Classification of milk by means of an electronic nose and SVM neural network

Brudzewski, K.; Osowski, S.; Markiewicz, Tomasz

doi:10.1016/j.snb.2003.10.028

Cited by 173 publications

(78 citation statements)

References 7 publications

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“…Classification models can be constructed from signal processing procedures using non-supervised techniques like principal components analysis (PCA) or supervised techniques such as linear discriminant analysis (LDA) and artificial neural network [1,2]. When compared with other analytical methodologies, this kind of devices also present interesting practical properties such as lower calibration costs, satisfactory accuracy for reasonable small sizes of the calibration data set and easy adaptability to different working conditions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Still, a small number of works on the application of sensor array devices for milk analysis are available in the literature, especially concerning e-tongue devices, although it is possible to find several works on electronic noses [3,[17][18][19][20]. Winquist and co-workers [21] used an e-tongue for the determination of bacterial counts in fresh milk during storage.…”

Section: Introductionmentioning

confidence: 99%

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An electronic tongue taste evaluation: Identification of goat milk adulteration with bovine milk

Dias

Peres

Veloso

et al. 2009

Sensors and Actuators B: Chemical

184

112

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a b s t r a c tAn electronic tongue with 36 cross-sensibility sensors was built allowing a successful recognition of the five basic taste standards, showing high sensibility to acid, salty and umami taste substances and lower performance to bitter and sweet tastes. The taste recognition capability was afterwards tested in the detection of goat milk adulteration with bovine milk, which is a problem for the dairy industry. This new methodology is an alternative to the classical analytical methods used to detect caprine milk adulterations with bovine milk, being a simpler, faster and economical procedure. The different signal profiles recorded by the e-tongue device together with linear discriminant analysis allowed the implementation of a model that could distinguish between raw skim milk groups (goat, cow and goat/cow) with an overall sensibility and specificity of 97% and 93%, respectively. Furthermore, cross-validation showed that the model was able to correct classify unknown milk samples with a sensibility and specificity of 87% and 70%, respectively. Additionally, the model robustness was confirmed since it correctly or incorrectly classified milk samples with, respectively, higher and lower probabilities than those that could be expected by chance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An electronic tongue taste evaluation: Identification of goat milk adulteration with bovine milk

Dias

Peres

Veloso

et al. 2009

Sensors and Actuators B: Chemical

184

112

View full text Add to dashboard Cite

show abstract

“…The application of this type of model is further discussed and investigated by the authors in Refs. [56][57][58][59]. Finally, LS-VSM model is a simplification of the computational calculations of SVM by implementation of a least squares version for SVM [51].…”

Section: Nonlinear Calibration Models For Near-infrared Spectroscopymentioning

confidence: 99%

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

Nomngongo¹,

Munonde²,

Mpupa³

et al. 2017

Developments in Near-Infrared Spectroscopy

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Environmental contamination by trace elements is becoming increasingly important problem worldwide. Trace metals such as cadmium, copper, lead, chromium, and mercury are major environmental pollutants that are predominantly found in areas with high anthropogenic activities. Therefore, there is a need for rapid and reliable tools to assess and monitor the concentration of heavy metal in environmental matrices. A nondestructive, cost-effective, and environmentally friendly procedure based on near-infrared reflectance spectroscopy (NIRS) and chemometric tools has been used as alternative technique for the simultaneous estimation of various heavy metal concentrations in environmental sample. The metal content is estimated by assigning the absorption features of metals associated with molecular vibrations of organic and inorganic functional groups in organic matter, silicates, carbonates, and water at 780-2500 nm in the near-infrared region.This chapter, reviewed the application of NIRS combined with chemometric tools such as multiple linear regression (MLR), principal component regression (PCR), and partial least squares (PLS) regression. The disadvantages and advantages of each chemometric tool are discussed briefly.

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“…Taste sensors are used to obtain data from the fermentation process to be classified using ANNs [17]. Similarly, sensors are used to smell milk, which is classified using SVMs [18].…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Weather Forecasting Using Data Mining Techniques

Shivaranjani¹

2016

ijecs

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:The vast dramatically changes occurs day by day in certain fields due to the development of advanced technology and nature one such among them is rainfall. The rainfall is the fragment of the agriculture and unable to understand the monsoon condition, predicating the crop yield and the soil fertility. Data mining is the techniques used to extract the knowledge from the set of data.This paper provides a survey of different data mining techniques being used in weather prediction or forecasting which helps the farmer for yield worthy productive and nourish the soil fertility such as artificial feed-forward neural networks (ANNs), fuzzy inference system, decision tree method, time series analysis, learning vector Quantization (LVQ) and biclustering technique.

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Classification of milk by means of an electronic nose and SVM neural network

Cited by 173 publications

References 7 publications

An electronic tongue taste evaluation: Identification of goat milk adulteration with bovine milk

An electronic tongue taste evaluation: Identification of goat milk adulteration with bovine milk

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

A Review of Weather Forecasting Using Data Mining Techniques

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