Partial least-squares regression study of the effects of wheat flour composition, protein and starch quality characteristics on oil content of steamed-and-fried instant noodles

2006 ◽  
Vol 44 (2) ◽  
pp. 117-126 ◽  
Author(s):  
J. Wu ◽  
R.E. Aluko ◽  
H. Corke
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Wheat Flour ◽  
Oil Content ◽  
Partial Least Squares Regression ◽  
Quality Characteristics ◽  
Least Squares Regression ◽  
Starch Quality ◽  
Instant Noodles

scholarly journals A partial least squares-based approach to assess the light penetration depth in wheat flour by near infrared hyperspectral imaging

2019 ◽  
Vol 28 (1) ◽  
pp. 25-36 ◽  
Author(s):  
Antoine Laborde ◽  
Benoît Jaillais ◽  
Ryad Bendoula ◽  
Jean-Michel Roger ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
...  
Keyword(s):  
Least Squares ◽  
Penetration Depth ◽  
Partial Least Squares ◽  
Polylactic Acid ◽  
Wheat Flour ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Sample Holder ◽  
Least Squares Regression ◽  
Detection Depth

Near infrared hyperspectral imaging technique has been used for adulteration detection in food samples for several years. However, the sensor cannot screen beyond a certain material thickness. This work studies a method to determine the penetration depth of near infrared radiations in the context of detection. The case of wheat flour in a polylactic acid sample holder is investigated. A sample holder is specially designed to have the wheat flour thickness vary from 0.5 to 3.5 mm. Hyperspectral images are acquired and the partial least squares regression method is used to determine the amount of polylactic acid in each spectrum. Partial least squares prediction results are interpreted using sensor considerations and the Kubelka–Munk theory. Thereafter, the Kubelka–Munk model is fitted and the penetration depth is determined for each wavelength using the reflectance profiles. Similarities between these results and partial least squares regression coefficients lead to the conclusion that partial least squares, combined with the near infrared spectra, is able to characterize the detection depth. The value is calculated by fitting two linear models on the partial least squares prediction results. As a result, 1.80 mm is found to be the detection depth, defined as the maximum thickness of wheat flour to ensure the detection of polylactic acid through the background. Reflectance profiles also show that the penetration depth is highly dependent on the wavelength. This study aimed at providing a method that could be used to evaluate the penetration depth and the detection depth in other contexts than a polylactic acid target in wheat flour.

Oil Content Estimation of Individual Kernels of Quercus Ilex Subsp. Rotundifolia [(Lam) O. Schwarz] Acorns by Fourier Transform near Infrared Spectroscopy and Partial Least Squares Regression

2007 ◽  
Vol 15 (4) ◽  
pp. 247-260 ◽  
Author(s):  
Cristina Sousa-Correia ◽  
Ana Alves ◽  
José C. Rodrigues ◽  
Suzana Ferreira-Dias ◽  
José M. Abreu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Oil Content ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Association Studies ◽  
Extraction Procedure ◽  
Coefficient Of Determination ◽  
Least Squares Regression

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak ( Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure ( n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm−1 and from 4478 to 4177 cm−1 of the vector normalised spectra, a PLSR model with a coefficient of determination ( r 2) of 0.992 and a root mean square error of cross-validation ( RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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