# Zero Defect Manufacturing in the Food Industry: Virgin Olive Oil Production
## 4 First Steps towards ZDM Implementation
### How to Obtain Data  
The first challenge for ZDM implementation is that of collecting the data gathered from different and heterogeneous sources distributed in the production line. The relevant data for VOO production include: the state of incoming fruits, the main process variables, operator impressions and an estimation of the product quality and quantity. The sensory technologies to be integrated at the different productive stages are detailed below:  
* State of the incoming fruits--Computer vision is viable technology to classify fruits according their ripening and sanitary state before the oil extraction process. Two types of image acquisition set-ups based on cameras working in the visible and infrared (IR) spectra, have to be installed before and after the washing stage. First, cameras working in the visible spectra and installed at the reception yard before the washing stage acquire images that will be used to differentiate between the fruits picked from the ground or from the tree. Second, two different technologies of cameras must be installed after the washing stage. Images from a camera working in the visible spectra will be later processed to detect the ripening index of the fruits; while images from the other set-up working in the IR spectra will be used to classify fruits according to their internal and external damage.
* Process parameters--In this regard, it has to be considered those manipulated and controlled variables of the process having an influence on the product quality or yield. Among the decisive process variables, there are the storage time and storage conditions of the fruits before their processing (e.g., humidity, cooling or pressure); kneading stage parameters (e.g., paste temperature, water or talc addition); and olive paste state and the storage conditions of the final product (e.g., temperature, exposure to oxygen and exposure to light). The acquisition of the process data, except for the paste state, is supported by an HMI to allow the operator to gather the remaining data. Finally, a computer vision set-up working in the visible spectra and installed inside the thermomixer provides images, which will later be used to extract information about the olive paste state (e.g., viscosity, granularity, and the presence of olive oil over the paste).
* Product quality--Since there is no in-line systems for oil mills to provide the VOO organoleptic and flavour features, the product quality has to be estimated. Based on previous studies [24; 29; 31; 37], it is possible to offer an estimation of the VOO quality supported by different sensory technologies and historical data. The information obtained from the images acquired with the set-ups installed after the washing stage are used to predict the maximum level of quality. If fruits are properly processed, the predicted quality can be reached. Data obtained from electronic nose technology can also be used to classify the obtained oil into its proper designation. This technology has to be installed after the centrifugation stage and before the oil storage. In addition, the historical data of the panel test assessments together with real-time data from the operator view and impressions are used for the quality estimation.
* Productivity--The quantity of VOO litres obtained from a predefined quantity of fruits can be inferred from the oil that remains in the pomace. New intelligent devices based on NIR technology enable the extraction of information on fat yield and moisture. This device has to be installed after the separation stage.  
Figure 3 shows examples of sensory technologies that are used to monitor and save data. They were installed by the ISR company in the Piculia oil mill.