A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy

2013 ◽  
Vol 765-767 ◽  
pp. 528-531
Author(s):  
Dan Peng ◽  
Qing Chen Nie
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Successful Model ◽  
Consensus Strategy

To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

scholarly journals Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 261 ◽  
Author(s):  
Maria Marques ◽  
Ana Álvarez ◽  
Pilar Carral ◽  
Iris Esparza ◽  
Blanca Sastre ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Prediction Ability ◽  
Greenhouse Gas Inventories ◽  
Starting Point

Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

scholarly journals Fault identification for chiller sensor based on partial least square method

2021 ◽  
Vol 233 ◽  
pp. 03057
Author(s):  
Bang Wu ◽  
Yunpeng Hu ◽  
Chuanhui Zhou ◽  
Guaiguai Chen ◽  
Guannan Li
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Fault Identification ◽  
Least Squares Regression

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

Prediction of Ice Fraction and Fat Content in Super-Chilled Salmon by Non-Contact Interactance near Infrared Imaging

2009 ◽  
Vol 17 (2) ◽  
pp. 77-87 ◽  
Author(s):  
Silje Ottestad ◽  
Martin Høy ◽  
Astrid Stevik ◽  
Jens Petter Wold
Keyword(s):  
Shelf Life ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Fat Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Multivariate Curve Resolution ◽  
Prediction Ability ◽  
Ice Fraction

It has been shown that super-chilling can increase the shelf life of fish and meat products without the reduction in quality encountered with normal freezing. The shelf life and quality achieved depend on the fraction of water frozen and how it is preserved in the product. In this paper, we report on the possibility of using non-contact near infrared (NIR) interactance spectroscopy in combination with multi-spectral imaging as a rapid, non-destructive way to predict the average ice fraction as well as the spatial distribution of ice in super-chilled salmon fillets. For average ice fraction calibrations, samples in the range of 0% to 30% ice were investigated. Partial least square regression (PLSR) models with high explained variance ( R2≈0.98) and root mean square error of cross-validation between 1.6% and 2.4% were obtained. The time of NIR measurement after super-chilling (0 h, 1/2 h, 2 h and 24 h) did not seem to affect the prediction ability significantly. The spatial ice distribution, as predicted by PLSR modelling, was affected considerably by the fat distribution. This was avoided by the use of multivariate curve resolution at pixel level and calibration against the amount of ice divided by the total amount of water in both liquid and solid state. Excellent images of ice distribution were achieved and allowed observation of changes during storage and thawing. The ability to predict fat content in super-chilled salmon fillets was also investigated. Fat content predictions were affected by ice fraction, but were observed to be independent of time of measurement after freezing.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression

2021 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Monitoring Networks ◽  
Concentration Estimation ◽  
The Impact

Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors’ responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

scholarly journals Wood species identification from Atlantic forest by near infrared spectroscopy

2019 ◽  
Vol 28 (3) ◽  
pp. e015
Author(s):  
José-Henrique Camargo Pace ◽  
João-Vicente De Figueiredo Latorraca ◽  
Paulo-Ricardo Gherardi Hein ◽  
Alexandre Monteiro de Carvalho ◽  
Jonnys Paz Castro ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Atlantic Forest ◽  
Wood Species ◽  
Native Species ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Planted Forests ◽  
Good Ability

Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.

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