Food forensics

The food market nowadays accounts for huge incomes and therefore it is an easy target for falsification. This recalls the urgency for reliable and powerful diagnostic techniques, in order to develop analytical protocols for identification of frauds. MS-based strategies of analysis are definitely suitable for this task and have become in the last years of paramount importance in the field of food forensics. Sophisticated techniques have been developed that request short times of analysis and allow the identification of specific parameters, useful as classification markers. The wide range of techniques available [i.e. isotopic analysis, inductively coupled plasma - mass spectrometry (ICP-MS), hyphenated systems, stand-alone systems] allow to address a wide range of analytical questions pertaining to food authentication and traceability

Wine Traceability

Wine traceability is a central theme in the current world market where consumers are increasingly demanding the quality and origin of food and drink. The wine production chain and wine composition are generally controlled by different laws (International Organization of Vine and Wine (OIV), European Union (EU), and national governments) and need specific documentation. Nevertheless, wine production is subject to fraud. Consequently, the improvement of the methods applied to verify the origin and quality of wines is very important to protect wine consumers and producers. In this book, eight different papers—six research papers and two reviews—address the topic from different points of view

Detection of adulterations in fruit juices using machine learning methods over FT-IR spectroscopic data

Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R2 greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test setPeer ReviewedPostprint (published version

Food Authentication: Techniques, Trends and Emerging Approaches

Multiple factors can directly influence the chemical composition of foods and, consequently, their organoleptic, nutritional, and bioactive properties, including their geographical origin, the variety or breed, as well as the conditions of cultivation, breeding, and/or feeding, among others. Therefore, there is a great interest in the development of accurate, robust, and high-throughput analytical methods to guarantee the authenticity and traceability of foods. For these purposes, a large number of sensorial, physical, and chemical approaches can be used, which must be normally combined with advanced statistical tools. In this vein, the aim of the Special Issue “Food Authentication: Techniques, Trends, and Emerging Approaches” is to gather original research papers and review articles focused on the development and application of analytical techniques and emerging approaches in food authentication. This Special Issue comprises 12 valuable scientific contributions, including one review article and 11 original research works, dealing with the authentication of foods with great commercial value, such as olive oil, Iberian ham, and fruits, among others

Determination of Trace Elements in Wine by Atomic Spectroscopy and Electroanalytical Methods

The chapter will outline the basic theory, advantages and disadvantages, experimental considerations and set up of various atomic spectroscopy, and electroanalytical quantification methods and their specific application to trace element determination in wines. The reader will gain an introduction to most popular elemental analysis methods used in beverage analysis. Copper, iron, manganese, and zinc will be used as examples of essential trace elements throughout the chapter that at high levels may affect the properties of wine as well as the sensory experience of the consumer. Furthermore, special considerations that should be given to wine as a sample matrix for quantitative analysis of inorganic elements and the use of standard addition methods will be described

Geochemical and spectroscopic fingerprinting for authentication and geographical traceability of high-quality lemon fruits.

Geochemical (mineral element and Sr isotope ratio) and spectroscopical fingerprinting (Near Infrared Spectroscopy) were proposed to authenticate and track the two high-quality lemon fruits from the Campania region (Limone di Sorrento PGI and Limone Costa d'Amalfi PGI) to protect them from frauds. Considering the geochemical indicators, we built different chemometric discriminant models based on mineral profile and 87Sr/86Sr isotope ratio. These two techniques were applied to discriminate fruits from different territorial scales, small territorial scales (region scale), and large territorial scales. The results of different discriminant models applied on mineral profiles of lemon juices, both on a small and large territorially scale, showed good discrimination according to provenance, especially for non-essential elements as Rb, Ba, Sr, Ti, and Co. These same elements have shown a good correlation with cultivation soils and stability between the two production years. It is worth noting that although, the performance of the whole elemental profile gave a better result than the profile of the non-essential elements, the reliability of the two models, calculated as the ratio between the percentage of correctly validated and classification samples, was similar. In addition, the Sr isotope ratio had shown a clear differentiation among the fruits from the Campania region and extra-regional samples, and by analysis of 86Sr/87Sr of soils, it was clear that the strontium isotope ratio of lemon juices was closely related to that of the bioavailable fractions of the soil. Furthermore, combining both isotopic and mineral profiles in lemon juices by a low-level data fusion approach, the results showed a better clustering according to geographical origins than the two-determination taken separately, although on an explorative level. In addition, the spectroscopical data (NIR) on intact lemon fruits showed the strong influence of environmental growing conditions on the samples. For this, the application of Linear Discriminant Analysis (LDA) models suggested building the discrimination models according to origins (PGI and not PGI productions) based on one production year. In the same way, the application of MLR models, that showed a strong relationship between quality properties of lemon fruits and NIR spectra, suggested the applicability of this technique to build predictive models for the quality properties. In addition, on a part of the total samples collected only in 2019 (intact lemons and juices), have been successfully applied two different chemometrics models i.e., LDA and Partial Least Square Discriminant Analysis (PLS-DA). The results showed better provenance discrimination using the lemon juices than the intact lemons. Comparing the results obtained, of the two approaches used, the results of geochemical fingerprinting have shown more stability for discriminate lemon fruits derived from two different production years, especially for not essential elements. However, considering the various vantages of the application of NIR spectroscopy (non-destructive, rapid, and cheap) and the results obtained, this technique can be used for rapid screening of samples in order to verify the quality and origins of lemon fruits during the year. The study of the pedoclimatic features was fundamental to understand the nature of discriminating variables, in both approaches. Additional research should be conducted to include a greater number of lemon farms (or sampling points) in the PGI area and to enlarge the existing database including lemon samples from other regions and validate the models built. These discriminant models based on geochemical and spectroscopical profiles of lemon fruits could substantially contribute to implementing a blockchain system for Campanian lemon traceability, providing real-time information not only to the final consumers but also to manufacturers, distributors, and retailers

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: a short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications

Unveilling the role of technological processes on the strontium isotopic ratio, fingerprint of wines' geographical origin

Mestrado Vinifera Euromaster - Instituto Superior de Agronomia - UL / Institut National d'Études Supérieurs Agronomiques, MontpellierBased on the close relationship of strontium isotopic ratio 87Sr/86Sr between soil and plants, this isotopic ratio has been reported as fingerprint tool to verify wine geographical origin and its authenticity In the last decade, some studies suggested that winemaking applications do not alter the 87Sr/86Sr isotopic ratios from vineyard to the wine despite the variations of mineral concentration. However, information about wood impact on 87Sr/86Sr is lacking in the literature. In this study, we investigated the wood ageing effect on 87Sr/86Sr, and also on the multi-elemental compositon of wine, which to our best knowledge are novelties, thus representing important advances to this field of knowledge. A red wine from Castelão grape variety was aged in stainless steel vats (34,000 L) with oak wood staves, in triplicate. The wines were sampled after 30, 60 and 90 days of ageing and evaluated in terms of: 87Sr/86Sr, by Q-ICP-MS after Sr and Rb separation by ion exchange chromatography; multi-elemental analysis (Be, Mg, Al, Sc, Ti, V, Mn, Co, Ni, Cu, Zn, Ga, Ge, As, Rb, Sr, Y, Zr, Mo, Sb, Cs, Ba, Pr, Nd, Sm, Eu, Dy, Ho, Er, Yb, Lu, Tl, Pb) by Q-ICP-MS; Na, K, Ca, Fe by FAAS. Wood ageingWood ageing effect on total polysaccharides concentration , chromatic characteristics and phenolic composition was also evaluated. Statistical analysis showed that there was no significant difference of the 87Sr/86Sr between the control wine and wine aged with wood. The results show that strontium isotopic ratio of wines was not altered even after ageing with wood (0.710 at initial time and 0.709 after 90 days) despite having a significant increase in Sr concentration by wood stage. This suggests that 87Sr/86Sr might be used as a reliable geographical indicator. Time had significant impact on Al, V, Zn, Ni, Cs, Pb and also on Na, K, Ca and Fe while wood stage only altered concentrations of Mg, V, Co, Ni and Sr. Our study suggests that wood ageing does not impact the 87Sr/86Sr, accordingly it will shed some light for further studies.N/

Pencarian Frequent Itemset pada Analisis Keranjang Belanja Menggunakan Algoritma FP-Growth

 Market basket analysis (also known as association rule mining) is one method of data mining that focuses on finding purchase patterns by extracting associations or transaction data from a store. Market basket analysis found products purchased together in the same bucket. Association rules is a procedure for finding relationships between items that exist on a dataset. This research uses Supermarket dataset and data processing using Rapid Miner software. The method used in the frequent itemset search is the FP-Growth Algorithm. Experimental results using FP-Growth Algorithm found that the combination of beer spirits-frozen foods and snack foods is a frequent itemset with an lift ratio of 2,477   Keywords: FP-Growth, Market Basekt Analysi