## 1 Introduction  
In this age of the \"Quantified Self\" [1], a new breed of \"automated food scanner\" apps, devices and methods is emerging that aims at identifying the exact nature (quantity and bromatological composition) of food and drinks in our diet (and even identify the medicines in our pillboxes), much like a \"Shazam\" service [2] for all items we might ingest. Methods used include the following: (i) _before ingesting food_, food/drink item barcode scanning [3, 4], food weighing by portable electronic scales [5], VBM (vision-based measurement) quantification of food volume/weight/portion size by smartphone camera photos [5, 6, 7, 8], remote food and drink recognition by human crowdsourced volunteers or dieticians using smartphone photos of meals sent over the Internet [9], and/or NIR (Near Infrared) spectroscopy (using a handheld sensor/scanner communicating wirelessly with a specialized smartphone app) [10, 11]; and (ii) _after ingesting food_, bioimpedance [12]. Used singly, these methods vary widely in their accuracy and comprehensiveness/suitability in different meal identification scenarios. A smart triangulation of more than one of these methods is likely to produce better and more reliable results in a wider range of meal identification scenarios (as described in \"ADAMILO--Automated Diet and Activity Monitoring for Intelligent Lifestyle Optimisation\" EU H2020 Research Proposal [13]). Many of these methods connect to cloud-based (sometimes also crowdsourced) lookup databases (e.g., of food item barcodes, of previously identified food images, or of previously classified NIR spectra of food and drink items) to match and identify a scanned food or drink item, and report the results back to the user. In this paper, we review a number of existing \"food ontologies\" that can, with appropriate modifications (or as a basis, to be added to and expanded) and together with other relevant non-food ontologies, supplement these lookup databases, in an attempt to progress from the mere automated identification of food and drinks in our meals to a more useful and advanced application, whereby we can automatically _reason with_ the identified food and drink items and details (quantities and ingredients/composition) in order to better assist users in making the correct, healthy food and drink choices that are tailored and personalized for their particular health condition, age, body weight/BMI (Body Mass Index), lifestyle and preferences, _etc._ (by \"_reason with_\" we mean \"infer and offer intelligent, personalized and context-sensitive dietary recommendations\"). Such an application can, for example and among other possibilities, help with personalized food menu planning (particularly useful for people with special dietary requirements dictated by their health condition and/or other factors such as meal cost/affordability), and advise users about any essential ingredients lacking in their diet (under-nutrition) or about their intake of substances with cumulative toxicity in daily food, so that they can always stay within the recommended limits. Users can also be warned regarding allergens or any other food intolerances in food they are about to ingest, based on their known medical history of such conditions (e.g., peanut allergy). People with diabetes would benefit from a closer look at the carbohydrates (amounts and composition) profile of their diet, while those with hypertension and cardiovascular disease would appreciate the automated checks on the amounts of salt and saturated fats in their meals.