Pietro Morozzi ◽  
Alessandro Zappi ◽  
Fernando Gottardi ◽  
Marcello Locatelli ◽  
Dora Melucci
Keyword(s):  
Gas Chromatography ◽  
Discriminant Analysis ◽  
Curcuma Longa ◽  
Low Cost ◽  
External Validation ◽  
Screening Method ◽  
Prediction Ability ◽  
Linear Discriminant ◽  
Chromatographic Data ◽  
Flame Ionization

Saffron is one of the most adulterated food products all over the world because of its high market prize. Therefore, a non-targeted approach based on the combination of headspace flash gas-chromatography with flame ionization detection (HS-GC-FID) and chemometrics was tested and evaluated to check adulteration of this spice with two of the principal plant-derived adulterants: turmeric (Curcuma longa L.) and marigold (Calendula officinalis L.). Chemometric models were carried out through both linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) from the gas-chromatographic data. These models were also validated by cross validation (CV) and external validation, which were performed by testing both models on pure spices and artificial mixtures capable of simulating adulterations of saffron with the two adulterants examined. These models gave back satisfactory results. Indeed, both models showed functional internal and external prediction ability. The achieved results point out that the method based on a combination of chemometrics with gas-chromatography may provide a rapid and low-cost screening method for the authentication of saffron.

scholarly journals Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data

Molecules ◽  
2020 ◽  
Vol 25 (10) ◽  
pp. 2332 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Martina Foschi ◽  
Angelo Antonio D’Archivio
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
External Validation ◽  
Central Italy ◽  
Accurate Solution ◽  
Crocus Sativus ◽  
Linear Discriminant ◽  
Ir Spectroscopic

One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation).

Application of Linear Discriminant Analysis to the Differentiation of Pure Milk from Different Species and Mixtures

1977 ◽  
Vol 60 (6) ◽  
pp. 1382-1385
Author(s):  
Johanna Smeyers-Verbeke ◽  
Désiré L Massart ◽  
Danny Coomans
Keyword(s):  
Fatty Acids ◽  
Discriminant Analysis ◽  
Milk Fat ◽  
Visual Inspection ◽  
Good Discrimination ◽  
Linear Discriminant ◽  
Chromatographic Data ◽  
Milk Samples ◽  
Pure Milk ◽  
High Degree

Abstract Discriminant analysis is used to identify different milk samples on the basis of the gas chromatographic data for fatty acids in 20 samples each of milk fat from cows, sheep, and goats. The method can differentiate mixtures and pure milks with a high degree of correct classifications. A good discrimination can also be obtained by using a reduced set of variables. The method is useful for the interpretation of gas chromatographic data and should allow a higher proportion of correct classifications than is possible by visual inspection of the chromatograms.

scholarly journals Classification models of structure - P-glycoprotein activity of drugs

2016 ◽  
Vol 62 (2) ◽  
pp. 173-179
Author(s):  
V.Yu. Grigorev ◽  
S.L. Solodova ◽  
D.E. Polianczyk ◽  
O.A. Raevsky
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
External Validation ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Classification Models ◽  
Qsar Modeling ◽  
Linear Discriminant ◽  
P Glycoprotein ◽  
Dragon Descriptors

Thirty three classification models of substrate specificity of 177 drugs to P-glycoprotein have been created using of the linear discriminant analysis, random forest and support vector machine methods. QSAR modeling was carried out using 2 strategies. The first strategy consisted in search of all possible combinations from 1¸5 descriptors on the basis of 7 most significant molecular descriptors with clear physico-chemical interpretation. In the second case forward selection procedure up to 5 descriptors, starting from the best single descriptor was used. This strategy was applied to a set of 387 DRAGON descriptors. It was found that only one of 33 models has necessary statistical parameters. This model was designed by means of the linear discriminant analysis on the basis of a single descriptor of H-bond (SCad). The model has good statistical characteristics as evidenced by results to both internal cross-validation, and external validation with application of 44 new chemicals. This confirms an important role of hydrogen bond in the processes connected with penetration of chemical compounds through a blood-brain barrier

scholarly journals An Authentication Study on Grappa Spirit: The Use of Chemometrics to Detect a Food Fraud

Analytica ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 84-92
Author(s):  
Silvia Arduini ◽  
Alessandro Zappi ◽  
Marcello Locatelli ◽  
Salvatore Sgrò ◽  
Dora Melucci
Keyword(s):  
Gas Chromatography ◽  
Principal Component ◽  
Volatile Fraction ◽  
Alcohol Content ◽  
Food Fraud ◽  
Linear Discriminant ◽  
Grape Marc ◽  
Class A ◽  
Flame Ionization ◽  
One Class Classification

An authenticity study on Italian grape marc spirit was carried out by gas chromatography (GC) and chemometrics. A grape marc spirit produced in Italy takes the particular name of “grappa”, a product which has peculiar tradition and production in its country of origin. Therefore, the evaluation of its authenticity plays an important role for its consumption in Italy, as well as for its exportation all around the world. For the present work, 123 samples of grappa and several kinds of spirits were analyzed in their alcohol content by electronic densimetry, and in their volatile fraction by gas-chromatography with a flame-ionization detector. Part of these samples (94) was employed as a training set to compute a chemometric model (by linear discriminant analysis, LDA) and the other part (29 samples) was used as a test set to validate it. Finally, two grappa samples seized from the market by the Italian Customs and Monopolies Agency and considered suspicious due to their aroma reported as non-compliant were projected onto the LDA model to evaluate the compliance with the “grappa” class. A further one-class classification method by principal component analysis (PCA) was carried out to evaluate the compliance with other classes. Results showed that the suspicious samples were not recognized as belonging to any of the analyzed spirit classes, confirming the starting hypothesis that they could be grappa samples adulterated in some way.

Comparative Study of NMR Spectral Profiling for the Characterization and Authentication of Cannabis

2017 ◽  
Vol 100 (5) ◽  
pp. 1356-1364 ◽  
Author(s):  
Xinyi Wang ◽  
Peter de B Harrington ◽  
Steven F Baugh
Keyword(s):  
Discriminant Analysis ◽  
Nmr Spectroscopy ◽  
Plant Extracts ◽  
Screening Method ◽  
Fuzzy Rule ◽  
Classification Performance ◽  
Support Vector ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Projected Difference Resolution

Abstract For the authentication of botanical materials, itis difficult to obtain representative reference materials because botanicals vary significantly with respect to cultivation conditions. Chemical profiling of plant extracts or spectral fingerprinting can differentiate botanicals and group them by their chemical profiles. NMR spectroscopy yields a powerful and useful method for profiling plant extracts. Both 500 MHz 1H and 1H-1H correlation NMR spectroscopy coupled with pattern recognition were used to discriminate among Cannabis samples. A rapid method of analysis was achieved by extracting directly into the deuterated solvent. Spectral ranges including or excluding the downfield region were compared to evaluate the effect on classification accuracy by projected difference resolution. Six classification methods—fuzzy rule-building expert system, linear discriminant analysis (LDA), super partial least-squares discriminant analysis, support vector machine (SVM), and SVMclassification trees (SVMTrees)—all gave better classification performance for proton NMR spectrathan for proton-proton correlation NMR spectra for seven Cannabis samples. Among the classification methods for a set of 25 Cannabis samples, the 0.5–7.2 plus 7.4–13.0 ppm ranges gave higher prediction rates of greater than 96% when compared to the reduced range of 0.5–7.2 ppm that excluded the downfield range. The LDA method had the best prediction accuracy of 99.8 ± 0.4%. SVMTree methods provide a robust tool, and classification trees are amenable to interpretation. Hence, NMR spectroscopy combined withchemometrics could be used as a fast screening method for the authentication of Cannabis samples.

scholarly journals Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (“Crusco”) Bell Pepper

2021 ◽  
Vol 11 (4) ◽  
pp. 1709
Author(s):  
Alessandra Biancolillo ◽  
Francesca Di Donato ◽  
Francesco Merola ◽  
Federico Marini ◽  
Angelo Antonio D’Archivio
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
External Validation ◽  
Classification Problem ◽  
Sweet Pepper ◽  
Bell Pepper ◽  
Spectroscopic Techniques ◽  
Sequential Data ◽  
Linear Discriminant

Bell pepper is the common name of the berry obtained from some varieties of the Capsicum annuum species. This agro-food is appreciated all over the world and represents one of the key ingredients of several traditional dishes. It is used as a fresh product, or dried and ground as a seasoning (e.g., paprika). Specific varieties of sweet pepper present organoleptic peculiarities and they have been awarded by quality marks as a further confirmation of their unicity (e.g., Piment d’Espelette, Pimiento de Herbón, Peperone di Senise). Due to the market value of this aliment, it can be subjected to frauds, such as adulterations and sophistication. The present study lays on these considerations and aims at developing a spectroscopy-based approach for authenticating Senise bell pepper and for detecting its adulteration with common paprika. In order to achieve this goal, 60 pure samples of bell pepper from Senise were analyzed by mid- and near-infrared spectroscopies. Then, in order to mimic the adulteration, 40 mixtures of Senise bell pepper and paprika were prepared and analyzed (by the same spectroscopic techniques). Eventually, two different multi-block classification approaches (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate between pure and adulterated Senise bell pepper samples. Both proposed procedures achieved extremely successful results in external validation, correctly classifying all the (thirty-five) test samples, indicating that both approaches represent a winning solution for the investigated classification problem.

scholarly journals Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis

2009 ◽  
Vol 92 (5) ◽  
pp. 1846-1855 ◽  
Author(s):  
R. Gutiérrez ◽  
S. Vega ◽  
G. Díaz ◽  
J. Sánchez ◽  
M. Coronado ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Milk Fat ◽  
Linear Discriminant

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

2021 ◽  
pp. 096703352199974
Author(s):  
Yue Ma ◽  
Yichao Xu ◽  
Hui Yan ◽  
Guozheng Zhang
Keyword(s):  
Discriminant Analysis ◽  
Short Wavelength ◽  
Near Infrared ◽  
Low Cost ◽  
Calibration Model ◽  
Linear Discriminant ◽  
Line Identification ◽  
Silkworm Pupae ◽  
Calibration Models ◽  
On Line

The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties (Hibiscus, Jingsong, 932, Xiang Hui, 7532×Xiang Hui, Haoyue B, Jingsong B, and 7532). The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98.44%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

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