Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment.

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

BDNF and cAMP are neuroprotective in a porcine model of traumatic optic neuropathy.

Traumatic optic neuropathy (TON) is a devastating condition that can occur after blunt or penetrating trauma to the head, leading to visual impairment or blindness. Despite these debilitating effects, no clinically available therapeutic targets neuroprotection or promotes axon regeneration in this or any optic neuropathy. Limited data in large-animal models are a major obstacle to advancing treatments toward clinical therapeutics. To address this issue, we refined a surgical model of TON in Yucatan minipigs. First, we validated the model by demonstrating visual impairment by flash visual-evoked potential and retinal ganglion cell degeneration and death. Next, we developed and optimized a delivery method and nontoxic dosing of intravitreal brain-derived neurotrophic factor (BDNF) and cAMP. Finally, we showed that intravitreal injection of BDNF and cAMP rescued visual function and protected against retinal ganglion cell death and optic nerve axon degeneration. Together these data in a preclinical large-animal model advance our understanding of and ability to model TON and further identify and develop candidate clinical therapeutics.

COVERT: A classless approach to generating balanced datasets for process modelling.

In this work, a classless oversampling technique, Covert, was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote, however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert, existing imbalanced historical data can be used to extend the range of applicability of any process model.

The Cousa objective: a long-working distance air objective for multiphoton imaging in vivo.

Multiphoton microscopy can resolve fluorescent structures and dynamics deep in scattering tissue and has transformed neural imaging, but applying this technique in vivo can be limited by the mechanical and optical constraints of conventional objectives. Short working distance objectives can collide with compact surgical windows or other instrumentation and preclude imaging. Here we present an ultra-long working distance (20 mm) air objective called the Cousa objective. It is optimized for performance across multiphoton imaging wavelengths, offers a more than 4 mm2 field of view with submicrometer lateral resolution and is compatible with commonly used multiphoton imaging systems. A novel mechanical design, wider than typical microscope objectives, enabled this combination of specifications. We share the full optical prescription, and report performance including in vivo two-photon and three-photon imaging in an array of species and preparations, including nonhuman primates. The Cousa objective can enable a range of experiments in neuroscience and beyond.

The Application of Artificial Intelligence and Big Data in the Food Industry.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

New insight into thermal hydrolysis of sewage sludge from solubilisation analysis.

Thermal hydrolysis, a sludge pre-treatment process prior to anaerobic digestion, is increasing in popularity in academia and industry due to the potential of biogas production enhancement. However, there is a limited understanding of the solubilisation mechanism, which significantly influences the biogas yield. This study evaluated the influence of flashing, reaction time, and temperature to understand the mechanism. It was found that while hydrolysis is the primary process (responsible for approximately 76-87% of sludge solubilisation), the sudden decompression via flashing at the end of the process, creating shear force to break the cell membrane, contributes a considerable percentage (approximately 24-13% depended on the treatment conditions) to the solubilisation of treated sludge. More importantly, the decompression helps significantly shorten the reaction time from 30 min to 10 min, which in turn reduces the sludge's colour, minimises energy consumption, and eliminates the formation of inhibitory compounds for anaerobic digestion. However, a considerable loss in volatile fatty acids (650 mg L⁻1 of acetic acid at 160 °C) during flash decompression should be considered.

The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development.

Sensory attributes are essential factors in determining the quality of wines. However, it can be challenging for consumers, even experts, to differentiate and quantify wines' sensory attributes for quality control. Soft sensors based on rapid chemical analysis offer a potential solution to overcome this challenge. However, the current limitation in developing soft sensors for wines is the need for a significant number of input parameters, at least 12, necessitating costly and time-consuming analyses. While such a comprehensive approach provides high accuracy in sensory quality mapping, the expensive and time-consuming studies required do not lend themselves to the industry's routine quality control activities. In this work, Box plots, Tucker-1 plots, and Principal Component Analysis (PCA) score plots were used to deal with output data (sensory attributes) to improve the model quality. More importantly, this work has identified that the number of analyses required to fully quantify by regression models and qualify by classification models can be significantly reduced. Based on regression models, only four key chemical parameters (total flavanols, total tannins, A520nmHCl, and pH) were required to accurately predict 35 sensory attributes of a wine with R2 values above 0.6 simultaneously. In addition, for classification models to accurately predict 35 sensory attributes of a wine at once with prediction accuracy above 70%, only four key chemical parameters (A280nmHCl, A520nmHCl, chemical age and pH) were required. These models with reduced chemical parameters complement each other in sensory quality mapping and provide acceptable accuracy. The application of the soft sensor based on these reduced sets of key chemical parameters translated to a potential reduction in analytical cost and labour cost of 56% for the regression model and 83% for the classification model, respectively, making these models suitable for routine quality control use.

Revisiting China's domestic greenhouse gas emission from wastewater treatment: A quantitative process life-cycle assessment.

The wastewater treatment industry could alleviate water pollution but consume a large amount of energy and resources. China has over 5000 centralized domestic wastewater treatment plants and produces an unignorable amount of greenhouse gases (GHG). By considering the wastewater treatment, wastewater discharge, and sludge disposal processes, and employing the modified process-based quantification method, this study quantifies wastewater treatment's on-site and off-site GHG emissions across China. Results showed that the total GHG emission was 67.07 Mt CO2-eq in 2017, with approximately 57% of on-site emissions. The top seven cosmopolis and metropolis (top 1%) emitted nearly 20% of the total GHG emission, while their emission intensity was relatively low due to the huge population. This means that a high urbanization rate may be a feasible way to mitigate GHG emissions in the wastewater treatment industry in the future. Furthermore, GHG reduction strategies can also focus on process optimization and improvement at WWTPs as well as the nationwide promotion of onsite thermal conversion technologies for sludge management.

DNA methylation-based biomarkers for ageing long-lived cetaceans.

Epigenetic approaches for estimating the age of living organisms are revolutionizing studies of long-lived species. Molecular biomarkers that allow age estimates from small tissue biopsies promise to enhance studies of long-lived whales, addressing a fundamental and challenging parameter in wildlife management. DNA methylation (DNAm) can affect gene expression, and strong correlations between DNAm patterns and age have been documented in humans and nonhuman vertebrates and used to construct "epigenetic clocks". We present several epigenetic clocks for skin samples from two of the longest-lived cetaceans, killer whales and bowhead whales. Applying the mammalian methylation array to genomic DNA from skin samples we validate four different clocks with median errors of 2.3-3.7 years. These epigenetic clocks demonstrate the validity of using cytosine methylation data to estimate the age of long-lived cetaceans and have broad applications supporting the conservation and management of long-lived cetaceans using genomic DNA from remote tissue biopsies.

Application of Three-Dimensional Digital Photogrammetry to Quantify the Surface Roughness of Milk Powder.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.

Comparison of struvite and K-struvite for Pb and Cr immobilisation in contaminated soil.

Struvite is a value-added by-product recovered from phosphorus-rich wastewater treatment by adding magnesium. Struvite is mainly used as slow-release fertilisers containing phosphate that can form insoluble salts with certain heavy metals. Hence, struvite may have potential application as a phosphate remediation agent for the immobilisation of heavy metals in contaminated soil, while the related study is limited. Similarly, an analogue compound of struvite, K-struvite, may also have this value but has not been reported elsewhere. This study investigated the effect of struvite and K-struvite on the remediation of Cr-spiked and Pb-spiked soil. To evaluate the feasibility, the agent dosage and two quality parameters (particle size and purity) of struvite and K-struvite were considered for the experimental design and statically analysed by principal component analysis (PCA) and partial least squares (PLS). The results show that the dosage significantly impacts the immobilisation process, while the effect of particle size and purity are negligible. Struvite and K-struvite have similar performance on heavy metals immobilisation, and both are significant in Pb immobilisation (up to 96% of F5, stable fraction) and are beneficial for reducing the most mobilised fractions (F1 and F2) of Cr to lesser than 3%. Struvite and K-struvite share similar performance due to their similar atomic radius, and the different performance between Cr and Pb immobilisation can be explained by the strong hydrolysis trend of chromium ion, which may inhibit the binding of the phosphate and chromium. The kinetic study finds that all three variables positively impact the free chromium ion, and the immobilisation process is fast so unlikely to be kinetically limited. These findings of this project will provide insight into how the immobilisation process changes in response to the dosage and quality of struvite compounds.

Non-permanent primary food packaging materials assessment: Identification, migration, toxicity, and consumption of substances.

Almost all processed food comes packaged in either plastic, glass, metal, or paper and paperboard materials, and many packaging materials are disposed of after a single use (linear economy). Based on the concept of a circular economy, the recycling of food packaging materials has become one of the main targets for industries and regulators around the world. However, recycling presents particular challenges, mainly related to the recycled material composition, which determines its reusability, application, functionality, and chemical safety. In this latter matter, it has been demonstrated that the use of recycled food packaging materials increases the number and possible sources of substances that could be present in the packaging material, which is of concern as substances that can migrate into food and cause health hazards upon consumption. This review compiles information regarding substances detected in non-permanent food packaging materials, focusing mainly on plastics, paper, and paperboards. The compilation of literature studies (110 research articles) on the presence of intentionally added substances (IAS) and non-intentionally added substances (NIAS) in food packaging materials, their migration, toxicity, and dietary exposure has been summarized, evaluated, and discussed. In addition, current sustainable food packaging trends have been mentioned. Finally, approaches to reduce the presence, migration, and potential exposure to substances that have migrated from packaging materials into food have been reviewed.

Assessing and Quantifying the Surface Texture of Milk Powder Using Image Processing.

Milk powders produced from similar spray dryers have different visual appearances, while the surface appearance of the powder is a key quality attribute because the smoothness of the milk powder also affects flowability and handling properties. Traditionally quantifying this nuanced visual metric was undertaken using sensory panelists, which is both subjective and time consuming. Therefore, it is advantageous to develop an on-line quick and robust appearance assessment tool. The aim of this work is to develop a classification model which can classify the milk powder samples into different surface smoothness groups. This work proposes a strategy for quantifying the relative roughness of commercial milk powder from 3D images. Photogrammetry equipment together with the software RealityCapture were used to build 3D models of milk powder samples, and a surface normal analysis which compares the area of the triangle formed by the 3 adjacent surface normals or compares the angle between the adjacent surface normals was used to quantify the surface smoothness of the milk powder samples. It was found that the area of the triangle of the smooth-surface milk powder cone is smaller than the area of the triangle of the rough-surface milk powder cone, and the angle between the adjacent surface normals of the rough-surface milk powder cone is larger than the angle between the adjacent surface normals of the smooth-surface milk powder cone, which proved that the proposed area metrics and angle metrics can be used as tools to quantify the smoothness of milk powder samples. Finally, the result of the support vector machine (SVM) classifier proved that image processing can be used as a preliminary tool for classifying milk powder into different surface texture groups.

Effect of saline water ionic strength on phosphorus recovery from synthetic swine wastewater.

Declining worldwide phosphate rock reserves has driven a growing interest in exploration of alternative phosphate supplies. This study involved phosphorus recovery from swine wastewater through precipitation of struvite, a valuable slow-release fertiliser. The economic feasibility of this process is highly dependent on the cost of magnesium source. Two different magnesium sources were used for phosphorus recovery: pure magnesium chloride and nanofiltration (NF) saline water retentate. The paper focuses on the impact of ionic strength on phosphorus recovery performance that has not been reported elsewhere. Experimental design with five numerical variables (Mg/P molar ratio, pH, PO43--P, NH4+-N, and Ca2+ levels) and one categorical variable (type of magnesium source) was used to evaluate the effect of ionic strength on phosphorus removal and struvite purity. The experimental data were analysed using analysis of variance (ANOVA) and principal component analysis (PCA). Results indicated that a magnesium source obtained from NF retentate was as effective as MgCl2 for struvite precipitation. It was also revealed that ionic strength had a more positive effect on struvite purity than on phosphorus removal. Within the range of parameters studied in this research, high ionic strength, high pH and wastewater with high phosphate, high ammonium and low calcium contents were found to be the most favourable conditions for struvite precipitation. Findings from this study will be beneficial to determine the feasibility of using high ionic strength saline water, such as NF seawater retentate, as a magnesium source for phosphorus recovery from wastewater that is rich in ammonium-nitrogen and phosphate.

An uncommon neuronal class conveys visual signals from rods and cones to retinal ganglion cells.

Neurons in the central nervous system (CNS) are distinguished by the neurotransmitter types they release, their synaptic connections, morphology, and genetic profiles. To fully understand how the CNS works, it is critical to identify all neuronal classes and reveal their synaptic connections. The retina has been extensively used to study neuronal development and circuit formation. Here, we describe a previously unidentified interneuron in mammalian retina. This interneuron shares some morphological, physiological, and molecular features with retinal bipolar cells, such as receiving input from photoreceptors and relaying visual signals to retinal ganglion cells. It also shares some features with amacrine cells (ACs), particularly Aii-ACs, such as their neurite morphology in the inner plexiform layer, the expression of some AC-specific markers, and possibly the release of the inhibitory neurotransmitter glycine. Thus, we unveil an uncommon interneuron, which may play an atypical role in vision.

Effect of acetic acid on struvite precipitation: An exploration of product purity, morphology and reaction kinetics using central composite design.

Phosphorus recovery has attracted increasing interest due to the potential depletion of phosphorus resources. One promising solution is to recover phosphorus via struvite precipitation from wastewater or other waste that is in rich of phosphate. However, product quality control during such process is always challenging due to the variation and complexity of wastewater compositions. For example, subcritical wet oxidation (SCWO) effluent is rich in phosphorus and nitrogen but contains a large amount of acetic acid, while its effect on struvite recovery is hardly known. Therefore, central composite design (CCD), considering pH, acetic acid level, Mg level and Ca level, was used to evaluate the effect of acetic acid on struvite purity, phosphorus removal, morphology and reaction kinetics. The experimental data were statistically analysed by analysis of variance (ANOVA) and principal components analysis (PCA). The results indicate that pH and Mg level have a significant impact on phosphorus removal (pH: p-value < 0.0001, Mg: p-value < 0.0001) and struvite purity (pH: p-value = 0.0410, Mg: p-value < 0.0001), Ca level only affects the struvite purity (p-value = 0.0333). The presence of acetic acid, within the studied range (8.77-34.53 mM), has a negligible effect on struvite morphology, phosphorus removal and reaction kinetics, but a slightly positive effect on struvite purity. Findings of this research would be beneficial to determine the feasibility of acetic acid-rich wastewater as a phosphorus source for struvite recovery.

Mechanisms, status, and challenges of thermal hydrolysis and advanced thermal hydrolysis processes in sewage sludge treatment.

Sewage sludge management has garnered interest in both academia and industry due to the challenges of overpopulation and its potential as a bioenergy source. Thermal hydrolysis is a promising technology for sludge pre-treatment prior to anaerobic digestion to enhance biogas production. However, the technology is facing two main problems; the dark colour of sludge can affect UV disinfection and the formation of methanogenesis inhibitors such as free ammonia and refractory compounds have a significant impact on methane production in anaerobic digestion processes. Advanced thermal hydrolysis, which is an oxidative thermal hydrolysis process, has been introduced to overcome these challenges. This study provides a comprehensive review of the mechanisms and reactions which occur during the hydrothermal hydrolysis and advanced thermal hydrolysis processes. Technical and implementation challenges of both technologies are discussed. Additionally, the prospects of the technologies are assessed through their technology readiness levels. An assessment of the relevant literature is also provided to illuminate the aspects in which research gaps exist and areas where additional studies could be performed.

Enhancing biogas production from caribbean pelagic Sargassum utilising hydrothermal pretreatment and anaerobic co-digestion with food waste.

The recurring inundation of beaches in the tropical North Atlantic by pelagic Sargassum and the associated social, ecological and economic challenges, have aroused great interest in its potential use as a marine energy crop. However, to date, the seasonal availability and low experimental methane potential of these invasive brown seaweeds have hindered their commercial exploitation as feedstock for sustainable energy production. This novel study evaluated Caribbean pelagic Sargassum and the synergistic interactions of hydrothermal pretreatment and co-digestion with food waste at different mixture ratios, on biogas production enhancement and bio-fertiliser recovery. Batch testing revealed that hydrothermal pretreatment promoted the hydrolysis of organics in pelagic Sargassum and food waste, thus increasing methane recovery from mono-substrate digestion by 212.57% and 10.16%, respectively, in comparison to the untreated samples. Co-digestion of pelagic Sargassum and food waste redistributed metal elements and raised the buffering capacity of the digester, facilitating high organic loadings without pH control. Food waste also provided lipids to the seaweed feed which augmented the digestion performance. The maximum cumulative methane yield of 292.18 ± 8.70 mL/gVS was obtained from a blend of co-pretreated pelagic Sargassum and food waste at the weight ratio 25:75. Screening of the whole digestate from co-digestion indicated bio-fertiliser potential. However, the solid fraction necessitates arsenic remediation to meet international soil standard guidelines. The findings of this study are promising and suggest opportunity for the design, scale up and optimisation of biogas systems, equipped with hydrothermal pretreatment for utilisation of Sargassum seaweeds during influx.

Oxidative hydrothermal surface modification of activated carbon for sevoflurane removal.

The emission of waste anaesthetic gas is a growing contributor to global warming and remains a factor in atmospheric ozone depletion. Volatile anaesthetics in medical waste gases could be removed via adsorption using suitable activated carbon materials possessing an enhanced affinity to anaesthetic molecules. In this work, the effects of surface physical and chemical properties on sevoflurane adsorption were investigated by oxidative hydrothermal surface modification of a commercial activated carbon using only distilled water. The hydrothermal surface modification was carried out at different treatment temperatures (150-300 °C) for varying durations (10-30 min), and adsorption was conducted under fixed conditions (bed depth = 10 cm, inlet concentration = 528 mg/L, and flow rate = 3 L/min). The hydrothermal treatment generally increased the BET surface area of the activated carbons. At oxidation temperatures above 200 °C, the micropore volume of the samples diminished. The relative amount of surface oxygen was enriched as the treatment temperature increased. Treatment duration did not significantly affect the introduction of relative amount of surface oxygen, except at higher temperatures. There were no new types of functional groups introduced. However, disappearance and re-formation of oxygen functional groups containing C-O structures (as in hydroxyl and ether groups) occurred when treatment temperature was increased from 150 to 200 °C, and when treatments were conducted above 200 °C, respectively. The ester/acetal groups were enriched under the temperature range studied. The findings suggested that the re-formation of surface oxygen functionalities might lead to the development of functional groups that improve sevoflurane adsorption.

Mercury in Ringed Seals (Pusa hispida) from the Canadian Arctic in Relation to Time and Climate Parameters.

Mercury is found in Arctic marine mammals that are important in the diet of northern Indigenous peoples. The objectives of the present long-term study, spanning a 45-yr period, were to 1) investigate the temporal trends of total mercury (THg; muscle and liver) and selenium (Se; liver) in ringed seals (Pusa hispida) from different regions of the Canadian Arctic; and 2) examine possible relationships with age, diet, and climate parameters such as air temperature, precipitation, climatic indices, and ice-coverage. Ringed seals were collected by hunters in northern communities in the Beaufort Sea, Central Arctic, Eastern Baffin Island, Hudson Bay, and Ungava/Nunatsiavut regions (Canada) between 1972 and 2017. Mercury levels did not change through time in seal liver, but THg levels in muscle decreased in seals from Hudson Bay (-0.91%/yr) and Ungava/Nunatsiavut (-1.30%/yr). Carbon stable isotope values in seal muscle decreased significantly through time in 4 regions. Selenium-to-THg ratios were found to be >1 for all years and regions. Variation partitioning analyses across regions indicated that THg trends in seals were mostly explained by age (7.3-21.7%), climate parameters (3.5-12.5%), and diet (up to 9%); climate indices (i.e., Arctic and North Atlantic Oscillations, Pacific/North American pattern) explained the majority of the climate portion. The THg levels had a positive relationship with Arctic Oscillation for multiple regions. Associations of THg with air temperature, total precipitation, and sea-ice coverage, as well as with North Atlantic Oscillation and Pacific/North American pattern were found to vary with tissue type and geographical area. Environ Toxicol Chem 2020;39:2462-2474. © 2020 Her Majesty the Queen in Right of Canada. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Reproduced with the permission of the Minister of Fisheries and Oceans Canada.