Non-destructive monitoring of quality attributes kinetics during the drying process: A case study of celeriac slices and the model generalisation in selected commodities.

The potential of Visual-NIR hyperspectral imaging (VNIR-HSI, 425-1700 nm) to predict celeriac quality attributes during the drying process was investigated. The HSI-Gaussian Process Regression (GPR) fusion method excellently predicted moisture content (MC, R2 ≈ 1.00, RMSE = 0.77 gw 100 gs-1) and water activity (aw, R2 = 0.98, RMSE = 0.04). Moreover, the rehydration ratio (RR, R2 = 0.89, RMSE = 0.04) and colour indices (R2 = 0.80-0.93, RMSE = 0.17-1.45) were reasonably predicted. However, antioxidant activity (AA) and total phenolic compounds (TPC) were poorly predicted. These results are potentially due to MC variations dominating the NIR region, masking phenolic compounds. Finally, the celeriac-based-trained model was assessed by predicting the MC of apple, cocoyam, and carrot slices. The results were encouraging; however, a GPR model trained on the data of all four commodities was more robust (R2 ≈ 1.00, RMSE = 1-2 gw 100 gs-1).

Simulation of transient heat transfer during forced convection cooling of cocoyam (Colocasia Esculenta (L.) Schott) tubers.

In this study, a prediction model based on transient heat transfer was modified and validated using experimental data. The time required to cool tubers from field temperature of 30 ± 2 °C to the target storage temperature of 12 ± 0.2 °C was predicted directly from the model. Moreover, total cooling time ranged from 127.8 - 154.2 min for small tubers and 190.8-262.2 min for large tubers while the field heat removed ranged from 9.61 - 10.17 kJ for small tubers and 24.78-31.90 kJ for large tubers between the extremes of the air velocity. Tuber orientation to airflow neither influenced the heat transfer coefficients and Biot numbers nor the cooling time and amount of field heat removed. The results from this study could be applied in the design and optimisation of forced convection cooling systems to precool tubers immediately after harvest and for extended duration storage.

The Effect of Pre-Drying Treatment and Drying Conditions on Quality and Energy Consumption of Hot Air-Dried Celeriac Slices: Optimisation.

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 (Es) 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.

Impact of drying temperature and salt pre-treatments on drying behavior and instrumental color and investigations on spectral product monitoring during drying of beef slices.

Drying behavior and instrumental color development of beef slices untreated or pretreated with salt or salt and vinegar solutions were monitored by determining the moisture content and the color change by measuring CIELAB values during drying at 50, 60, and 70 °C. Time-series hyperspectral imaging (400-1000 nm) was applied with regard to the development of non-invasive measurement systems based on robust models to predict moisture and color independent of the pre-treatment and drying temperature. Samples pretreated with salt dried the slowest which became more prominent at increasing drying temperatures and the least color change (∆E = 23) was observed at 60 °C drying temperature. Robust prediction models for moisture content and CIELAB values irrespective of pre-treatment and processing conditions were developed successfully and improved by wavelengths selection with high R2 (0.94-0.98) and low RMSEP (1.05-5.22) which will support the future development of simple and cost-effective applications regarding non-invasive product monitoring systems for beef drying processes.

Influence of pre-drying storage time on essential oil components in dried hops (Humulus lupulus L.).

BACKGROUND: It is well known that duration of pre-drying storage impacts on hop quality. However, little knowledge exists regarding its actual effects on valuable hop components. To investigate these effects, fresh hop cones were stored for 5 or 24 h and dried for 210 min at 65 °C thereafter. Furthermore, to understand the effect of freezing hop cones on the essential oil content, both fresh and stored samples were frozen before and after drying. RESULTS: The results from gas chromatography analysis show an increase in linalool, ß-caryophyllene, humulene, geraniol content and decrease in myrcene content dependent on the period of storage. Total colour difference ΔE values of 4.61 and 5.27 were obtained for fresh and stored hops respectively, indicating discoloration of hops during storage. Modelling of moisture curves revealed the Wang and Singh model to be suitable, with R adj 2 values of 0.978 and 0.989 and root-mean-square error values of 0.037 and 0.019 for fresh and stored hops respectively. CONCLUSION: The results from this study provide an in-depth understanding on the changes occurring within the hop cones both during storage and drying and will further help hop processors optimize their storage times.

Effect of Sieve Particle Size and Blend Proportion on the Quality Properties of Peeled and Unpeeled Orange Fleshed Sweet Potato Composite Flours.

Orange fleshed sweet potato (OFSP) has great potentials to improve the nutritional benefits of bakery products when processed into quality flour. This study investigated the effects of sieve particle sizes (250 µm and 500 µm) and flour blend proportions on the physicochemical, nutritional, functional and pasting properties of peeled and unpeeled OFSP composite flours. Peeled OFSP composite flours had significantly (p < 0.05) higher crude protein (CP), lightness (L*), oil absorption capacity (OAC) and water solubility (WS) but lower crude fiber (CF), bioactive compounds (except ascorbic acid), water absorption capacity (WAC) and swelling capacity (SC) than the unpeeled OFSP composite flours. The sieve particle size had no significant (p> 0.05) effect on nutritional and pasting properties. However, OFSP-based flours sieved with 500 µm mesh particle size had a significantly (p < 0.05) higher WAC and SC but a lower WS than corresponding 250 µm mesh flours. The proportions of flour blends greatly (p < 0.001) influenced all quality properties of OFSP composite flours. Generally, OFSP composite flours had higher CF, bioactive compounds, WAC, SC and WS, but lower CP, fat, OAC and pasting properties than wheat flour. The quality properties indicated that peeled and unpeeled OFSP flours sieved with a 250 µm or 500 µm mesh size have great potentials as ingredients in the bakery industry.

Optimisation of Physical and Chemical Treatments to Control Browning Development and Enzymatic Activity on Fresh-cut Apple Slices.

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.

Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs.

Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.

Nutritional and sensory quality of composite extruded complementary food.

Complementary foods in Ethiopia have nutritional and sensory limitations which can be attributed to cereal-dominated ingredients and lack of appropriate processing techniques. This study aimed to optimize the nutritional and sensory quality of complementary food product through compositing and extrusion of various local ingredients. A constrained D-optimal mixture experiment with 13 runs was designed. Accordingly, 55-65 g/100 g oats, 11-23 g/100 g soybean and 6-11 g/100 g linseed, and a premix of 9.9 g/100 g sugar, 0.6 g/100 g table salt, three g/100 g moringa and 1.5 g/100 g fenugreek were blended and extruded using a co-rotating twin screw extruder with set parameters. Statistical model evaluation and optimization were done using Minitab version 16 software package. There is a statistically significant (p < 0.05) association between the blend of oats and soybean, oats and linseed, soybean and linseed, and the protein, fat, carbohydrate, fiber, ash, ß-carotene content as well as aroma, taste, and consistency. On the contrary, there is a no statistically significant (p < 0.05) association between the blends and moisture, energy, and zinc content together with appearance and overall acceptability. The optimal blending ratio was 55.0 g/100 g oats, 21.0 g/100 g soybean, and 9.0 g/100 g linseed plus 15.0 g/100 g premix. Evidence-based selection of locally grown plant-based ingredients, an optimal mixture of these ingredients and optimal processing, can result in a complementary food product with an improved dietary quality for children in low-income settings.

Nutritional Characteristics of Four Underutilized Edible Wild Fruits of Dietary Interest in Ghana.

Malnutrition has been a serious issue in Ghana and Africa as a whole. However, the potential of many indigenous fruits to combat it has not yet been tested. Therefore, this study aimed to determine the nutritional characteristics of four underutilized wild fruits (Gardenia erubescens, Sclerocarya birrea, Diospyros mespiliformis, and Balanites aegyptiaca) of dietary interest in Ghana. The nutritional and antinutritional characteristics of the fruits were analyzed according to standard methods (laid down by the Association of Official Analytical Chemists and other well-known researchers) on a dry weight (dw) basis. The nutritional value of the fruits was high enough to contribute to the nutrient requirements of humans, with their iron (0.34⁻1.46 mg/100 g), zinc (0.81⁻2.97 mg/100 g), vitamin A (0.84⁻2.03 mg/100 g), and ß-carotene (64.84⁻176.89 mg/100 g) contents worth special mention. The antinutrient content also ranged between 0.06⁻1.82 mg/g. Therefore, it is evident from the study that the fruits, although containing some levels of antinutrients, are nutrient-dense, suggesting their potency in fighting malnutrition in humans.

Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging.

BACKGROUND: The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). RESULTS: The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ1 ]-R[λ2 ] and R[λ1 ]:R[λ2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). CONCLUSIONS: Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry.

High pH thresholding of beef with VNIR hyperspectral imaging.

Initial quality grading of meat is generally carried out using invasive and occasionally destructive sampling for the purposes of pH testing. Precise pH and thresholds exist to allow the classification of different statuses of meat, e.g. for detection of dry, firm, and dark (DFD) (when dealing with cattle and sheep), or pale, soft exudative meat (when dealing with pork). This paper illustrates that threshold detection for pH level in beef with different freshness levels (fresh, fresh frozen-thawed, matured, and matured frozen-thawed). Use of support vector machine (SVM) analysis allowed for the classification of beef samples with a pH above 5.9, and below 5.6, with an accuracy of 91% and 99% respectively. Biochemical and physical conditions of the meat concerning the pH are discussed.

Classification of organic beef freshness using VNIR hyperspectral imaging.

Consumer trust in the food industry is heavily reliant upon accurate labelling of meat products. As such, methods, which can verify whether meat is correctly labelled are of great value to producers, retailers, and consumers. This paper illustrates two approaches to classify between, fresh and frozen thawed, and in a novel manner matured and matured frozen-thawed, as well as fresh and matured beef using the 500-1010nm waveband, captured using hyperspectral imaging, and CIELAB measurements. The results show successful classification based upon CIELAB between 1) fresh and frozen-thawed (CCR=0.93), and 2) fresh and matured (CCR=0.92). With successful classification between matured and matured frozen-thawed beef using the entire spectral range (CCR=1.00). The performance of reduced spectral models is also investigated. Overall it was found that CIELAB co-ordinates can be used for successful classification for all comparisons except between matured and matured frozen-thawed. Biochemical and physical changes of the meat are thoroughly discussed for each condition.