Effects of Feed Moisture Content on the Physical and Nutritional Quality Attributes of Sunflower Meal-based High-Moisture Meat Analogues.

Adding value to food industry by-products, like sunflower meal (SFM), through their utilization as ingredients in new food products can improve sustainability of food systems. This research investigated extrusion cooking to produce high-moisture meat analogues (HMMAs) made from blends of soy protein isolate and expeller-pressed SFM. The effects of feed moisture content [FMC] (60, 65, and 70%, wet basis) and SFM concentration (37.5, 50, and 62.5%, total blend weight basis) on physical and protein nutritional quality attributes of HMMAs were investigated. The processing temperatures (including cooling die), screw speed and feed rate were kept constant at 60-80-115-125-50-25 °C (from feeder to the die end), 200 rpm and 0.5 kg/h (dry basis), respectively. An increase in SFM concentration and FMC significantly (p < 0.05) reduced the mechanical energy requirements for extrusion. Cutting strength and texture profile analysis of HMMAs indicated softer texture with increases in SFM and FMC. X-ray microcomputed tomography analysis revealed that the microstructure of the HMMAs at the centre and towards the surface was different and affected by SFM concentration and FMC. The in vitro-protein digestibility corrected amino acid score of the HMMAs ranged between 85 and 91% and did not show significant (p < 0.05) changes as a function of FMC or SFM concentration. HMMAs produced from 37.5% SFM at 70% FMC showed no deficiency in essential amino acids for all age categories except for infants, suggesting the high potential of SFM and soy protein blends for creating nutritious meat alternative products. Overall, this work provided valuable insights regarding the effects of soy protein replacement by SFM on the textural, microstructural and nutritional quality of HMMA applications, paving the way for value-addition to this underutilized food industry by-product.

Characterization of Near Infrared-Dye Colored Fabrics Using Hyperspectral Imaging.

Near-infrared (NIR) dyes have a unique ability to interact favorably with light in the NIR region, which is particularly interesting where stealth and camouflage are paramount, such as in military uniforms. Characterization of cotton fabric dyed with NIR-absorbing dyes using visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging was done. The aim of the study was to discern spectral changes caused by variations in dye concentration and dyeing temperature as these parameters directly influence color- and crocking-fastness of fabrics impacting the camouflage effect. The fabric was dyed at three concentrations (2.5, 5, and 10%) and two dyeing temperatures (55 °C and 85 °C) and principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on the spectra to discriminate the fabrics based on dye concentrations. The PCA models successfully segregated the fabrics based on the dye concentration and dyeing temperature, while PLS-DA models demonstrated classification accuracies between 75 and 100% in the Vis-NIR range. Spectra in the SWIR region could not be used to detect the differences in the concentrations of the NIR dyes. This finding is promising, as it aligns with the objective of creating NIR-dyed camouflage fabrics that remain indistinguishable under varying dye concentrations. These results open possibilities for further exploration in enhancing the stealth capabilities of textiles in military applications.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages.

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

Non-Destructive Assessment of Microstructural Changes in Kabuli Chickpeas during Storage.

The potential of hyperspectral imaging (HSI) and synchrotron phase-contrast micro computed tomography (SR-µCT) was evaluated to determine changes in chickpea quality during storage. Chickpea samples were stored for 16 wk at different combinations of moisture contents (MC of 9%, 11%, 13%, and 15% wet basis) and temperatures (10 °C, 20 °C, and 30 °C). Hyperspectral imaging was utilized to investigate the overall quality deterioration, and SR-µCT was used to study the microstructural changes during storage. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used as multivariate data analysis approaches for HSI data. Principal component analysis successfully grouped the samples based on relative humidity (RH) and storage temperatures, and the PLS-DA classification also resulted in reliable accuracy (between 80 and 99%) for RH-based and temperature-based classification. The SR-µCT results revealed that microstructural changes in kernels (9% and 15% MC) were dominant at higher temperatures (above 20 °C) as compared to lower temperatures (10 °C) during storage due to accelerated spoilage at higher temperatures (above 20 °C). Chickpeas which had internal irregularities like cracked endosperm and air spaces before storage were spoiled at lower moisture from 8 wk of storage.

Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging.

As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible-near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries.

Advanced Oxidative Decontamination of Flax and Its Impacts on Storage.

The metabolic actions of storage fungi and other microorganisms can cause spoilage and post-harvest losses in agricultural commodities, including flaxseed. These microbial contaminants are oxidized with hydroxyl radicals that are efficiently generated when ozone, hydrogen peroxide (H2O2) and ultraviolet (UV) light react in an advanced oxidative process (AOP). The present work explores what we believe is the first application of an AOP technology to reduce mould on whole brown and yellow flaxseed. The impact of AOP on storage and quality parameters was assessed by measuring the fatty acid value (FAV), germination rate, moisture content (MC) and visible mould growth after 12 weeks of storage at 30°C and 75% relative humidity (RH). Under these conditions, the yellow decontaminated flaxseed showed a 31% decrease in the number of seeds with visible mould without any adverse effect on germination rate, FAV and MC. In contrast, the same AOP treatment created an insignificant decrease in mould in stored brown flaxseed, at the cost of decreasing the germination rate and increasing FAV. The adverse effects of AOP on brown flaxseed were not readily apparent but became measurable after storage. Moreover, Fourier transform infrared (FTIR) spectroscopy was utilized to explore the rationale behind the different reactions of flaxseed varieties to AOP. The corresponding results indicated that the tolerance of yellow flaxseed to AOP might be related to its richness in olefins. The authors believe that technologies that harness advanced oxidative processes open new horizons in decontamination beyond ozone alone and towards increasing the shelf life of various agri-food products.

Enhancing traceability of wheat quality through the supply chain.

With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

Non-destructive characterization of pulse flours-A review.

The consumption of plant-based proteins sourced from pulses is sustainable from the perspective of agriculture, environment, food security, and nutrition. Increased incorporation of high-quality pulse ingredients into foods such as pasta and baked goods is poised to produce refined food products to satisfy consumer demand. However, a better understanding of pulse milling processes is required to optimize the blending of pulse flours with wheat flour and other traditional ingredients. A thorough review of the state-of-the-art on pulse flour quality characterization reveals that research is required to elucidate the relationships between the micro- and nanoscale structures of these flours and their milling-dependent properties, such as hydration, starch and protein quality, components separation, and particle size distribution. With advances in synchrotron-enabled material characterization techniques, there exist a few options that have the potential to fill knowledge gaps. To this end, we conducted a comprehensive review of four high-resolution nondestructive techniques (i.e., scanning electron microscopy, synchrotron X-ray microtomography, synchrotron small-angle X-ray scattering, and Fourier-transformed infrared spectromicroscopy) and a comparison of their suitability for characterizing pulse flours. Our detailed synthesis of the literature concludes that a multimodal approach to fully characterize pulse flours will be vital to predicting their end-use suitability. A holistic characterization will help optimize and standardize the milling methods, pretreatments, and post-processing of pulse flours. Millers/processors will benefit by having a range of well-understood pulse flour fractions to incorporate into food formulations.

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review.

One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods. These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

Enhancing the Techno-Functionality of Pea Flour by Air Injection-Assisted Extrusion at Different Temperatures and Flour Particle Sizes.

Industrial applications of pulses in various food products depend on pulse flour techno-functionality. To manipulate the techno-functional properties of yellow pea flour, the effects of flour particle size (small vs. large), extrusion temperature profile (120, 140 and 160 °C at the die) and air injection pressure (0, 150 and 300 kPa) during extrusion cooking were investigated. Extrusion cooking caused the denaturation of proteins and gelatinization of starch in the flour, which induced changes in the techno-functionality of the extruded flour (i.e., increased water solubility, water binding capacity and cold viscosity and decreased emulsion capacity, emulsion stability, and trough and final viscosities). In general, the large particle size flour required less energy input to be extruded and had higher emulsion stability and trough and final viscosities compared to the small particle size flour. Overall, among all of the treatments studied, extrudates produced with air injection at 140 and 160 °C had higher emulsion capacity and emulsion stability, making them relatively better suited food ingredients for emulsified foods (e.g., sausages). The results indicated air injection's potential as a novel extrusion technique combined with modification of flour particle size distribution and extrusion processing conditions to effectively manipulate product techno-functionality and broaden the applications of pulse flours in the food industry.

Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging.

The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting.

Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster.

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios.

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner.

Application of Vis-NIR and SWIR spectroscopy for the segregation of bison muscles based on their color stability.

The study objective was to investigate the potential for using visible near-infrared (Vis-NIR) and short wave infrared (SWIR) spectroscopy to segregate bison portions based on muscle types and storage periods. In the Vis-NIR range, the principal component analysis showed clear segregation of the muscles based on storage at retail display d 4 whereas the discrimination based on muscle type was better portrayed in the SWIR region. Furthermore, partial least squares discriminant analysis (PLS-DA) models classified muscles based on muscle type and storage in the Vis-NIR range with the classification accuracy of 97% for calibration and 86% for cross-validation. Finally, the PLS-regression models were developed for the successful prediction of a* value with an R2 of 0.88 (RMSEC: 1.57), 0.84 (RMSECV: 1.88), and 0.90 (RMSEP: 1.41), color score with an R2 of 0.96 (0.25), 0.95 (0.27), and 0.92 (0.32), and discoloration score with an R2 of 0.96 (0.47), 0.93 (0.63), and 0.93 (0.56) for calibration, cross-validation, and prediction, respectively.

Implications of Blending Pulse and Wheat Flours on Rheology and Quality Characteristics of Baked Goods: A Review.

Bread is one of the most widely consumed foods in all regions of the world. Wheat flour being its principal ingredient is a cereal crop low in protein. The protein content of a whole grain of wheat is about 12-15% and is deficit in some essential amino acids, for example, lysine. Conversely, the protein and fibre contents of legume crops are between 20 and 35% and 15 and 35%, respectively, depending on the type and cultivar of the legume. The importance of protein-rich diets for the growth and development of body organs and tissues as well as the overall functionality of the body is significant. Thus, in the last two decades, there has been a greater interest in the studies on the utilization of legumes in bread production and how the incorporation impacts the quality characteristics of the bread and the breadmaking process. The addition of plant-based protein flours has been shown to produce an improved quality characteristic, especially the nutritional quality aspect of bread. The objective of this review is to synthesize and critically investigate the body of research on the impact of adding legume flours on the rheological attributes of dough and the quality and baking characteristics of bread.

Principal component analysis of lipid and protein oxidation products and their impact on color stability in bison longissimus lumborum and psoas major muscles.

The study aims were to compare lipid (malondialdehyde [MDA], 4-hydroxy-2-nonenal [HNE]) and protein (carbonyl content [CAR]) oxidation products between two bison muscles (longissimus lumborum [LL] and psoas major [PM]) at different aging and retail display time and determine their influence on muscle color stability. Regardless of the aging and retail display time, LL showed greater redness (a* value; P = 0.04) and lower surface discoloration (P < 0.01) than PM as well as LL exhibited lower MDA, HNE, and CAR content compared to PM (P < 0.05). In both muscles, MDA showed the highest correlation to a* (r = -0.78; P < 0.01) and discoloration (rs = 0.82; P < 0.01) scores, particularly in PM muscle compared to LL muscle. In conclusion, the principal component analysis revealed 4 distinct color deterioration clusters within steaks displayed at d 4 according to the muscle and aging time, and MDA critically influences color deterioration patterns in bison muscles.

Development of Cellular High-Protein Foods: Third-Generation Yellow Pea and Red Lentil Puffed Snacks.

This study aimed to evaluate how extrusion cooking conditions and microwave heating play a role in enhancing physical and thermal properties of third-generation expanded cellular snacks made from yellow pea (YP) and red lentil (RL) flours for the first time. Increasing temperature and moisture content during extrusion resulted in darker, crunchier and crispier products with higher expansion index (EI). Microwave heating after extrusion led to an increase in cell size and porosity of YP and RL products when qualitatively compared to extrusion alone. Additionally, extrusion followed by microwave heating resulted in extensive damage to starch granular structure and complete denaturation of proteins. Using microwave heating, as a fast and inexpensive process, following partial cooking with extrusion was demonstrated to greatly improve the physical and thermal properties of YP and RL snacks. Microwave heating following mild extrusion, instead of severe extrusion cooking alone, can potentially benefit the development of high quality nutritionally-dense expanded cellular snacks made from pulse flours.

New Method for Accurate Determination of Polyphenol Oxidase Activity Based on Reduction in SERS Intensity of Catechol.

Rapid and accurate measurement of polyphenol oxidase (PPO) activity is important in the food industry as PPOs play a vital role in catalyzing enzymatic reactions. The aim of this study was to develop surface-enhanced Raman scattering (SERS) approach for accurate determination of PPO activity in fruit and vegetables using the reduction in SERS intensity of catechol in reaction medium. Within a certain catechol concentration, when a purified PPO solution was analyzed, the reduction in SERS intensity (Δ I) was linear to PPO activity ( Ec) in a wide range of 500-50 000 U/L, and a linear regression equation of log Δ I/Δ t = 0.6223 log Ec + 0.8072, with a correlation coefficient of 0.9689 and a limit of detection of 224.65 U/L, was obtained. The method was used for detecting PPO activity in apple and potato samples, and the results were compared with those obtained from colorimetric assay, which demonstrated that the proposed method could be successfully used for detecting PPO activity in food samples.