Detection of total viable count in spiced beef using hyperspectral imaging combined with wavelet transform and multiway partial least squares algorithm

2017 ◽  
Vol 38 (1) ◽  
Author(s):  
Dong Yang ◽  
Anxiang Lu ◽  
Dong Ren ◽  
Jihua Wang
Keyword(s):  
Wavelet Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Viable Count ◽  
Total Viable Count ◽  
Least Squares Algorithm

Flight Control System improvement based on a software sensor derived from Partial Least Squares Algorithm

2012 ◽  
Vol 45 (20) ◽  
pp. 1041-1046
Author(s):  
Florian Cazes ◽  
Marie Chabert ◽  
Corinne Mailhes ◽  
Patrice Michel ◽  
Philippe Goupil ◽  
...  
Keyword(s):  
Control System ◽  
Least Squares ◽  
Partial Least Squares ◽  
Flight Control ◽  
Flight Control System ◽  
Software Sensor ◽  
System Improvement ◽  
Least Squares Algorithm

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.

Quantification of protein in wheat using near infrared hyperspectral imaging: Performance comparison with conventional near infrared spectroscopy

2018 ◽  
Vol 26 (3) ◽  
pp. 186-195 ◽  
Author(s):  
Ana Morales-Sillero ◽  
Juan A. Fernández Pierna ◽  
George Sinnaeve ◽  
Pierre Dardenne ◽  
Vincent Baeten
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Wavelength Range ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Performance Comparison ◽  
Imaging Instrument ◽  
Leave One Out

Hyperspectral imaging is a powerful technique that combines the advantages of near infrared spectroscopy and imaging technologies. Most hyperspectral imaging studies focus on qualitative analysis, but there is growing interest in using such technique for the quantitative analysis of agro-food products in order to use them as universal tools. The overall objective of this study was to compare the performance of a hyperspectral imaging instrument with a classical near infrared instrument for predicting chemical composition. The determination of the protein content of wheat flour was selected as example. Spectra acquisition was made in individual sealed cells using two classical near infrared instruments (NIR-DS and NIR-Perstop) and a near infrared hyperspectral line-scan camera (NIR-HSI). In the latter, they were also acquired in open cells in order to study the possibility of accelerating the measurement process. Calibration models were developed using partial least squares for the full wavelength range of each individual instrument and for the common range between instruments (1120–2424 nm). The partial least squares models were validated using the “leave-one-out” cross-validation procedure and an independent validation set. The results showed that the NIR-HSI system worked as well as the classical near infrared spectrometers when a common wavelength range was used, with an r2 of 0.99 for all instruments and Root Mean Square Error in Prediction (RMSEP) values of 0.15% for NIR-HSI and NIR-DS and 0.16% for NIR-Perstop. The high residual predictive deviation values obtained (8.08 for NIR-DS, 7.92 for NIR-HSI, and 7.56 for NIR-Perstop) demonstrate the precision of the models built. In addition, the prediction performance with open cells was almost identical to that obtained with sealed cells.

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