Measuring The Tensile Strain of Wood By Visible And Near-Infrared Spatially Resolved Spectroscopy

2021 ◽  
Author(s):  
Ma Te ◽  
Tetsuya Inagaki ◽  
Masato Yoshida ◽  
Mayumi Ichino ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Prediction Accuracy ◽  
Tensile Strain ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Calibration Model ◽  
Spatially Resolved ◽  
Squared Error ◽  
Stiffness Evaluation ◽  
Spatially Resolved Spectroscopy

Abstract Wood has various mechanical properties, so stiffness evaluation is critical for quality management. Using conventional strain gauges constantly is high cost, also challenging to measure precious wood materials due to the use of strong adhesive. This study demonstrates the correlation between light scattering changes inside the wood cell walls and tensile strain. A multifiber-based visible-near-infrared (Vis–NIR) spatially resolved spectroscopy (SRS) system was designed to rapidly and conventiently acquire such light scattering changes. For the preliminary experiment, samples with different thicknesses were measured to evaluate the influence of thickness. The differences in Vis–NIR SRS spectral data diminish with an increase in sample thickness, which suggests that the SRS method can successfully measure the whole strain (i.e., surface and inside) of wood samples. Then, for the primary experiment, 18 wood samples with the same thickness (2 mm) were tested to construct a strain calibration model. The prediction accuracy was characterized by a determination coefficient (R2) of 0.86 with a root mean squared error (RMSE) of 297.89 με for five-fold cross-validation; for test validation, The prediction accuracy was characterized by an R2 of 0.82 and an RMSE of 345.44 με.

scholarly journals Measuring the tensile strain of wood by visible and near-infrared spatially resolved spectroscopy

Cellulose ◽  
2021 ◽  
Author(s):  
Te Ma ◽  
Tetsuya Inagaki ◽  
Masato Yoshida ◽  
Mayumi Ichino ◽  
Satoru Tsuchikawa
Keyword(s):  
Tensile Strain ◽  
Near Infrared ◽  
Spatially Resolved ◽  
Spatially Resolved Spectroscopy

Demonstration of the applicability of visible and near-infrared spatially resolved spectroscopy for rapid and nondestructive wood classification

Holzforschung ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Te Ma ◽  
Tetsuya Inagaki ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Short Wave ◽  
Physical Structure ◽  
Support Vector ◽  
Spatially Resolved ◽  
Standard Normal Variate ◽  
Spatially Resolved Spectroscopy

AbstractAlthough visible and near-infrared (Vis-NIR) spectroscopy can rapidly and nondestructively identify wood species, the conventional spectrometer approach relies on the aggregate light absorption due to the chemical composition of wood and light scattering originating from the physical structure of wood. Hence, much of the work in this area is still limited to further spectral pretreatments, such as baseline correction and standard normal variate to reduce the light scattering effects. However, it should be emphasized that the light scattering rather than absorption in wood is dominant, and this must be effectively utilized to achieve highly accurate and robust wood classification. Here a novel method based on spatially resolved diffuse reflectance (wavelength range: 600–1000 nm) was demonstrated to classify 15 kinds of wood. A portable Vis-NIR spectral measurement system was designed according to previous simulations and experimental results. To simplify spectral data analysis (i.e., against overfitting), support vector machine (SVM) model was constructed for wood sample classification using principal component analysis (PCA) scores. The classification accuracies of 98.6% for five-fold cross-validation and 91.2% for test set validation were achieved. This study offers enhanced classification accuracy and robustness over other conventional nondestructive approaches for such various kinds of wood and sheds light on utilizing visible and short-wave NIR light scattering for wood classification.

scholarly journals IR spectroscopy coupled with chemometrics used as a simple and rapid method to determine the caffeine content of tea products

2021 ◽  
pp. 195-200
Author(s):  
Lestyo Wulandari ◽  
Diana Hanifiyah Sutipno ◽  
Dwi Koko Pratoko
Keyword(s):  
Ir Spectroscopy ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Black Tea ◽  
Calibration Model ◽  
Chemometric Method ◽  
Simple Method ◽  
Caffeine Content ◽  
Squared Error ◽  
Significant Difference

Introduction: Tea is a popular beverage that comes from Camellia sinensis. Tea is generally categorised into four types: black tea, oolong tea, green tea, and white tea. These four types are distinguished based on the presence or absence of a fermentation process during their processing. One of the compounds that play a role in providing freshness to tea is caffeine. Aims: The purpose of this study was to determine the caffeine content in the tea samples that are on the market. Methods: This was done using the near-infrared (NIR)-chemometric method and using the TLC-Densitometry method as a comparison. Infrared (IR) spectroscopy combined with chemometrics has been developed as a simple method to analyse the caffeine content in a tea sample. IR spectra of tea samples were correlated with caffeine content using chemometrics. Results: In this study, the partial least squares (PLS) model of the NIR model that showed the best calibration with r-square was 0.958, and the root mean squared error of calibration (RMSEC) value was 0.070. The PLS calibration model of the NIR models was further used to predict the unknown caffeine content in commercial samples. The significance of the caffeine content that had been measured with NIR and TLC-Densitometry was evaluated using a two paired sample t-test. Conclusion: The caffeine content measured with both methods gave no significant difference.

scholarly journals Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

2005 ◽  
Vol 56 (4) ◽  
pp. 417 ◽  
Author(s):  
J. A. Guthrie ◽  
D. J. Reid ◽  
K. B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Sample Selection ◽  
Model Performance ◽  
Soluble Solids ◽  
Internal Quality ◽  
Calibration Model ◽  
Validation Population

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

Frontal cortical oxygenation changes during gravity-induced loss of consciousness in humans: a near-infrared spatially resolved spectroscopic study

2007 ◽  
Vol 103 (4) ◽  
pp. 1326-1331 ◽  
Author(s):  
Koichi Kurihara ◽  
Azusa Kikukawa ◽  
Asao Kobayashi ◽  
Toshio Nakadate
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Cerebral Oxygenation ◽  
Oxygenation Index ◽  
Loss Of Consciousness ◽  
Spatially Resolved ◽  
Hemoglobin Saturation ◽  
Significant Difference ◽  
Concentration Changes ◽  
Spatially Resolved Spectroscopy

Gravity (G)-induced loss of consciousness (G-LOC), which is presumably caused by a reduction of cerebral blood flow resulting in a decreased oxygen supply to the brain, is a major threat to pilots of high-performance fighter aircraft. The application of cerebral near-infrared spectroscopy (NIRS) to monitor gravity-induced cerebral oxygenation debt has generated concern over potential sources of extracranial contamination. The recently developed NIR spatially resolved spectroscopy (SRS-NIRS) has been confirmed to provide frontal cortical tissue hemoglobin saturation [tissue oxygenation index (TOI)]. In this study, we monitored the TOI and the standard NIRS measured chromophore concentration changes of oxygenated hemoglobin and deoxygenated hemoglobin in 141 healthy male pilots during various levels of +Gz (head-to-foot inertial forces) exposure to identify the differences between subjects who lose consciousness and those who do not during high +Gz exposure. Subjects were exposed to seven centrifuge profiles, with +Gz levels from 4 to 8 Gz and an onset rate from 0.1 to 6.0 Gz/s. The SRS-NIRS revealed an ∼15% decrease in the TOI in G-LOC. The present study also demonstrated the TOI to be a useful variable to evaluate the effect of the anti-G protection system. However, there was no significant difference found between conditions with and without G-LOC in subjects with terminated G exposure. Further studies that elucidate the mechanism(s) behind the wide variety of individual differences may be needed for a method of G-LOC prediction to be effectively realized.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces.Methods: Vis-NIR spectra were obtained from worm-free sheep faeces from different environments in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations collected. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected Queensland (QLD) faeces. Naturally occurring blood in QLD samples was quantified using Hemastix® and FAMACHA© scores.Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions in sheep faeces. The calibration models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood.Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic for the accurate prediction of H. contortus infections in these regions.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces. Methods: Vis-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). QLD samples were quantified using Hemastix® and FAMACHA © scores. Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for blood in naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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