TECH OFFERS

Singapore is currently only producing 13% of its vegetable consumption. With little farming land available, Singapore relies heavily on imports from other countries. Due to increasing focus on food security, the alternative to solve land scarcity problem is to build greenhouses on concrete rooftop. Although concrete rooftop greenhouse are able to keep pests out, there is a signifcant heating problem which severely inhibits the growth of the vegetables. Therefore, there is a need for a rooftop greenhouse that is able to actively cool itself to avoid such problem. This technology offer is a modular rooftop greenhouse farming system (hydroponics) capable of producing vegetables on concrete roofs to meet the local demand while reducing over-reliance on imports. The design of the greenhouse farming system enables cooling and does not heat up, thus allowing the growth of pest-free vegetables. The system is approximately the size of a typical carpark lot (2.5 x 5 m). The production rate is 30 kg per month (2.5 x 5 m size) and requires minimal human intervention. The technology offer comprises both the farming system and its operation know-how. The modular rooftop greenhouse farming system can be set-up within 3 days or scaled-up when required with guaranteed vegetable growth. The break-even cost of one greenhouse is about 3 years. The technology owner is seeking to out-license their technology. This technology offer is a temperature regulated and modular farming system (hydroponics) for rooftop farming. The features and specifications are as follows: Modular and scalable Flexible sizes (as small as 2.5 x 5 m) 30 kg/month (2.5 x 5 m size) Active cooling design (20% reduction in temperature) Passive system operation (minimal manpower) Anti-pest  Quick setup (3 days) Applicable for wide range of crops The main application for this technology is for those that are interested in rooftop farming. The potential applications are: Conversion of barren and unused concrete space into temporary/permanent arable land (eg. Carpark rooftops, schools, apartments, factories and floating platforms etc.) Can be applied to high-value crops Minimal human intervention is needed as the modular greenhouse farming system encompasses Internet of Thing (IoT) and automation Ability to scale-up immediately greenhouse, vegetable, urban farming, rooftop farming, bak choy, deep water culture hydroponics system, hydroponics, pesticide-free, farming IoT, farming automation Life Sciences, Agriculture & Aquaculture, Sustainability, Food Security

Different plant species have different nutrient requirements. The current practice of urban farming uses a generic hydroponic nutrient solution that is suitable to most plant types, and a crude sensing system that only measures total ion content in the solution. This approach often results in nutrients deficiency and/or overloading and hence requires consistently monitoring. Overloading of nutrients not only increases the input costs, it also results in greater quantities of contamination in effluent to be disposed after harvest.  A targeted hydroponic nutrient solution reduces the need to periodically adjust the nutrient. The technology provider has studied and formulated different nutrient recipes that had shown improved yield compared to commercial products. This ensures the best growth for each crop type. It also reduces common problem stemming from imbalanced nutrient, e.g. leaf chlorosis due to nutrient deficiency. All these translate to a better yield and a more marketable produce for the farmers. Formulations developed include Mizuna, Kale, Lettuce, Mustard, Kalian, and Caixin. The technology provider is seeking for licensing partners from the agriculture industry. The common practice of urban farming is to discard the spent nutrient solution after a few cycles of plant growth. A targeted hydroponics nutrient solution reduces the frequency needed to periodically adjust the nutrient. The technology provider has formulated the nutrient formulation that has considered the rate of nutrient uptake by the desired plant, the nutrient’s ratio and its availability in the solution. The formulation is specific to individual crop types and ensures the best growth for each crop type, e.g. lettuce or kale.  Indoor hydroponics farmers  Fertiliser / chemical production company may wish to market this as solution to farmers or to produce as off-the-shelf products for mass market consumer Agriscience company may package this as a solution to downstream clients   Increases yield (up to 20%) Reduces the manpower needed to monitor the nutrients intake of the plant More resource-efficient with lesser nutrient adjustment Reduces common problems stemming from imbalanced nutrients Sustainable, eco-friendly solution that can potentially lead to zero-wastage Better nutrition for the consumer nutrients solution, plant growth, crop optimisation, plant nutrients, nutrients recipes, nutrients formulations, kale, caixin, mizuna, kailan, mustard, lettuce Life Sciences, Agriculture & Aquaculture, Waste Management & Recycling, Food & Agriculture Waste Management, Sustainability, Food Security

The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing. This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples. The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.    The features and specifications of this rapid screening technology include: Spectroscopic methods to collect unique spectral measurements from samples based on their chemical compositions Heavy metal classification between cadmium and lead Generation of datasets from spectral measurements to create predictive model to identify heavy metal presence and predict concentration levels Predictive model is trained on spectral measurements for increased accuracy 95% accuracy in heavy metal detection, with trace concentration detection of as low as 4ppm This technology is further customisable to include other classes of heavy metals e.g. mercury, and to include other food types e.g. seafood, meats etc. Detection and measurement of heavy metal species in food/feed powder products such as: Insect powders Animal feeds Milk powders Protein supplement powders Plant-based nutritional supplements Rapid detection of heavy metal species with minimal sample preparation Screening of large amounts of food or feed powder samples within a short period of time Model performance and prediction results are comparable to industry accepted method to measure heavy metal content  Rapid screening, heavy metals, food powders, food safety Infocomm, Video/Image Analysis & Computer Vision, Big Data, Data Analytics, Data Mining & Data Visualisation, Foods, Quality & Safety, Processes

The latest Singapore National Population Health Survey has reported a concerning diabetes trend. From 2019-2020, 9.5% of the adults had diabetes, slightly dropping to 8.5% from 2021-2022. About 1 in 12 (8.5%) of residents aged 18 to 74 were diagnosed, with an age-standardised prevalence of 6.8% after accounting for population ageing. Among the diabetes patients, close to 1 in every 5 (18.8%) had undiagnosed diabetes, and 61.3% did not meet glucose control targets. Prediabetes is also prevalent, with 35% progressing to type 2 diabetes within eight years without lifestyle changes. Untreated Type 2 diabetes can lead to severe health issues. Tackling this challenge requires a holistic approach, focusing on awareness, early diagnosis, and lifestyle adjustments for diabetes and prediabetes. Recognising the need for innovation to address this, the technology owner develops a cost-effective and non-invasive AI-powered solution, Blood Glucose Evaluation And Monitoring (BGEM), that detects glucose dysregulation in individuals to monitor and evaluate diabetic risks. BGEM allows users to track their blood glucose levels regularly, identify any adverse trends and patterns, and adopt early intervention and lifestyle changes to prevent or delay the onset of diabetes. Clinically validated in 2022, with a research paper published in October 2023, the technology is open for licensing to senior care/home care providers, telehealth platforms, health wearables companies, and more. The BGEM technology is an end-to-end managed AI platform that leverages Photoplethysmography (PPG) enabled wearable sensors to monitor various heart rate variability (HRV) features associated with blood glucose fluctuation. The solution comprises the following features: Optimised and validated AI algorithm Mobile Demo App Including UI/UX design guideline User-friendly visualisations SaaS Scalability Security API Integration The BGEM technology offers a cost-effective, non-invasive approach to predicting an individual's diabetes risk. The applications include: Population Health Perspective: The technology leverages the high growth rate of smart wearables and hearables, presenting an opportunity to identify undiagnosed diabetes individuals within the population. Preventive Health Monitoring: With the ability to monitor blood glucose changes regularly at minimal cost, the technology empowers high-risk users to adopt a healthier lifestyle and, therefore, prevent or delay the onset of diabetes. Diabetes around the world in 2021: 537 million adults (20-79 years) are living with diabetes, 1 in 10. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is responsible for 6.7 million deaths in 2021 - 1 every 5 seconds. Diabetes caused at least USD 966 billion dollars in health expenditure – a 316% increase over the last 15 years. 541 million adults have Impaired Glucose Tolerance (IGT), which places them at high risk of type 2 diabetes. Overview of the wearable technology market: The market is projected to expand at a compound annual growth rate (CAGR) of approximately 12.5% between 2023 to 2030. Estimated to be worth USD 55.5 billion in 2022, with a projected revenue of USD 142.4 billion by 2030. Current blood glucose monitoring technologies either require finger pricking for blood extraction or the insertion of sensors into the skin and discomfort through wearing patches for extended periods. Instead, the technology uses external sensors and algorithms to detect and predict diabetes risk. No object needs to be inserted into the user's body or continuously worn throughout the day, resulting in minimal pain and discomfort. Additionally, the only equipment required for testing is the wearable device. No additional disposable equipment needles or test strips are needed, which makes blood glucose monitoring much more convenient and cost-effective than other "State-of-the-Art" solutions. The Unique Value Proposition of BGEM include: Market-ready: It is a market-ready non-invasive diabetes risk detection and prediction AI solution that leverages consumer-grade wearables to detect blood glucose dysregulation. Performance: Demonstrates outstanding prediction and detection capabilities. Cloud-based: Operates on a cloud-based platform for seamless integration. Third-party compatibility: Easily implemented with third-party devices and apps. Sustainability: Reduction in bio-medical waste such as needles, test strips etc User-friendly: Non-invasive, convenient and allows frequent measurement. Non-invasive measurement, blood glucose, diabetes mellitus, preventive healthcare, AI, ML, Wearables, PPG, Blood Glucose Monitoring, Diabetes Monitoring, Diabetes Evaluation, Non-Invasive Diabetes Monitoring, Diabetic Risk Assessment Infocomm, Artificial Intelligence, Healthcare, Diagnostics

In Singapore, more than 30,000kg of okara are generated from soya milk and tofu production. Due to the high amount of insoluble dietary fiber and a unique, poignant smell of okara, it is often discarded as a waste product. Despite okara's low palatability, it is rich in nutrients. Therefore, the technology owner has developed a cost-effective formulation to include okara in feed for abalone. The formulation can potentially be adapted and customised for other aquatic species. The technology owner is seeking potential partners to license and commercialise the technology. The technology allows for an alternative nutrient source for animal feed allowing for the sustainability of food supply and reduction of food waste. The formulation consists of a cost-effective plant-based functional ingredient, lowering the costs of feed for aquaculture farms. The nutritional composition can be tailored for different species to increase growth rates and survivability. Okara is used as a cost-effective feed for high-value abalone, a commonly cultured species of mollusc. Okara-based feed results in the beautiful purple colouration of the shell and increased growth and survivability of abalone. In comparison, the okara-based feed costs ~30% less than commercial feed used in the industry. There is potential for okara to be included in feed for other aquatic species such as shrimp and fish. The success of this method will valorise okara, transferring them into a nutrient-dense aquatic feed while promoting a more environmentally sustainable food production chain.       okara, aquatic feed Foods, Ingredients

Most existing training applications offer good programmes for guiding users to achieve individual fitness goals, some even come with guided video workouts led by professional trainers. However, such applications lack or have limited capability to assess whether the correct posture is maintained during exercise - poor posture can reduce exercise effectiveness and may even cause injury, e.g. arched back during push-ups. This solution is a synergistic combination of video/image processing, human pose recognition, and machine learning technologies to deliver a solution that addresses the twin challenges of accurate count and correct execution of exercises in an automated manner, without having to wear any additional hardware/sensors. The software-only solution is able to advise users on the correct execution of repetitive movement sequences, e.g. sit-ups, and push-ups, and is deployable on a wide range of affordable camera-enabled hardware devices such as mobile phones, tablets, and laptops, and it can be easily integrated into existing applications to enhance functionality. It is applicable to the sports and healthcare industry to help users perform exercises correctly and effectively in an unencumbered manner. This software-only solution is able to identify the correct or incorrect execution of repetitive movement sequences using camera inputs from devices such as mobile phones, tablets, or laptops. The solution can be deployed on any Python and JavaScript-capable platform; providing flexibility for deployment across a range of devices and operating systems, including web browsers. Video/Image Processing Pre-processing of video frames as inputs for Human Pose Estimation and Machine Learning Built-in algorithm used to assess real-time movement direction, i.e., up, down, right and left, thereby providing inputs for a more accurate count Human Pose Estimation Based on BlazePose, OpenCV and TensorFlow for real-time tracking of up to 32 human pose keypoints, e.g. shoulder, wrist, hip, knee, ankle, etc Measurement of angles between pose keypoints as input parameters to determine correct posture, e.g. is the user's back straight, are the knees bent Enhances original image from camera with accentuated pose keypoints and connections between keypoints, i.e., stickman diagram, used as input AI classification to determine correct posture. Machine Learning Train and test AI models using human subject video footage for correct and automatic classification of exercises AI models classify exercise based on angles and distance of pose landmarks and enhanced images with accentuated pose landmarks and connections between landmarks   Primarily for fitness and healthcare (rehabilitation) industries - can also be applied for any activity that requires assessment of posture Repetitive exercise-specific counting e.g. push-up, sit-up, additional exercises can be included, requiring specific customisation Posture assessment and analysis of the movement of human subjects - personalised calibration to initial posture can be explored  Enables intelligent coaching functionality in fitness/healthcare applications to promote healthy or active living Enables real-time posture assessment and monitoring Does not require additional body-worn sensors or wearables; simply requires a camera-enabled mobile device Provides value added-service to an existing healthcare/fitness application to help end-users perform exercises correctly and effectively in an unencumbered manner  Provides remote assessment of exercise without any over-reliance on the expertise of a professional coach The technology partner is looking for test-bedding opportunities in the fitness and rehabilitation industries, which would involve studying specific exercises individually. Additionally, the technology owner is looking for co-development with companies that have adjacent use for this posture assessment software, e.g. heavy load lifting, combining the software with additional body-worn sensors to detect uneven weight distribution on the workman's back. Human Pose Estimation, OpenCV, Tensorflow, Dense Optical Flow, Sports and Leisure, Rehabiliation Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence, Personal Care, Wellness & Spa, Data Processing

The current clean meat technologies grow lab meat with conventional 2D cell culture. However, the conventional cell culture technique has an overall low yield of cells, as the cells are restricted to growth on surface areas.  A new 3D scaffolding method has been developed to overcome this problem with the use of microcarrier beads that provide cells with additional surface area to attach onto and proliferate. The microcarrier beads are suspended in the cell culture thus maximizing the 3D volume of the cell culture, leading to an increased yield. A microcarrier type has been identified to yield the highest number of porcine cells. The conditions of the cell culturing process have been optimised to improve the cell viability in a 3D environment Companies interested in cell-cultured meat development could consider using this method to grow cell-cultured meat at a larger scale with a potentially lower cost of production. The technology developer is seeking companies that are keen to scale up lab-grown meat applications.  The technology owner uses microcarrier beads, which are small (approx. 75 um diameter) polymer beads. They collectively provide a significantly larger surface area than standard tissue culture flasks for cell growth. Typically used in the pharmaceutical industry to scale up cells for protein expression, the use of microcarrier beads for the scale-up of cell-cultured meat is a relatively new application.   Some key features of the technology: Increases cell yield by 7-fold from lab-scale Microcarrier beads hold their shape throughout Made up of inert and food grade material  Different cell types require specific types of microcarrier beads to optimize growth The method is suitable for:   Clean meat industry / Cultured meat companies Pharmaceutical companies that apply such technologies towards protein manufacturing, drug discovery or antibody production. Other potential clean meat applications using other cell line sources. With the increasing demand for meat production for population consumption, there is a need to look at other more sustainable meat source. Lab grown ‘clean meat’ can be an alternative to livestock farming. The development of ‘clean-meat’ can help to reduce carbon footprint, improve animal welfare and prevent foodborne illnesses. The cultured meat market size was valued at $1.6 million in 2021 and is projected to reach $27 billion by 20301.   1Cultured meat market size, share - global industry report, 2022-2030. Allied Market Research. April 2021.   Increased yield of viable cells as compared to 2D cell culture Scalable Up to 7x lower in cost of production at a larger scale Pathogen-free source of meat produced High reproducibility Low usage of microcarrier beads: 10g microcarriers/20 litres of cell culture clean meat, microcarrier, dynamic culture, cultured, meat, scaffolding, 3d, scaffold Chemicals, Polymers, Foods, Ingredients, Processes

This technology offer presents an autonomous materials handling system. The technology could reduce manual labor requirements and increase working efficiency. The solution consists of a Lidar Elevator Stand (LES) system, which will trigger the autonomous actuators (e.g., trolley puller, tray return robots) via the robot command center whenever no goods (e.g., trolley, tray, and stock) is detected in the designated area. Currently, the technology has been demonstrated in autonomous trolley return solutions. Generally, trolley replenishment requires deploying manual labour to monitor the available quantity at the trolley bay and replenishing it by physically operating an electric trolley puller to transport the new stack of trolleys. Therefore, the system was developed to solve the problem by triggering an autonomous trolley puller to replenish the new stack of trolleys whenever the trolley quantity is depleting. The system can be further customized and repositioned based on clients’ requirements. The  Machine-to-Machine communication protocol for Lidar Elevator Stand system (Client), remote autonomous trolley puller (Client) and Robot Command Centre (Server). The 2D Lidar sensor has a field-of-view in Lidar Elevator Stand (LES) system with detection range from 0.05m to 10m, and a horizontal aperture angle of 200 degree, which is suitable for deployment in the application at airport Ability to avoid false triggering requests for trolleys replenishment from LES System. LES system’s operations and processing are able to work under different lighting conditions. Replenishment of trolleys can be customised for multiple location points. Material handling with delivery in airports, supermarkets, warehouses, logistics, hotels and factories. Improve and simplify work operations. Free up existing manpower to take up new higher value-added tasks. Seamless communication between monitoring and replenishment of trolley. Safe mobility replenishment of trolleys, trolleys, material handling, autonomous trolley puller, trolley puller, trolley Infocomm, Networks & Communications, Mobility, Robotics & Automation, Logistics, Inventory Management, Transportation

The proliferation of e-commerce, ride-hailing and food-delivery services have fueled the need for more accurate and reliable estimation of delivery times. The current common estimation of delivery time is based on Estimated Time of Arrival (ETA) which relies on route distance that is calculated between the origin and the desired destination. It only considers the duration from pick up to drop off, and does not consider the additional time needed for preparing and offloading the goods. This technology offer is a Machine Learning (ML) model that is able to calculate the stop duration (job completion duration), which together with the ETA, provides the Estimated Time of Completion (ETC). This ML model is for Singapore use only. For the Machine Learning (ML) model to calculate the stop duration, users will need to key in the input parameters such as building name, block number, road name, postal code, day of week, day of month and time. The system will predict the stop duration (job completion duration) in minutes. ML model enables prediction of ETC based on historical data and a small set of input parameters Highly correlated with ETA which can be easily obtained from API services such as Google or Onemap Takes in consideration of temporal data including hours of the day and day of the week. Can be integrated with existing web/mobile based solutions. Apart from being a productive tool for route planning systems, the software can also be used in various situations such as: Customer Services/Call Centres Fleet Management  Loading Bay Assignment This model can be used to improve the existing route planning systems as it provides additional job completion duration prediction on top of estimated ETA.  Low cost - the model is easy to implement and incorporate into other applications. Simple to use - only a small set of input parameters are required. Able to predict the stop duration (job completion duration) with reasonable accuracy. The technology owner is keen to out-license this technology to collaborators in the field of last mile-logistics, or collaborators who are providers of software for last-mile logistics. This ML model is for Singapore use only. Last Mile Delivery, Estimated Time of Completion (ETC) Infocomm, Artificial Intelligence, Logistics, Delivery & Distribution, Value-Added Services