Identification of coffee leaves using FT-NIR spectroscopy and SIMCA

Mees, Corenthin; Souard, Florence; Delporte, Cédric; Deconinck, Éric; Stoffelen, Piet; Stévigny, Caroline; Kauffmann, Jean‐Michel; Braekeleer, Kris De

doi:10.1016/j.talanta.2017.09.056

Cited by 69 publications

(30 citation statements)

References 34 publications

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“…The genus Coffea L. comprises more than 100 species, of which only two species, that is, Coffea arabica (arabica coffee) and Coffea canephora (robusta coffee), are commercially cultivated on a large scale [ 1 ]. If the chemical composition of green coffee beans is well-known, less attention has been paid to the phytochemistry content of coffee leaves [ 2 , 3 , 4 , 5 , 6 , 7 ]. Interesting ethno-pharmacological uses and biological activities, which can partly be related to the presence of high amounts of chlorogenic acids (CGAs), have been described for coffee leaves [ 3 , 4 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Determination of Three Main Chlorogenic Acids in Water Extracts of Coffee Leaves by Liquid Chromatography Coupled to an Electrochemical Detector

et al. 2018

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Coffee is a beverage widely consumed in the world. The coffee species most commercialized worldwide are Arabica (Coffea arabica) and Robusta (Coffea canephora). Roasted coffee beans are the most used, but coffee leaves are also consumed as infusion in several countries for traditional medicinal purposes. They contain several interesting phenolic antioxidant compounds mainly belonging to chlorogenic acids (CGAs). In the present work, a liquid chromatography-electrochemical detection (LC-EC) method was developed for the determination of three main chlorogenic acid isomers, namely 3-, 4-, and 5-caffeoylquinic acids (CQA), in coffee leaves aqueous extracts. Samples from eight coffee species, namely; Coffea arabica, Coffea canephora, Coffea liberica, Coffea humilis, Coffea mannii, Coffea charrieriana, Coffea anthonyi, and Coffea liberica var. liberica, were grown and collected in tropical greenhouses. Linearity of the calibration graphs was observed in the range from the limit of quantification to 1.0 × 10−5 M, with R2 equal to 99.9% in all cases. High sensitivity was achieved with a limit of detection of 1.0 × 10−8 M for 3-CQA and 5-CQA (i.e., 3.5 µg/L) and 2.0 × 10−8 M for 4-CQA (i.e., 7.1 µg/L). The chromatographic profile of the samples harvested for each Coffea species was studied comparatively. Obtained raw data were pretreated for baseline variations and shifts in retention times between the chromatographic profiles. Principal Component Analysis (PCA) was applied to the pretreated data. According to the results, three clusters of Coffea species were found. In the water sample extracts, 5-CQA appeared to be the major isomer, and some species contained a very low amount of CQAs. Fluctuations were observed depending on the Coffea species and harvesting period. Significant differences between January and July were noticed regarding CQAs content. The species with the best CQAs/caffeine ratio was identified. The LC-EC data were validated by liquid chromatography-high resolution mass spectrometry (LC-HRMS).

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Section: Introductionmentioning

confidence: 99%

Determination of Three Main Chlorogenic Acids in Water Extracts of Coffee Leaves by Liquid Chromatography Coupled to an Electrochemical Detector

et al. 2018

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“…The principal component analysis (PCA), unsupervised technique, has been widely applied in data dimension reduction and exploratory data analysis [37,63]. From PCA-loading analysis, we can also extract the characteristic variables, which lead to differences between the samples.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Shen

Wang

2020

Molecules

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Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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“…C. Mees et al . have described a relevant study using NIRS to discriminate leaves, in a powder format, of various coffee plants 18 . Cannabis aerial parts have already been analysed by NIRS and classifications have already been made.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of legal and illegal Cannabis spp. according to European legislation using near infrared spectroscopy and chemometrics

Duchateau

Kauffmann

Canfyn

et al. 2020

Drug Testing and Analysis

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Aerial parts containing cannabidiol can be purchased in a legal way but cannabis used as recreational drug is illegal in most European countries. Δ9‐tetrahydrocannabinol is one of the main cannabinoids responsible for the psychotropic effect. European Union countries and Switzerland authorize a concentration of THC of 0.2 % and 1.0 % w/w, respectively, for smoking products and industrial hemp. Public health inspectors and law enforcement officers need to check the legality of samples. Therefore there is a need for innovative approaches, allowing quality control of these products in an easy way and preferably on site. In many countries, cultivation of industrial hemp is permitted if the THC content does not exceed 0.2 % w/w. A portable equipment could be a useful measuring tool for farmers to check for the THC content at regular time. In this work, 189 samples were analysed with a benchtop and a handheld NIR device in order to create two classification methods according to European and Swiss laws. All samples were also analysed by GC‐FID to determine their THC concentration. Supervised analysis was applied in order to establish the best model. For the first classification, the accuracy was 91% for the test set with the benchtop data and 93 % for the test set with the handheld data. For the second classification, the accuracies were respectively 91 % and 95 %. The obtained models, hyphenating spectroscopic techniques and chemometrics, enable to discriminate legal and illegal cannabis samples according to European and Swiss laws.

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Identification of coffee leaves using FT-NIR spectroscopy and SIMCA

Cited by 69 publications

References 34 publications

Determination of Three Main Chlorogenic Acids in Water Extracts of Coffee Leaves by Liquid Chromatography Coupled to an Electrochemical Detector

Determination of Three Main Chlorogenic Acids in Water Extracts of Coffee Leaves by Liquid Chromatography Coupled to an Electrochemical Detector

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Discrimination of legal and illegal Cannabis spp. according to European legislation using near infrared spectroscopy and chemometrics

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