Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

Gessell, Austin; Small, Gary W.

doi:10.1080/00032719.2021.1939362

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Spectral transformations were performed on the calibration set to increase the signal-to-noise ratio and decrease the influence of undesired light-scattering effects ( Figures S1 and S2 ) [ 54 , 62 ]. The applied transformations included multiplicative scatter correction (MSC), area normalisation, de-trending, orthogonal signal correction (OSC), and standard normal variate (SNV) [ 63 , 64 , 65 , 66 ]. Partial least squares regression (PLSR) models were developed for each mineral nutrient, using both raw and transformed data, to correlate foliar mineral nutrient concentrations with relative reflectance in the full spectral range of 400–1000 nm [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition

Silva

Trueman

Kämper

et al. 2023

Plants

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Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400–1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R2P) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R2P = 0.55, RPD = 1.52; P: R2P = 0.77, RPD = 2.11; K: R2P = 0.77, RPD = 2.12; Ca: R2P = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion.

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Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition

Silva

Trueman

Kämper

et al. 2023

Plants

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“…In the process of spectral acquisition, in order to reduce random noise and disorder fluctuations in spectral data due to factors unrelated to the nature of the sample to be measured, De-trending (DT) [33], Savitzky-Golay polynomial smoothing (S-G) [34], Standard Normal Variate (SNV) [33], Multiplicative Scatter Correction (MSC) [33], and Orthogonal Signal Correction (OSC) [35], 5 preprocessing algorithms to eliminate noise in the original spectral data, and establish the corresponding SVR prediction model respectively. All data preprocessing and modeling use matlab2020b.…”

Section: Preprocessing Methods Of Spectral Datamentioning

confidence: 99%

Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology

et al. 2022

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The dry matter test of mango has important practical significance for the quality classification of mango. Most of the common fruit and vegetable quality nondestructive testing methods based on fluorescence hyperspectral imaging technology use a single algorithm in algorithms such as Uninformative Variable Elimination (UVE), Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS) and Continuous Projection Algorithm (SPA) to extract feature spectral variables, and the use of these algorithms alone can easily lead to the insufficient stability of prediction results. In this regard, a nondestructive detection method for the dry matter of mango based on hyperspectral fluorescence imaging technology was carried out. Taking the ‘Keitt’ mango as the research object, the mango samples were numbered in sequence, and their fluorescence hyperspectral images in the wavelength range of 350–1100 nm were collected, and the average spectrum of the region of interest was used as the effective spectral information of the sample. Select SPXY algorithm to divide samples into a calibration set and prediction set, and select Orthogonal Signal Correction (OSC) as preprocessing method. For the preprocessed spectra, the primary dimensionality reduction (UVE, SPA, RF, CARS), the primary combined dimensionality reduction (UVE + RF, CARS + RF, CARS + SPA), and the secondary combined dimensionality reduction algorithm ((CARS + SPA)-SPA, (UVE + RF)-SPA) and other 12 algorithms were used to extract feature variables. Separately constructed predictive models for predicting the dry matter of mangoes, namely, Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN) model, were used; The results show that (CARS + RF)-SPA-BPNN has the best prediction performance for mango dry matter, its correlation coefficients were RC2 = 0.9710, RP2 = 0.9658, RMSEC = 0.1418, RMSEP = 0.1526, this method provides a reliable theoretical basis and technical support for the non-destructive detection, and precise and intelligent development of mango dry matter detection.

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The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

Song,

Yu,

Wang

et al. 2024

Microchemical Journal

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Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

Cited by 4 publications

References 28 publications

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition

Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology

The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

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