Conference Agenda

Tribo-electrostatic separation (TES) as a dry fractionation technique for the separation of protein was performed in this work. The experiments were carried out using a custom-built solid particle dispenser, coupled with a triboelectrostatic separator, with the latter comprising of a cubic chamber with two electrodes on which the separated particles were deposited. Both cereal and protein flours of known particle sizes were tested with the electrostatic separator via the particle dispenser with air as the dispersing medium. Using a tribocharging tube, Faraday cup, and an electrometer, the type of charge (positive or negative) acquired by the components of the flour sample and their chargeability in coulombs per unit mass were established prior to separation. A high voltage generator was utilized to supply the electric field required to ensure proper separation within the separator. The effects of electric field strength (100 – 200 kV/m) and air flow rate, varied between 5 – 15 l/min on the yield, protein enrichment and separation efficiency were explored. Scanning Electron Microscopy (SEM) analysis of the structure was carried out on the pre- and post-TES materials which further confirmed high separation efficiency of the process. The positive results from these experiments prove that the TES technique is a viable means for the commercial fractionation of both cereal and pulse flours without the drawbacks associated with existing wet fractionation techniques. Further experiments comparing the functional properties of the post-TES with the pre-TES materials are currently underway to explore the effect of this technique on these and other feed materials.

Peanut is the one of the top 10 allergens that is causing product recalls in Canada. Current strategies to identify such allergens are mostly reactive and often compounded by the delays in sampling, laboratory testing, decision making and traceability challenges. The current study focuses on using NIR spectrometry to identify the peanut allergen contamination by automating data collection, apply machine learning algorithm to predict the allergen contamination and to provide real-time feedback. The current study leverages the Texas Instruments DLP® NIRscan™ Nano Evaluation Module(900 – 1700 nm) that is integrated with Raspberry Pi Module 3 to collect the spectral signature of the wheat flour . The samples contained two categories: pure and contaminated wheat flour. First, the spectral signature of pure wheat sample are obtained. The wheat flour is contaminated with Peanut contaminated samples are acquired. Applying a machine learning pipeline that is being split into the following phases - training-test split, feature preprocessing, model selection, hyperparameter tuning, feature selection and model evaluation on the test set. The Light Gradient Boosting Machines (LGBM) and eXtreme Gradient Boosting machines (XGBoost) achieved the best average balanced accuracy. The final trained models were then evaluated on the test set, which was also used to calculate their final SHAP values. The random forest and XGBoost models were found to be the most effective classifiers, outperforming SVCs, decision trees, and the more computationally efficient Light Gradient Boosting Machine (LGBM) algorithm. The model predictions achieve above 90% balanced accuracy that predicts the presence or absence of allergen in question

The present antinutrients and unpleasant flavours constrain consumers’ acceptance of kidney bean flour. Extrusion can remove antinutrients by heat, pressure, and shear. However, its ability to modify beany flavour has not been well-investigated. While researchers have investigated the effects of extrusion temperature and feed moisture, the effects of acid or alkali injection into the extruder are yet to be systematically elucidated. This study investigated the effects of injecting acetic acid or sodium carbonate solutions, at three levels of concentration (0.05, 0.10, 0.15 mol/L), combined with three levels of temperature profile (die temperature set at 90, 110, 130°C) and two levels of feed moisture (25, 30%) during extrusion on the removals of antinutrients (condensed tannins, trypsin inhibitor activity, raffinose family oligosaccharides) and beany flavour (volatile compounds) in whole kidney bean flour. Results showed that all concentration levels of acetic acid and sodium carbonate solution increased the reduction of condensed tannin compared with water, especially at 130°C extrusion temperature. The addition of acetic acid and sodium carbonate at 0.15 mol/L concentration resulted in 72 and 90% reduction of total raffinose oligosaccharide content, as compared with 17% when water alone was added. The addition of sodium carbonate under three investigated concentrations reduced the total volatile compound in the bean flours by 45-58% as compared with water (23-33%) and acetic acid (11-27%), primarily due to aldehydes, alcohols, and aromatic hydrocarbons. These results suggest that the injection of sodium carbonate solution during extrusion could effectively reduce antinutrients and beany flavour compounds in kidney bean flour.

Ensuring food safety of eggs is crucial to preventing the risk of foodborne illnesses for consumers. However, the traditional egg surface decontamination method, washing with hot water containing disinfecting chemicals can inadvertently damage the eggshell cuticle, acting as a natural barrier, preventing bacterial access inside egg, and maintaining egg quality during storage. To address this challenge, it is essential to explore innovative technologies for egg surface decontamination. Non-thermal emerging technologies, such as engineering water nanostructures (EWNS) and cold plasma, offer promising alternatives, in which their effectiveness in deactivating bacteria on egg surface without any adverse impact on egg quality have been proved in our previous studies. However, understanding the impact of these technologies on the eggshell cuticle structure is crucial for informed decision-making regarding their practical application. In this study, the effects of non-thermal emerging technologies on eggshell cuticle chemical composition and coverage were evaluated, utilizing ATR Infrared spectroscopy and synchrotron-based X-ray micro-CT tomography. Our findings indicate that unwashed eggs (control) with intact cuticles exhibit a highly intense polysaccharide band, signifying strong glycosylation of the cuticle. In contrast, washed eggs show a weak polysaccharide band and strong carbonate absorption peaks due to cuticle thickness reduction during the washing process, and significantly lower cuticle coverages. On the other side, no significant differences in the eggshell chemical compositions and coverage of unwashed eggs and those treated with EWNS, and cold plasma were observed, suggesting that these methods do not have any adverse impacts on the eggshell cuticle chemical composition and coverage.

Leafy vegetables, particularly lettuce (Lactuca sativa), are prone to postharvest deterioration due to high water content and rapid loss of green color following harvest. This research aimed to investigate the effects of different storage light spectra on shelf life and visual characteristics of lettuce while minimizing postharvest quality losses. Since green light does not induce stomatal opening and as the consequence minimal impact on wilting of the product, in the present study the impact of two wavelengths of green LEDs with peaks at 500 nm and 530 nm, compared to the white LEDs (400–700 nm), and dark storage (control) over a 14-day storage period were assessed. Lettuce leaves were exposed to constant light intensity (10 µmol m-2 s-1), photoperiod (12 h-d), and air temperature (5°C). Lettuce stored under 500 nm and 530 nm green LEDs exhibited a significantly extended shelf life compared to dark storage. Noteworthy improvements in stiffness, and maintaining color were obvious by postharvest exposure to green LEDs. Exposure to 530 nm green LEDs led to notably higher chlorophyll index compared to other treatments. Photosynthesis was run over an extended duration, resulting in higher levels of total soluble sugar (TSS), while lower transpiration compared to samples under dark storage and white LEDs. Under green light, relative water content (RWC) was higher than its value under dark storage and white LEDs. These findings highlight the positive impact of green LEDs on quality retention and visual attributes of lettuce, offering valuable insights for postharvest practices.

The bin piler is a mechanical arm used to pile tubers within a bulk storage facility. This study evaluates a localization system designed to track the position of the bin-piler, enabling the creation of a 3D quality map of a tuber storage facility. Conducted at Dalhousie University, the research involved two experiments to determine localization accuracy in a 32 m^3 grid using stationary anchors at varied and uniform heights. The varied setup had a total anchor height range of 2.09 m, while the uniform setup held all anchors at an equal height of 0.52 m. Initial tests yielded accuracies of 1.19 m and 75 m, respectively, revealing geometric limitations in the localization algorithm.

Implementing a multivariate Support Vector Machine regressor model improved localization accuracy to 0.23 m and 0.49 m for varied and uniform anchor heights. Further optimization of hyperparameters was conducted, training models using a series of values for both the regularization parameter and epsilon, ranging from 0.1 – 100 and 0.1 – 1, respectively. With optimized regularization and epsilon, the accuracy further improved to 0.18 m for the varied anchor height setup, and 0.32 m for uniform heights. The synchronization of the localization information with other sensors that detect quality information of tuber, such as size, would create storage quality maps for better traceability.

Key Words:

Food traceability

UWB sensors

Wireless sensing

Yield quality

A wide range of environmental issues associated with traditional plastics have resulted in the need for efficient pathways that focus on sustainability. Therefore, attention has been drawn to naturally-derived polymers such as starch. To date, commercial starches from potatoes, corn, and rice have been dominating the polymer industry. Faba bean starch, on the other hand, has received little material application despite its strong gelling ability and high final viscosity. The present study utilized tunicate-sourced cellulose nanocrystals to mitigate the water barrier properties of faba bean starch of different degrees of purity (air-classified and isolated). Tunicate CNC (varying between 0.2 and 0.6 mL) was added to starch (3% w/v) and glycerol (1 mL). 25 mL of the starch/glycerol/tunicate CNC was then crosslinked with 5 mL of sucrose and 5 mL of citric acid, basically minimizing retrogradation and enhancing antimicrobial properties. The films were characterized for their physico-chemical, mechanical, and thermal properties. The environmental footprint of the film specimens provides a competitive advantage in reducing carbon emissions over low-density polyethylene.

Plant proteins are gaining in popularity and are increasingly being considered as an alternative to animal protein, thus a sustainable, yet comparable source of plant protein is highly recommended. To this end, the current study was conducted to investigate the structural, thermal, physical, and functional attributes, as well as the volatile profile of sugar maple leaves (SML) protein. To this end, SML protein was extracted by homogenization (10000 rpm, 5 min) pretreated ultrasound-assisted extraction (120000 J, 60% amplitude, 5:5 pulse, 25°C, and 15 min) at varying pH (8−10) and compared with conventionally extracted SML protein. Among the different protein extracts, sonicated extract (pH9) provided a good protein yield (up to 7%) having higher protein content (210 mg Bovine serum albumin/g dry matter), thermal stability (onset of denaturation temperature:114°C), and functional properties (solubility, emulsion capacity, water holding capacity, and antioxidant activity). On contrary, the characteristic green aroma, caused by 3-hexen-1-ol and nonanal, was found higher in sonicated protein extracts than that of macerated extracts. Having said that, this study showed the versatile properties of SML protein fractions that can be used as plant-based food ingredients.

Structure and texture formation in plant proteins based meat-analog (MA), is still a big challenge. This study utilized different plant-based composites to develop restructured MA. Physicochemical, thermal, mechanical, structural, and sensory properties of formulated MA as well as batter-coated fried MAs were studied, and compared with a commercial product. Protein (23.27-24.68%), moisture (57.05-58.78%), pH (7.19-7.57), color (L:64.76-66.84, a:0.62-1.98, b:18.84-20.49), and textural (MF:0.22-0.52N, GF:0.07-0.24N/sec, FA:0.74-1.92 N.sec) attributes of formulated MAs were substantially impacted by the ratio of soy-protein-isolate (SPI) and wheat-gluten (WG). Incorporation of higher WG and lower SPI resulted in the formation of chicken-like fibrous and porous structure, hence, increased consumers acceptability of MA-based coated fried products. Microporosity (crust:51.14-58.35%, core: 63.57-71.55%), surface opening (5.67-14.75%), and fractal dimension (2.586-2.402) of coated fried MAs were dependent on the formulation of batter-coating. MA-based coated fried products surface moisture-fat (SMR:0.51-187.20 au; SFR: 2.01-20.17 au) profile significantly (p<0.05) varied with the formulations of batter-coating. Negative glass-transition-temperature (around -23°C) is prime concern for MA-based fried products stability at room environment.

Micronization is vital in altering the microstructural properties of pulse seeds, aiming to tailor them for various applications. This study evaluated the impact of micronization processing on the microstructural and physico-chemical attributes of three key pulses: chickpeas, lentils, and yellow peas, using different infrared (IR) exposure times (60, 80, 100, and 120 s) at a surface temperature of 180°C. Morphometric parameters (porosity, pore size, and pore distribution) were quantitatively and qualitatively analyzed using X-ray micro-CT, while physico-chemical parameters such as moisture loss (%), color change (ΔE), hardness (N), swelling/hydration capacity, and cooking time (min) were assessed using AOAC methods. Results demonstrated a significant increase (p<0.05) in moisture loss, ranging from 2.16 ± 0.29% to 12.24 ± 1.62% at prolonged IR exposure, along with noticeable discoloration of micronized seeds at 100 s and 120 s. Chickpeas and yellow peas exhibited increased hardness with extended IR exposure, whereas green lentils showed a significant reduction (p<0.05) in hardness at 120 s (158.01 ± 41.47N). All three pulses showed a substantial reduction in cooking time when micronized at 100 s, showing optimal cooking times of 120 ± 2 min, 50 ± 2 min, and 20 ± 2 min by chickpeas, yellow peas, and green lentils, respectively. Pulses micronized at 100 s demonstrated improved porosity, swelling/hydration capacity, and decreased cooking time, indicating their suitability for diverse applications. This study underscores the importance of understanding the microstructural and physico-chemical changes induced by pulse micronization, offering insights to develop predictive models to meet specific consumer/industrial requirements.

The growing need for plant protein has resulted in the increased demand for many pulse grain fractionation and refining industries. Field pea grains, which are produced worldwide have high protein concentration ranging from 25% to 30% (dry basis). However, the pea starch, a byproduct of the protein refining industry does not find any place in the agri-food sector because of their high amylose concentration (~40%), which could lead to rapid retrogradation. Therefore, in this study, a novel in-house spraying procedure was used to synthesise starch nanoparticles, to encapsulate natural antimicrobial compounds such as essential oils to find a value-added application of the underutilized starch byproduct. The synthesis of oil-encapsulated starch nanoparticles (OESNP) was further optimized using a Box-Behnken experimental design to study the influence of the processing parameters such as the initial starch concentration (10, 30 and 50 mg/ml), homogenization speed (5000 to 15000 rpm), duration of homogenization (1 to 10 min), sample injection rate (100 to 1000 µl/min), and quantity of antisolvent (1:1 to 1: 10). The produced OESNP were further characterized to investigate their molecular interactions, size and structure of the nanoparticles using Dynamic light scattering (DLS), X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and Fourier transform infrared (FTIR) spectrophotometry. The optimized sample showed an 80-90 % entrapment efficiency and particle size (<500 nm). The OESNP also showed significant antimicrobial properties against common plant pathogens suggesting their potential use in agri-food sector.

The growing demand for plant protein ingredients has increased the attention towards pulse starch, a major byproduct of protein fractionation. North America is the largest producer of pinto beans in the world. Different milling techniques are used to produce pinto bean flour which is further processed for protein or starch isolation. Each mill has a unique impact on the starch molecules that influence the yield and properties of isolated starches. In this study, the effect of blade mill, burr mill, stone mill, and hammer mill on the physical properties of pinto bean flour was correlated with conventional isoelectric precipitation-based starch isolation, and the characteristics of starch isolates. In all mills, the increase in milling intensity resulted in lower particle sizes and subsequent increase in starch damage in flour. However, the degree of size reduction and starch damage varied among different mills. The larger particles were observed in burr milled flours followed by stone mill, blade mill and hammer mill. The starch yield significantly increased with reduction in flour particle size. The degree of crystallinity, conclusion temperature during gelatinization and the enthalpy of gelatinization of starch isolates were also reduced with a reduction in particle size of flours.

In Canadian prairie cropping systems, most farmers apply urea during the fall before freezing. This practice is inspired by lower fertilizer prices in the fall and the need to minimize tillage and other farm operational tasks in the spring. Farmers are advised to apply fertilizers at a temperature of 5°C or lower to reduce nitrogen (N) losses. However, N losses such as nitrous oxide (N2O) emissions occur during thawing even when fertilizers are treated with inhibitors. This is due to the coupled effects of soil moisture and temperature in driving these emissions. However, there is a lack of information on the extent of these effects in Manitoba. The objective of this study was to evaluate the effects of soil freezing and varying thawing temperatures on N2O emissions from urea treated with double inhibitors (SuperU) under varying moisture contents. Treatment effects were tested using a sandy loam soil collected from Carman, Manitoba. The soil was homogenized and packed into Nalgene containers at a bulk density of 1.2 g/cm-3, followed by band-application of SuperU. The units were watered with deionized water to attain moisture levels of 25%, 50%, and 100% saturation. The soils were then frozen at -20 °C, followed by thawing at 4, 8, 12, and 16°C. Gas samples were taken at 0, 1, 3, 6, 10, 20, 30, 40, 50, and 60 h using the static chamber technique. Results on the effects of temperature and moisture levels on N2O emission will be reported. Findings will help design nutrient management practices.

Soybean is the third most important crop in Manitoba based on seeded area and farm income. However, drought and excess moisture affects potential crop yield. Tile drainage is becoming popular as a method to remove excess moisture. To overcome drought conditions, subirrigation through tile drains was investigated as a viable alternative to overhead irrigation. This study investigated the effects of subirrigation through the drainage systems on soybean (Glycine max (L.) Merr.) grain yield and yield components, actual crop evapotranspiration, yield response factor (Ky), and harvest index (HI). Field experiments were conducted in 2023 at Prairies East Sustainable Agriculture Initiative (PESAI), Arborg, Manitoba, Canada. Two subsurface irrigation treatments (on tile and midway between tiles) and rainfed conditions were evaluated with soybean seeded at 444,789 plants per ha (180,000 plants/ac) in three replicated plots. Soybean yield, nodule counts, above and below ground biomass, height, number of pods, number grains, and grain weight were significantly higher (p < 0.05) in the subirrigated through tile drainage treatment compared to the rainfed control treatment. Grain yield on the tile treatment was significantly higher at 2820.9 kg ha-1 compared to 2216.9 kg ka-1 in the rainfed treatment. Nodule counts ranged from 67 for the rainfed to 614 for on tile treatment. Seasonal ETa was 189.6 mm for the rainfed compared to 299.7 mm in the midway between tiles. The average irrigation water requirement was 182.8 mm. This study showed that subirrigation through tile drains is important for better soybean yield in an area subjected to drought.

The study of watershed management has become essential in an era of increasing environmental challenges and developed agricultural activity. Increasing attention is being given to the complex relationships that exist between the water quality of freshwater and agricultural activities. The role of non-point source (NPS) pollution is crucial to understand since it is an important issue for preserving the environmental quality of water bodies. The relationship between agricultural practices and the quality of water is being examined more closely, considering NPS pollution. The spatio-temporal patterns of precipitation events that significantly contribute to sediment and phosphorus loads are the focus of the comparative study. The frequency as well as the magnitude of threshold events, both annually and seasonally, are examined for different watersheds in Southern Ontario (Canada). The primary emphasis of this study is on threshold precipitation events, which create significant hydrological responses, including higher levels of sediment and phosphorus loads and surface runoff. These events are critical to understand the NPS pollution and their correlation with land use changes. By focusing on threshold events, the study elucidates the direct relationship between precipitation events and the subsequent hydrological processes, such as surface runoff, sediments, and phosphorus loads. This work contributes to our understanding of the diverse patterns exhibited by watersheds in response to NPS pollution during these threshold events. The ultimate objective of this research is to contribute towards the assessment of effective of Best Management Practices.

Subsurface drainage is a water management strategy that uses tile drains to remove excess water from poorly drained soils and maintain soil conditions near field capacity following heavy rainfall. Due to spring snowmelt, Manitoba experiences excess soil water during the early growing season. High-intensity storm events during the growing season also led to fluctuating water table depths. This research aimed to evaluate soybean yield under different water table depths under subsurface drainage and no drainage conditions in heavy clay soil in the Interlake region of Manitoba. Tile drains were installed at a depth of 0.9 to 1.1 m at 4.5, 9, 13.7 m (14, 30, 45 ft) spacing. The soybean yield was obtained over the tile, midway between tiles, and in no tile plots. Water table depths were continuously monitored. During the dry years of 2020 and 2021, the yield was lower in all the plots. During the wet year of 2022, the over the tile yield was significantly higher than the control because the watertable was maintained lower during vegetative stage.

Soil quality plays a crucial role in sustainable agricultural practices and the mitigation of climate change. A major indicator of soil quality and fertility is soil organic matter (SOM) which is derived from decomposed organic material. High levels of SOM have been shown to resist erosion, preventing nutrient loss and retain water. However, conventional practices like soil tilling degrade arable lands by disrupting soil structure and depleting organic matter. Current research on agricultural applications of nanoparticles (NPs) is gaining attention from farmers and agrochemical industries, providing successful alternatives to chemical-based fertilizers. NPs improve nutrient release timing, duration, and rate, enhancing plant absorption. Their use reduces soluble salt volumes in fields, leading to increased crop growth, biomass, and nutritional content. The work presented here has focused on the development and application of iron oxide nanoparticles (IONPs) as soil amendments with the intention of enhancing soil organic matter and retaining nutrients. The interaction of IONPs with various nutrients (phosphate and nitrate) along with organic matter was assessed using breakthrough experiments and soil columns under various conditions including NP condemnation, water flow rate, and mixing style. Additionally, the impact of NPs on the microbial community will be discussed.

Globally, climate change poses substantial challenges to agricultural sustainability. The Upper Humber River Watershed (UHRW), a vital agricultural producing region in western Newfoundland, similarly faces significant challenges due to climate change. This study investigates the potential impacts of climate change on agricultural sustainability in UHRW using the SWAT model and downscaled CMIP5 climate data from General Circulation models. Based on emission scenarios RCP8.5 and RCP4.5, by the 2080s, the annual average temperature is projected to increase by 4.8°C and 2.6°C, respectively and the annual average precipitation to increase by 146 mm and 65 mm, respectively. Spring temperature and winter precipitation show the most significant increases with 0.04°C/10a (0.03°C/10a) and with 0.59 mm /10a (0.35 mm /10a) under scenario RCP8.5 (RCP4.5). Simulations indicated an increase in mean values of flow across RCP scenarios by the 2030s, 2050s, and 2080s, where mean streamflow projections increased from the baseline by 11.1% (RCP8.5) and 3.7% (RCP4.5) by the 2080s. Due to this, it is expected that the monthly average total nitrogen and phosphorous loads will increase from May to November and decrease from December to April by the 2080s. Additionally, anticipated climate fluctuations are likely to result in substantial alterations to crop yields within UHRW, with resultant outcomes influenced by the inherent uncertainties associated with predictive modeling. Increased winter precipitation and temperature will lead to earlier melting of spring snowpack and increasing trends in non-point source pollution. Consequently, the paper emphasizes the imperative of implementing adaptation measures to ensure agricultural sustainability in the watershed.

As the competing demand for water increases, effective water management is necessary to optimize water use in agricultural fields. Knowledge of soil water content and crop water uptake pattern with different drain spacing will help farmers optimize water use and maintain a stable ground water table to maximize production by protecting the crops from water stress. On the other hand, the challenge posed by poor drainage in heavy clay soils affect crop performance. This research aims to analyze soil moisture patterns within the wheat root zone at different growth stages using multi-layer remote sensing, comparing different drain spacings (13.7 m (45ft) and 9.1 m (30ft) and control). METER soil moisture sensors were installed at depths of 20, 60, and 90 cm to measure soil moisture and soil temperature. Continuous measurement of water table elevations was carried out using Solinst dataloggers. The field data will be simulated using DRAINMOD. Weather parameters were collected throughout the season. The data collected will be used in the simulation model, predicting the impact of drain tile spacing and soil moisture on wheat crop performance, estimating maximum yield, and comparing it with actual yields. This research has the potential to optimize water management and contribute valuable insights into global agricultural sustainability.

Amending soils with hydrogels is known to improve soil fertility. Use of acrylamide-based synthetic hydrogels (SH) in soil could be problematic due to concerns about their toxicity and slower biodegradability. Therefore, the development of natural hydrogels as soil amendments is gaining popularity as a greener alternative. A two-year field study using pots, filled with sandy soil, was conducted to compare the effects of SH and wastepaper-based natural (NH) hydrogels on green pepper plant growth and yield under water stress levels. Both hydrogels were applied at the rate of 0.5% (w/w), and the pots were arranged in a randomized complete block design with three treatments (SH, NH, and non-amended control) and four blocks (rows). Both hydrogels increased green pepper yield by improving water-use and nutrient-use efficiencies. No significant differences (p<0.05) were observed in the performance of SH and NH hydrogels in terms of plant yield and nutrient leaching at 100% irrigation level. Therefore, the wastepaper-based natural hydrogel can serve as an environment-friendly alternative to synthetic acrylamide-based commercial hydrogels as it eliminates the potential threat of acrylamide monomer to the environment.

Dill (Anethum Greveolens L.), a biennial or annual herb is globally known for its utilization in various food industries, including refrigeration and food concentrates, to impart its distinctive flavor and seasoning to a range of dishes. Dill seed also offers nutritional benefits as it contains various essential nutrients such as vitamin A, calcium, magnesium, sodium, potassium, fiber, protein and niacin. Dill seed is usually adulterated with fennel, cumin seeds, Indian dill (Anethum sowa) and other resembling dill cultivars or varieties for economic gain. In this study, shortwave infrared (SWIR) hyperspectral imaging was deployed to differentiate three dill varieties based on reflectance features. Three hundred hyperspectral images captured in the wavelength range of 900 to 2500 nm were processed, segmented and subjected to machine learning algorithms. Multiclass classifier was used to classify the dill varieties based on reflectance spectra. All the three dill varieties were classified correctly with a classification accuracy of 100%.

In recent years, frequent reports of food adulteration have increased the concerns of consumers, food industries and regulatory agencies. Food adulteration is termed as a deliberate offensive act to delude people for monitory benefits. Chickpea is broadly consumed pulse in the world. It is rich in protein and also a good source of dietary fiber, carbohydrates, vitamins, minerals, and several bioactive components. It is often adulterated with low cost and low protein resembling materials like corn flour. In the present study, chickpea flour was adulterated with different percentage levels (1, 3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 97, 99, and 100%) of corn flour. Images of mixed flour samples were acquired with 25 replications using shortwave infrared (SWIR) hyperspectral imaging (HSI) in the wavelength range between 900 and 2500 nm. The captured images were processed, segmented and subjected to machine learning algorithms. The classification of different levels of corn flour adulteration was achieved with 98% accuracy using multiclass classifier. Variations in protein and starch contents of adulterated samples can be attributed to the higher classification accuracy.

The significant shift in customer eating habits towards high-protein, low- carb, gluten-free alternatives has forced the food processing industry to come up with more innovative solutions to meet rising demands. Specifically, the pasta industry is currently developing techniques to incorporate more protein per serving while ensuring the palatability of the product. Potato flour (PF) is a gluten- free, versatile flour that can be incorporated into baked goods such as pasta and gnocchi, while chickpea flour (CF), known for its low glycemic index, high protein and essential vitamins which is ideal for fortification. In this research, potato and chickpea flour blends of various concentrations were prepared and analyzed for their physiochemical properties; specifically starch and protein content, which could help pasta- making. The NIR hyperspectral imaging system was used in the range of 900-2500 nm to scan 17 different flour blends in reflectance mode. Spectral features were extracted from the acquired images which were further processed and analyzed using machine learning approaches. The regression analysis predicted protein and starch content with a correlation coefficient of 0.999 and 0.996 respectively.

Potatoes are the widest grown vegetable in Canada, in addition to being the fifth largest primary agricultural crop. Due to their unique end-uses and value addition to the market as processed potatoes, it becomes vital to ensure potato quality from harvest to the consumption. This study intended to understand the effect of storage on dry matter (DM) and accumulated sugars. Hence, the tubers were stored at two different temperatures 2℃ and 15℃, in boxes that admitted little to no light. For the DM study, 277 Russet Burbank (RB) potatoes were scanned in the short-wave infrared (SWIR) of 900-2500 nm range using a hyperspectral imaging system. The relationship between tuber specific gravity (SG) vs DM for individual potatoes was also explored by using a contraption attached to the texture analyzer, that was developed in-house. Similarly, a separate batch containing 130 RB potatoes (stored at 2℃ and 15℃) was scanned for their sugar content; followed by wet chemical techniques to estimate total glucose and sucrose. A 10-fold cross-validation test conducted on the dataset revealed a 90.2% and 82.8% classification accuracy, with a root mean square error of 0.105 and 0.2403, respectively, for observed DM and sugar values, imaged on various days.

Glyphosate, a widely used organophosphate herbicide, poses significant concerns globally due to its persistent presence in pulse crops, leading to food safety challenges. This research investigates the potential of Near-Infrared (NIR) hyperspectral imaging, operating between 900 to 2500 nm wavelengths, for identifying glyphosate residues in red lentil flour. Red lentil flour samples were tested at five different glyphosate concentrations (0 ppm, 5 ppm, 10 ppm, 15 ppm, and 20 ppm) to assess residue levels. Spectral data preprocessing involved smoothing and first derivate techniques. Partial Least Square Regression (PLSR) models were developed incorporating chemical reference measurements and spectral data, refined using a variable importance in projection (VIP)-based method to identify crucial wavelengths for glyphosate detection. Results demonstrated robust predictive capabilities, with a correlation coefficient of 0.930 and a root mean square error of cross-validation (RMSEP) of 0.871 across the concentration range. These outcomes underscore the potential of NIR hyperspectral imaging in accurately quantifying glyphosate residue levels in red lentil flour, thereby mitigating food safety risks associated with agricultural products. This study highlights the efficacy of this non-destructive technique, providing swift and precise detection methods. Future research could explore integrating this approach into routine monitoring practices in food processing facilities to ensure strict adherence to food safety standards.

Rapid, non-destructive and precise prediction of yellow pea flour adulteration in chickpea flour is crucial for food inspection agencies. In present study, short wave infra-red (SWIR) hyperspectral imaging (HSI) in the wavelength range between 900 nm and 2500 nm was deployed to predict the protein and starch in the chickpea flour adulterated with yellow pea. Adulteration was formulated at different levels of yellow pea, i.e. 1, 3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 97, 99, and 100%. Gaussian process regression (GPR), support vector machine regression (SVMR) and neural network models based on different spectral preprocessing techniques were developed by correlating the hyperspectral data with measured reference protein and starch values of the adulterated samples. Competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV) algorithms were used to select the effective wavelengths with full spectrum. The optimum prediction model for protein was obtained using GPR yielding correlation coefficient of prediction (R2p) and root mean square error of prediction (RMSEP) values of 0.99 and 0.089, respectively. Similarly, the same GPR model predicted the starch content with R2p and RMSEP values of 0.86 and 0.549, respectively.

Natural polymers, including proteins and polysaccharides, have been receiving increasing attentions recently as sustainable materials for the development in biomedical applications. Natural polymers extracted from plants or animal products provide great biocompatibility and biodegradability, which are highly desirable in the biomedical field. Hydrogels are highly hydrated, porous, and soft materials with a touch similar to that of the human skin and have therefore attracted extensive interest in the development of wearable biosensors. Egg white is a major nutrient source that has been utilized for extraction of functional proteins. However, the costly and complex process of isolation and purification limits the utilization of egg white and its derivatives. Recently, there has been immense interests in using raw egg white with advanced applications such as supercapacitors and semiconductors, although they still involve complex fabrication methods. Here, we prepared a physically crosslinked egg white (EW) hydrogel with extraordinary stretchability using a simple method. The prepared EW hydrogel showed shear-thinning and self-healing properties that enabled its 3D-printing to fabricate the complex architectures of the biosensor. Incorporation with carbon nanotubes, the versatile EW hydrogel-based biosensors can capture a wide range of human motion including finger bending, wrist pulse, and respiration rate. The biosensor also effectively discerned the cardiovascular system's radial augmentation and stiffness indexes. A highly sensitive humidity-responsive reversible actuator was fabricated through a design of gradient crosslinking density structure. This low-cost natural material provides an easy and effective way for the tailored fabrication of multifunctional biosensors and actuators.