Metal and metalloid profile as a fingerprint for traceability of wines under any Galician protected designation of origin

 Journal of Food Composition and Analysis, 102, 104043, 2021

Effective and cheap methods for detecting fraud and, guaranteeing wine authenticity, are of paramount importance in the sector. In this sense, three different kinds of prediction models (random forest, artificial neural networks, and support vector machines) were developed to classify wines, according to their element contents (metals and metalloids, obtained using an inductively coupled plasma with a quadrupole mass spectrometer, and an optic emission spectrophotometer). One models were developed using 45 inputs variables, and then the models were subjected to a process of reducing variables to simplify models and save material and time costs. A total accuracy was reached in all phases for the white wines-random forest models. From a practical point of view, the accuracy and the errors obtained by the selected models (except for red wines-artificial neural network developed using reduced variables) are acceptable. The models developed with fewer variables, can make the prediction task easier.

Prediction Models to Control Aging Time in Red Wine

Molecules 2019, 24(5), 826

DOI: 10.3390/molecules24050826

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

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.

Future challenges on the use of blockchain for food traceability analysis

Trends in Analytical Chemistry
DOI: 10.1016/j.trac.2018.08.011

The steady increase in food falsification, which has caused large economic losses and eroded consumers’ trust, has become a pressing issue for producers, researchers, governments, consumers and other stakeholders. Tracking and authenticating the food supply chain to understand provenance is critical with a view to identifying and addressing sources of contamination in the food supply chain worldwide. One way of solving traceability issues and ensuring transparency is by using blockchain technology to store data from chemical analysis in chronological order so that they are impossible to manipulate afterwards. This review examines the potential of blockchain technology for assuring traceability and authenticity in the food supply chain. It can be considered a true innovation and relevant approach to assure the quality of the third step of the analytical processes: data acquisition and management.

A review on the application of chromatographic methods, coupled to chemometrics, for food authentication

Food Control, 93, 165-182, 2018

DOI: 10.1016/j.foodcont.2018.06.015

The increase of food adulteration, inducing losing a large amount of money as well as of the confidence of consumers, has become an urgent issue for producers, researchers, governments and consumers. Chromatographic methods, in combination with chemometrics, are usually developed and applied throughout the food chain to verify the nature or origin of food, with both targeted (metabolomics) and non-targeted (profiling) approaches. Their operation, together with their advantages and drawbacks, will be discussed in this review to show strategies to solve food authentication issues.

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.