A process for detecting foreign objects present in a stream comprising a food product, the process comprising: A) forwarding a product stream comprising the food product; B) illuminating the product stream with incident electromagnetic energy; C) generating raw data based on electromagnetic energy reflected from the product stream, using a camera (22, 22'); D) processing the raw data with a machine learning classification algorithm with regression analysis to form image data, the processing of the raw data including unsupervised learning comprising grouping pixels into similar categories by their spectra, in order to generate classified data by: (i) classifying a first portion of the raw data as corresponding with the food product; characterized in that the processing of the raw data including unsupervised learning comprising grouping pixels into similar categories by their spectra, in order to generate classified data further includes: (ii) classifying a second portion of the raw data as corresponding with the foreign objects; (iii) classifying a third portion of the raw data as corresponding with background which is behind the product stream; and in that the process further comprises E) removing the foreign objects from the product stream. A) forwarding a product stream comprising the food product; B) illuminating the product stream with incident electromagnetic energy; C) generating raw data based on electromagnetic energy reflected from the product stream, using a camera (22, 22'); D) processing the raw data with a machine learning classification algorithm with regression analysis to form image data, the processing of the raw data including unsupervised learning comprising grouping pixels into similar categories by their spectra, in order to generate classified data by: (i) classifying a first portion of the raw data as corresponding with the food product; characterized in that the processing of the raw data including unsupervised learning comprising grouping pixels into similar categories by their spectra, in order to generate classified data further includes: (ii) classifying a second portion of the raw data as corresponding with the foreign objects; (iii) classifying a third portion of the raw data as corresponding with background which is behind the product stream; and in that the process further comprises (i) classifying a first portion of the raw data as corresponding with the food product; characterized in that the processing of the raw data including unsupervised learning comprising grouping pixels into similar categories by their spectra, in order to generate classified data further includes: (ii) classifying a second portion of the raw data as corresponding with the foreign objects; (iii) classifying a third portion of the raw data as corresponding with background which is behind the product stream; and in that the process further comprises characterized in that the processing of the raw data including unsupervised learning comprising grouping pixels into similar categories by their spectra, in order to generate classified data further includes: (ii) classifying a second portion of the raw data as corresponding with the foreign objects; (iii) classifying a third portion of the raw data as corresponding with background which is behind the product stream; and (ii) classifying a second portion of the raw data as corresponding with the foreign objects; (iii) classifying a third portion of the raw data as corresponding with background which is behind the product stream; and in that the process further comprises E) removing the foreign objects from the product stream.