Recent advances in Chinese food authentication and origin verification using isotope ratio mass spectrometry

 Food Chem. 398, 133896, 2023

Over the last decade, isotope ratio mass spectrometry (IRMS) using up to 5 light stable isotopes (13C/12C, 2H/1H, 15N/14N, 18O/16O, 34S/32S) has become more widely applied for food origin verification as well as food authentication in China. IRMS technology is increasingly used to authenticate a range of food products including organic foods, honey, beverages, tea, animal products, fruits, oils, cereals, spices and condiments that are frequently unique to a specific region of China. Compared to other food authenticity and traceability techniques, IRMS has been successfully used to characterize, classify and identify many Chinese food products, reducing fraud and food safety problems and improving consumer trust and confidence. IRMS techniques also provides scientific support to enhance China’s strict government regulatory policies. Isotope testing verifies geographical origin labelling of domestic and imported foods, protects and verifies high value foods that are unique to China, and indicates environmentally friendly farming practices such as ‘green’ or ‘organic’ methods. This paper reviews recently published Chinese research to highlight the recent advances of IRMS as a regulatory and verification tool for Chinese food products.

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.

A critical review on the use of artificial neural networks in olive oil production, characterization and authentication

Critical Reviews in Food Science and Nutrition 

doi.org/10.1080/10408398.2018.1433628

Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology.

The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.