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Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies.
The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains.
Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management.
Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.
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Computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.
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Veröffentlichungsjahr: 2024
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PREFACE
This edited book is aimed to be used by undergraduate or postgraduate students, researchers and industry professionals from a wide range of backgrounds like finance, e-learning, agriculture, social media, healthcare and many more.
Artificial Intelligence is a multidisciplinary field with an intrinsic relationship as a subfield to computer science. AI algorithms are preferred for faster and more accurate results, which enables AI to do many complicated and extensive tasks.
Machine learning has been playing a vital role in almost all major fields, such as finance, marketing, security, healthcare, social sciences, automobile, e-learning and many more. Machine learning, one of the most significant advances of this century, refers to an emerging area related to the collection, preparation, analysis, visualization, management, and preservation of both - structured and unstructured data. This book covers a vast range of topics critical to the field of Artificial Intelligence and Machine Learning in an easy-to- understand language.
This edited book will be ideal for both - beginners and experts related to Artificial Intelligence and Machine Learning.
List of Contributors
Artificial Taste Perception of Tea Beverage Using Machine Learning
Amruta Bajirao Patil1,Mrinal Rahul Bachute1,*Abstract
Nowadays, an artificial perception of beverages is in high demand as working hours increase, and people depend on readymade food and beverages. An assurance of quality, safety, and edibility of food and drink products is essential both for food producers and consumers. Assurance of unique beverage taste and consistent taste uniformity creates a distinct identity in the market. India is the second largest tea producer country in the world. Based on geographic location, the tea has a specific flavor and aroma. Artificial Intelligence (AI) can contribute to the feature identification and grading of tea species. The taste, aroma, and color are the three main attributes that can be sensed with the help of E-tongue, E-nose and E-vision, and can be processed further for automatic tea grading. The various potentiometric, voltammetric, Metal Oxide Semiconductor (MOS) and acoustic sensors are available with Principal Component Analysis (PCA). For tea analysis, various reviews are mentioned, like User Experience (UX evaluation, literature review, bibliometric review, and patent review. An in-depth analysis of artificial taste perception using machine learning has been described in the chapter. The topic covered almost all possible approaches to the artificial perception of tea with various interesting facts.
INTRODUCTION
Nowadays, several new diseases are caused due to changing lifestyles. Everyone is just running behind the money by keeping daily basic needs aside or making compromises. There is no use of such blindfolded thinking, as gaining money cannot assure a healthy life. A person can buy required readymade things and products with money, but the quality of the product needs to be verified from time
to time. Today is the world of ready-to-eat or ready-to-drink products. Quality is a big concern for both food producers and consumers.
So, assurance of the quality and taste uniformity of food and beverages is very much necessary. Technology must be incorporated for artificial perception and quality grading as it is directly related to human health awareness and care. Artificial taste perception will set the benchmark for flavors in the food industry. It also assures the safety and edibility of food and beverages. The food and beverage industries depend upon brand popularity and standards to sustain a good rapport with the end user. Businesses can be clogged by goods recall or contamination. The adulteration may harm consumers’ lives badly. Ultimately, “Artificial taste perception and verification” means food brand protection. It also helps reduce wastage and recall due to taste variation [1].
The sensor assembly can be used to monitor results in beverage development, beverage purity authentication, flavor aging analysis, alcoholic, or non-alcoholic drinks, measure the effect of process control variables, establish devotion to government standards, measure levels of spice, flavors, dissolved compounds, and compute taste-masking success. Taste sensors have artificial polyvinyl chloride (PVC)/lipid membranes that react with a test liquor such as beverages, blood, caffeine, etc. The voltage of the lipid membrane varies the sensor output or measurement monitoring potential variation results in measuring the “taste” provided by the production of the chemical substances. With the sensor assembly, multiple sensors provide a change in conductivity and form a complete response [2-5]. Artificial taste perception is about various taste patterns that can be assessed for uniformity.
India is the second largest tea-producing country on the globe. Tea is the national drink of India and its agro-asset as well. According to IMARC (a leading market research company), the global tea market extended a value of US$ 21 Billion in 2020 and expected a Compound Annual Growth Rate (CAGR) of 5.1% during 2021-2026. In India, “the tea board of India” decides the policies for tea farming, manufacturing to marketing. Three important varieties of tea have been produced in India- Black Tea, Green Tea, and Oolong Tea. Each species has its own advantages.
Due to several advantages, tea is treated as a herbal medicine and is used in many health care products. In the last two decades, the second variety of tea has gained more popularity, named green tea. Nowadays the world is growing with the information technology sector, and due to this, desk jobs or motionless jobs are increasing daily. We face many issues, such as increased blood pressure, high blood sugar, excess body fat around the waist, and abnormal cholesterol levels. So, sometimes prescribed tea consumption is helpful for the human body. Daily, it should be monitored for each consumption.
Three primary attributes of tea taste, color, and fragrance are essential. In artificial taste perception, the tea taste is analyzed for its pH parameter [2-5].
Fig. (1) shows the hardware and software requirements of the tea testing. This research aims to automate tea tasting and classification with the help of electronic gadgets and Machine learning algorithms.
Fig. (1)) Hardware and software requirement of artificial taste perception.Fig. (2) shows the classification of tea according to its size. The broken and tiny tea leaves form a mixture of tea dust, making the tea sample dark and have a strong flavor. The other type has been formed by whole tea leaves, which causes a lighter and softer taste of tea liquor. For the tea Industry, it is difficult to predict the actual age of the tea sample. No such electronic technology has been implemented yet. In India, an electronic technique is implemented for artificial odor perception of black tea grading as “E-nose” using a microcontroller by CDAC and Jadavpur University, which is under verification. Alpha Mos is the manufacturing industry of France, which provides a global solution for beverage analysis. In India, tea analysis is not implemented with the machine learning concept [2-5].
The tea grading depends on three essential attributes of tea- its flavor, fragrance, and color. The fusion of all three features still needs to be developed [1-3, 6].
Four essential reviews are considered for this research on the Indian Tea Industry, as listed in Fig. (3).
Fig. (2)) Tea samples classification according to the leaf size. Fig. (3)) Types of reviews required and carried out for tea testing.User Experience (UX) Evaluation
UX evaluation is the reviews from users/customers for their experience with the product. In this case, it was a tea sample. UX evaluation is the study to analyze the target market for various tea brands. This analysis can answer several questions related to tea, such as:
• Who is the tea customer?
• What is the age range of tea customers?
• What is the motivation behind their tea-drinking habit?
• What is the daily usage of tea by them?
• Does the cost of tea products cause a change in the demand for tea?
• Which tea brand is on demand?
• What are the reasons behind it?
• What is the percentage of online and offline customers?
• Why do they prefer that media?
• How can we launch a new service for tea products?
Tea is most popular in the rural area of the North-West side in the age range 18 to 60 years. The motivations vary according to age groups, such as refreshing flavor, diet, daily routine or habit, and herbal medicine. Offline customers are more observed in urban areas, as urban people don’t want to spend time shopping, and the taste and brand are almost fixed. So, online offers or specific motivations can cause changes in the demand from their side. Offline customers are scared about packaging and the quality of the product. They want to physically verify the tea taste and quality and prefer local shops for tea tasting [2, 3].
LITERATURE REVIEW
The literature review is the study of literature available to find the research gaps and identify the related problem that can serve with the help of technology. This kind of study can construct the research objectives and goals. These research objectives lead to research planning and methodology:
An in-depth literature review was done on the artificial perception of tea. The following research gaps were found:
• The sensor’s (taste, color, and odor) response needs to be more consistent, and sensors can be reformed for stability.
• Individual artificial perception is possible for all three tea attributes, but the fusion of them has not been made successful yet.
• The grading of tea can be done according to health requirements as well.
• The age of tea leaves can be determined to find the storage time of leaves.
• Machine intelligence can be possible for the tea grading, which will help to knob the vast production and marketing of the tea.
• In many tea-producing countries like India and Shri Lanka, there is no availability of such in-house equipment that can uniquely represent the product globally for international marketing.
• The tea-producing countries are still dependent on human tea tasters. Sometimes, it is possible due to human limitations like personal biasing, psychological effects; the tea grading prediction may get affected. The sensitivity of human tea tasters may degrade with time.
• The Alpha MOS Headquartered in Toulouse, France, is the solution provider for an artificial taste perception for various agriculture, food, and beverage products. An Artificial Neural Network (ANN) and biochemical sensors have created a digital signature in these solutions.
E-tongue, E-nose, and E-vision are artificial intelligent sensor systems that can predict food or beverage taste, smell, and color or beverages [2-5].
The human nose can sense the smell of gases, but the response is dependent and biased. Also, verifying the evaluation of response to toxic gases is not possible with the human nose. In an E-nose, the gas molecules react with the sensing material of gas sensors and cause a change in conductivity. This change is analyzed with the help of pattern recognition algorithms, and gas classification and grading take place accordingly. Some of the available conventional types of equipment are GC-MS, High Performance Liquid Chromatography (HPLC), and Fourier Transforms Infrared (FT-IR) spectrometry. These types of equipment need skilled operators, and their response time is also high. E-noses are preferable as, without any human intervention, the response is predictable with less time with the effective recording [7].
Many gas sensors are available in the market and they are classified according to the sensing material used. The types are Conducting Polymers (CP), Metal-Oxide Semiconductors (MOS), Quartz Crystal Microbalance (QCM), and Surface Acoustic Wave (SAW) sensors [8].
Metal Oxide Semiconductor (MOS) Sensors
Sensing material is of two types-1) reduction and 2) oxidation. The MOS sensors are classified into n-mos and p-mos as per the sensing material [8]. The n-type sensors have formed with zinc, tin, or iron oxides that react mainly to reducing compounds (e.g., H2, CH4, CO, C2H5, or H2S). The p-type sensors have formed from oxides of nickel oxides or cobalt oxides that react mainly to oxidizing compounds (O2, NO2, and Cl2) [9, 10].
(1)(2)Equations (1) and (2) represent the reactions occurring between materials and gases, where he is the electron of oxide, R(g) is reducing gas, ‘g’ is gas, and ‘s’ is sensing material.
In equation (1), Oxygen from the environment is integrated with the surface of the sensor’s semiconductors lattice, fixing its electrical resistance to a stable state. In equation (2), the reducing gas molecules react with the sensing material surface (oxidation/reduction) with the integrated oxygen species causing a change in the electrical properties, like capacitance and sensor resistance [4, 5, 8].
Conducting Particle (CP) Sensors
CP sensors consist of conducting particles like polypyrrole, polyaniline, and polythiophene interspersed in an insulating polymer matrix. The reaction happens when the sensing material comes in contact with gas formed by the test sample. This reaction causes doping of sensing material that will transfer electrons to or from the gas analytes. The conductivity will get affected and be further used as a measurement attribute. MOS sensor needs extra sensing element heating and consumes higher power than the CP sensors [11]. CP sensors are more durable. CP sensors are vulnerable to humidity and require a high-temperature environment for reaction happenings [8, 11].
Acoustic Wave Sensors
Acoustic wave sensors are formed with piezoelectric substrates like quartz crystal, ZnO, and lithium niobite. The substrate is encrusted with sensing material like polymeric film and two transducers- input and output). The reaction between gas and sensing material causes a change in the mass of the gas-sensitive membrane. This effect creates a shift in SAW velocity and attenuation [8].
Acoustic wave sensors are of two types- Surface Acoustic Wave (SAW) transducers sensors and Bulk Acoustic Wave (BAW) transducers sensors. The sensors are shown in Figs. (4 and 5). The wave propagates on the surface of the substrate is the SAW type sensor and when it propagates through the substrate is a BAW type sensor [8].
Fig. (4)) A SAW Sensor (Source: [8]). Fig. (5)) A BAW Sensor (Source: [8]).Quartz crystal microbalance (QCM) sensors were used to detect tea aroma for the chemical gases linalool, geraniol, linalool oxide, Methyl salicylate, and Trans-2- hexenal in the process of black tea fermentation [12].
(3)Equation (3) is the propagation delay variation concerning wave number, where
γ is the complex propagation coefficient,
k0 is the wavenumber in an unperturbed state,
(Δm) is the mass change,
(ΔPmec) is the change of mechanical factors (e.g., viscosity and elasticity),
(ΔPele) are the electric factors (e.g., conductivity and permittivity),
(ΔPenv) are the environmental factors (e.g., temperature and humidity).
Taste sensors are chemical sensors.
Potentiometric Sensor
The two electrodes dipped into the test liquid, of which one is the Working Electrode (WE), and the other is the Reference Electrode (RE). The RE is submerged in the reference solution, and its voltage of it is always constant. The WE voltage depends on the concentration of a test liquid which creates the potential difference between WE and RE [8, 13, 14].
(4)The electrode potential (E) is the function of the concentration of ratio of the oxidized (Co) to the reduced form (Cr) of the analyte. Equation (4) represents the Nernst equation, where Eo (V) is the potential of the electrode at standard conditions, and T(°C) is the temperature [8].
Fig. (6) shows the potentiometric taste sensor. One end of the WE is covered by an ion-selective membrane sensitive to chemical concentration. Due to ionization, the potential difference between WE and RE is measured in terms of voltage. Glass membrane, crystalline/solid-state membrane, liquid membrane, and polymer membrane are commonly coverings used in potentiometric E-tongue.
Fig. (6)) The Potentiometric Sensor (Source: [8]).Potentiometric sensors are available for a wide variety of applications. The major disadvantage of this sensor is that it is susceptible to temperature.
Voltammetric Sensor
The voltammetric sensor works the same as the potentiometric sensor. It requires WE and RE [8].
(5)The potential difference between electrodes is measured in terms of current. The relation between potential E and current I is given in equation (5).
Rs is the resistance of the test chemical, t is the time elapsed after the onset of a voltage pulse, and B is an electrode-related equivalent capacitance constant. The two types of pulse voltammetry, Large Amplitude Pulse Voltammetry (LAPV) and Small Amplitude Pulse Voltammetry (SAPV), are used in voltammetry E-tongue [14].
Commercial Solutions
The pictures of the Alpha MOS solution are given below in Fig. (7).
Fig. (7)) ASTREE electronic tongue (Alpha MOS, France) (Source: [15, 16]).ASTREE E-tongue is based on the principle of potentiometric measurement using the WE and RE. The electrodes are cross-sensitive to various taste-forming molecules. The analytical conditions have been mentioned in the manual of ASTREE E-tongue (Alpha MOS, France), such as sample volume is 100 mL, Acquisition time is 120 sec, Ambient temperature is required, and the time between 2 analyses is 180 sec. Fig. (8) shows the result panel where the X-axis is for tea taste type and Y-axis is for steeping time to detect taste.
Fig. (8)) Result panel for tea grading (Source: [15, 16]).Heracles NEO E-nose is the commercial E-nose available by Alpha MOS for aroma characterization. The E-nose automatically heated for a few minutes to obtain the aromatic compounds. The ultra-fast Gas Chromatography (GC) is used to separate molecules in the gas mixture. Fig. (9) shows the Ultrafast GC-based Heracles NEO electronic nose.
Fig. (9)) Ultrafast GC-based Heracles NEO electronic nose (Source: [4, 17]).The analytical conditions of the Heracles E-nose are mentioned in Table 1.
Fig. (10) shows the classification of the coffee and odor map.
Fig. (10)) Principal Component Analysis (PCA) of coffee obtained with Heracles NEO E-nose (Source: [2, 16]).Color and Image Sensors
The system is established to detect impurities in water, based on E-tongue and E-nose with additive wavelet transform and homomorphism image processing. This electronic sensor system can extract the required information from a water sample. Image enhancement is beneficial to advance the visual quality of an image. The detection of water impuritiesmentioned above uses infrared image processing. Infrared image processing consists of hefty dark areas and tiny details. An additive wavelet transform is used as a decomposition algorithm to separate these small image information details into several frequency sub-bands. In addition, homomorphic enhancement algorithms are used for changing these small details to illumination and reflectance components, and then reflectance components are amplified, showing the details correctly. With this, infrared image reconstruction is performed at the end, and by using the MATLAB tool, the Peak Signal to Noise Ratio (PSNR) is determined. Pure water PSNR is very high (62.59 dB), and in the water, with increasing percentage impurities, PSNR becomes low [7].
PATENT REVIEW
A variety of devices and methods are developed for multiple causes in food industries. The inventors and manufacturers of these devices and processes had registered them as Intellectual Property Rights (IPR) and patents. Intellectual property (IP) and patent registration save the rights of the first actual inventors and give them the liberty to create wealth from their inventions. The patent life duration is 20 years; in that duration, the inventor has to use rights granted by law to generate wealth from invention commercialization.
There are standard patent databases like US PTO, WIPO, European Patent Office, and Indian Patent Office to find genuine inventions. Some online accessible databases are available for patent searches, such as www.worldwide.espacenet .com and www.lens.org.
The patent survey for artificial tea tasting was done atwww.lens.org for the query keywords “artificial intelligence and tea” on 17 May 2021. A total of 10 related patents were found on the tool. For the other keyword “artificial taste perception of tea”, overall 24 patents were received on the lens portal.
For any patent, all the details like patent status- published and filed dates, family patents, cited works, applicants, inventors, and full patent documents are available on the lens portal and all those are openly accessible. Suppose any researcher wants to start research for any specific objective. In that case, he needs to survey the prior work done in the research field and find the research gaps that should be explored more, having a substantial economic impact on the related industry.
Also, direct patent analysis is available on the lens portal and that is helpful to new researchers to get the research support connections for collaboration, for guidance and research funding.
BIBLIOMETRIC REVIEW
The standard repositories like Web of Science, Scopus, IEEE Xplore, ResearchGate, and J-gate collect the research data from various fields. The warehouses are mainly of two types, subscription-based and open-access. The subscription-based databases are generally paid for by users or researchers who want to read current techno-scientific documents such as journal papers, conference proceedings, books, book chapters, editorial notes, and newsletters. The subscription-based databases are web platforms for research authors wishing to publish their research in the public domain without fees. The second type of database is open access, which demands a certain amount of fees from authors for review and document maintenance and publishes articles accessible to readers who have registered under the platform through research organizations and educational institutes. The need and utilization of various processors have been studied by referring to other papers [2, 5 13, 14, 18, 19, 21].
The research topic “An artificial perception of tea” is reviewed under the Scopus repository. A total of 602 publications were found for the keyword “tea”, out of which 12 are for an “artificial perception of tea”. The survey duration was of last decade. In the Scopus portal, various filters are available to filter out unwanted material such as language, document types, time, author-wise, country-wise, funding sponsor, and organization affiliation.
This kind of search is helpful to find the research trend, state of the art, and prior work done in the research domain. In the Scopus database total of 489 articles are available for tea quality evaluation, 57 conference papers, and 31 review papers contribute to the environment mainly.
“Molecular definition of black tea taste using quantitative studies, taste reconstitution, and omission experiments,” by Schubert S. and Hofmann T., published in 2005 in the journal “Journal of Agricultural and Food Chemistry,” had been cited 227 times. A detailed bibliometric review is given in the paper [1, 20], which covers current research trends in the tea industry, geographical research locations, Keyword analysis and mapping, word network analysis using various tools like VOSviewer, Google spreadsheet, and some online tools like- https://medialab.github.io/sciencescape/scopus2net/. The research topic “Artificial Taste Perception of Tea Beverage using Machine Learning” consists of three important terms: Tea Beverage, Artificial taste perception, and Machine Learning [1-3].
Tea Beverage
India is the more prominent producer, consumer, and exporter of tea. Tea is the national agro-asset of India and its national drink as well.
As India is a developing country, a new era of Artificial Intelligence (AI) has yet to come. A crucial need of any developing country is to establish standards for market products and services. In India, the quality of tea and tea standards are decided by the ‘Tea Board of India’. The tea Board of India also decides the tea cultivation and marketing strategies. However, once the tea is sold in case of loose tea, the problem of contamination and quality degradation is prevalent. The proposed research uses a model to identify tea types and capture impurities based on their pH value. In between trading chains, such quality checks can reduce the chances of adulteration [1-3]. Table 2 describes the comparison between traditional and AI approaches in tea taste perception.
There are three main types of tea found in India- Black, Green, and Oolong. Each of them has its own advantage. So, the usage and demand of tea depend on these advantages:
i. Black tea has antioxidant properties, and it helps to reduce chronic diseases and improves health and immunity. It also improves heart health, reduces high cholesterol, reduces blood sugar, reduces chances of stroke and cancer, and reduces stress. Black tea is fermented and is highly demanded [1-3].
ii. Green Tea is used to increase metabolism. It also improves brain function. Green tea is semi-fermented and has been famous for the last decade.
iii. Oolong Tea is good for digestion and is generally drunk after a heavy meal. It is prepared without milk and sugar. Oolong tea is unfermented and is rarely used.
Artificial Taste Perception
The capability of the human tongue to analyze the taste of different flavors has been achieved with the help of sensors and software algorithms. The proposed hardware will be able to find different concentrations’ effects on taste and additives effect and will also provide liquid parameters with taste mapping [1-3].
Machine Learning (ML)
It is the ability developed in the machine, and with the help of that ability, it can achieve self-learning, and then it will be proficient at making a decision based on that knowledge of newly acquired similar nature inputs for their categorization. There are four prime ML approaches- Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning, as mentioned in Fig. (11). According to the complexity of categorization, the software follows the approaches. In supervised learning, input and output attribute mapping is directly possible with some mathematical and logical relationships, and the output is in terms of error percentage and standard deviation. In the case of unsupervised learning, the clustering of data or association of frequent co-occurrence of data has been analyzed in terms of scatter and purity or support and confidence of the model. Semi-supervised is the middle approach between supervised and unsupervised learning, and it follows both these approaches partially. So, units such as error, standard deviation, scatter and purity, or support and confidence are also shared. The last approach is reinforcement learning, where a system can independently sense the environment, self-learn, and then apply solutions for similar problems. The outcome of this approach is measured in terms of cost and rewards [19].
Fig. (11)) Machine learning approaches.Initially, each ML model started with supervised learning and gradually grew towards reinforcement learning. For each approach, two phases are compulsorily followed- data verification and data validation. This verification comes under the development of the model, and validation comes under the testing of the model. Review at each stage is very much required as the full model functionality depends on data verification and validation. After the development of the ML model, the training criteria are decided by developers as per the requirement of result coverage. Accordingly, the data is ordered for training and testing [1-3]. So, testing validates the ML functionality in terms of the confusion matrix. The confusion matrix is a matrix that will plot the ML-predicted results against the actual results. Various parameters such as accuracy, precision, recall, and F1 factors have been derived from the confusion matrix to analyze the performance of the ML model [1-3]. Fig. (12) describes Machine Learning Model Building. It covers the software process flow for building the model for tea grading as well as the evaluation of the model for various parameters.
Fig. (12)) Machine Learning Model Building.IMPLEMENTATION
Experiment Requirement
The basic hardware and software requirement of this project is given in Fig. (13). It includes four different tea species as test samples, namely Orange Pekoe (OP-black tea), Cut-Tear-Curl (CTC-black tea), Green Tea, and Energy Drink Mix.
Fig. (13)) Tea species under test with the hardware requirement.Fig. (13) also describes the hardware that includes the pH sensor with its signal conditioning unit (DIY more make), Arduino UNO, Universal Serial Bus (USB) cable, and some connecting wires. Fig. (14) indicates liquefied tea samples, which will be tested further for their pH value. The Arduino has its own open-source Integrated Development Environment (IDE). The IDE accepts the input voltage signal from the signal conditioning board and converts it into an equivalent pH value [1-3]. Fig. (15) shows the connection of the hardware with a laptop.
Fig. (14)) Tea liquid samples under test. Fig. (15)) Sensory Mechanism for pH value detection.Table 3 shows the ML Dataset for the pH attribute of four different tea species, and Table 4 shows the ML Dataset for the pH attribute of four different tea additives with temperature effect.
Proportion Sample Sets
Set 1 – 10 g of tea boiled with 100 mL water for 5 minutes.
Set 2 – 10 g of tea boiled with 170 mL water for 5 minutes.
Set 3 – 10 g of tea boiled with 230 mL water for 5 minutes.
Set 4 – 10 g of CTC tea boiled with four different additives and 100 mL water for 5 minutes.
Results
These datasets are processed with various ML algorithms like k-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), Decision tree, and Logistic Regression for tea species and additives separately. Figs. (16 and 17) demonstrate ML Results of tea species grading and tea additives, respectively, given by the ML mentioned above classifiers. Figs. (18 and 19) show the plotting of Voltage Vs. pH for the tea species and additives, respectively, with the k-NN classifier. Tables 5 and 6 show color indicators used in Figs. (18 and 19), respectively.
Fig. (16)) ML Results of grading tea species.The k-NN classifier accuracy is 98% for the training set and 100% for the test set for tea grading, whereas it is 98% for the training set and 93% test set for tea additives. It is the most suitable and efficient ML algorithm for this problem. Its Implementation is easy in Python.
The aim of this experiment is to create a standard pH dataset for different tea species satisfied here, and same data is utilized for ML training and evaluation. This kind of solution is required at the individual level and also in-between consumer trading.
Fig. (17)) ML Results of grading tea Additives. Fig. (18)) Tea species grading -plot by K-NN classifier. Fig. (19)) Tea additives grading -plot by K-NN classifier.
