Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of food preservation and monitoring systems. More specifically, it pertains to a food spoilage detection system for refrigerators.
BACKGROUND OF THE DISCLOSURE
[0002] Refrigerators have been an indispensable appliance in modern households, playing a pivotal role in preserving food, minimizing waste, and extending the shelf life of perishable items.
[0003] However, despite advancements in refrigeration technology, one persistent challenge that continues to plague consumers and industries alike is the problem of undetected food spoilage.
[0004] Even within a controlled cold environment, certain food items deteriorate over time due to microbial growth, enzymatic reactions, or exposure to fluctuating temperatures.
[0005] This issue is further exacerbated by human factors such as forgetting to check expiry dates, improper storage practices, or failing to notice subtle signs of degradation.
[0006] Consequently, spoiled food not only results in unnecessary wastage but also poses serious health risks due to the potential for foodborne illnesses.
[0007] Traditional indicators of spoilage, such as changes in color, texture, odor, or visible mold growth, are often subjective and dependent on the user's sensory perception.
[0008] In some cases, spoilage occurs without overt physical signs, leaving consumers unaware of underlying bacterial contamination or biochemical changes that render the food unsafe for consumption.
[0009] Moreover, certain food categories—particularly meats, dairy, and seafood—may harbor pathogenic microorganisms even before visible signs appear, thus making reliance solely on human senses inadequate for reliable detection.
[0010] The inability to accurately and timely assess food quality in household refrigerators leads to increased food waste, unnecessary expenditure, and potential health hazards, highlighting an urgent need for improved spoilage monitoring mechanisms.
[0011] In response to these challenges, recent years have witnessed growing research interest in the integration of sensor technologies within food storage and packaging solutions.
[0012] Sensor-based detection systems have been developed across various contexts, such as smart packaging materials embedded with freshness indicators or biosensors capable of detecting specific microbial metabolites.
[0013] However, most existing technologies remain confined to industrial or commercial applications and have yet to be widely adopted within domestic settings.
[0014] Refrigerators equipped with freshness indicators or connected IoT platforms are emerging concepts; however, they typically rely on indirect proxies such as elapsed storage time or preset temperature thresholds rather than real-time biochemical indicators of spoilage.
[0015] The concept of utilizing sensors directly embedded within a refrigerator's storage environment offers a transformative approach to overcoming the limitations of conventional spoilage detection methods.
[0016] By employing dedicated sensors capable of detecting chemical and physical markers of food degradation, it becomes feasible to provide users with objective, real-time assessments of food freshness.
[0017] Among various detection modalities, color sensors and odor sensors have emerged as highly promising tools due to their direct alignment with natural spoilage indicators: color change and odor emission.
[0018] As food degrades, biochemical reactions often result in the production of volatile organic compounds (VOCs), such as amines, sulfides, ketones, and aldehydes, which contribute to characteristic foul odors associated with spoilage.
[0019] Concurrently, pigment degradation, microbial colonization, and oxidative reactions lead to visible discoloration of food surfaces.
[0020] By leveraging these inherent spoilage signals, a sensor system can effectively translate sensory cues into quantifiable data, enabling early detection and actionable alerts.
[0021] Color sensors operate by detecting reflected light intensity across different wavelengths, capturing variations in color shades, hue, saturation, and brightness.
[0022] In the context of food spoilage, color sensors can be calibrated to monitor key chromatic changes associated with specific degradation pathways—for example, the browning of meat due to myoglobin oxidation, yellowing of dairy products, or greening of vegetables due to chlorophyll breakdown.
[0023] By continuously scanning stored food items, a color sensor array integrated within a refrigerator can monitor gradual deviations from baseline freshness colors, thereby providing an early indication of quality decline before severe spoilage sets in. Such a system can be designed to either scan specific regions of interest within the refrigerator or employ mobile sensor modules that adjust their position to monitor different shelves dynamically.
[0024] On the other hand, odor sensors—commonly referred to as electronic noses—simulate the olfactory detection mechanism by utilizing sensor arrays that respond to various volatile compounds.
[0025] Technologies such as metal oxide semiconductor (MOS) sensors, conductive polymer sensors, quartz crystal microbalance sensors, and surface acoustic wave sensors are widely utilized to detect VOCs with high sensitivity.
[0026] Each type of odor sensor offers distinct advantages in terms of selectivity, response time, stability, and cost. By incorporating an odor sensor within the refrigerator’s interior, the system can detect accumulating VOCs associated with microbial metabolism and enzymatic breakdown of organic matter.
[0027] Elevated concentrations of biogenic amines such as putrescine and cadaverine, for example, serve as chemical signatures of proteinaceous food spoilage, while sulfur compounds such as hydrogen sulfide and dimethyl sulfide indicate degradation of seafood and eggs.
[0028] By correlating sensor responses with established spoilage thresholds, the system can generate timely alerts that notify users of potential spoilage risks, prompting them to inspect, consume, or discard affected items.
[0029] The integration of color and odor sensors into a unified spoilage detection system offers a synergistic advantage by enabling multimodal monitoring. Whereas color sensors provide surface-level assessments linked to visible deterioration, odor sensors capture gaseous emissions reflective of internal biochemical changes.
[0030] This complementary approach enhances the system’s sensitivity and specificity, minimizing false positives or negatives that may arise if relying on a single sensing modality.
[0031] For instance, certain spoilage scenarios may exhibit odor emission prior to visible discoloration (as in some meat products), while others may involve color changes without significant VOC production (as in cut fruits exposed to oxidation).
[0032] By fusing data from both sensors, a more comprehensive and robust assessment of spoilage status can be achieved.
[0033] A critical aspect of developing a functional food spoilage detection system lies in addressing the environmental factors inherent to refrigerator interiors.
[0034] Refrigerators operate under varying temperature and humidity conditions, and these variables influence both sensor performance and spoilage kinetics. Color sensors must be calibrated to account for varying illumination conditions within the refrigerator—whether through built-in LED lighting or ambient light leakage—while maintaining accurate chromatic readings.
[0035] Odor sensors, meanwhile, must be designed to function under low-temperature and high-humidity environments, where condensation or sensor fouling could otherwise compromise performance.
[0036]
[0037] Sensor housings, protective coatings, and heating elements may be incorporated to prevent moisture accumulation on sensor surfaces, thereby preserving sensitivity and prolonging sensor lifespan.
[0038] In addition to sensor hardware considerations, the system requires a robust signal processing and data interpretation framework capable of translating raw sensor outputs into actionable insights.
[0039] Sensor signals must be pre-processed to remove noise, normalized against baseline measurements, and subjected to pattern recognition algorithms that map sensor responses to known spoilage profiles.
[0040] Machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), or artificial neural networks (ANN) may be employed to enhance classification accuracy, especially in distinguishing between acceptable natural odors and spoilage-related VOCs.
[0041] Over time, the system can be designed to adaptively refine its detection thresholds based on user preferences, dietary habits, and storage patterns, thus offering a personalized spoilage monitoring experience.
[0042] The practical implementation of a food spoilage detection system using color and odor sensors necessitates seamless integration with the refrigerator’s structural and electronic ecosystem.
[0043] Sensor modules may be embedded into refrigerator walls, mounted on adjustable rails, or designed as detachable units for cleaning and maintenance.
[0044] Connectivity features such as Wi-Fi or Bluetooth may enable the system to interface with smartphones, allowing users to receive notifications remotely regarding food status or impending spoilage events.
[0045] Integration with smart home ecosystems could further automate actions—for example, triggering reminders to consume certain items, adjusting refrigerator temperatures to slow spoilage, or generating shopping lists based on items nearing expiration.
[0046] From a consumer standpoint, the adoption of such a system addresses critical pain points associated with food waste, cost savings, and health assurance.
[0047] According to reports by the Food and Agriculture Organization (FAO) and other global agencies, household food waste constitutes a significant proportion of overall food loss, with perishable items being the most frequently discarded.
[0048] By providing consumers with early and objective spoilage alerts, the proposed system empowers proactive consumption decisions, reducing unnecessary discards of edible food while safeguarding against accidental consumption of spoiled items.
[0049] For vulnerable populations such as the elderly or immunocompromised individuals, enhanced spoilage detection offers an additional layer of protection against foodborne illnesses arising from spoiled food consumption.
[0050] At an environmental level, reducing food spoilage and waste contributes to broader sustainability goals by lowering the carbon footprint associated with food production, transportation, and disposal.
[0051] Each kilogram of food wasted translates into lost resources—including water, land, energy, and labor—underscoring the societal relevance of technologies that promote responsible food utilization.
[0052] Additionally, minimizing spoiled food disposal alleviates burdens on municipal waste systems, reducing methane emissions from decomposing organic waste in landfills.
[0053] The incorporation of spoilage detection systems into consumer appliances represents an actionable step toward achieving circular economy principles and fostering sustainable consumption behaviors.
[0054] Major appliance manufacturers are actively exploring embedded sensor solutions and IoT connectivity to differentiate their products in competitive markets. By embedding food spoilage detection functionality into refrigerators, manufacturers can offer added value propositions while addressing unmet consumer needs.
[0055] This innovation holds market potential across residential, commercial, and institutional segments—ranging from household kitchens to school cafeterias, hospitals, hotels, and foodservice establishments where maintaining food safety and quality is paramount.
[0056] In addition to consumer and industrial applications, the way for future research opportunities into advanced sensing materials, miniaturized electronics, and adaptive sensing algorithms.
[0057] Research into nanostructured sensing films, bio-inspired sensor arrays, or hybrid optoelectronic sensors may further enhance detection sensitivity, selectivity, and durability under refrigeration conditions.
[0058] The integration of predictive spoilage models based on microbial kinetics, chemical degradation pathways, or environmental parameters may enable the system to forecast spoilage timelines rather than merely detect current status, thus supporting more informed decision-making regarding food management.
[0059] One of the primary disadvantages of a food spoilage detection system based on color and odor sensors lies in the inherent limitations of sensor accuracy and reliability.
[0060] Color sensors rely heavily on the visible appearance of food to detect changes associated with spoilage, such as discoloration or mold growth. However, not all spoilage is visually apparent, and some food items may undergo chemical or microbial deterioration without noticeable color changes.
[0061] Similarly, odor sensors function by detecting volatile organic compounds (VOCs) released during spoilage. Yet, the sensitivity and selectivity of these sensors can be problematic, as they may fail to differentiate between spoilage-related odors and benign odors from aromatic foods like cheeses, fermented products, or spices.
[0062] This could lead to false positives, where the system incorrectly signals spoilage, or false negatives, where genuine spoilage goes undetected. The implications of such errors are serious: either leading to unnecessary food waste or posing a health risk if spoiled food is consumed due to system oversight.
[0063] Moreover, odor sensors are particularly susceptible to cross-sensitivity and sensor drift. Cross-sensitivity occurs when the sensor responds to gases or compounds not related to spoilage, resulting in erroneous readings.
[0064] For example, the presence of strong-smelling but safe food items may saturate the sensor, reducing its ability to detect actual spoilage indicators. Sensor drift refers to the gradual decline in sensor performance over time, leading to degraded sensitivity and specificity.
[0065] Calibration routines can mitigate this issue but require regular maintenance, technical expertise, and potentially expensive recalibration equipment. The burden of ensuring ongoing accuracy can be inconvenient for users and raises concerns about the system’s long-term reliability.
[0066] Consequently, the technological promise of odor sensors is compromised by their vulnerability to environmental conditions, cumulative contamination, and performance degradation.
[0067] Another disadvantage arises from the complexity of integrating such detection systems into existing refrigerator designs. Refrigerators are typically designed with an emphasis on cooling efficiency, storage optimization, and ease of cleaning.
[0068] Embedding color and odor sensors into these appliances involves additional hardware, wiring, power requirements, and software control mechanisms. Ensuring that sensors have unobstructed lines of sight for color monitoring or adequate airflow for odor sampling without compromising the refrigerator’s insulation or energy efficiency is a significant design challenge.
[0069] Retrofitting existing refrigerators with such systems could be impractical or cost-prohibitive, thereby limiting the technology’s applicability primarily to new, high-end refrigerator models.
[0070] This technological exclusivity widens the accessibility gap between affluent and average consumers, rendering the benefits of the system unattainable for many households.
[0071] In addition to integration challenges, the maintenance and cleaning of the sensors themselves pose significant drawbacks. Refrigerators are prone to spills, moisture accumulation, and residue buildup, especially around perishable items. Color sensors may become obstructed by condensation, stains, or packaging materials, impeding their ability to capture accurate readings.
[0072] Odor sensors, meanwhile, risk fouling or saturation from repeated exposure to food vapors, oils, and particulate matter present in the refrigerator environment.
[0073] Regular cleaning of these sensors may not only be inconvenient for users but may also introduce risks of damaging sensitive components if improper cleaning methods or agents are used.
[0074] The need for specialized cleaning protocols or professional servicing adds another layer of complexity and potential cost, undermining the system’s appeal as a maintenance-free solution.
[0075] The economic disadvantages of such a system are also substantial. The incorporation of advanced sensors, microcontrollers, communication modules, and software analytics increases the production cost of refrigerators equipped with spoilage detection systems.
[0076] For manufacturers, this translates into higher research and development investments, more complex quality assurance protocols, and potential warranty liabilities.
[0077] These costs are inevitably passed on to consumers, resulting in a price premium that may deter widespread adoption. The higher upfront cost may not be justified for many consumers, especially considering the uncertainties regarding sensor lifespan, accuracy, and practical impact on food management.
[0078] Furthermore, if the system experiences frequent false alarms or technical malfunctions, consumer frustration may lead to abandonment of the feature, thereby nullifying its intended benefit despite the added expense.
[0079] From a user experience perspective, the system may inadvertently create confusion, distrust, or over-reliance. For instance, users unfamiliar with the biochemical complexity of food spoilage may misinterpret sensor alerts as definitive rather than probabilistic.
[0080] If the system frequently issues warnings for safe foods or misses spoiled items, users may lose confidence in its recommendations, defeating the purpose of the technology.
[0081] Alternatively, users may become excessively dependent on the system’s alerts, diminishing their personal vigilance in checking food manually. This over-reliance poses a risk if the system malfunctions or fails to detect spoilage, leading to consumption of unsafe food under the false reassurance of technological oversight.
[0082] Balancing automation with user education remains a persistent challenge for such sensor-based systems.
[0083] The privacy and ethical implications of connected spoilage detection systems introduce further disadvantages. Many of these systems integrate with mobile applications or smart home ecosystems, transmitting data about food inventory, consumption patterns, and spoilage events to cloud servers.
[0084] While such connectivity enables remote monitoring and automated grocery replenishment, it also raises concerns about data privacy and surveillance. Companies may exploit this data for targeted advertising, market research, or even dynamic pricing strategies without explicit user consent.
[0085] The aggregation of food-related data could inadvertently reveal sensitive insights about household habits, cultural practices, or dietary preferences, creating vulnerabilities for exploitation or discrimination.
[0086] Ensuring robust data protection, transparency, and ethical governance becomes a prerequisite but adds further regulatory and compliance burdens for manufacturers.
[0087] An often-overlooked disadvantage is the environmental footprint of incorporating additional electronic components into household appliances. Sensors, microcontrollers, printed circuit boards, and communication chips contribute to electronic waste at the end of the refrigerator’s life cycle.
[0088] Many of these components contain rare earth elements or non-biodegradable materials, complicating recycling and disposal efforts. The accelerated obsolescence of sensors compared to the longer operational lifespan of refrigerators may also result in premature system failures, requiring partial or full replacements.
[0089] The environmental cost of manufacturing, maintaining, and disposing of these embedded systems conflicts with sustainability goals unless accompanied by comprehensive recycling and circular economy strategies.
[0090] The system’s compatibility challenges with diverse food types and storage conditions represent another major disadvantage. Refrigerators store a wide variety of foods—fruits, vegetables, dairy, meats, leftovers—each with distinct spoilage markers, odors, and color changes.
[0091] A sensor calibrated for specific spoilage compounds in meat may not be effective in detecting spoilage in produce or dairy. Similarly, color changes in fresh produce may reflect natural ripening rather than spoilage, leading to false alarms if the system lacks sophisticated differentiation capabilities.
[0092] Achieving universal detection accuracy across all food types stored under varying humidity, temperature, and packaging conditions is a daunting task.
[0093] Without extensive calibration for diverse food matrices, the system risks underperformance in real-world settings compared to controlled laboratory environments.
[0094] The software algorithmic complexity required to interpret sensor data introduces its own disadvantages. Sensor readings are inherently noisy and influenced by environmental factors such as humidity, temperature fluctuations, or electromagnetic interference from other devices. \
[0095] Developing robust algorithms that can filter noise, compensate for confounding variables, and deliver reliable spoilage predictions requires significant computational resources and machine learning models trained on extensive, diverse datasets.
[0096] Continuous updates to these models may necessitate internet connectivity, periodic software upgrades, and technical support infrastructure.
[0097] For users, these updates could create additional complications if software bugs, incompatibility issues, or subscription-based features are introduced, turning a passive detection system into an actively managed technology.
[0098] Additionally, the system’s cultural and behavioral fit with users’ food management practices may be limited. In many households, sensory cues such as smell, texture, and taste remain the primary methods for assessing food quality, rooted in cultural traditions and personal experience.
[0099] Introducing an automated detection system may clash with these intuitive practices, leading to skepticism or rejection. Users may resist relying on an abstract digital alert over their own sensory evaluation, especially if the system’s alerts contradict their perception.
[0100] Furthermore, the system may promote unnecessary food disposal if it issues conservative warnings to avoid liability, exacerbating food waste rather than reducing it.
[0101] Lastly, the durability and robustness of such systems under long-term refrigerator conditions present critical disadvantages. Refrigerators operate under cold, humid, and variable environments, which can challenge the operational stability of sensitive electronic components.
[0102] Condensation, frost buildup, vibration from compressors, and exposure to cleaning agents may degrade sensor performance or cause hardware failures. Designing sensors that can withstand such harsh conditions without compromising accuracy or requiring frequent replacements raises engineering and material science challenges.
[0103] If sensors fail prematurely or degrade in performance, the reliability of the entire detection system is compromised, and repair or replacement may not be user-friendly.
[0104] Presently available solutions, such as temperature and humidity controls, ethylene absorbers, and air filters, only help slow down spoilage rather than detecting it. They cannot alert users to actual spoilage events occurring within specific compartments, making it easy for users to overlook when perishable items have started to deteriorate.
[0105] Some smart refrigerators use barcode scanning or manual input to track expiration dates, but these systems rely on users to input accurate information. Expiration reminders are based on preset dates rather than real-time indicators of freshness or spoilage, making these reminders less reliable for identifying when food actually goes bad.
[0106] Existing patented systems generally focus on monitoring environmental factors like temperature, humidity, or internal atmosphere. They do not incorporate the direct use of color and odor sensors, which are critical for detecting early spoilage markers that occur before food visually or texturally degrades.
[0107] Currently available solutions do not offer compartment-specific alerts for different food categories (fruits, vegetables, leafy greens). This one-size-fits-all approach fails to account for the unique spoilage rates and conditions needed for various types of food, leading to either premature alerts or missed spoilage detections.
[0108] Without an early warning system, users are not informed of spoilage until it’s often too late to salvage the food. Real-time alerts triggered by color and odor changes would allow users to address spoilage at the earliest signs, reducing waste and avoiding unpleasant odors.
[0109] Thus, in light of the above-stated discussion, there exists a need for a food spoilage detection system for refrigerators using color and odor sensors.
SUMMARY OF THE DISCLOSURE
[0110] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0111] According to illustrative embodiments, the present disclosure focuses on a food spoilage detection system for refrigerators using color and odor sensors which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0112] An objective of the present disclosure is to provide early detection and timely alerts of food spoilage by identifying discoloration and odor-related gases specific to fruits, vegetables, and leafy greens.
[0113] Another objective of the present disclosure is to develop an intelligent food spoilage detection system that uses color and odor sensors to monitor spoilage indicators in refrigerator compartments.
[0114] Another objective of the present disclosure is to design a sensor-based system that enables real-time monitoring of perishable food items to reduce unnoticed spoilage and prevent the spread of offensive odors inside the refrigerator.
[0115] Another objective of the present disclosure is to integrate a pre-buzzer alert mechanism that notifies users at the early stages of spoilage, allowing them to take corrective action before food becomes inedible.
[0116] Another objective of the present disclosure is to enable compartment-specific spoilage detection so that users can identify and remove only the affected food items without wasting other uncontaminated foods.
[0117] Another objective of the present disclosure is to customize the detection parameters of the system to accommodate the varying spoilage rates and storage conditions of different food categories.
[0118] Another objective of the present disclosure is to minimize food waste and household expenses by proactively alerting users about impending spoilage, thus improving food management and reducing unnecessary disposal.
[0119] Another objective of the present disclosure is to enhance food safety and hygiene within the refrigerator by preventing the multiplication of harmful bacteria resulting from unnoticed spoilage.
[0120] Another objective of the present disclosure is to improve refrigerator storage efficiency by transforming it into a smart system that assists users in maintaining optimal freshness and quality of stored food items.
[0121] Yet another objective of the present disclosure is to contribute to creating a healthier kitchen environment by integrating technology-driven solutions that promote sustainable food storage and consumption practices.
[0122] In light of the above, a food spoilage detection system for refrigerators using color and odor sensors comprises a plurality of color sensors positioned in different food compartments of the refrigerator for detecting color changes indicative of spoilage in respective food categories. The system also includes a plurality of odor sensors positioned in said compartments for detecting gases released during spoilage. The system also includes a microcontroller operatively configured to process sensor data and determine spoilage status for each compartment. The system also includes an alert unit comprising a buzzer and/or notification module operatively connected to the microcontroller. The system also includes a dual-sensor approach integrating both color and odor sensors in each food compartment to provide real-time, compartment-specific spoilage detection and proactive pre-buzzer alerts for fruits, vegetables, and leafy greens individually.
[0123] In one embodiment, the plurality of color sensors is calibrated to detect specific color changes corresponding to spoilage thresholds unique to each food category.
[0124] In one embodiment, the microcontroller is further configured to generate a pre-buzzer alert prior to activation of the alert unit, upon detecting an initial spoilage indicator based on combined data from the color sensors and odor sensors.
[0125] In one embodiment, the dual-sensor approach is configured to correlate odor sensor data and color sensor data to reduce false positives and improve detection accuracy for each compartment.
[0126] In one embodiment, the microcontroller is programmed to prioritize alerts for certain food compartments based on predefined spoilage sensitivity profiles for fruits, vegetables, and leafy greens.
[0127] In one embodiment, the system is further configured to display the spoilage status for each compartment on an integrated display panel of the refrigerator or on a remote mobile application.
[0128] In one embodiment, the alert unit comprises a notification module adapted to transmit alerts to a user device.
[0129] In one embodiment, the plurality of odor sensors is configured to detect spoilage-related gases selected from the group consisting of ethylene, ammonia, and other volatile organic compounds emitted during food decay.
[0130] In one embodiment, a method for detecting food spoilage in a refrigerator comprises continuously monitoring color changes of food stored in a plurality of compartments using one or more color sensors disposed in each compartment. The method also includes continuously monitoring odor emissions from the food stored in said compartments using one or more odor sensors disposed in each compartment. The method also includes analyzing color and odor data from said sensors via a microcontroller to determine spoilage status for each compartment. The method also includes generating a compartment-specific alert, comprising at least an audible or visual notification, when spoilage is detected in a respective compartment based on predefined thresholds for color and odor changes. The method also includes optionally transmitting a notification to a remote device when spoilage is detected.
[0131] These and other advantages will be apparent from the present application of the embodiments described herein.
[0132] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0133] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0134] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0135] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0136] FIG. 1 illustrates a flowchart outlining sequential step involved in a food spoilage detection system for refrigerators using color and odor sensors, in accordance with an exemplary embodiment of the present disclosure;
[0137] FIG. 2 illustrates the images of a food spoilage detection system for refrigerators using color and odor sensors, in accordance with an exemplary embodiment of the present disclosure.
[0138] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0139] The food spoilage detection system for refrigerators using color and odor sensors, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0140] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0141] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0142] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0143] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0144] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0145] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in a food spoilage detection system for refrigerators using color and odor sensors, in accordance with an exemplary embodiment of the present disclosure.
[0146] A food spoilage detection system for refrigerators using color and odor sensors 100 comprises a plurality of color sensors 102 positioned in different food compartments of the refrigerator for detecting color changes indicative of spoilage in respective food categories. The plurality of color sensors 102 is calibrated to detect specific color changes corresponding to spoilage thresholds unique to each food category.
[0147] The system also includes a plurality of odor sensors 104 positioned in said compartments for detecting gases released during spoilage. The plurality of odor sensors 104 is configured to detect spoilage-related gases selected from the group consisting of ethylene, ammonia, and other volatile organic compounds emitted during food decay.
[0148] The system also includes a microcontroller 106 operatively configured to process sensor data and determine spoilage status for each compartment. The microcontroller 106 is further configured to generate a pre-buzzer alert prior to activation of the alert unit 108, upon detecting an initial spoilage indicator based on combined data from the color sensors 102 and odor sensors 104. The microcontroller 106 is programmed to prioritize alerts for certain food compartments based on predefined spoilage sensitivity profiles for fruits, vegetables, and leafy greens.
[0149] The system also includes an alert unit 108 comprising a buzzer and/or notification module operatively connected to the microcontroller. The alert unit 108 comprises a notification module adapted to transmit alerts to a user device.
[0150] The system also includes a dual-sensor approach 110 integrating both color and odor sensors in each food compartment to provide real-time, compartment-specific spoilage detection and proactive pre-buzzer alerts for fruits, vegetables, and leafy greens individually. The dual-sensor approach 110 is configured to correlate odor sensor data and color sensor data to reduce false positives and improve detection accuracy for each compartment.
[0151] The system also displays the spoilage status for each compartment on an integrated display panel of the refrigerator or on a remote mobile application.
[0152] A method for detecting food spoilage in a refrigerator comprises continuously monitoring color changes of food stored in a plurality of compartments using one or more color sensors disposed in each compartment. The method also includes continuously monitoring odor emissions from the food stored in said compartments using one or more odor sensors disposed in each compartment. The method also includes analyzing color and odor data from said sensors via a microcontroller to determine spoilage status for each compartment. The method also includes generating a compartment-specific alert, comprising at least an audible or visual notification, when spoilage is detected in a respective compartment based on predefined thresholds for color and odor changes. The method also includes optionally transmitting a notification to a remote device when spoilage is detected.
[0153] FIG. 1 illustrates a flowchart outlining sequential step involved in a food spoilage detection system for refrigerators using color and odor sensors.
[0154] At 102, At the initial stage of the system's operation, the flowchart begins with the activation of the sensors within the refrigerator compartments. A plurality of color sensors is strategically positioned across different food compartments, each calibrated to monitor color variations specific to the food category it oversees.
[0155] At 104, similarly, a plurality of odor sensors is also embedded within these compartments to continuously sense the presence and concentration of gases typically emitted during food spoilage, such as ethylene from ripening fruits, ammonia from decaying leafy greens, or other volatile organic compounds. This dual arrangement ensures that each food category is monitored both visually and chemically, enhancing the sensitivity and specificity of spoilage detection. From the flowchart’s perspective, this stage represents the sensor initialization and environmental scanning phase, where baseline data is established and continuous monitoring is initiated.
[0156] At 106, as the system enters the data acquisition phase, the color and odor sensors begin transmitting real-time data streams to the microcontroller. The microcontroller acts as the central processing unit, receiving analog or digital signals from the sensors at regular intervals. Each sensor reading represents a snapshot of the current state of the food within its compartment. For example, a color sensor monitoring leafy greens may track changes in green pigment intensity or browning, while an odor sensor may detect an increase in ammonia concentration beyond normal ripening thresholds. At this stage illustrates parallel data streams being routed from multiple sensors to the microcontroller, emphasizing the simultaneous processing of sensory inputs from different compartments.
[0157] Following data acquisition, the microcontroller initiates the data processing and analysis phase, a critical step where raw sensor readings are interpreted to evaluate spoilage status. Within the microcontroller’s firmware or software, predefined threshold values are programmed for both color and odor parameters. These thresholds are scientifically determined to correlate with early spoilage indicators for different food types. For instance, a drop in chlorophyll intensity below a specific value in leafy greens, coupled with a rise in ammonia gas concentration, may indicate initial spoilage onset. Using conditional logic and comparison algorithms, the microcontroller compares incoming data against these thresholds, continuously updating the status of each compartment. This phase represents decision nodes, where the system evaluates whether the sensor data crosses spoilage thresholds or remains within safe limits. If no spoilage is detected, the system loops back to continue monitoring, maintaining an uninterrupted sensing cycle.
[0158] However, once the microcontroller identifies that the combination of color change and odor emission in any compartment exceeds the spoilage threshold, the system transitions to the spoilage detection confirmation phase. This phase ensures that transient anomalies or false readings do not trigger unnecessary alerts. The microcontroller may incorporate averaging algorithms, time-delay confirmation, or require multiple consecutive readings indicating spoilage before proceeding. This step, depicted as a confirmation checkpoint, prevents false positives and reinforces system reliability. Only after spoilage is confirmed beyond statistical doubt does the system proceed to the alert generation phase.
[0159] At 108, the subsequent phase involves the activation of the alert unit, which comprises a buzzer and/or a notification module operatively connected to the microcontroller. Depending on the system configuration, the microcontroller triggers an auditory alert via the buzzer located inside or outside the refrigerator, and/or sends a digital notification to a user interface such as a display panel on the refrigerator door or a connected mobile application. The alert is compartment-specific, meaning that the system explicitly identifies which compartment—fruits, vegetables, or leafy greens—has detected spoilage. This specificity enables users to address the issue without unnecessary disturbance to other unaffected compartments. In the flowchart, this phase is represented as the alert generation and user notification output, completing the detection-to-alert cycle for the spoiled compartment.
[0160] At 110, a unique aspect is the incorporation of a pre-buzzer alert mechanism, part of the system’s proactive design. The pre-buzzer alert operates as an early warning, triggered when sensor data trends towards, but does not yet surpass, the spoilage threshold. For example, if color degradation or odor concentration is approaching spoilage levels but has not yet crossed the defined limit, the microcontroller issues a softer or preliminary alert to inform the user that the food may soon spoil. This feature provides users with an opportunity to consume or utilize food before it reaches an unusable state, thus actively reducing food waste. In the flowchart, the pre-buzzer alert stage branches from the data analysis phase, acting as an intermediary output prior to full spoilage detection confirmation.
[0161] Once the alert has been issued, the system re-enters the continuous monitoring cycle, as indicated in the flowchart by feedback loops connecting the alert phase back to the sensor monitoring phase. This cyclical architecture ensures that spoilage detection remains a real-time, dynamic process, adapting to ongoing environmental and biological changes within the refrigerator. Additionally, the system is designed to accommodate optional functionalities such as remote access through IoT integration. In such configurations, the flowchart extends to include pathways for sending notifications via Wi-Fi or Bluetooth modules to a smartphone app, enabling users to monitor refrigerator status even while away from home.
[0162] From a broader perspective, it also encapsulates system initialization routines, where sensors undergo calibration during power-up to establish baseline color and odor values for each compartment. This calibration phase is critical to account for natural variations in food color and odor profiles that do not indicate spoilage but may otherwise influence sensor readings. Similarly, it includes error-handling routines, allowing the microcontroller to detect and report sensor malfunctions, communication errors, or power disruptions, thereby maintaining system integrity and informing users of maintenance needs.
[0163] FIG. 2 illustrates the images of a food spoilage detection system for refrigerators using color and odor sensors.
[0164] FIG. 2A is a hand-drawn schematic that outlines the internal layout of a refrigerator equipped with this spoilage detection system. In this depiction, the refrigerator is divided into multiple compartments, each designed for storing different categories of food such as fruits, vegetables, dairy, meat, and eggs. Prominently marked within each compartment are "indicators," labeled next to a small graphical bar displaying durations like "3 weeks," "1 week," and "3 days." These indicators visually represent the estimated remaining freshness or storage life of the food stored in each compartment. Positioned beside these indicators are color sensor modules that monitor any visible changes in the color of food items, which are often an early sign of spoilage. For example, the browning of leafy greens or the discoloration of fruits is effectively detected by the color sensors, providing a quantifiable parameter to determine freshness.
[0165] Further analysis of this schematic reveals the presence of an odor detection unit labeled as "prebuzzer" at the bottom section of the refrigerator. The prebuzzer acts as an early-warning system by using odor sensors to detect volatile organic compounds (VOCs) or gases typically emitted during the spoilage process. Substances such as ammonia, hydrogen sulfide, and ethanol, which are characteristic of decaying proteins and fermenting carbohydrates, are identified by the odor sensors, prompting the prebuzzer to alert the user even before visible signs of spoilage occur. This proactive notification provides a valuable window of time for users to take corrective actions such as removing spoiled items or adjusting storage conditions to prevent cross-contamination.
[0166] Each compartment's dual-sensor configuration—a combination of a color sensor and an odor sensor—feeds data into a microcontroller situated within the system. The microcontroller serves as the processing hub, analyzing input signals from the sensors, correlating changes in color spectra and odor profiles, and applying pre-set spoilage thresholds to determine the status of each food category. Based on these analyses, the microcontroller triggers the respective indicators and alert mechanisms, facilitating compartment-specific monitoring rather than a generalized assessment for the entire refrigerator. This modular design ensures tailored spoilage detection for fruits, vegetables, leafy greens, dairy, and meats, each of which exhibits different spoilage timelines and biochemical markers.
[0167] FIG. 2B, a digitally rendered illustration, further expands upon the conceptual framework by providing a more detailed, visual depiction of a commercially styled refrigerator equipped with spoilage detection indicators. In this image, the refrigerator showcases neatly organized shelves, each storing different food products such as dairy, canned goods, fresh produce, meats, and beverages. Superimposed on the upper compartment is a graphical gauge resembling a meter, labeled as "Energy levels to determine how many days we can store the items in it." This meter functions similarly to the indicator bars in the first image, offering a dynamic and intuitive visual representation of remaining storage time based on the combined analysis of color and odor sensor data. The gauge's needle moves along a gradient, shifting from a green "safe" zone toward a red "critical" zone as the freshness declines, providing an at-a-glance assessment of food viability.
[0168] On the refrigerator door, prominently displayed near the lower section, is an "odor buzzer"—a large red button designed to audibly alert users when the odor sensors detect gas emissions surpassing predefined spoilage thresholds. This buzzer complements the prebuzzer from the first image, but in this depiction, it is represented as a more accessible, user-interactive component, capable of immediately drawing attention to potential spoilage through sound alerts. The strategic placement of the odor buzzer on the door ensures that users are notified during their routine interactions with the refrigerator, such as opening the door to retrieve items.
[0169] A key feature illustrated in the second image is the integration of food category-specific monitoring through compartmentalization. For example, leafy greens and vegetables stored in the lower crisper drawers benefit from tailored detection algorithms sensitive to ethylene gas emissions and color degradation specific to plant-based products. Meanwhile, protein-rich items like poultry and red meats, housed in middle shelves, are monitored for sulfurous gas emissions and color changes such as browning or greying, indicative of microbial spoilage. This customized detection ensures the dual-sensor system provides accurate, compartment-specific spoilage assessment, reducing false positives and enabling more reliable notifications.
[0170] The overall architecture of this food spoilage detection system revolves around a synergistic interaction between sensor technologies, data processing, and user alert mechanisms. The color sensors, positioned strategically to capture maximum exposure of stored items, utilize RGB and multispectral imaging techniques to detect subtle chromatic variations over time. Changes in hue, saturation, and brightness are analyzed against baseline color profiles established for each food type. Simultaneously, the odor sensors employ metal-oxide semiconductor (MOS) or electrochemical detection principles to identify gaseous biomarkers associated with enzymatic degradation and microbial activity.
[0171] Once sensor data is collected, the microcontroller employs embedded algorithms to process the inputs in real-time. These algorithms consider factors such as rate of color change, gas concentration trends, ambient temperature, and humidity, all of which influence spoilage kinetics. The microcontroller's decision-making engine maps sensor readings against spoilage thresholds to classify the freshness status into categories such as "Safe," "Approaching Spoilage," and "Spoiled." Depending on the classification, corresponding actions are initiated: visual indicators are updated, pre-buzzer alerts are activated for items nearing spoilage, and the main odor buzzer is triggered for critical spoilage events.
[0172] Importantly, the system embodies proactive food management by not only detecting spoilage after it occurs but also by forecasting spoilage trends. The integration of indicators displaying durations like "3 weeks," "1 week," and "3 days" empowers users to plan consumption and prioritize usage of perishable items. This predictive capability enhances household food inventory management, reducing unnecessary waste and optimizing grocery planning.
[0173] Furthermore, the design implicitly addresses cross-contamination risks by isolating detection at the compartment level. For example, spoilage detected in the meat compartment triggers an alert specific to that section, preventing misinterpretation or generalized spoilage warnings affecting unrelated compartments. This localized detection and notification facilitate targeted interventions, such as selective removal of spoiled items, without necessitating full refrigerator clearance.
[0174] In terms of usability, both images highlight a user-centric interface that emphasizes intuitive visual cues and audible alerts. The indicators, gauges, and buzzers are designed for immediate recognition, catering to diverse user demographics including elderly individuals or users unfamiliar with technical systems. The absence of complex manual settings or calibration requirements ensures accessibility and ease of use, promoting widespread adoption of the system in domestic settings.
[0175] A further extension of this system could involve wireless connectivity, enabling remote monitoring through smartphone applications. While not explicitly depicted in the images, such integration aligns with the underlying architecture, allowing users to receive spoilage alerts and freshness status updates even when away from home. Data logging and trend analysis features could provide historical insights into food storage patterns, further enhancing food management strategies.
[0176] The holistic approach embodied in these images encapsulates a paradigm shift in food storage practices, transitioning from passive preservation to active monitoring. By embedding dual-sensor technologies directly into refrigerator compartments, the system transforms the refrigerator from a mere storage appliance into an intelligent food management ecosystem. The combination of visual indicators, odor buzzers, and predictive spoilage assessment introduces a multi-modal alert system that caters to both immediate and anticipatory user actions.
[0177] Moreover, the modularity evident in the images allows scalability across different refrigerator models and configurations. Whether implemented in compact single-door units or large multi-compartment systems, the underlying principles remain applicable. Manufacturers could customize sensor densities, indicator designs, and alert configurations to align with specific user needs and market segments, broadening the applicability of this innovation.
[0178] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0179] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0180] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0181] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A food spoilage detection system for refrigerators (100) comprising:
a plurality of color sensors (102) positioned in different food compartments of the refrigerator for detecting color changes indicative of spoilage in respective food categories;
a plurality of odor sensors (104) positioned in said compartments for detecting gases released during spoilage;
a microcontroller (106) operatively configured to process sensor data and determine spoilage status for each compartment;
an alert unit (108) comprising a buzzer and/or notification module operatively connected to the microcontroller;
a dual-sensor approach (110) integrating both color and odor sensors in each food compartment to provide real-time, compartment-specific spoilage detection and proactive pre-buzzer alerts for fruits, vegetables, and leafy greens individually.
2. The system (100) as claimed in claim 1, wherein the plurality of color sensors (102) is calibrated to detect specific color changes corresponding to spoilage thresholds unique to each food category.
3. The system (100) as claimed in claim 1, wherein the microcontroller (106) is further configured to generate a pre-buzzer alert prior to activation of the alert unit (108), upon detecting an initial spoilage indicator based on combined data from the color sensors (102) and odor sensors (104).
4. The system (100) as claimed in claim 1, wherein the dual-sensor approach (110) is configured to correlate odor sensor data and color sensor data to reduce false positives and improve detection accuracy for each compartment.
5. The system (100) as claimed in claim 1, wherein the microcontroller (106) is programmed to prioritize alerts for certain food compartments based on predefined spoilage sensitivity profiles for fruits, vegetables, and leafy greens.
6. The system (100) as claimed in claim 1, wherein the system is further configured to display the spoilage status for each compartment on an integrated display panel of the refrigerator or on a remote mobile application.
7. The system (100) as claimed in claim 1, wherein the alert unit (108) comprises a notification module adapted to transmit alerts to a user device.
8. The system (100) as claimed in claim 1, wherein the plurality of odor sensors (104) is configured to detect spoilage-related gases selected from the group consisting of ethylene, ammonia, and other volatile organic compounds emitted during food decay.
9. A method for detecting food spoilage in a refrigerator comprising:
continuously monitoring color changes of food stored in a plurality of compartments using one or more color sensors disposed in each compartment;
continuously monitoring odor emissions from the food stored in said compartments using one or more odor sensors disposed in each compartment;
analyzing color and odor data from said sensors via a microcontroller to determine spoilage status for each compartment;
generating a compartment-specific alert, comprising at least an audible or visual notification, when spoilage is detected in a respective compartment based on predefined thresholds for color and odor changes;
optionally transmitting a notification to a remote device when spoilage is detected.