scholarly journals Prediction of starch content in adulterated fresh cheese using Partial Least Squares Regression, Multiple Linear Regression and Hyperspectral Imaging

2021 ◽  
Vol 3 (1) ◽  
pp. 15-20
Author(s):  
Greta Tejada ◽  
Cinthya Velasquez ◽  
Víctor Aredo ◽  
J.P. Cruz-Tirado ◽  
Raúl Siche
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Starch Content ◽  
Least Squares Regression ◽  
Fresh Cheese

scholarly journals Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Siong Fong Sim ◽  
Min Xuan Laura Chai ◽  
Amelia Laccy Jeffrey Kimura
Keyword(s):  
Fourier Transform ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Palm Olein ◽  
Least Squares Regression ◽  
Simple Linear Regression ◽  
Olein Oil

Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. The marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. The prediction performance was measured based on the percentage root mean square error (%RMSE). The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. The bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.

scholarly journals Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology

2021 ◽  
Vol 12 ◽  
Author(s):  
Stien Mertens ◽  
Lennart Verbraeken ◽  
Heike Sprenger ◽  
Kirin Demuynck ◽  
Katrien Maleux ◽  
...  
Keyword(s):  
Water Potential ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Physiological Traits ◽  
Plant Phenotyping ◽  
Least Squares Regression ◽  
Proximal Sensing ◽  
Induced Changes

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.

scholarly journals Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015 ◽  
pp. 55 ◽  
Author(s):  
Ll. Pérez-Planells ◽  
J. Delegido ◽  
J. P. Rivera-Caicedo ◽  
J. Verrelst
Keyword(s):  
Linear Regression ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Partial Least Squares Regression ◽  
Gaussian Process Regression ◽  
Least Squares Regression ◽  
Kernel Ridge Regression

<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>

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