Classification and authentication of tea according to their harvest season based on FT-IR fingerprinting using pattern recognition methods

 J. Food Comp. Anal. 115, 104995 (2023)

The potential of FT-IR spectral fingerprinting was investigated to classify tea samples based on the harvest season (May and September). Tea samples were collected from five geographical regions (north of Iran) during the harvesting period 2019–2020. Principal component analysis (PCA), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least square-linear discriminant analysis (PLS-LDA) were employed in order to assess the feasibility of discrimination of tea samples based on their harvest season using their FT-IR spectral data. The results showed that the tea samples from two harvest seasons can be identified based on FT-IR spectral fingerprints. All calibration samples were correctly classified (100.0 %) by the PCA-LDA and PLS-LDA models using leave-one-out cross validation. The mean sensitivity and specificity (for prediction set) were both 98.6 % for PCA-LDA model and 100.0 % for PLS-LDA mode. A high percentage of correct classifications for the training set shows the strong relationship between the FT-IR spectral fingerprinting and the harvest season, while the satisfactory results for the prediction set demonstrates the ability to identify the harvest season of an unknown tea sample based on its FT-IR spectral data.

Classification and authentication of tea according to their harvest season based on FT-IR fingerprinting using pattern recognition methods

 J. Food Comp. Anal. 115, 104995, 2023

The potential of FT-IR spectral fingerprinting was investigated to classify tea samples based on the harvest season (May and September). Tea samples were collected from five geographical regions (north of Iran) during the harvesting period 2019–2020. Principal component analysis (PCA), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least square-linear discriminant analysis (PLS-LDA) were employed in order to assess the feasibility of discrimination of tea samples based on their harvest season using their FT-IR spectral data. The results showed that the tea samples from two harvest seasons can be identified based on FT-IR spectral fingerprints. All calibration samples were correctly classified (100.0 %) by the PCA-LDA and PLS-LDA models using leave-one-out cross validation. The mean sensitivity and specificity (for prediction set) were both 98.6 % for PCA-LDA model and 100.0 % for PLS-LDA mode. A high percentage of correct classifications for the training set shows the strong relationship between the FT-IR spectral fingerprinting and the harvest season, while the satisfactory results for the prediction set demonstrates the ability to identify the harvest season of an unknown tea sample based on its FT-IR spectral data.

Fatty Acids-Based Quality Index to Differentiate Worldwide Commercial Pistachio Cultivars

Molecules 2019, 24(1), 58

DOI: 10.3390/molecules24010058

The fatty acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi, and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty acid data yielded three significant PCs, which together account for 80.0% of the total variance in the dataset. A linear discriminant analysis (LDA) model that was evaluated with cross-validation correctly classified almost all of the samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty acid chromatographic fingerprint.

Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products

Food Control, 2018, 91, 100-112

DOI:10.1016/j.foodcont.2018.03.031

Spectroscopic methods are efficient tools for food authentication due to the advantages of high sensitivity, rapidness, simplicity and their convenience. The combined used of spectroscopic methods and linear discriminant analysis has provided powerful tools for detecting food fraud. This review discusses their operational details, advantages and disadvantages.