•Heat flux sensor based on Peltier element (PE) was introduced.•Apple was used to validate heat flux during cooling, temperature fluctuations and re-warming.•PE results reliably capture minor heat ...fluxes with small temperature differences (<0.5 °C).•PE connected in series is recommended for average heat flux determination.
Climate control mechanisms in the postharvest chain of fruit and vegetables are predominantly based on air temperature measurements. These, however, do not reflect the real heat exchange between the product and the environment in all situations. The direct recording of the incoming and outgoing heat flux should enable higher-quality statements to be made about the interaction between the food product and the environment since other influencing parameters are also taken into account (e.g. air velocity and radiation). Commercial heat flux sensors are readily available but expensive, and for a variety of reasons, are not suitable for use directly on horticultural products, or are suitable only to a limited extent (e.g. large products, such as melons). In this study, small and low-cost Peltier elements (PE) were primarily tested and successfully validated as an alternative for their use in measuring heat exchange. Cooling, fluctuating temperature, and re-warming tests were applied to evaluate and validate the use of PE during the heat absorption and release of individual apples. The results confirmed the reliable detection of minor heat fluxes, regardless of direction, with small temperature differences (<0.5 °C) between the fruit and the air. The new heat flux measurement methodology has potential applications in agricultural technology, including optimizing packaging, and designing storage equipment.
•Scoring of lateral and sternal lying posture of pigs is presented.•Image processing and SVM were used to monitor lying postures of each pig.•Area and perimeter of boundaries and convex hull were ...used as inputs to the SVM.•High overall accuracies show the possibility of using the SVM.
The behaviour of animals provides information on their health, welfare and environmental situation. In different climatic conditions, pigs adopt different lying postures; at higher temperatures they lie laterally on their side with their limbs extended, while in lower temperatures they will adopt a sternal or belly lying posture. Machine vision has been widely used in recent years to monitor individual and group pig behaviours. So, the aim of this study was to determine whether a two-dimensional imaging system could be used for lateral and sternal lying posture detection in grouped pigs under commercial farm conditions. An image processing algorithm with Support Vector Machine (SVM) classifier was applied in this work. Pigs were monitored by top view RGB cameras and animals were extracted from their background using a background subtracting method. Based on the binary image properties, the boundaries and convex hull of each animal were found. In order to determine their lying posture, the area and perimeter of each boundary and convex hull were calculated in lateral and sternal lying postures as inputs for training of a linear SVM classifier. The trained SVM was then used to detect the target postures in binary images. By means of the image features and the classification technique, it was possible to automatically score the lateral and sternal lying posture in grouped pigs under commercial farm conditions with high accuracy of 94.4% for the classification and 94% for the scoring (detection) phases using two-dimensional images.
This paper seeks to investigate the potential use of laser-based imaging techniques in non-invasive quality inspection of apple slices during drying. For this purpose, Laser-light Backscattering ...(LLBI) and Biospeckle (BSI) Imaging techniques were investigated. Images at 980 nm and 1450 nm were captured. The data was used to develop Gaussian Process Regression (GPR) and Partial Least Square Regression (PLSR) models in order to estimate quality attributes of moisture content, vitamin C, and soluble solids content (SSC). Overall, the predictions accuracies were slightly in favor of GPR models. Moisture content was best estimated with R-squared = 0.92 and RMSE = 7%, and R-squared = 0.93 and RMSE = 6.54% when GPR models were trained on 1450/980 nm data acquired by LLBI or 980 nm data acquired by BSI, respectively. LLBI-based GPR models predicted vitamin C with R-squared ≥ 0.79 and RMSE ≤ 0.83 mg/100 g fresh weight (FW), slightly better than that of BSI (R-squared = 0.80 and RMSE ≤ 1.01 mg/100 g). The most accurate predictions of SSC for LLBI (R-squared = 0.88 and RMSE = 6.39%) and BSI (R-squared = 0.82 and RMSE = 7.99%) were achieved at 980 nm. Furthermore, BSI monitoring system was embedded into an experimental dryer to study the interaction between sample temperature and biospeckle activity. Results showed an increase in biospeckle activity at the beginning of drying followed by a gradual decrease towards the end of drying. The findings of present study indicate the potential use of simple and promising monochrome imaging systems in monitoring of the drying process that can be considered as alternatives to more expensive imaging sensors such as hyperspectral imaging.
Display omitted
•Laser-based imaging techniques are promising tools for drying process monitoring.•Moisture content was best estimated with R-squared = 0.93 and RMSE = 6.54%.•Vitamin C and SSC can be well estimated by laser-based imaging techniques.•Biospeckle activity increased initially due to the increase of sample temperature.
Drying and physicochemical characteristics of nectarine slices were investigated using hot-air and hybrid hot air-microwave drying methods under fixed air temperature and air speed (50 °C and ...0.5 m/s, respectively). Microwave power levels for the combined hot air-microwave method were 80, 160, 240, and 320 W. Drying kinetics were analyzed and compared using six mathematical models. For both drying methods the model with the best fitness in explaining the drying behavior was the Midilli–Kucuk model. The coefficient of determination (
R
2
), root mean square error (RMSE) and reduced chi square (
χ
2
) for this model have been obtained greater than 0.999 and less than 0.006 and 0.0001 for hybrid hot air-microwave drying while those values for hot-air drying were more than 0.999 and less than 0.003 and 0.0001, respectively. Results showed that the hybrid method reduced the drying time considerably and produced products with higher quality. The range of effective moisture diffusivity (
D
eff
) of hybrid and hot-air drying was between 8.15 × 10
−8
and 2.83 × 10
−7
m
2
/s and 1.27 × 10
−8
m
2
/s, respectively. The total color difference (ΔE) has also been obtained from 36.68 to 44.27 for hybrid method; however this value for hot-air drying was found 49.64. Although reduced microwave power output led to a lower drying rate, it reduced changes in product parameters i.e. total color change, surface roughness, shrinkage and microstructural change and increased hardness and water uptake.
•The appearance of intelligent potato friers is imminent.•Computer vision is promising in quality inspection of raw and processed potatoes.•New emerging pre-treatment technologies increase quality of ...potato chips.•Dynamic optimization is superior to static optimization in potato frying process.•Fully fledged Digital Twins will facilitate predictive control of product quality.
Potato chips production is a traditional food process. To achieve uniform product quality, raw materials are usually rigorously sorted. Traditionally, the process is conducted in a single stage approach leading to high quality losses. Recently, dynamically optimized frying processes have been found to result in higher product quality. Consequently, industrial continuous deep-fat fryers convey potato disks through several zones pre-set at different temperatures.
However, these improved systems still do not take the variabilities in frying kinetics among potatoes into consideration. To address this issue and decrease uncertainties in end-product quality, frying conditions of each zone must be optimized, physiochemical properties of the various raw tubers and their frying kinetics taking into account.
This paper, therefore, presents a novel approach for an intelligent frying process with embedded computer vision systems providing continuous monitoring of product quality and, therefore, facilitate dynamic control of frying conditions in order to meet desired quality attributes in the final product. An extensive literature review of the key physiochemical attributes of raw potato tubers is presented, followed by an introduction to novel pre-treatment technologies, and the importance of optimal frying conditions. An overview of the potentials for using computer vision systems for the assessment of said quality criteria is given, followed by a detailed description of the envisioned frying process. The paper concludes that the realization of intelligent frying processes necessitates the development of fully fledged digital twins of the process and the products, combining physics based and data driven modelling with real time sensing and control.
Terminology: Chips refer to thin slices of potato while French fries refers to wedges/stripes.
The convergence of drying technology and Digital Twins is enabling a transition from traditional drying methods to a new phase where the process can be continuously monitored. A crucial aspect is the ...development of digital model of kinetic reactions. In this context, hyperspectral imaging has been widely used, however, it has limitations due to its high cost and computational intensity, which restrict its application to lab-scale settings. To address this limitation, this study introduces technically simple and cost-effective imaging techniques of Light-emitting diode (LED) and Band-pass filter (BPF). These techniques were implemented at 980 nm and 1450 nm to measure moisture content, Soluble Solids Content (SSC), and shrinkage of apple slices undergoing drying at 60 °C. Images of apple slices during the drying process were captured to train Gaussian Process Regression (GPR) models. Moisture content achieved the highest accuracy, with GPR models yielding R-squared values of 0.996 and 0.992, and RMSE values of 1.74% and 2.21% for LED and BPF, respectively. Similarly, SSC (R-squared ≥0.874 and RMSE ≤7.85%) and shrinkage (R-squared ≥0.967 and RMSE ≤5.23%) were well predicted. Furthermore, accurate prediction results for external apples demonstrated the models reliability and robustness. In line with the concept of digital twins-based smart drying, the 980 nm LEDs embedded into an experimental dryer exhibited high accuracy (R-squared ≥0.968 and RMSE ≤4.6%) in inline prediction of moisture content. This represents a significant step towards the development of digital twins-based smart dryers using cost-effective and technically simple imaging sensors.
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, that ...is, 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 Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, 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 a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots.
Novelty impact statement
non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results.
Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots.
A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.
•A machine vision approach was used for detection of lying position of pigs.•An ellipse fitting technique was applied to localize each pig in images.•The lying patterns of pigs in enriched and barren ...pens were compared.•Rooting material provision significantly changed the diurnal activity pattern and lying location.
Visual monitoring of pig behaviours over long periods is very time consuming and has possibility for observer bias. Automated image processing techniques now give the potential to carry out behavioural research in a more effective way. To illustrate this, an image processing technique was applied to identify whether any changes in pig lying behaviour which might be detrimental to welfare resulted from an enrichment provision treatment. The lying patterns of pigs in 6 enriched pens were compared with those of 6 control pens, which had only a suspended enrichment toy, to determine whether daily provision of a rooting material (maize silage) onto a solid plate in the lying area of a fully slatted pen resulted in changed lying time and location. Pigs were monitored by top view CCTV cameras and animals were extracted from their background using image processing algorithms. An ellipse fitting technique was applied to localize each pig and the centre of each fitted ellipse was used in x–y coordinates to find the lying positions after use of an algorithm to remove images in motion preceding the scan. Each pen was virtually subdivided into four zones and the position of each lying pig obtained at 10min intervals over a series of 24h periods. Results of a validation study showed that the image processing technique had an accuracy of 93–95% when compared to visual scoring. Results from image processing indicated that once daily provision of rooting material significantly changed the diurnal activity pattern (p<0.001) and resulted in a modified diurnal pattern of resting location. The study demonstrates that machine vision can be used as a precise and rapid method for quantifying pig lying behaviour for research or practical applications.
•Physics-based modeling combined with sensor data and used in Monte Carlo simulations.•Influence of drying tray is relevant for hygrothermal behavior of produce particles.•The distribution of the ...water activity of the carrot slice population determines the minimum drying time required.•Insights into the distribution of moisture and beta-carotene changes during drying.
Digital twins allow non-invasive access to the hygrothermal processes of fruits and vegetables during drying. However, existing methods do not consider the heterogeneity of product particles throughout the process. This study uses physics-based and Monte Carlo simulations to predict natural variability in carrot slices from raw material to the final product. Data from literature to model sorption isotherm and effective diffusivity, to validate the hygrothermal model, and to provide input ranges for Monte Carlo simulations was used. Nusselt correlations define heat and mass transfer coefficients, while conductive heat transfer accounts for thermal contact between the mesh tray and carrot slice. Validation shows good agreement between model and experimental data. A first-order reaction kinetic describes the thermal decay of β-carotene satisfactorily. Sensitivity analysis identifies the parameters that impact drying time and carotene retention most: slice thickness and initial moisture content. The study also introduces a water activity assessment method for the carrot slice population in the dryer, applying it to assess convective drying efficiency under different conditions. Raising the drying air temperature from 50 °C to 70 °C, along with a shift to product temperature-controlled drying, achieves a 45 % reduction in drying time, a 27 % decrease in required heat energy, and an 8 % improvement in β-carotene retention. The described approach holds promising insights for optimizing drying processes without additional equipment. In addition, the combination of physical and Monte Carlo simulations will enable progress in areas where variability plays a decisive role.
PDF
