Selecting optimal wavelength intervals for an optical sensor: A case study of milk fat and total protein analysis in the region 400–1100nm

Galyanin, Vladislav; Melenteva, Anastasiia; Bogomolov, Andrey

doi:10.1016/j.snb.2015.03.101

Cited by 34 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The capability of simultaneous determination of water and oil hydrocarbons content up to 13% using an ATR CIR probe with a ZrO 2 crystal has been illustrated. Limited accuracy of analysis intrinsic to the MIR spectroscopy of solid samples through an ATR probe can be essentially reduced by the application of the variable intervals selection method recently reported by the authors in combination with an appropriate spectral preprocessing. The prediction accuracies achieved in this study are sufficient for a wide range of practical applications.…”

Section: Discussionmentioning

confidence: 99%

“…At 0 derivative order, the algorithm performs spectral smoothing. This flexibility makes it one of the most effective data modification algorithms, which is particularly efficient in combination with the variable selection …”

Section: Methodsmentioning

confidence: 99%

“…This is a recently developed approach that solves general optimization problem of selecting spectral intervals jointly with the preprocessing parameters. It makes use of the genetic algorithm (GA) with our modifications reported by Galyanin et al There are 2 main distinctions of the applied algorithm from the commonly used GA‐based variable selection. Our algorithm makes use of an interval‐based variable coding instead of the binary notation.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative analysis of total hydrocarbons and water in oil‐contaminated soils with attenuated total reflection infrared spectroscopy

Guryanova

Ermakov

Galyanin

et al. 2016

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

The feasibility of direct (ie, without sample preparation) quantitative analysis of total hydrocarbons and water in oil-contaminated soils using mid-infrared spectroscopy and an attenuated total reflection (ATR) probe has been investigated. Spectral characteristics of unpolluted and oil-contaminated soils composed of sand, clay, dolomite, and humus have been studied over the full mid-infrared range (4000-400 cm). Spectra of 25 typical soil samples containing varying levels of oil and water have been analyzed using a chalcogenide infrared fiber-based probe with a ZrO 2 crystal as an ATR element. The spectral data were used to build calibration models for the analysis of hydrocarbon contamination as well as moisture content of soil samples. The low quality of ATR spectra of drier samples and variable spectral intensity inherent in the ATR measurement of solids has been overcome by suitable data processing. Further improvement of the model performance has been achieved using a variable selection based on the modified genetic algorithm. Our proposed method allows the determination of oil and moisture content in soils with accuracies of 1.1% and 0.6%, respectively, which is sufficient for a number of practical applications. The reported results may be used to develop portable devices for measuring petroleum and water content of soils.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative analysis of total hydrocarbons and water in oil‐contaminated soils with attenuated total reflection infrared spectroscopy

Guryanova

Ermakov

Galyanin

et al. 2016

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The method is now routinely used as an exploratory analysis tool for selecting suitable predictor variables in a many different tasks . R esearch has been conducted in the detection and quantification of the adulteration of sesame and vegetable oils , for selecting optimal wavelength intervals for an optical sensor for milk fat and total protein analysis , for the prediction of raspberry puree quality , for correlating physical and chemical properties of pork ham and in other areas of the food industry. In general, the precision of model parameters improves with the increasing number of relevant variables and observations .…”

Section: Introductionmentioning

confidence: 99%

PLSR modelling of quality changes of lager and malt beer during storage

Gagula¹,

Magdić

Horvat

2016

J. Inst. Brew.

View full text Add to dashboard Cite

The aim of this research was to create mathematical models for describing the changes in beer properties by using two chemometric methods applied on the experimental data. The models are intended to be useful and trustworthy for calculating four beer properties based on three easily measured ones. For that purpose, lager and malt beer were packaged in glass bottles, while lager beer was also packaged in polyethylene terephthalate (PET). Samples were placed at room temperature in the dark for 6 months. Fifteen physical and chemical properties of the beer were measured before bottling, immediately after bottling and once per month for the next 6 months. Standard MEBAK and Analytica‐EBC methods of analysis were applied. During the 6 month period, seven properties changed >1%. Two partial least squares regression methods [polynomial regression, partial least squares regression with polynomial regression (PLSR‐PR) and response surface method (PLSR‐RSM)] were used for modelling the relationships amongst multivariate measurements. Models with high statistical significance were determined and two PLSR methods were compared. Both chemometric methods were found to be suitable for modelling physical and chemical changes in the beers during their commercial shelf‐life. The PLS‐RSM method was found to be the more precise and confident method in describing property changes for lager and malt beer in glass bottles, while the polynomial regression model was found to be better for the lager beer packaged in PET. The R2 values determined for polynomial regression model were up to 0.939, while for the random surface method model the values were up to 1.000. Copyright © 2016 The Institute of Brewing & Distilling

show abstract

“…[9][10][11] Diagnostic optical devices and sensors developed from LED-based optical technique finds quite a vast field of applications in recent years which makes us focus towards NIR-LED sensor in diagnosing sinusitis and to validate with the help of wavelet analysis along with radiographic results. [12][13][14][15] This paper focuses on signal analysis and discrimination of sinusitis affected patients from healthy individuals with the help of Dyadic Wavelet transform. Dyadic Wavelet transform (DyWT) pioneered by Mallat and Zhong proves to be a powerful tool in signal analysis and processing.…”

mentioning

confidence: 99%

Dyadic wavelet analysis and detection of sinusitis using near infrared sensor

Kamatchi

Sundararajan

2018

Int J Otorhinolaryngol Head Neck Surg

View full text Add to dashboard Cite

Background: Sinusitis is a chronic infection or inflammation which affects the paranasal sinus cavities and the associated nasal cavities. As the symptoms of sinusitis greatly resemble upper respiratory infections, diagnosing sinusitis clinically is a major issue. Though imaging techniques serves as a standard in confirming the diagnosis of chronic sinusitis, the availability at the primary care settings, affordability and diagnosing acute cases calls upon an alternative technique in practice. Recent researches confirming the diagnosis of sinusitis using Near-infrared imaging gives us hope in taking up the research using optical sensing. The objective of the study was to successfully diagnose sinusitis using NIR-LED optical sensor and to signal process the data obtained from the patients using Dyadic Wavelet Transform (DyWT) to confirm and to validate diagnosis using regression analysis. The study also correlates the plain radiographic findings with the NIR device sensing to make the device feasible.Methods: This was a one year pilot study (June 2014–May 2015) conducted with forty patients suspected of sinusitis and with clinical history along with ten healthy individuals as controls. Results: Patients age ranged from 18-65 years were included in the study. Results from NIR sensing device well correlate with the radiographic examination of the registered candidates. The regression result perfectly matches with the dyadic wavelet results of the patients, confirming the diagnosing of sinusitis using near- infrared sensor. Radiographic examination well correlates with the results from the NIR diagnostic device providing a valuable evidence of the hardware.Conclusions: NIR-LED sensor device can provide qualitative evidence in differentiating the mild and severe patients based on air-fluid level present in the sinus. The results strongly recommend that NIR sensing device can be a best alternative in case of frequently sinus affected patients and for the unaffordable patients without the risk of radiation.

show abstract

Selecting optimal wavelength intervals for an optical sensor: A case study of milk fat and total protein analysis in the region 400–1100nm

Cited by 34 publications

References 16 publications

Quantitative analysis of total hydrocarbons and water in oil‐contaminated soils with attenuated total reflection infrared spectroscopy

Quantitative analysis of total hydrocarbons and water in oil‐contaminated soils with attenuated total reflection infrared spectroscopy

PLSR modelling of quality changes of lager and malt beer during storage

Dyadic wavelet analysis and detection of sinusitis using near infrared sensor

Contact Info

Product

Resources

About