Geographical origin authentication of fruits: A decadal review (2014–2024) of technological progress and outlook

Summarized the detection technology used in the identification of fruit origin. Summarized the relevant factors affecting the identification of fruit origin. Summarized the articles on the origin of the fruit from 2014 to 2024. Provided critical insights for the development of future research. Food fraud is an increasing deliberate act of deception for profits. Hence, it is highly necessary to develop powerful analytical approaches to assess the authenticity of foods. In recent years, the geographical origin authenticity of fruits has become a particular public concern. The geographical origin of fruit is generally determined based on specific indicators such as elements, stable isotopes, and metabolites. Many studies have demonstrated that mineral elements and stable isotope ratios are effective indictors for geographical origin authentication as they are directly related to the geographical environment. Some other techniques, such as spectroscopy and chromatography, have shown potential in identifying geographical origin and assessing the authenticity of fruits. Nonlinear PCA, PLS-DA, and LDA are commonly used for geographical origin authentication of fruits. A modern approach for data classification is machine learning, including SVM, RF, and ANN, which have been applied to identify the origin of fruits with high accuracies.