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Machine Learning and Its Application in Food Processing and Preservation
Published in Utku Kose, V. B. Surya Prasath, M. Rubaiyat Hossain Mondal, Prajoy Podder, Subrato Bharati, Artificial Intelligence and Smart Agriculture Technology, 2022
Babatunde Olawoye, Oyekemi Popoola, Oseni Kadiri, Jide Ebenezer Taiwo Akinsola, Charles Taiwo Akanbi
Adulteration is a growing safety concern in the food industry around the globe. In adulteration, food quality is incidentally or intentionally degraded through the addition of extraneous matter or chemicals, etc. Figure 9.1 shows the major types of adulteration. Natural adulteration occurs when certain chemicals, radicals, or organic compounds that are injurious to health are present in the food. These types of adulterants are not intentionally added to food but occur naturally in it. Examples of unintentional adulterants include microbiological contamination and pesticide residues while ripening agents and food additives are typical examples of adulterants intentionally added to food.
Near-infrared (NIR) Spectroscopy for Food Processing Applications
Published in Azharul Karim, Sabrina Fawzia, Mohammad Mahbubur Rahman, Advanced Micro-Level Experimental Techniques for Food Drying and Processing Applications, 2021
Azharul Karim, Sabrina Fawzia, Mohammad Mahbubur Rahman
Spices are used to improve the organoleptic qualities of food and culinary dishes, making them more appealing to consumers. The use of illegal, low-cost colorants may be lucrative in the food supply chain, but it puts human health at risk. NIR spectroscopy, combined with chemometrics, can be used as a fast, simple, non-destructive and low-cost screening method for detecting adulterants in food samples. NIR spectroscopy has also been used to screen out the adulterated samples within the food supply chain. Thereby, this technique has been beneficial in the on-line quality checking of the food products.
Thermal Phase Transitions in Food
Published in Mohammed M. Farid, Mathematical Modeling of Food Processing, 2010
Crystallization and melting temperatures of food products have been used to characterize and detect adulterant. Ferari et al. [16] studied isothermal crystallization for olive oil authentication. In general, any change in oil composition due to chemical or physical treatment affects in a typical way, the freezing heat flow curve. In other words, the nucleation and growth of the polymorphous crystalline fractions of triacyl glycerols are dependent on oil molecular composition. Crystallization and melting have been used to characterize composite foods like caramel by Ahmed et al. [10]. The melting behavior of caramels was mainly controlled by the nature of fats used in the formulation. The sharp melting point peak of one of the caramel samples has been attributed to the fractionated palm kernel oil-based fat in its formulation, while milk fat-based caramels exhibited mixed behavior. Caramels exhibit a broad endothermic peak with an onset and peak temperature. The crystal expected to depress the solubility of sucrose and the nature of fat. The presence of corn syrup also affects the nucleation and growth rate kinetics and may influence the relative shapes of the crystallization curves [65].
Quantification of anhydrous ethanol and detection of adulterants in commercial Brazilian gasoline by Raman spectroscopy
Published in Instrumentation Science & Technology, 2019
Andressa Cristina de Mattos Bezerra, Danieli de Oliveira Silva, Gustavo Henrique Machado de Matos, Josuel Pereira dos Santos, Claudio Neves Borges, Landulfo Silveira, Marcos Tadeu Tavares Pacheco
Another quality test for gasoline C is the gas chromatography, consisting on the identification of adulterants through the physical separation process in a heated column until its vaporization.[7] A chromatogram shows several peaks, in which each peak represents one compound. To determine the presence of an adulterant, the peaks of the sample are compared to the chromatographic peaks of possible adulterants. The gasoline quality has been tested by gas chromatography (GC), which also presented positive results to be applied in other fuels and to quantify mixtures of biodiesel.[8] Although this test is efficient depending on the concentration of the solvent, it is not possible to identify the presence of adulterant depending on its concentration, and some samples with low concentration (i.e., 1%) may be considered reliable, presenting 95% compatibility with the standard sample.[9]