Description:TECHNICAL FIELD
The present disclosure relates generally to an aroma analysis. More particularly, the present disclosure relates to aroma analysis system and method thereof.
BACKGROUND
Ayurveda considers the use of aroma as an important tool for prevention and healing; practitioners use it for protecting the vital force, prana; regulating digestion and metabolism, Agni; and increasing resistance to disease, Ojas. The formulation of the ayurvedic products requires proper evaluation of aromas to determine and analyse the presence of the volatile organic compounds existing in the formulation.
So before this innovation, there was a lack of objective and reliable methods for evaluating the aroma of Ayurvedic formulations that led to subjectivity and inconsistency in quality assessment of the ayurvedic formulations. Traditional methods relied heavily on subjective human perception, lacking standardization and consistency. This subjectivity led to variations in quality assessment across different practitioners and manufacturers, hindering the reliability of aroma analysis. Without objective and reliable methods, there was no consistent way to measure the effectiveness of aromas in Ayurvedic formulations, impeding progress in understanding their therapeutic potential.
Furthermore, the lack of standardized aroma evaluation methods created difficulties in ensuring the quality and potency of Ayurvedic products. Inconsistent assessments made it challenging for manufacturers to maintain consistent product quality, potentially compromising the efficacy of treatments and diminishing consumer confidence. Without a reliable means of assessing aroma, practitioners and consumers alike faced uncertainty regarding the authenticity and effectiveness of Ayurvedic formulations, impacting their willingness to use and recommend these products for health and wellness purposes
The absence of objective aroma evaluation methods also posed obstacles in research and development within the Ayurvedic industry. Without standardized approaches to analyze aromas, researchers struggled to validate the therapeutic claims of various formulations and identify optimal blends for specific health conditions. This lack of scientific rigor limited advancements in understanding the mechanisms of action behind aromatic compounds in Ayurveda, hindering the exploration of new therapeutic possibilities and innovative treatment approaches.
Moreover, the reliance on subjective assessment methods left Ayurvedic practitioners vulnerable to biases and inconsistencies in their diagnoses and treatment recommendations. Without objective tools to evaluate aromas, practitioners may have inadvertently overlooked important nuances in scent profiles, potentially leading to suboptimal treatment outcomes for their patients. The absence of standardized aroma analysis methods thus impeded the ability of practitioners to harness the full potential of aromatic therapies in promoting health and well-being according to Ayurvedic principles.
Thus, there is a need for a system, an apparatus, and a method capable of providing an automated approach to aroma analysis, ensuring accuracy, reliability, and compliance with industry standards, which demands a need for improvised technical solution that overcomes the aforementioned problems.
SUMMARY
In an aspect of the present disclosure, a system for anlaysing aromas is disclosed. The system includes a plurality of sensors, a database, processing circuitry, and an output unit. The plurality of sensors are configured to sense aromas from a mixture. the database that is coupled to the plurality of sensors are configured to store one or more predefined readings. The processing circuitry is coupled to the plurality of sensor and the database and configured to compare readings of the plurality of sensors with the one or more predefined readings, such that upon comparison, the VOC corresponding to the reading of each sensor of the plurality of the sensors is determined. The processing circuitry further combine the readings of the plurality of sensors to generate a combined set of readings. The processing circuitry further applies one or more pattern recognition techniques to the combined set of readings to determine a composition of at least one VOC of the one or more VOCs. The output unit coupled to the processing circuitry and configured to display one or more parameters related to the one or more VOC.
In some aspects of the present disclosure, the plurality of sensors are metal oxide sensors.
In some aspects of the present disclosure, the one or more parameters include any one or a combination of a concentration level of the one or more VOCs, relative ratios, quality metrics, temporal trends, and classification results.
In some aspects of the present disclosure, the processing circuitry includes a comparator unit that facilitates comparison of the readings of the plurality of sensors 102 with the predefined readings.
In some aspects of the present disclosure, the database further includes a calibration data for each sensor of the plurality of sensors to enhance accuracy in quantification of the one or more VOCs.
In some aspects of the present disclosure, the system further includes an analog front end (AFE) to process the analog signals which are generated from the sensors 102.
In some aspects of the present disclosure, the processing circuitry (110) further includes a merging unit (114) configured to combine the readings from the plurality of sensors (102) to generate the combined set of readings.
In some aspects of the present disclosure, the system (100) as claimed in claim 1, the output from the merging unit (114) is transferred to the field-programmable gate array (FPGA) of a Specific Integrated Circuit (APSOC).
In some aspects of the present disclosure, the pattern recognition techniques include machine learning (ML) techniques to improve accuracy in identifying and quantifying the one or more VOCs.
In an aspect of the present disclosure, a method for analysing one or more VOCs in a mixture is disclosed. The method begins with sensing aromas evolving from a mixture by way of a plurality of sensors. The method further includes comparing readings of the plurality of sensors with one or more predefined readings by way of a processing circuitry. The method further includes combining the readings of the plurality of sensors to generate a combined set of readings by way of the processing circuitry. The method further includes applying one or more pattern recognition techniques to the combined set of readings by way of the processing circuitry. The method further includes displaying one or more parameters related to the one or more VOCs by way of the processing circuitry. Furthermore, method further includes employing dimensionality reduction techniques on the combined set of readings to enhance computational efficiency in pattern recognition.
BRIEF DESCRIPTION OF DRAWINGS
The above and still further features and advantages of aspects of the present disclosure becomes apparent upon consideration of the following detailed description of aspects thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
FIG. 1 illustrates a block diagram of a system for aroma analysis, in an accordance with an aspect of a present disclosure.
FIG. 2 illustrates a process of analysing one or more VOCs present in a mixture by the system of the FIG. 1, in an accordance with an aspect of the present disclosure; and
FIG. 3 illustrates a flow chart of a method for analysing the one or more VOCs that are present in the mixture by the system 100 of the FIG. 1, in an accordance with an aspect of the present disclosure.
To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures.
DETAILED DESCRIPTION
Various aspect of the present disclosure provides aroma analysis system and method thereof. The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.
The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
It is understood that when an element is referred to as being “on,” “connected to,” or “coupled to” another element, it can be directly on, connected to, or coupled to the other element or intervening elements that may be present. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventor/inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various aspects including the example aspects relate to an aroma analysis system and a method thereof.
As mentioned, there is a need for a system that is capable of protecting crops and increase the productivity of the food grains. The present aspects, therefore: provides a system for protection of the crops from the intruders (animals, reptiles, pests, and birds) to overcome the aforementioned problems.
The aspects herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting aspects that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the aspects herein. The examples used herein are intended merely to facilitate an understanding of ways in which the aspects herein may be practiced and to further enable those of skill in the art to practice the aspects herein. Accordingly, the examples should not be construed as limiting the scope of the aspects herein.
FIG. 1 illustrates a block diagram of a system for aroma analysis (hereinafter referred to and denoted as “the system 100”). The system 100 may include an electronic nose for analysis and evaluation of the aroma evolving from Ayurvedic formulations. Specifically, the system 100 may include the electronic nose for the analysis and the evaluation of the aroma evolving from volatile organic compounds (VOCs) that may be present in the mixture containing Ayurvedic formulations. The electronic nose may facilitate the reliable method for quality assessment of the ayurvedic formulations by identifying and quantifying the various aroma compounds or VOCs present in the mixture of ayurvedic formulations. The system 100 enables consistent and accurate evaluation of product quality, thereby improving standards in the Ayurvedic industry. The system 100 may provide automated analysis of the aroma compounds that may be present in the ayurvedic formulations. In other words, the system 100 may provide automated analysis of the VOCs present in the mixture of the ayurvedic formulations.
The system 100 may include a plurality of sensors 102a-102n (hereinafter referred to and denoted as “the sensors 102”), an information processing apparatus 104, and an output unit 106. The information processing apparatus 104 may include processing circuitry 110 and a database 108. The sensors 102, the information processing apparatus and the output unit 106 may be communicatively coupled to each other by way of the communication network 112.
The sensors 102 may be configured to sense aromas that may be evolving from a mixture. Specifically, the sensors 102 may be configured to sense the aromas that may be evolving from the one or more volatile organic compounds (VOCs) that might be present in the mixture. The sensors 102 may be metal oxide sensors.
The information processing apparatus 104 may be coupled to the sensors 102. The database 108 may be configured to store one or more predefined readings or values that may correspond to one or more predefined VOCs. The database 108 may further include a calibration data for each sensor of the sensors 102 to enhance the accuracy in VOC quantification.
In some aspects of the present disclosure, the calibration data may include, one or more baseline readings, one or more sensitivity factors, one or more response curves, one or more environmental factors, and drift correction. Aspects of the present disclosure are intended to include and/or otherwise include all calibration data.
The one or more baseline readings may be the initial readings of each sensor of the sensors 102 which when exposed to a clean or known reference air or gas. The one or more baseline readings may serve as a reference point for comparison during subsequent measurements. The one or more sensitivity factors may include varying sensitivities to different VOCs or environmental conditions. The response curve may often exhibit nonlinear responses to changes in VOC concentrations. The Response curves in calibration data describe the relationship between the input (concentration of VOC) and the output of the readings of the sensors 102. The environmental factors such as temperature, humidity, and pressure, which can influence sensor performance. Compensation algorithms based on environmental conditions may be included in the calibration data. Each sensor of the sensors 102 may experience drift, where their responses gradually change even in the absence of changes in the environment. Calibration data may include one or more drift correction to adjust the readings of the sensors 102 and maintain accuracy over extended periods.
The processing circuitry 110 may be configured to receive the one or more readings of the sensors 102. Specifically, the processing circuitry 110 may be configured to receive the one or more readings of the sensors 102 that may correspond to the one or more VOCs that may be present in the mixture. The processing circuitry 110 may be further configured to compare the one or more readings of the sensors 102 with the one or more predefined readings that may be stored in the database 108.
The processing circuitry 110 may employ a comparator unit 116 that may facilitate the comparison of the readings of the sensors with the one or more predefined readings. The processing circuitry 110 may be configured to determine and quantify the one or more VOCs that may be present in the mixture based on the comparison of the one or more readings of the sensors 102 with the one or more predefined readings that may be stored in the database 108. For example, the readings of sensor A are 0.7, 0.5, 0.9, 0.8, 0.6, sensor B 0.6, 0.8, 0.7, 0.9, 0.5, sensor C are 0.8, 0.6, 0.8, 0.7, 0.4, sensor D are 0.9, 0.4, 0.7, 0.6, 0.8 and sensor E are 0.5, 0.7, 0.6, 0.5, 0.9. The processing circuitry 110 may access the predefined readings (or values) for each VOC for example, for Eucalyptol with readings 0.8, 0.6, 0.9, 0.7, 0.5. Now, the correlation of the Eucalyptol with the readings of the Sensor A to Sensor D may be determined based on comparison. Now the sensor with a higher correlation value with the predefined readings may indicate the higher concentration of the Eucalyptol in the mixture and conversely, the sensor with a lower correlation value with the predefined readings may indicate the lower concentration of the Eucalyptol in the mixture.
The processing circuitry 110 may be further configured to combine the one or more readings of the sensors 102 to generate a combined set of readings. Specifically, the processing circuitry 110 may be configured to combine the readings of the sensors 102 to generate a combined set of readings by way of a merging unit 114. The processing circuitry 110 may be configured to identify the one or more characteristics of the one or more VOCs that may be present in the combined set of readings by applying one or more pattern recognition techniques. The processing circuitry 110 may further employ one or more dimensionality reduction techniques to enhance the computational efficiency in pattern recognition.
The one or more dimensionality reduction techniques may include, any one of, or a combination of, a principal component analysis (PCA) technique, a Linear Discriminant Analysis (LDA) technique, A t-distributed Stochastic Neighbor Embedding (t-SNE) technique, and a Non-negative Matrix Factorization (NMF) technique. Aspects of the present disclosure are intended to include and/or otherwise include all other dimensionality reduction techniques, without deviating from the scope of the present disclosure.
In some aspects of the present disclosure, the pattern recognition techniques may include one or more machine learning techniques which may be trained on the readings of the sensors 102 to improve accuracy in quantifying and the identification of the VOCs.
In some aspects of the present disclosure, the one or more characteristics of the one or more VOCs may include, the presence or absence, concentration levels, quality and purity of the VOCs, and temporal dynamics. Aspects of the present disclosure are intended to include and/or otherwise include all other characteristics that may be identified by applying the pattern recognition techniques, without deviating from the scope of the present disclosure.
The output unit 106 may be coupled to the information processing apparatus. The output unit 106 may be configured to display one or more parameters related to the one or more VOCs that may be present in the mixture. The one or more parameters may provide valuable information about the composition, concentration, and characteristics of VOCs present in the mixture.
In some aspects of the present disclosure, the one or more parameters may include, the one or more VOCs, concentration level of the one or more VOCs, Relative Ratios, Quality Metrics, Temporal Trends and classification results. Aspects of the present disclosure are intended to include and/or otherwise include all other parameters that may provide valuable information about the composition, concentration, and characteristics of VOCs present in the mixture, without deviating from the scope of the present disclosure.
In some aspects of the present disclosure, the output unit 106 may include, any one of, a display screen, an LED/LCD panel, a printer, an augmented reality device, and a virtual reality device. Aspects of the present disclosure may be intended to include and/or otherwise include all other output unit that may facilitate the display of the one or more parameters, without deviating from the scope of the present disclosure.
In operation, the sensors 102 may be configured to sense the aroma that may be evolving from the mixture. the readings of the plurality of the sensor is transmitted to the processing circuitry. The readings of the sensors 102 may be processed parallelly with dedicated analog and digital comparator units 116. The reading of each sensor of the sensors 102 may be transmitted to the analog front end (AFE). The AFE may facilitate the processing of the analog signals that may be generated from the sensors 102. The AFE may facilitate amplification, filtering, and other signal conditioning to prepare the signals for further processing. where the readings may be compared with the one or more predefined readings that may be stored in the database 108. Based on the comparison, the one or more VOCs that may be present in the mixture may be identified. Now, merging unit 114 may be configured to combine the readings of each sensor of the plurality of sensors 102 to generate a unified signal which may facilitate the training of a machine learning (ML) model. The output from the merging unit 114 may be transferred to the field-programmable gate array (FPGA) of a Specific Integrated Circuit (APSOC).
FIG. 2 illustrates a process of analyzing the one or more VOCs that may be present in the mixture by the system 100 of FIG. 1, in accordance with the aspect of the present disclosure.
The readings of the sensors 102 may be processed parallelly with dedicated analog and digital comparator units 116. The reading of each sensor of the sensors 102 may be transmitted to the analog front end (AFE). The AFE facilitates the processing of the analog signals that may be generated from the sensors 102. The AFE may facilitate amplification, filtering, and other signal conditioning to prepare the signals for further processing. The merging unit 114 may be configured to combine the readings that may be obtained from the sensors 102 into a unified signal which may facilitate the training of the Machine learning (ML) model. The output from the AFE and the merging unit 114 may be transferred to the field-programmable gate array (FPGA) that may facilitate the customizable digital processing. The comparator that may be present in the FPGA may facilitate comparison of the readings of the sensors 102 against the one or more predefined values. Upon comparison, the comparator may identify the microvariations in the readings of the sensors 102 without any loss. The employment of the analog and digital comparator units may facilitate parallel processing of the readings of the sensors 102. The system 100 may further employ a common heating bed mechanism that may prevent false triggers that may be caused by the setup time variations of the sensors 102. By maintaining a stable environment fluctuation in the readings of the sensors readings due to changes in temperature may be minimized which enhances the reliability of the system 100. The output from both the AFE and the merging unit is transferred to the FPGA block of the Application-Specific Integrated Circuit (APSOC).
FIG. 3 illustrates a flow chart of a method 300 for analysing the one or more VOCs that may be present in the mixture by the system 100 of the FIG. 1, in accordance with the aspect of the present disclosure.
At step 302, the system 100 may be configured to sense the aromas that may be evolving from the mixture. Specifically, the system 100 may be configured to sense the aromas evolving from the one or more volatile organic compounds (VOCs) that may be present in the mixture by way of the sensors 102.
In some aspects of the present disclosure, the sensors may include, any one of, or a combination of the one or more Metal Oxide Sensors (MOS), the one or more Piezoelectric Sensors, the one or more surface Acoustic Wave (SAW) Sensors, one or more Optical Sensors, the Gas Chromatography Sensors, the Mass Spectrometry Sensors, and the one or more Quartz Crystal Microbalance (QCM) Sensors. Aspects of the present disclosure are intended to include and/or otherwise include all the sensors that may be employed by the system to sense the aromas evolving from the mixture, without deviating from the scope of the present disclosure.
At step 304, the system 100 may be configured determine the one or more VOCs present in the mixture by comparing the readings of the plurality of sensors 102 with the one or more predefined readings. Specifically, the system 100 may be configured to determine the one or more VOCs present in the mixture by comparing the readings of the plurality of sensors 102 with the one or more predefined readings by way of the processing circuitry 110.
At step 306, the system 100 may be configured to combine the readings of the sensors 102 to generate the combined set of readings. Specifically, the system 100 may be configured to combine the readings of the sensors 102 to generate the combined set of readings by way of the processing circuitry 110.
At step 308, the system 100 may be configured to determine the composition of the one or more VOCs that may be present in the mixture by applying the one or more pattern recognition techniques to the combined set of readings by way of the processing circuitry 110.
At step 310, the system 100 may be configured to employ the dimensionality reduction technique on the combined set of readings. Specifically, the system 100 may be configured to employ the dimensionality reduction techniques on the combined set of readings by way of the processing circuitry.
At step 312, the system 100 may be configured to display the one or more parameters related to the one or more VOCs. Specifically, the system 100 may be configured to display the one or more parameters related to the one or more VOCs by way of the output unit 106.
The foregoing discussion of the present disclosure has been presented for purposes of illustration and description. It is not intended to limit the present disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the present disclosure are grouped together in one or more aspects, configurations, or aspects for the purpose of streamlining the disclosure. The features of the aspects, configurations, or aspects may be combined in alternate aspects, configurations, or aspects other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention the present disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, configuration, or aspect. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate aspect of the present disclosure.
Moreover, though the description of the present disclosure has included description of one or more aspects, configurations, or aspects and certain variations and modifications, other variations, combinations, and modifications are within the scope of the present disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights that include alternative aspects, configurations, or aspects to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.
As one skilled in the art will appreciate, the system 100 includes a number of functional blocks in the form of a number of units and/or engines. The functionality of each unit and/or engine goes beyond merely finding one or more computer algorithms to carry out one or more procedures and/or methods in the form of a predefined sequential manner, rather each engine explores adding up and/or obtaining one or more objectives contributing to an overall functionality of the system 100. Each unit and/or engine may not be limited to an algorithmic and/or coded form, rather may be implemented by way of one or more hardware elements operating together to achieve one or more objectives contributing to the overall functionality of the system 100. Further, as it will be readily apparent to those skilled in the art, all the steps, methods and/or procedures of the system 100 are generic and procedural in nature and are not specific and sequential.
Certain terms are used throughout the following description and claims to refer to particular features or components. As one skilled in the art will appreciate, different persons may refer to the same feature or component by different names. This document does not intend to distinguish between components or features that differ in name but not structure or function. While various aspects of the present disclosure have been illustrated and described, it will be clear that the present disclosure is not limited to these aspects only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the present disclosure, as described in the claims. ,

Claims:

1. A system (100) for aroma analysis, the system (100) comprising:
a plurality of sensors (102) configured to sense aromas evolving from one or more volatile organic compounds (VOCs) that are present in a mixture;
a database (108) coupled to the plurality of sensors (102), and configured to store one or more predefined readings corresponding to one or more predefined VOCs;
processing circuitry (110) coupled to the plurality of sensors (102) and the database (108), and configured to:
determine the one or more VOCs present in the mixture by comparing readings of the plurality of sensors (102) with the one or more predefined readings;
combine the readings of the plurality of sensors (102) to generate a combined set of readings;
determine a composition of at least one VOC of the one or more VOCs by employing one or more pattern recognition techniques to the combined set of readings; and
an output unit (106) coupled to the processing circuitry (110) and configured to display one or more parameters related to the one or more VOCs.

2. The system (100) as claimed in claim 1, wherein the plurality of sensors (102) are metal oxide sensors.

3. The system (100) as claimed in claim 1, wherein the one or more parameters comprises a concentration level of the one or more VOCs in the mixture, relative ratios, quality metrics, temporal trends, and classification results.

4. The system (100) as claimed in claim 1, wherein the processing circuitry (110) comprises a comparator unit (116) configured to compare the readings of the plurality of sensors (102) with the one or more predefined readings.

5. The system as claimed in claim 1, wherein the database (108) further comprising a calibration data for each sensor of the plurality of sensors (102) to enhance accuracy in quantification of the one or more VOCs.

6. The system as claimed in claim 1, further comprises an analog front end (AFE) to process the analog signals which are generated from the sensors 102.

7. The system (100) as claimed in claim 1, wherein the processing circuitry (110) further comprises a merging unit (114) configured to combine the readings from the plurality of sensors (102) to generate the combined set of readings.

8. The system (100) as claimed in claim 1, the output from the merging unit (114) is transferred to the field-programmable gate array (FPGA) of a Specific Integrated Circuit (APSOC).

9. The system as claimed in claim 1, wherein the pattern recognition techniques include one or more machine learning (ML) techniques to improve accuracy in identifying and quantifying the one or more VOCs.

10. A method (300) for analysing one or more VOCs in a mixture:
sensing (302), by way of a plurality of sensors (102), aromas evolving from one or more volatile organic compounds (VOCs) that is present in a mixture;
determining (304), by way of processing circuitry (110), the one or more VOCs present in the mixture;
combining (306), by way of the processing circuitry (110), the readings of the plurality of sensors (102) to generate a combined set of readings; and
determining (308), by way of the processing circuitry (110), composition of the one or more VOCs by applying one or more pattern recognition techniques;
employing (310), by way of the processing circuitry (110), one or more dimensionality reduction techniques on the combined set of readings to enhance computational efficiency in pattern recognition; and
displaying (312), by way of an output unit (106), one or more parameters related to the one or more volatile organic compounds (VOCs).