*Proceeding Paper* **Detection of Chocolate Properties Using Near-Infrared Spectrophotometry †**

**Brais Galdo \*, Enrique Fernandez-Blanco and Daniel Rivero**

Faculty of Computer Science, CITIC, University of A Coruna, 15071 A Coruna, Spain; enrique.fernandez@udc.es (E.F.-B.); daniel.rivero@udc.es (D.R.)

**\*** Correspondence: brais.cgaldo@udc.es

† Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.

**Abstract:** Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive technique that analyzes the electromagnetic spectrum in search of waves of a certain length. The aim of this project is to combine this technology with machine learning techniques to try to detect the presence of milk, as well as the level of cocoa present in an ounce of chocolate. This has given satisfactory results in both cases, so it is considered that the combination of these techniques offers great possibilities.

**Keywords:** near infrared spectroscopy; machine learning; artificial neural networks; intensity; absorbance; reflectance; chocolate; milk; cocoa

**1. Introduction**

Near-infrared spectrophotometry [1] is a technique whith which we can measure the chemical compositions of substances, and its main object of measurement is carbon. This technique is non-destructive, which gives the possibility of repeating the analysis.

The devices used for this analysis are usually very large and are housed in laboratories dedicated to this type of analysis. It is for this reason that portable spectrophotometers of reduced size and accuracy were developed. In this way, the device can be moved and measurements can be taken in the field.

Chocolate [2] is a food that is the result of the combination of various ingredients. These can vary, from nuts to different additives. Among the possible ingredients, two main ones stand out, milk and cocoa.

The aim of this work is to detect different properties in chocolate, such as the presence of milk or the level of cocoa contained in an ounce, using machine learning [3].
