Studies on factors relating to the development of defects in commercial cheese

Defects in commercial cheese result in a downgrading of the final cheese and a consequential economic loss to the cheese producer. Developments of defects in cheese are often not fully understood and therefore not controllable by the producer. This research investigated the underlying factors in the development of split and secondary fermentation defect and of pinking defects in commercial Irish cheeses. Split defect in Swiss-type cheese is a common defect associated with eye formation and manifests as slits and cracks visible in the cut cheese loaf (Reinbold, 1972; Daly et al., 2010). No consensus exists as to the definitive causes of the defect and possible factors which may contribute to the defect were reviewed. Models were derived to describe the relationship between moisture, pH, and salt levels and the distance from sample location to the closest external block surface during cheese ripening. Significant gradients within the cheese blocks were observed for all measured parameters in cheeses at 7 day post/after manufacture. No significant pH gradient was found within the blocks on exit from hot-room ripening and at three months post exit from the hot-room. Moisture content reached equilibrium within the blocks between exit from hot-room and 3 months after exit from hot-room while salt and salt-to-moisture levels had not reached equilibrium within the cheese blocks even at three months after exit from hot-room ripening. A characterisation of Swiss-type cheeses produced from a seasonal milk supply was undertaken. Cheeses were sampled on two days per month of the production year, at three different times during the manufacturing day, at internal and external regions of the cheese block and at four ripening time points (7 days post manufacture, post hot-room, 14 days post hot-room and 3 months in a cold room after exit from hot-room). Compositional, biochemical and microbial indices were determined, and the results were analysed as a splitplot with a factorial arrangement of treatments (season, time of day, area) on the main plot and ripening time on the sub-plot. Season (and interactions) had a significant effect on pH and salt-in-moisture levels (SM), mean viable counts of L. helveticus, propionic acid and non-starter lactic acid bacteria, levels of primary and secondary proteolysis and cheese firmness. Levels of proteolysis increased significantly during hot-room ripening but also during cold room storage, signifying continued development of cheese ripening during cold storage (> 8°C). Rheological parameters (e.g. springiness and cohesiveness) were significantly affected by interactions between ripening and location within cheese blocks. Time of day of manufacture significantly affected mean cheese calcium levels at 7 days post manufacture and mean levels of arginine and mean viable counts of NSLAB. Cheeses produced during the middle of the production day had the best grading scores and were more consistent compared to cheeses produced early or late during day of manufacture. Cheeses with low levels of S/M and low values of resilience were associated with poor grades at 7 days post manufacture. Chesses which had high elastic index values and low values of springiness in the external areas after exit from hot-room ripening also obtained good commercial grades. Development of a pink colour defect is an intermittent defect in ripened cheese which may or may not contain an added colourant, e.g., annatto. Factors associated with the defect were reviewed. Attempts at extraction and identification of the pink discolouration were unsuccessful. The pink colour partitioned with the water insoluble protein fraction. No significant difference was observed between ripened control and defect cheese for oxygen levels and redox potential or for the results of elemental analysis. A possible relationship between starter activity and defect development was established in cheeses with added coulourant, as lower levels of residual galactose and lactose were observed in defective cheese compared to control cheese free of the defect. Swiss-type cheese without added colourant had significantly higher levels of arginine and significantly lower lactate levels. Flow cell cytometry indicated that levels of bacterial cell viability and metabolic state differed between control and defect cheeses (without added colourant). Pyrosequencing analysis of cheese samples with and without the defect detected the previously unreported bacteria in cheese, Deinococcus thermus (a potential carotenoid producer). Defective Swiss-type cheeses had elevated levels of Deinococcus thermus compared to control cheeses, however the direct cause of pink was not linked to this bacterium alone. Overall, research was undertaken on underlying factors associated with the development of specific defects in commercial cheese, but also characterised the dynamic changes in key microbial and physicochemical parameters during cheese ripening and storage. This will enable the development of processing technologies to enable seasonal manipulation of manufacture protocols to minimise compositional and biochemical variability and to reduce and inhibit the occurrence of specific quality defects.

Fundamental studies on the application of enzymes when brewing with unmalted oats and sorghum

The use of unmalted oats or sorghum in brewing has great potential for creating new beer types/flavors and saving costs. However, the substitution of barley malt with oat or sorghum adjunct is not only innovative but also challenging due to their specific grain characteristics. The overall objectives of this Ph.D. project were: 1) to investigate the impact of various types and levels of oats or sorghum on the quality/processability of mashes, worts, and beers; 2) to provide solutions as regards the application of industrial enzymes to overcome potential brewing problems. For these purposes, a highly precise rheological method using a controlled stress rheometer was developed and successfully applied as a tool for optimizing enzyme additions and process parameters. Further, eight different oat cultivars were compared in terms of their suitability as brewing adjuncts and two very promising types identified. In another study, the limitations of barley malt enzymes and the benefits of the application of industrial enzymes in high-gravity brewing with oats were determined. It is recommended to add enzymes to high-gravity mashes when substituting 30% or more barley malt with oats in order to prevent filtration and fermentation problems. Pilot-scale brewing trials using 10–40% unmalted oats revealed that the sensory quality of oat beers improved with increasing adjunct level. In addition, commercially available oat and sorghum flours were implemented into brewing. The use of up to 70% oat flour and 50% sorghum flour, respectively, is not only technically feasible but also economically beneficial. In a further study on sorghum was demonstrated that the optimization of industrial mashing enzymes has great potential for reducing beer production costs. A comparison of the brewing performance of red Italian and white Nigerian sorghum clearly showed that European grown sorghum is suitable for brewing purposes; 40% red sorghum beers were even found to be very low in gluten.

Characterisation and application of fruit by-products as novel ingredients in gluten-free products

The physicochemical and nutritional properties of two fruit by-products were initially studied. Apple pomace (AP) contained a high level of fibre and pectin. The isolated AP pectin had a high level of methylation which developed viscous pastes. Orange pomace also had high levels of fibre and pectin, and it was an abundant source of minerals such as potassium and magnesium. Due to the fibrous properties of orange pomace flour, proofing and water addition were studied in a bread formulation. When added at levels greater than 6%, the loaf volume decreased. An optimised formulation and proofing time was derived using the optimisation tool; these consisted of 5.5% orange pomace, 94.6% water inclusion and with 49 minutes proofing. These optimised parameters doubled the total dietary fibre content of the bread compared to the original control. Pasting results showed how orange pomace inclusions reduced the final viscosity of the batter, reducing the occurrence of starch gelatinisation. Rheological properties i.e. the storage modulus (G') and complex modulus (G*) increased in the orange pomace batter compared to the control batter. This demonstrates how the orange pomace as an ingredient improved the robustness of the formulation. Sensory panellists scored the orange pomace bread comparably to the control bread. Milled apple pomace was studied as a potential novel ingredient in an extruded snack. Parameters studied included apple pomace addition, die head temperature and screw speed. As screw speed increased the favourable extrudate characteristics such as radical expansion ratio, porosity and specific volume decreased. The inclusion of apple pomace had a negative effect on extrudate characteristics at levels greater than 8% addition. Including apple pomace reduced the hardness and increased the crispiness of the snack. The optimised and validated formulation and extrusion process contained the following parameters: 7.7% apple pomace, 150°C die head temperature and a screw speed of 69 rpm.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.