Since food adulteration is a huge concern in all parts of the globe, there is voluminous literature on various aspects of food adulteration including its detection. In fact, methodological procedures of food adulteration detection have been reviewed extensively (Druml and Cichna-Markl, 2014; Li and Sheng, 2014; Cheng et al., 2015; Qu et al., 2015; Feng and Sun, 2012).
Many of the methods for detection of food adulteration require elaborate steps of sample preparation prior analysis involving high-end technologies and that makes the whole process difficult to perform and time consuming.
Therefore, considerable interest has emerged in developing rapid methods for food-adulteration detection (Rodriguez-Saona and Allendorf, 2011). Thus, rapid online detection of food quality, in a nondestructive manner becomes even more relevant (Ruiz et al., 2008; Sun et al., 2009). The need of the hour is to develop composite in silico tools and computer-vision systems with minimal analytical technology for rapid, nondestructive, highly efficient, and economic food-adulteration detection maneuvers that may be used in the field/at point of use by less trained personnel to generate data with significant reproducibility (Huang et al., 2007; Ma et al., 2016).
Sometimes, reading the list of ingredients or the nutrition facts label on a foodstuff may not tell the real story. Moore et al. (2012) reported the definition of food adulteration, or food fraud, as “a collective term that encompasses the deliberate substitution, addition, tampering, or misrepresentation of food, food ingredients, or food packaging, or false or misleading statements made about a product for economic gain.” Often, adulteration is motivated by economic reasons, eg, maximizing profit by adding a cheaper ingredient. Addressing a more specific type of fraud, the United States Pharmacopeial (USP) Expert Panel on Food Ingredient Intentional Adulterants defined the intentional or economically motivated adulteration of food ingredients as “the fraudulent addition of non-authentic substances or removal or replacement of authentic substances without the purchaser's knowledge for economic gain of the seller” (Moore et al., 2012).
Chromatographic-based methods are able to detect different kinds of food adulteration that can be classified as resulting from ingredient replacement and addition. Based on the type of fraud, they can be listed as follows:
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Complete or partial substitution of a food ingredient or one authentic food by a similar but cheaper one. Examples include substituting bonito for tuna, sea trout for salmon, or addition of seed oils to extra virgin olive oil.
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Adding food with a cheaper base material. Examples include adding water, sugar, acids, and coloring to fruit juices and addition of melamine to milk to artificially increase apparent protein content.
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False declaration of geographic, species, botanical, or varietal origin. Examples include substitution of less-expensive cow's milk for sheep or goat's milk; substitution of common wheat for durum wheat; substitution of Greek or Turkish olive oil for Italian olive oil; and substitution of synthetically produced vanillin for botanically derived natural vanillin.
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False declaration of raw material origin or production process used to manufacture a food.
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Nondeclaration or false declaration of processes. Examples include fraudulent labeling of a synthetically derived flavor chemical as being naturally derived; food falsely labeled as organically produced; poor quality, filtered honey or a honey substitute product labeled as honey.
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Addition of a nonauthentic substance to mask an inferior quality ingredient for color and/or taste enhancement. An example is the addition of a color additive (such as Sudan Red dyes) to enhance the color of poor quality paprika.
Table 10.1 reports some examples of chromatographic methods applied to food authenticity and fraud analysis reported in recently published papers.
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