Food fraud is an increasingly prevalent deliberate act of deception for profit.  Hence, it is highly necessary to develop robust analytical methods to assess the authenticity of foods.  In recent years, the geographical origin authenticity of fruits has attracted considerable 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 indicators for geographical origin authentication as they are directly related to the geographical environment.  Other techniques, such as spectroscopy and chromatography, also exhibit promising potential for fruit origin discrimination and authenticity assessment.  Omics technologies have emerged as a key approach for authenticating the geographical origin of fruit.  The integration of instrumental analysis techniques with machine learning enables high-precision discrimination of fruit geographical origin, and the growing trend toward combining multiple analytical techniques further enhances identification accuracy.  Commonly used methods for geographical origin authentication include linear techniques such as PCA, PLS-DA, and LDA.  Machine learning algorithms, including SVM, RF, and ANN, have also been applied to identify fruit origin with high accuracy.  Future developments in this field should prioritize the consideration of agricultural practices to ensure reliable and practical authentication.  

phytopathogen–insect interactions  /  transmission mechanism  /  insect vector borne diseases  /  host vulnerability  /  environmental influences