Optimisation of processing time and pre-treatments are crucial factors prior to apple drying to produce a high-quality product. The purpose of the present study was to test the utility of physical (hot-water, HWB and steam blanching, SB) and chemical (1% ascorbic acid, AA; and 1% citric acid, CA) treatments, alone or in combination in reducing surface discolouration as well as oxidative enzyme activity in apple slices (cv. Golden Delicious and Elstar) exposed to air at room temperature for 0, 30 and 60 min. The total colour change (ΔE) for Golden Delicious was equal to 2.38, 2.68, and 4.05 after 0, 30 and 60 min of air exposure, respectively. Dipping in AA solution (1% w/v) was found to be the best treatment to limit surface discolouration of both apple cultivars. The best heat treatments to inhibit polyphenol oxidase/peroxidase enzymes activity were 70 °C HWB for Golden Delicious and 60 °C HWB for Elstar slices, both in combination with a solution of 1% AA and 1% CA. The tested apple cultivars were found to require different treatments at minimum ambient air exposure to obtain the best surface colour condition.

Claw lesions are a serious problem on dairy farms, affecting both the health and welfare of the cow. Automated detection of lameness with a practical, on-farm application would support the early detection and treatment of lame cows, potentially reducing the number and severity of claw lesions. Therefore, in this study, a method was proposed for the detection of claw lesions based on the acoustic analysis of a cow's gait. A panel was constructed to measure the impact sound of animals walking over it. The recorded impact sound was edited, and 640 sound files from 64 cows were analyzed. The classification of animal-lameness status was performed using a machine-learning process with a random forest algorithm. The gold standard was a 2-point scale of hoof-trimming results (healthy vs. affected), and 38 properties of the recorded sound files were used as influencing factors. A prediction model for classifying the cow lameness was built using a random forest algorithm. This was validated by comparing the reference output from hoof-trimming with the model output concerning the impact sound. Altering the likelihood settings and changing the cutoff value to predict lame animals improved the prediction model. At a cutoff at 0.4, a decreased false-negative rate was generated, and the false-positive rate only increased slightly. This model obtained a sensitivity of 0.81 and a specificity of 0.97. With this procedure, Cohen's Kappa value of 0.80 showed good agreement between model classification and diagnoses from hoof-trimming. In summary, the prediction model enabled the detection of cows with claw lesions. This study shows that lameness can be detected by machine learning from the impact sound of hoofs in dairy cows.

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques i.e. Vis-NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot-air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots while Passing Bablok and Deming regressions alongside with Blant Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R²_T and R²_v at 0.92 and 0.90 with values of RMSE_T and RMSE_V at 8.15 % and 8.16 %. Satisfactory prediction accuracies were observed for total carotenoids (R²_v = 0.64 and RMSE_v = 32.62) µg/g, L* (R²_v = 0.68 and RMSE_v =32.62 ), a* (R²_v = 0.69 and RMSE_v = 1.18 ), and b* (R² _v = 0.60 and RMSE_v = 1.45 ). Selected wavelengths for total carotenoids, moisture content, L*, a* and b * based on the highest score of VIP loadings were 531 nm, 973 nm, 531 nm, 531 nm and 680 nm, respectively. An adequate agreement of Blant Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 µg/g to 82.34 µg/g, moisture content (25.18 % to 22.93 %), L (2.88 to -3.30), a* (4.15 to 3.43) and b* (4.53 to -3.11) with mean differences at 6.67,1.12, -0.21, 0.36 and 0.71 respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78 and 0.83 for moisture content, L*, a* and b* with moderate correlation of total carotenoids at 0.69. The results indicating potential feasibility of using non-invasive measurement of quality attributes using hyperspectral imaging during drying of carrots.

Celeriac is a good source of fibre, trace minerals, and phenolic compounds; it has a pleasant aroma but is a perishable material, prone to discolouration. This research investigated the optimisation of the quality and energy demand in hot-air dried celeriac slices. The experiment utilised the I-optimal design of response surface methodology with 30 experiment runs. Pre-drying treatments (blanching at 85 °C, three minutes; dipping in 1% citric acid solution, three minutes; no pre-drying treatment), drying temperatures (50, 60, and 70 °C), air velocities (1.5, 2.2, and 2.9 m/s), and thickness (three-, five, and seven-mm) were applied. The drying conditions affected drying time significantly (p < 0.0001). The model by Midilli and others and the logarithmic model fitted best with celeriac slices drying kinetics. Blanched samples had a higher ΔE*_ab (total colour difference) and BI (browning index) but lower WI (whiteness index) than samples with other pre-drying treatments. The rehydration ratio decreased with the increase of sample thickness and blanching (p < 0.0001). A quadratic model described the specific energy consumption (E_s) best. The dried samples compared with fresh samples had increased antioxidant activity but decreased total phenolic compound value. The optimisation solution chosen was 58 °C drying temperature, 2.9 m/s air velocity, and 4.6 mm sample thickness with acid pre-drying treatment.

The effect of air temperature and sample thickness on the color changes and total carotenoids content of carrot slices was investigated. Temperature, exposure time, and moisture levels significantly affected the dynamic changes of total carotenoids and color. A slow and linear decrease in total carotenoids was observed at higher moisture content until reaching an inflection point at around 0.45 g _w/ g _dm for all temperatures studied _. Thereafter, the retention in total carotenoids decreased rapidly. The highest retention for a final product was 66.2% when drying at 60°C while retention was between 42.2 and 51.1% when drying at 50 and 70°C. These changes occurred alongside a noticeable change in color at moisture contents below the inflection point of 0.45 g _w /g _dm for all drying temperatures. Design of experiment based optimization of the drying process resulted in an ideal temperature of 59.8°C and 3.5 mm slice thickness with the predicted values for La * b *; Δ E of 62.18 ± 5.12, 22.46 ± 1.98, 40.35 ± 6.64, 6.31 ± 4.74; rehydration ratio of 0.48 ± 0.07; and total carotenoids of 163.83 ± 17.38 μg/g or 67.38%, respectively, all at a 95% prediction interval. Practical applications: Convective drying is the most widely used drying technique for food preservation due to its easy application and readily available technology. However, the quality of the end product is currently lower than it would be achievable due to a lack of direct integration of quality characteristics into process design. Monitoring the quality changes as a function of moisture content throughout the drying process is essential to developing advanced process parameters with a view to increasing final product quality. This study describes the experimentally determined dynamic quality changes of carrots throughout the drying process in order to identify the critical points at which the process settings should be changed to increase the retention of valuable components and sensory quality attributes, and thus, to provide consumers with higher quality products.