Sabine Wittmann

Visible-Near-Infrared Scanners enable a noninvasive prediction of quality properties of fruit and vegetable based on previously created models. A combination of NIR scanners andmachine learning methods can lead to economic improvements and reduction of food waste by strategies like "first expired, first out" and dynamic pricing. In order to identify parameterscapable of showing dynamic postharvest development, three horticultural products with different postharvest behavior (e. g. strawberry, table grape and mango) were chosen formorphological and statictical analysis. According to the results, a graduation of spectra in correspondence to the day of measurement was noticeable for strawberry regarding the a-value as well as presumingly mass loss for both mango and table grape. Furthermore, a PLS model for the a-values r2cv = 0.80 was developed for strawberries.

Several lighting strategies in an indoor farm are compared regarding the annual electrical energy consumption. Based on measurements of the energy consumption during one experiment, conducted inside the indoor farm at the HSWT, an energy balancing model was calibrated and subsequently validated by a second experiment. The model was then used to calculate the potential reduction of the annual electrical energy consumption concerning several different lighting strategies. Furthermore, different settings of temperature set points and the impact of an optimized heat transfer coefficient as well as a more efficient performance for heating and cooling are considered. The measured and the modeled energy consumption and the values regained by the model showed a high regression coefficient (R2=0.936). The prediction of the energy consumption during the second experiment was also possible (R2=0.738) with a total difference to the measured consumption of 29.1 kWh. Regarding the given technical settings of the indoor farm, the annual electrical energy consumption can be reduced by up to 16% by an adjustment of the temperature strategy while reaching a similar yield. By assuming an optimized technical setting, the relevance of the lighting strategy increased significantly. Based upon this an annual reduction of the electrical energy consumption of up to 21% seemed conceivable.

Plant production in indoor farming systems offers significant advantages compared to open field orgreenhouse production systems. Especially in terms of quality and the ability for automation the system is superior to the conventional production systems. Concerning resource consumption indoor farming has considerable advantages in regard to water consumption and the use of pesticides.The main disadvantage is the high consumption of electrical energy. Taking advantage of the specific benefits or eliminating the disadvantages, for example by using renewable energies, different potentials and fields of application for indoorfarming arise.The paper outlines the potentials and future fields of application of indoor farming considering the specific differences to conventional production systems related to resource consumption, quality and automation.

Climate change and increasing global urbanization accelerate the expansion of protected cultivation systems. However, certain dependences to external weather conditions remain even in modern greenhouses. Indoor vertical farming, on the other hand, pursues complete inde-pendence from external weather conditions with the aim for highly accurate control of all crop parameters. Particularly with regard to the advancing climate change and the need for sustainable resource consumption, there are clear advantages due to the year-round and independent cultivation of plants and raw materials under optimal conditions. The complexity in the optimal networking of the plant-technology systems offers intensive development opportunities for dig-itization and interdisciplinary collaboration.

In the past few years, portable and smartphone-based diagnostic technologies have found their way into the agri-food industry. The aim of this research was to evaluate the perfor-mance of portable near-infrared (NIR) spectrometers, so called food-scanners, with regard to their predictive accuracy of important quality parameters of fruit and vegetables. Food-scan-ner measurements were performed in combination with destructive measurements of the corresponding quality trait (sugar content, dry matter, relative water content) on a wide range of produce from the fruit and vegetable assortment. This study evaluated dry matter content of apple, avocado, blueberry, table grape and tangerine, which yielded cross validation re-sults (r²) of up to 0.95, 0.87, 0.94, 0.92 and 0.92 respectively. Furthermore, the evaluation of food-scanner spectra for the prediction of sugar content of blueberry, kiwi, mango, persim-mon, table grape, tangerine and tomato yielded cross validations (r²) of up to 0.95, 0.84, 0.80, 0.75, 0.95, 0.93, and 0.87. Furthermore, relative water content of ginger obtained a cross val-idation correlation of r² = 0.91. The results show that these traits can be predicted with a high degree of accuracy using non-destructive measurements performed with three commercially available food-scanners SCiOTM, F-750 Produce Quality Meter, and H-100F. Consequently, food-scanners can be used as objective measurement tools along the supply chain of fresh produce to quickly determine fruit quality. In addition, a practical example shows the poten-tial of these instruments for non-destructive quality assessment in incoming goods control at fruit and vegetable wholesalers over a time period of several weeks. Furthermore, possible areas of application of food-scanners along the supply chain of fresh produce are discussed, possibilities for practical applications are presented and time-saving means are highlightedLANDTECHNIK 76(1), 2021, 52–67Food-scanner applications in the fruit and vegetable sectorSimon Goisser, Sabine Wittmann, Heike MempelIn the past few years, portable and smartphone-based diagnostic technologies have found their way into the agri-food industry. The aim of this research was to evaluate the perfor-mance of portable near-infrared (NIR) spectrometers, so called food-scanners, with regard to their predictive accuracy of important quality parameters of fruit and vegetables. Food-scan-ner measurements were performed in combination with destructive measurements of the corresponding quality trait (sugar content, dry matter, relative water content) on a wide range of produce from the fruit and vegetable assortment. This study evaluated dry matter content of apple, avocado, blueberry, table grape and tangerine, which yielded cross validation re-sults (r²) of up to 0.95, 0.87, 0.94, 0.92 and 0.92 respectively. Furthermore, the evaluation of food-scanner spectra for the prediction of sugar content of blueberry, kiwi, mango, persim-mon, table grape, tangerine and tomato yielded cross validations (r²) of up to 0.95, 0.84, 0.80, 0.75, 0.95, 0.93, and 0.87. Furthermore, relative water content of ginger obtained a cross val-idation correlation of r² = 0.91. The results show that these traits can be predicted with a high degree of accuracy using non-destructive measurements performed with three commercially available food-scanners SCiOTM, F-750 Produce Quality Meter, and H-100F. Consequently, food-scanners can be used as objective measurement tools along the supply chain of fresh produce to quickly determine fruit quality. In addition, a practical example shows the poten-tial of these instruments for non-destructive quality assessment in incoming goods control at fruit and vegetable wholesalers over a time period of several weeks. Furthermore, possible areas of application of food-scanners along the supply chain of fresh produce are discussed, possibilities for practical applications are presented and time-saving means are highlighted.

Indoor vertical farming offers great opportunities regarding a sustainable and consistent production of high-quality herbs and raw materials all year round for the perfume, chemical, or food industry. Cultivation takes place in an enclosed structure, operating predominantly independent from external conditions in multi-layer systems equipped with artificial lighting, enabling extremely high resource use efficiencies with a simultaneous increase in yield. On the other hand, field production in terms of plant quality and harvesting times is highly influenced by environmental conditions, making it difficult to maintain homogenous raw material qualities throughout the year. To show how different light qualities affect the overall efficiency and quality of Origanum majorana grown in an indoor farm, the resource consumption, yield, and cultivation time as well as the essential oil quantity was analyzed, and the efficiencies in terms of energy and land use efficiency calculated. The experimental setup clearly demonstrated that the yield regarding fresh as well as dry matter and oil content was comparable to one square meter of open field production. Based on this, the multi-layer system and the noticeable lowered growth period result in a significantly higher area efficiency compared to the open field, leading to a potential increase of annual yields of dried leave weight and oil contents by up to 21 times. It was also shown that a white spectrum (W) showed similar influence on plant growth and yield as a spectrum consisting of blue and red (B/R). Nevertheless, the LED treatment W did show higher light use efficiencies as well as a better working conditions inside the cultivation chamber. By an integration of indoor vertical farming into existing industrial processes, new and innovative opportunities for a flexible and low-risk supply chain seem feasible and according to German food industry meet the interests of existing stakeholders.

The assessment of tomato fruit quality depends on a variety of extrinsic and intrinsic quality parameters such as color, firmness and sugar content. Conventional measurement methods of these quality parameters are time consuming, require various measurement devices, and in case of intrinsic quality, involve destructive measurements. Latest research focused on the non-destructive determination of these parameters by using spectroscopic measurements. The goal of this study was to evaluate the capability of three commercial-ly available portable and miniaturized VIS/NIR spectrometers, socalled food-scanners, in predicting various tomato quality attributes in a non-destructive way. Additionally, this study evaluated the software provided by manufacturers for building of prediction models by comparing the results derived from those software tools to state-of-the-art software for multivariate analysis. Evaluation of food-scanner spectra resulted in prediction models of high accuracy (r² > 0.90) for tomato fruit firmness, dry matter, total soluble solids and color values L*, a* and h°. Prediction models computed with manufacturer’s software showed similar accuracy to those derived from state-of-the-art evaluation software. Results of this study illustrate the great potential of commercial food-scanners for non-destructive quality measurement. Further important features of food-scanners with respect to the application along the fresh produce supply chain are addressed.

Lycopene, the red colored carotenoid in tomatoes, has various health benefits for humans due to its capability of scavenging free radicals. Traditionally, the quantification of lycopene requires an elaborate extraction process combined with HPLC analysis within the laboratory. Recent studies focused simpler methods for determining lycopene and utilized spectroscopic measurement methods. The aim of this study was to compare non-destructive methods for the prediction of lycopene by using color values from colorimeter measurements and Vis/NIR spectra recorded with three commercially available and portable Vis/NIR spectrometers, so called food-scanners. Tomatoes of five different ripening stages (green to red) as well as tomatoes stored up to 22 days after harvest were used for modeling. After measurement of color values and collection of Vis/NIR spectra the corresponding lycopene content was analyzed spectrophotometrically. Applying exponential regression models yielded very good prediction of lycopene for color values L*, a*, a*/b* and the tomato color index of 0.94, 0.90, 0.90 and 0.91, respectively. Color value b* was not a suitable predictor for lycopene content, whereas the (a*/b*)² value had the best linear fit of 0.87. In comparison to color measurements, the cross-validated prediction models developed for all three food-scanners had coefficients of determination (r²CV) ranging from 0.92 to 0.96. Food-scanners also can be used for additional measurements of internal fruit quality, and therefore have great potential for fruit quality assessment by measuring a multitude of important fruit traits in one single scan.