the input variables "response of the MSFK channels" and "process properties" (air temperature, air humidity, drying time and product surface temperature) and the prod-uct property to be predicted (prodprod-uct moisture) (3). The models were used to predict the corresponding product moisture content from further measuring points of the input variables. The system multispectral area scan camera plus machine learning was ap-plied to a further drying process in the cabinet dryer. There, further mechanical and chemical product properties and the colour were predicted.
The spectral reconstruction is shown in the lower part of Figure 1 (5). With a few sup-port points of the multispectral area scan camera, the twelve channels, spectra can be reconstructed very accurately (Figure 1 bottom left).
With the models found with Random Forest, quality criteria could be predicted with high quality. For example, coefficients of determination R² of up to 0.99 were achieved for the prediction of the product moisture content of mangos in single layer dryers. The range of parameters required for the prediction was also minimized. For the prediction of product moisture, a coefficient of determination R² of 0.98 could be achieved by exclusively using the most influential channels of the multi-spectral area scan camera and the easily measurable variables of relative air humidity, air temperature of the dryer and surface temperature of the product. If only the channels of the multispectral area scan camera were used to predict the product moisture content, the coefficient of de-termination R² was 0.91.
The data of the multispectral camera were pre-processed for the evaluation of the tests with pineapple. Image processing methods such as software filters made it possible to evaluate only relevant areas of the product. Dark fibres and shadows were faded out and not considered for the evaluation. This is a big advantage of the multispectral area scan camera compared to point spectrometers, which always measure areas of low interest, which makes their measurements less meaningful. Thanks to the image pro-cessing and the Random Forest approach, good results could be achieved for the pre-diction of the quality criteria of pineapples. For the product moisture content of the pineapple a very high prediction quality was achieved with a coefficient of determina-tion R² of up to 0.98. This was possible if only the most influential channels of the multispectral area scan camera and easily measurable variables such as the relative humidity and the air temperature of the dryer as well as the surface temperature of the product and the drying time were used for modelling. If only the channels of the multi-spectral area scan camera were used for the prediction of the product moisture con-tent, the coefficient of determination R² was 0.90.
The multispectral area scan camera could not be installed directly in the cabinet dryer.
Therefore a model was developed to calculate the channel responses from the meas-ured spectra. This model can be used for samples with a uniform surface, such as mangos. Coefficients of determination R² of 0.70 (only channels of the MSFK), 0.94 (only drying time, process parameters, surface temperature) and 0.89 (all input varia-bles) were achieved in the prediction of the product moisture content of mangos during drying in a cabinet dryer. When predicting the content of total soluble solids in the
rehydration water of mangos dried in the cabinet dryer, coefficients of determination of 0.64 were achieved when using the channels of the multispectral area scan camera and 0.81 when using the channels, drying time, process parameters and surface tem-perature. The colour could be predicted exclusively via the channels of the multispec-tral area camera with coefficients of determination of 0.91 (Δa*), 0.58 (Δb*) and 0.47 (ΔE00). With additional use of the process variables, drying time and surface tempera-ture, the coefficients of determination were 0.93 (Δa*), 0.56 (Δb*) and 0.61 (ΔE00).
When predicting the mechanical properties, the pH-values as well as the content of total soluble solids in the rehydrated mango samples, only low coefficients of determi-nation could be achieved. A prediction of these criteria via the input variables used is not possible with Random Forest.
The correlations found between prediction data from the model and measurement data in the cabinet dryer showed a lower coefficient of determination R² compared to the product moisture content in the single layer dryer. This was mainly due to the much smaller sample size and the fact that the same product was never measured twice in the cabinet dryer. Due to the process, both could not be realized differently. In addition, the cabinet dryer showed a strong dependence on the drying time.
The models developed with machine learning allow to draw many other values on the basis of a few easily measurable variables. This procedure is shown in Figure 2.
Figure 2: Result of the work
The spectral reflectance measured with the new MSFK is a physical surface property that describes how incoming radiation is reflected on a surface. An RGB camera can only describe the reflection spectrum with the help of three interpolation points, which is too imprecise for further-reaching statements. Compared to point spectrometers, however, the multispectral area scan camera has all the advantages of a conventional RGB camera: an image is generated and software filters can be used to select areas
of interest. At the same time, a spectrum can be output for each pixel of the captured image by spectral reconstruction. With the newly developed multispectral image pro-cessing system, a device-independent, location-specific spectral color measurement is thus possible.
The present work marks a further development for the food industry, since after a cer-tain training phase the product quality can be determined by a robust optical procedure.
For this purpose, no samples have to be destroyed or taken, and the process does not have to be interrupted. By the use of multispectral area scan camera and machine learning, many quality criteria can be recorded at once with one measuring device.
There is a great interest in making products of high quality without additives as durable as possible and at the same time being able to monitor the process in detail at any time and control it if necessary. For example, it is possible to react quickly to problems in production without having to dispose of entire batches afterwards.