Investigating the Quality of Milk using Spectrometry Technique and Scattering Theory

Kamil, Nur Ain Insyirah Muhamad; Nor’aini, Z. S.; Ismail, Wan Abdul Fattah Wan; Balakrishnan, Sharma Rao; Jamaludin, Juliza; Ismail, Ishak; Sahrim, Musab

doi:10.48084/etasr.4084

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…Thus, water quality is an important element to assist the growth of crops. Many methods have been adopted such as using spectroscopy [4,5], optics, and laser sensing [6,7], Arduino [8], Internet of Things (IoT) and real time monitoring [9][10][11], multi-sensor array [12], and cyber physical systems [13][14][15]. Reviews on water quality monitoring methods have been done recently involving electronics and optical sensing [16,17].…”

Section: Introductionmentioning

confidence: 99%

Integration of Sensing Framework with a Decision Support System for Monitoring Water Quality in Agriculture

Zainurin

Ismail

Mahamud³

et al. 2023

Agriculture

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Water is an essential element for every plant to survive, absorb nutrients, and perform photosynthesis and respiration. If water is polluted, plant growth can be truncated. The aim of this research is to develop a water quality monitoring system for agriculture purposes based on integration of sensing framework with a smart decision support method. This research consists of three stages: (1) the first stage: developing sensing framework which has four different water quality parameter sensors such as potential hydrogen (pH), electrical conductivity (EC), temperature, and oxidation-reduction potential (ORP), (2) the second stage: developing a hardware platform that uses an Arduino for sensor array of data processing and acquisition, and finally (3) the third stage: developing soft computing framework for decision support which uses python applications and fuzzy logic. The system was tested using water from many sources such as rivers, lakes, tap water, and filtered machine. Filtered water shows the highest value of pH as the filtered machine produces alkaline water, whereas tap water shows the highest value of temperature because the water is trapped in a polyvinyl chloride (PVC) pipe. Lake water depicts the highest value of EC due to the highest amount of total suspended solids (TSS) in the water, whereas river water shows the highest value of ORP due to the highest amount of dissolved oxygen. The system can display three ranges of water quality: not acceptable (NA), adequate (ADE) and highly acceptable (HACC) ranges from 0 to 9. Filtered water is in HACC condition (ranges 7–9) because all water quality parameters are in highly acceptable ranges. Tap water shows ADE condition (ranges 4–7) because one of the water quality parameters is in adequate ranges. River and lake water depict NA conditions (ranges 0–4) as one of the water quality parameters is in not acceptable ranges. The research outcome shows that filtered water is the most reliable water source for plants due to the absence of dissolved solids and contaminants in the water. Filtered water can improve pH and reduce the risk of plant disease. This research can help farmers to monitor the quality of irrigated water which eventually prevents crop disease, enhances crop growth, and increases crop yield.

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Section: Introductionmentioning

confidence: 99%

Integration of Sensing Framework with a Decision Support System for Monitoring Water Quality in Agriculture

Zainurin

Ismail

Mahamud³

et al. 2023

Agriculture

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“…Meanwhile, spectroscopic techniques for detecting contaminants are continuously upgraded in terms of detection sensitivity, quantitatively and qualitatively. There are several methods of spectroscopy that have been analyzed for monitoring water quality, such as vibrational spectroscopy, light emission or luminescence spectroscopy, fluorescence spectroscopy, near-infrared (NIR) spectroscopy, and others [ 23 , 34 , 76 , 77 , 78 , 79 , 80 ]. The techniques are extremely sensitive by producing accurate detection results of matter composition and determining physical structures through light propagation.…”

Section: Common Methods Of Water Quality Monitoring Systemmentioning

confidence: 99%

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Zainurin

Ismail

Mahamud³

et al. 2022

IJERPH

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Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

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“…Capillary zone Electrophoresis (CE) with ultraviolet (UV) identification has also been employed for classification [15]. VIS (visible) and NIR (Near Infrared) spectroscopy can quantify the milk quality [16]. To deal with some of the limitations of these analytical methods, novel sensing methods are proposed.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Urea Adulteration in Milk with Deep Neural Networks

Ghodinde,

Chaskar

2024

Eng. Technol. Appl. Sci. Res.

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Milk is a major food constituent. However, the existing discrepancy between milk demand and supply leads to adulteration, which can be dangerous since it causes detrimental effects on health implicating lethal diseases. Although classical methods for adulteration detection are very accurate, their implementation requires skilled technicians as well as expensive and sophisticated instruments. These reasons trigger the need for improved techniques in uncovering adulteration. Urea is a natural component in milk and accounts for a substantial share of adulteration in the non-protein content of milk. The current research proposes and employs a sensor system utilizing the Electrical Impedance Spectroscopy (EIS) method to determine the presence of urea. The classification system was developed using different machine learning algorithms. Three classifiers, Extreme Gradient Boosting (XGBoost), Extreme Learning Machines (ELM), and Deep Neural Networks (DNN) were considered for various levels of urea adulteration. Milk samples were assessed by deploying the developed EIS sensor assembly and the results derived were employed in the training of the machine learning algorithms. The estimated classifiers displayed promising outcomes, involving up to 98.33% classification accuracies, outshining frequently used existing learning approaches like logistic regression.

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Investigating the Quality of Milk using Spectrometry Technique and Scattering Theory

Cited by 8 publications

References 20 publications

Integration of Sensing Framework with a Decision Support System for Monitoring Water Quality in Agriculture

Integration of Sensing Framework with a Decision Support System for Monitoring Water Quality in Agriculture

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Detection and Classification of Urea Adulteration in Milk with Deep Neural Networks

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