chemometrics in food authentication

The guarantee of food safety and quality along the food chain is demanding great attention fromthe food industry and consumers. regulatory authorities, retailers, food producers, and processorsare all actively interested in ensuring that food quality matches by the claims they are posing.fraud and adulteration detection, so-called food authentication, is a process that verifies foodcompliance with its label description [1]. this may include the geographical origin, productionmethod, processing technologies, and food composition. various types of food fraud, includingsubstitution, dilution, tampering, misrepresentation, and mislabeling, are often used to derivefinancial profits by reducing manufacturing costs or extending shelf lives. authenticatingfoodstuffs that hold a certificate of specific character (csc), protected designation of origin(pdo), or protected geographical indication (pgi) is essential with agro-food products [2]. foodadulteration is a growing challenge for food manufacturers and analysts because most adulterantsare unknown and difficult to recognize using the typical targeted screening methods. this industryurgently needs non-targeted screening methods for food samples to provide proof of origin andprevent deliberate or accidental undeclared additions to those samples. nowadays, the rapidgrowth of the capabilities of modern analytical instruments provides a large amount of datainvolving a wide range of factors (features) that need chemometrics for the extraction ofmeaningful information. chemometrics provides powerful tools in calibration and classificationanalysis, applied in targeted and non-targeted approaches to identify various food fraud situationsor verify of their geographic or biological origin. the most common multivariate methods andprinciples for food authentication can be classified into three categories: exploratory data analysis;data description and visualization, discrimination, and classification; and regression andprediction. classification methods based on multivariate data analysis could be supervised orunsupervised. in unsupervised methods, the purpose is to identify clusters or relationships betweensamples without prior knowledge of classes or groups. in contrast, supervised methods requireinformation on class membership and a training stage to build a proper mathematical model.classification techniques are of either of two main types. one is known as “discriminant analysis”or “hard modeling” and partitions the data space into isolated regions (classes); the other, knownas “class modeling” or “soft modeling”, models each category or class separately [3].