A Low-Cost NIR Digital Photometer Based on InGaAs Sensors for the Detection of Milk Adulterations With Water

Moreira, Maurício Alves; França, José Alexandre de; Filho, Dari de Oliveira Toginho; Beloti, Vanerli; Yamada, Alberto Koji; França, Maria Bernadete de Morais; Ribeiro, Lucas de Souza

doi:10.1109/jsen.2016.2530873

Cited by 28 publications

(17 citation statements)

References 23 publications

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“…The IR radiations are transmitted through milk samples to detect the water content in the milk. The microcontroller portable device which uses 3 LED emits in the NIR region [8]. It is used to detect urea in milk.…”

Section: Literature Surveymentioning

confidence: 99%

Milk Adulteration Detection System

Chandrashekar¹

2019

IJRASET

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Milk is nutrient-rich white liquid that is essential for every mammal. People use not just cow's milk but various other animals such as milk of goat, sheep, deer, camel, mares. As agriculture, milk is used as diary milk which is used for consumption in daily basis. Milk is also used for the commercial purpose such production of curd, cheese, chocolates and other dairy products. Composition of cow's milk contains 87.7% of water, 4.9% of lactose, 3.4% of fat, 3.3% of protein and 0.7% of minerals. Adulteration is major problem existing in the food industry at present. Milk is one of the highly adulterated products. Up to 68.2% of India's milk is adulterated. The traditional methods of testing the adulterant are time consuming, less accurate and causes wastage. This paper proposes an innovative idea of detecting adulterants by using a system based on Image processing. This method has advantage of time efficiency, high accuracy and sample is unaltered by the system.

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Section: Literature Surveymentioning

confidence: 99%

Milk Adulteration Detection System

Chandrashekar¹

2019

IJRASET

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“…Moreover, the data set is small, and inhomogeneity may exist. Considering the problems mentioned above, the traditional classification methods cannot meet the requirements of accurate and effective identification [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Lei

Rao

et al. 2019

Applied Sciences

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Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.

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“…In [10] there is a need for new and more robust techniques for the detection of adulterated milk; therefore, they propose a prototype of a digital, microcontrolled and portable photometer, which uses three infrared LEDs, with this equipment, they measure the transmittance of infrared radiation in milk samples, which evaluate whether it has water or not. The team demonstrated an average absolute error less than 1%.…”

Section: Introductionmentioning

confidence: 99%

Milk Purity Recognition Software through Image Processing

Alvarado-Diaz¹,

Brian²,

Román-Gonzalez³

2019

IJACSA

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Currently in Peru, there is a per capita milk consumption of 87 kg per year; however, the Food and Agriculture Organization of the United Nations (FAO) recommends a consumption of 120 kg per person; the industry, when the milk is acquired from small livestock suppliers, does not analyze the milk before buying it, which there is a high risk that the milk is adulterated with water, in this sense, it proposes an alternative way of preliminary detection of the presence of water in milk, only through a laser a photograph, which greatly reduces the costs of milk analysis. Milk contains different nutrients, vitamins and minerals, which are beneficial for people, so it is very known if it is adulterated or not, that way to prevent diseases. In this document, the reader will read an alternative to the existing methods for the analysis of milk, for the presented method the application of Matlab Classification Learner and the fine K-Nearest Neighbors (KNN) algorithm were used, in which a success rate of 95.4% was obtained.

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A Low-Cost NIR Digital Photometer Based on InGaAs Sensors for the Detection of Milk Adulterations With Water

Cited by 28 publications

References 23 publications

Milk Adulteration Detection System

Milk Adulteration Detection System

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Milk Purity Recognition Software through Image Processing

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