Dear Sudhakar Muthusamy

Various laboratory detection methods for adulteration in food goods include High Performance Liquid Chromatography (HPLC) for analysis, HPTLC for inquiry, GCMC, LCMS-MS for chemical analysis, and many others.

I suggest you to go through these articles:

1. https://indiametrics.com/tackling-food-adulteration-with-artificial-intelligence/

2. https://www.researchgate.net/publication/344511903_A_Qualitative_Analysis_of_the_Machine_Learning_Methods_in_Food_Adultery_A_Focus_on_Milk_Adulteration_Detection

3. Article Food Adulteration Detection using Artificial Intelligence: A...

4. https://ift.onlinelibrary.wiley.com/doi/full/10.1111/1541-4337.12492

Kind Regards

Qamar Ul Islam

Qamar Ul Islam Thank you sir... ya i am surveying papers too, Are there any rapid detection mechanisms with sensors connected to ML algorithms, up to date?

Dear Sudhakar,

Yes, many algorithms are already been used by several researchers and are available in a published source.

https://www.turcomat.org/index.php/turkbilmat/article/view/6790

Article E-sensing and nanoscale-sensing devices associated with data...

https://www.sciencedirect.com/science/article/abs/pii/S092540052031474X

https://www.sciencedirect.com/science/article/abs/pii/S0308814621005537

you may build your own too.

Good luck!

Imtiaz Husain Thank you sir, what about Pesticide residue detection - I am already collecting papers, but even then I am in a dilemma, of how to extend it to research point of view as many IoT related pesticide contamination, detection projects are available - proposals have been built. could you help me to decide how to extend them to research purpose or what methodology it would be best and worthful.

Sure, would you kindly share a brief synopsis (the plan of work) it will help me to advise you more precisely?

Further, have you enrolled in any research courses? or just for publications purposes.

Imtiaz Husain yes sir. I have enrolled for full time Researcher at VIT, vellore. I need to start up research. I have selected the domain as IoT. I am in a dilemma to choose IoT with medical Image processing or Agri based work like pesticide detection. but since I have personal interest on nature and Food specific, I thought that I would contribute for society or something useful. today's world with filled with contaminated food mostly, it creates lot of health issues for human as well as environment.

If you click on the following link, you will find that this is one of the relevant items on food adulteration and how to detect it:

Andreas Schieber, in Modern Techniques for Food Authentication (Second Edition), 2018

https://www.sciencedirect.com/topics/food-science/food-adulteration Abstract Food adulteration occurs globally and in many facets and affects almost all food commodities. Adulteration not only constitutes a considerable economic problem but also may lead to serious health issues for consumers. As the methods of adulterating foods have become more sophisticated, very efficient and reliable techniques for the detection of fraudulent manipulations are required. Based mainly on relevant review articles, this chapter provides a brief overview of the history of food adulteration and reviews select analytical methods that have been used for food authentication, including chromatography, spectroscopy, stable isotope analysis, metabolomics, proteomics, enzymatic methods, and DNA-based techniques. For each method, recent applications in food authenticity control are presented. As studies indicate that adulteration is even increasing, it is concluded that more research efforts need to be made to protect consumers from health risks and honest producers from economic loss. View chapter/Purchase book

https://www.sciencedirect.com/topics/food-science/food-adulteration

Nancy Ann Watanabe Thank you mam, I'll go through it

This is the Abstract and also the link which lead you to an an interesting scientific article on Artificial Intelligence for the detection of adulteration which is posted on ResearchGate: Article Food Adulteration Detection using Artificial Intelligence: A...

Abstract

"Food Adulteration is a deceptive act of misleading food buyers for economic gain. It has been a major concern due to its risk to public health, reduction of food quality or nutritional value. It is a food fraud that has incensed the food industry and has attracted the attention of the community since the last century. To ensure consumer protection against fraudulent activities, authentication of food and the detection of adulterants in various food items should be taken into consideration. Artificial Intelligence has been proved to be an advanced technology in food science and engineering. In this paper, we intend to proclaim the role of artificial intelligence in food adulteration detection in a systematic way. The potential for machine learning and deep learning in food quality has been analyzed through its applications. Various data sources that are available online to detect food quality have been discussed in this review. The different techniques used to detect food adulteration and the parameters considered while evaluating the food quality have been highlighted. The various comparisons have been done among the state-of-the-art methods along with their datasets sets and results. This study will assist the researchers in analyzing the best method available to detect food quality. It will help them in finding the food products that are studied by different researchers along with relevant future research directions.

Article Food Adulteration Detection using Artificial Intelligence: A...

This is yet another resource on the topic of Artificial Intelligence for the detection of adulteration and how to carry out scientific research:

https://www.sciencedirect.com/science/article/abs/pii/S1466856420304732

📷Innovative Food Science & Emerging Technologies

Volume 66, December 2020, 102527

📷Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration

Author links open overlay panel: GokceIymena Gizem Tanrivera Yusuf Ziya Hayirlioglua Onur Ergenabcd

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Cite

https://doi.org/10.1016/j.ifset.2020.102527Get rights and content

Highlights

•

A simple artificial intelligence based food fraud, or economically motivated adulteration, technology is introduced.

•

An innovative sound vibration based detection mechanism was designed.

•

Very good performance achieved to verify high quality products with no additives, as well as organic food products.

•

Proof of concept is developed towards a simple smart phone application to make every consumer an inspector of their own food.

Abstract

The demand for high-quality food products is increasing globally at unprecedented rates in response to growing health concerns and consumer awareness about healthy food options. Yet, the tools for determining food quality remain restricted to well-equipped laboratories, not readily accessible to consumers. Unfortunately, the current inspection mechanisms are limited and cannot keep track of all the products continuously, which exposes weakness in the system towards adulteration, falsification, and mislabeling products. Consumers rely on manufacturer labeling alone, with no convenient and user-friendly tool to confirm quality, especially for organic products. The advancement of Artificial Intelligence (AI) provides an opportunity for these tools to be developed. In this study, we demonstrate that simple sound vibrations traversing the food products can be used in conjunction with deep learning models to verify high quality products with no additives, as well as organic food products. Our neural network models, namely Parallel CNN-RNN and CRNN, achieve high accuracy on the defined classification tasks. To our knowledge, this is the first report of an AI-based tool utilizing simple sound vibrations to identify adulteration in food products.

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Keywords

Artificial intelligence

Machine learning

Food fraud

Adulteration

Mobile application

Organic food products

Frequency response

MFCCs

Parallel CNN-RNN

CRNN

View full text

© 2020 Elsevier Ltd. All rights reserved."

Source link: https://www.sciencedirect.com/science/article/abs/pii/S1466856420304732

This Abstract for an article in the Journal of Food Processing and Technology is accessible at the following link: Article Comparative migration kinetics into different food simulants...

Detecting, identifying, and quantifying adulterations in foods through computational artificial intelligence

9th Euro-Global Summit & Expo on Food & Beverages July 11-13, 2016 Cologne, Germany

John C Cancilla, Regina Aroca-Santos, Gemma Matute, Enrique S Pariente, Kacper W Wierzchos and Jose S Torrecilla

Complutense University of Madrid, Spain University of South Florida, USA

Posters & Accepted Abstracts: J Food Process Technol

Abstract :

In the past, many food sectors have suffered the impact of fraudulent activities which involve the adulteration of foods, which not only endanger the economy and prestige of the producers or the quality of the goods, but in occasions the health of the consumers. One of the main fields our research group focuses is on the design of different mathematical tools based on algorithms such as artificial neural networks (ANNs), which are a relevant part of the computational artificial intelligence field, to aid in this particular sense. They allow the detection of adulterated foods, the identification of particular adulterants, and even their quantification. In this work, an array of applications will be covered, as they are designed with the intention to help protect the integrity of the food sector. Specifically, regarding extra virgin olive oil (EVOO), various tools based on supervised ANNs to model spectroscopic data have been created and optimized to quantify and identify lower grade olive oils (pomace and refined) or oils with different botanical origins (corn and sunflower). On the other hand, non-supervised ANNs combined with calculations based on chaotic parameters have also been employed to detect edible oils that are used to adulterate EVOO. Finally, an assortment of linear and non-linear mathematical models have been employed to characterize binary mixtures of 6 vinegars with different origins (red wine, white wine, molasses, apple, apple cider, and rice). All of these models have been validated accordingly, resulting in reliable and accurate tools that can be useful for many phases during the distribution chain of foods, ranging from producers all the way to the final consumers.

Biography :

Email: [email protected]

SOURCE LINK: Article Comparative migration kinetics into different food simulants...

You may want to access this article in _Nature_:

https://www.nature.com/articles/s41598-020-68156-2#content

"nature

Article Open Access Published: 08 July 2020 A machine learning workflow for raw food spectroscopic classification in a future industry Panagiotis Tsakanikas, Apostolos Karnavas, Efstathios Z. Panagou & George-John Nychas Scientific Reports volume 10, Article number: 11212 (2020) Cite this article3558 Accesses 18 Citations 5 Altmetric Metrics details / Abstract https://www.nature.com/articles/s41598-020-68156-2#content

This is a brief extract from a scientific research article that should prove helpful:

"📷

Food Eng Rev. 2021 Aug 9 : 1–42.

doi: 10.1007/s12393-021-09290-z [Epub ahead of print]

PMCID: PMC8350558

Application of Artificial Intelligence in Food Industry—a Guideline

Nidhi Rajesh Mavani,1 Jarinah Mohd Ali,📷1 Suhaili Othman,1,2 M. A. Hussain,3 Haslaniza Hashim,4 and Norliza Abd Rahman1

Author information Article notes Copyright and License information Disclaimer

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Abstract

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Keywords: AI in food industry, Review, NIR, Food sensors, Model development guidelines

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Introduction

Artificial intelligence (AI) is defined as a field in computer science that imitates human thinking processes, learning ability, and storage of knowledge [1, 2]. AI can be categorized into two types which are strong AI and weak AI. The weak AI principle is to construct the machine to act as an intelligent unit where it mimics the human judgments, while the strong AI principle states that the machine can actually represent the human mind [3]. However, strong AI does not exist yet and the study on this AI is still in progress. The gaming industry, weather forecasting, heavy industry, process industry, food industry, medical industry, data mining, stem cells, and knowledge representation are among the areas that have been utilizing AI methods [4–11]. AI has a variety of algorithms to choose from such as reinforcement learning, expert system, fuzzy logic (FL), swarm intelligence, Turing test, cognitive science, artificial neural network (ANN), and logic programming [3]. The alluring performance of AI has made it the most favorable tool to apply in industries including decision making and process estimation aiming at overall cost reduction, quality enhancement, and profitability improvement [7, 12].

As the population in the world is rising, food demand is predicted to rise from 59 to 98% by 2050 [13]. Thus, to cater for this food demand, AI has been applied such as in management of the supply chain, food sorting, production development, food quality improvement, and proper industrial hygiene [14–16]. Sharma stated that the food processing and handling industries are expected to grow about CAGR of 5% at least until 2021 [15]. ANN has been used as a tool in aiding real complex problem solving in the food industry according to Funes and coworkers [17], while based on Correa et al., the classification and prediction of parameters are simpler when using ANN, which leads to higher usage demand of ANN over the past years [18]. Besides, FL and ANN have also acted as controllers in ensuring food safety, quality control, yield increment, and production cost reduction [19, 20]. AI technologies have also known to be beneficial in food drying technology and as process control for the drying process [21–23].

Previous studies have shown many usages of AI in food industries focusing on individual target and aims. A study has been conducted on the various ANN applications in food process modeling where it has only highlighted the food process modeling using ANN [24]. Apart from that, the implementation of AI such as ANN, FL, and expert system in food industries have been reviewed but specifically focusing on the drying of fresh fruits [23]. A review has been conducted on how food safety has been one of the main concerns in the food industry which leads to the development of smart packaging systems to fulfill the requirements of the food supply chain. Intelligent packaging monitors the condition of foods to give details on the quality of the food during storage and transportation [25]. Another study reviewed on intelligent packaging as a tool to minimize food waste where about 45 recent advances in the field of optical systems for freshness monitoring have been reported. Meat, fish products, fruits, and vegetables were covered in the study as they are the most representative fields of application [25]. Few different studies have been conducted on intelligent packaging, and these studies proved that the usage of intelligent packaging systems plays an important role in the food factory in the context of the food chain as they are able to monitor the freshness of food products and crops [23, 26–30].

There are also several other studies that have been conducted on the application of AI and sensors in food; however, the coverage is rather limited. Therefore, a comprehensive review that assembles all AI applications in the food industry as well as its combinations with appropriate sensor will be a great advantage, all of which are unavailable as to the knowledge of the author. Such review will assist in gathering the advantages, limitations, and methodologies as a one-stop guideline and reference for food industry players, practitioners, and academicians. To be exact, different types of AI and their recent application in food industries will be highlighted which comprises several AI techniques including expert system, fuzzy logic, ANN, and machine learning. In addition, the integration of AI with electronic nose (E-nose), electronic tongue (E-tongue), near infrared spectroscopy (NIRS), and computer vision system (CVS) is also provided. This paper is organized as follows. The introduction of AI is explained in the first section followed by the application of different types of AI in the food industry. Following that, the fusion of the AI with the external sensors in the food industry is presented. In the latter part, a critical review is conducted where discussion on the main application of the AI algorithms in the food industry is carried out. A flowchart is presented to assist the researchers on establishing the most appropriate AI model based on their specific case study. Then, the trends on the application of AI in the food industry are illustrated after that section. Finally, a brief conclusion is discussed in this paper.

AI in Food Industry

The application of AI in the food industry has been growing for years due to various reasons such as food sorting, classification and prediction of the parameters, quality control, and food safety. Expert system, fuzzy logic, ANN, adaptive neuro-fuzzy inference system (ANFIS), and machine learning are among the popular techniques that have been utilized in the food industries. Prior to AI implementation, studies related to food have been going on over the years to educate the public about food knowledge as well as to improve the final outcomes related to food properties and the production of foods [31–36]. A lot of benefits can be obtained by using the AI method, and its implementation in the food industry has been going on since decades ago and has been increasing till today [37–39, 31, 32]. Nevertheless, this paper will focus on the application of AI in food industries from the year 2015 onwards since tremendous increase and innovation are seen in the implementation recently. It is worth noting that several methods such as partial least square, gastrointestinal unified theoretical framework, in silico models, empirical models, sparse regression, successive projections algorithms, and competitive adaptive reweighted sampling which have been used for prediction and enhancement of the food industries are not discussed here; instead it is narrowed down to the wide application of AI in the food industry.

Knowledge-based Expert System in Food Industry

The knowledge-based system is a computer program that utilizes knowledge from different sources, information, and data to solve complicated problems. It can be classified into three categories which are expert systems, knowledge-based artificial intelligence, and knowledge-based engineering. The breakdown of the knowledge-based system is presented in Fig. 1. The knowledge-based expert system which is widely used in the industries is a decisive and collective computer system that is able to imitate the decision-making ability of human expert [40]. It is a type of knowledge-based system that is known as among the first successful AI models. This system depends on experts for solving the complicated issues in a particular domain. It has two sub-systems, which are knowledge base and inference engine. The facts about the world are stored in the knowledge base, and the inference engine represents the rules and conditions regarding the world which are usually expressed in terms of the IF–THEN rules [41]. Normally, it is able to resolve complicated issues by the aid of a human expert. This system is based on the knowledge from the experts. The main components of the expert system (ES) are human expert, knowledge engineer, knowledge base, inference engine, user interface, and the user. The flow of the expert system is shown in Fig. 2.

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Open in a separate window

Fig. 1

Knowledge-based system

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Fig. 2

Expert system

The food industry has been utilizing ES for various objectives as this system is proven to be useful especially in the decision-making process. The knowledge-based expert system has been applied in white winemaking during the fermentation process for the supervision, intelligent control, and data recovery [42]. Apart from that, a web-based application was developed by implementing the ES to calculate the nutritional value of the food for the users, and the development of ES was able to help the SMIs in obtaining the details required for the qualification in obtaining the food production certificates [43]. Food safety is very important in the food industry,thus, the application of ES that is linked closely to food safety has been used extensively ranging from process design, safety management, quality of food, and risk assessment [44]. Furthermore, a prototype information technology tool and guidelines with corrective actions that considered ES in the model were developed for the food industry where few essential factors such as food safety, nutrition, quality, and cost were studied [45]. In addition, a digital learning tool, namely, MESTRAL, was developed to assist people in food processing by using models developed from research in food science and technology and simulators. This tool is based on the knowledge engineering and reflected real applications which can be mapped with the system scale and knowledge frameworks [46]. A comprehensive review was conducted by Leo Kumar on the application of the knowledge-based expert system in manufacturing planning. The paper has also discussed the utilization of ES in decision making in three wide areas which are the process planning activities, diverse applications, and manufacturing planning [41]. Moreover, Table ​Table11 gathers some of the recent application of ES in the food industry ranging from the raw material to the final production as well as the food safety.

Table 1 - Click on the link to view this table and the entire article.

Source link: Article Application of Artificial Intelligence in Food Industry—a Guideline

This looks like a promising resource which combines machine learning and the detection of adulteration: The entire article is at: https://www.ripublication.com/ijaerspl2019/ijaerv14n5spl_05.pdf

"Detecting food adulterants using the concepts of machine learning"

Anjana. M. P Student, 2nd year, Department of Information Technology, LICET Pradeepa. K Student, 2nd year, Department of Information Technology, LICET Abstract -Food adulteration is a process in which the quality of food products is degraded by the addition or substitution of certain chemicals which are injurious for human health. It not only includes the intentional addition or substitution of the ingredient but also the contamination during the period of growth, storage and distribution of food products. Adulteration has become a big business. We belong to a land where our ancestors taught that “food is medicine.” But the reality is that the fruits and vegetables that we consume today no more have vitamins and minerals in them rather they are injected and polished with poisonous chemicals. Adulterants in adulterated food have resulted in a number of diseases and premature deaths. This paper focuses on the detection of adulterated food products using the techniques of image processing using the techniques of image processing using the methods of machine learning like open CV and matplotlib. Index Terms: Machine Learning, Open CV, Matplotlib and Adulteration detection. I. INTRODUCTION [1]In a recent report, the Public Health Foundation of India attributed 80 percent of all premature deaths in contaminated food and water. Food adulteration in India starts from the field itself where fertilizers and pesticides are overused. But pesticide residues are not the only problem. Fruits and vegetables once contained vitamins and minerals in them. But now they are filled with poisonous chemicals like endo-sulphite which spoil our health. [2]Chemicals like carbide used for quick ripening of fruits faster have created a number of health hazards. [3]The following are a few health hazards due to adulterated food products  Mineral oil may be added to edible oil and fats and can cause cancers.  Lead chromate added to turmeric powder and spices can cause anemia, paralysis, brain damage and abortions.  Lead added to water, natural and processed food can lead to lead poisoning. Lead poisoning causes foot drop, insomnia, constipation, anemia, and mental retardation.  Cobalt added to water and liquors and can cause cardiac damage. Copper, tin, and zinc can cause colic, vomiting and diarrhea.  Mercury in mercury fungicide treated grains, or mercury-contaminated fish can cause brain damage, paralysis, and death.  Non-permitted color or permitted food color like metal yellow, beyond the safe limit in colored food can cause allergies, hyperactivity, liver damage, infertility, anemia, cancer and birth defects. II.RELATED WORK (a) Surface Enhanced Raman Spectroscopy: [5]Surface-enhanced Raman spectroscopy (SERS) is an advanced Raman technique that enhances the vibrational spectrum of molecules adsorbed on or in the vicinity of metal particles and/or surfaces. Because of its readiness, sensitivity, and minimum sample preparation requirements, SERS is being considered as a powerful technique for food inspection. Ocean Optics gold and silver SERS substrates enable us to make, repeatable SERS measurements for the identification and quantification of SERS-active analytes. Typical applications include precision screening of food ingredients for controlled additives such as melamine and pesticides. . . . "

Source link: https://www.ripublication.com/ijaerspl2019/ijaerv14n5spl_05.pdf

Finally, this is a Springer-Nature resource which looks interesting!

"Methodology Open Access Published: 08 July 2019Article On the utilization of deep and ensemble learning to detect m...

Article On the utilization of deep and ensemble learning to detect m...

Nancy Ann Watanabe Thank you for all your recommendations, mam. looks interesting... I'll refer it