scholarly journals Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4645
Author(s):  
Asma Khan ◽  
Muhammad Tajammal Munir ◽  
Wei Yu ◽  
Brent Young
Keyword(s):  
Particle Size ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Principal Components ◽  
Milk Powder ◽  
Complete Spectrum ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Partial Least Squares Model

Hyperspectral imaging (HSI) in the spectral range of 400–1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC1 and PC2) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (Rp2) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had Rp2 of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.

scholarly journals Research on Partial Least Squares Method Based on Deep Confidence Network in Traditional Chinese Medicine

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Wang-ping Xiong ◽  
Tian-ci Li ◽  
Qing-xia Zeng ◽  
Jian-qiang Du ◽  
Bin Nie ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Components ◽  
Least Squares Method ◽  
Model Parameters ◽  
Partial Least Squares Model ◽  
Partial Least Squares Method

Partial least squares method has many advantages in multivariate linear regression modeling, but its internal cross-checking method will lead to a sharp reduction of the principal component, thereby reducing the accuracy of the regression equation, and the selection of principal components about the traditional Chinese medicine data is particularly sensitive. This paper proposes a kind of partial least squares method based on deep belief nets. This method mainly uses the deep learning model to extract the upper-level features of the original data, putting the extracted features into the partial least squares model for multiple linear regression and evading the problem that selects the number of principal components, continuously adjusting the model parameters until satisfied well-pleased accuracy condition. Using Dachengqitang experimental data and data sets in the UCI Machine Learning Repository, the experimental results show that the partial least squares analysis method based on deep belief nets has good adaptability to TCM data.

Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB201-WB211 ◽  
Author(s):  
S. Buchanan ◽  
J. Triantafilis ◽  
I. O. A. Odeh ◽  
R. Subansinghe
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Soil Mapping ◽  
Additive Models ◽  
Digital Soil Mapping ◽  
Unbiased Estimation ◽  
Ancillary Data ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Log Ratio

The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.

CHARACTERIZATION OF PARTICLE SIZE FRACTIONS OF PEAT. AN INTEGRATED BIOLOGICAL, CHEMICAL, AND SPECTROSCOPIC APPROACH

Soil Science ◽  
1992 ◽  
Vol 153 (5) ◽  
pp. 382-396 ◽  
Author(s):  
B O NORDEN ◽  
ELISABET BOHLIN ◽  
MATS NILSSON ◽  
ÅSA ALBANO ◽  
CHRISTINA RÖCKNER
Keyword(s):  
Particle Size ◽  
Size Fractions ◽  
Particle Size Fractions

ANALYSIS OF AIRBORNE PARTICULATE MATTER COLLECTED AFTER AN ERUPTION EPISODE OF THE POPOCATÉPETL VOLCANO

2003 ◽  
Vol 13 (03n04) ◽  
pp. 133-139 ◽  
Author(s):  
F. ALDAPE ◽  
J. FLORES M.
Keyword(s):  
Particle Size ◽  
Particulate Matter ◽  
Mexico City ◽  
Airborne Particulate Matter ◽  
Airborne Particulate ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Two Samples ◽  
Popocatépetl Volcano ◽  
Popocatepetl Volcano

Samples of airborne particulate matter were collected in four sites along an east-west line from the Popocatépetl volcano after the eruption episode of June 30, 1997. The Popocatépetl volcano, with variable activity since it was known, is currently under low but continuous activity prolonged for almost one decade, with occasional moderate eruption episodes producing mainly fumes, ashes and volcanic dusts. The main objective of this study is to determine whether or not some elements have increased their presence in the atmosphere as a result of the volcanic activity, and also if some others, not usually found in urban aerosols, have appeared because of the same reason. In addition, the information obtained will be a source of scientific data for health risk assessment of the population exposed to volcanic emanations. The sample collection was performed on alternate days from July 10 to August 13 1997 in Puebla and Atlixco in Puebla State. Tlalpan within Mexico City, and Salazar in the State of Mexico. Two samples a day were taken in two periods: 7-19 h and 19-7 h. The samplers separated particles into two particle size fractions. PM25 and PM15. Elemental concentrations were determined by PIXE and the results obtained showed increased concentrations of mainly Ti and Fe in all sampling sites, thus indicating a long range transportation of volcanic dusts in both particle size fractions. Concentrations of Ti were found clearly above the average values of urban areas such as Mexico City, and although this element can be considered of low toxicity, the biological, metabolic and toxic effects on human beings are still under investigation.

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