scholarly journals Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4905
Author(s):  
Hongbo Li ◽  
Dapeng Jiang ◽  
Jun Cao ◽  
Dongyan Zhang
Keyword(s):  
Lipid Content ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component ◽  
Two Dimensions ◽  
Breeding Value ◽  
Pinus Koraiensis ◽  
Small Scale ◽  
Important Indicator ◽  
Chemometric Algorithms

Lipid content is an important indicator of the edible and breeding value of Pinus koraiensis seeds. Difference in origin will affect the lipid content of the inner kernel, and neither can be judged by appearance or morphology. Traditional chemical methods are small-scale, time-consuming, labor-intensive, costly, and laboratory-dependent. In this study, near-infrared (NIR) spectroscopy combined with chemometrics was used to identify the origin and lipid content of P. koraiensis seeds. Principal component analysis (PCA), wavelet transformation (WT), Monte Carlo (MC), and uninformative variable elimination (UVE) methods were used to process spectral data and the prediction models were established with partial least-squares (PLS). Models were evaluated by R2 for calibration and prediction sets, root mean standard error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Two dimensions of input data produced a faster and more accurate PLS model. The accuracy of the calibration and prediction sets was 98.75% and 97.50%, respectively. When the Donoho Thresholding wavelet filter ‘bior4.4’ was selected, the WT–MC–UVE–PLS regression model had the best predictions. The R2 for the calibration and prediction sets was 0.9485 and 0.9369, and the RMSECV and RMSEP were 0.0098 and 0.0390, respectively. NIR technology combined with chemometric algorithms can be used to characterize P. koraiensis seeds.

scholarly journals Agricultural products quality determination by means of near infrared spectroscopy

2022 ◽  
Vol 951 (1) ◽  
pp. 012112
Author(s):  
A A Munawar ◽  
Z Zulfahrizal ◽  
R Hayati ◽  
Syahrul
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Agricultural Products ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Non Destructive

Abstract Cocoa is one of main agricultural products cultivated in many tropical countries and processed onto several derivative products. To determine cocoa beans qualities, laboratory procedures based on solvent extractions were mainly used, however most of them are destructive and may cause environmental pollutions. The main purpose of this present study is to employ near infrared spectroscopy (NIRS) for rapid and non-destructive assessment of cocoa beans in form of fat content. Near infrared spectral data of cocoa bean samples were measured as diffuse reflectance in wavelength range from 1000 to 2500 nm. Reference fat contents were measured using standard laboratory methods. Prediction models were developed using principal component regression with raw and baseline corrected spectra data. The results showed that fat contents of cocoa beans can be predicted and determined with maximum correlation coefficient (r) of 0.89 and ratio prediction to deviation (RPD) index of 2.87 for raw spectra and r of 0.91, RPD of 3.18 for baseline spectra correction. It may conclude that NIRS was feasible to be applied as a rapid and non-destructive method for cocoa bean quality assessment.

scholarly journals Identification of Copper in Stems and Roots of Jatropha curcas L. by Hyperspectral Imaging

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 823
Author(s):  
Juan Francisco García-Martín ◽  
Amanda Teixeira Badaró ◽  
Douglas Fernandes Barbin ◽  
Paloma Álvarez-Mateos
Keyword(s):  
Jatropha Curcas ◽  
Near Infrared ◽  
Prediction Models ◽  
Plant Stress ◽  
Principal Component ◽  
Linear Discriminant ◽  
Jatropha Curcas L ◽  
Potential Applications

The in situ determination of metals in plants used for phytoremediation is still a challenge that must be overcome to control the plant stress over time due to metals uptake as well as to quantify the concentration of these metals in the biomass for further potential applications. In this exploratory study, we acquired hyperspectral images in the visible/near infrared regions of dried and ground stems and roots of Jatropha curcas L. to which different amounts of copper (Cu) were added. The spectral information was extracted from the images to build classification models based on the concentration of Cu. Optimum wavelengths were selected from the peaks and valleys showed in the loadings plots resulting from principal component analysis, thus reducing the number of spectral variables. Linear discriminant analysis was subsequently performed using these optimum wavelengths. It was possible to differentiate samples without addition of copper from samples with low (0.5–1% wt.) and high (5% wt.) amounts of copper (83.93% accuracy, >0.70 sensitivity and specificity). This technique could be used after enhancing prediction models with a higher amount of samples and after determining the potential interference of other compounds present in plants.

scholarly journals Near Infrared Hyperspectral Imaging in Transmission Mode: Assessing the Weathering of Thin Wood Samples

2016 ◽  
Vol 24 (6) ◽  
pp. 595-604 ◽  
Author(s):  
Knut Arne Smeland ◽  
Kristian Hovde Liland ◽  
Jakub Sandak ◽  
Anna Sandak ◽  
Lone Ross Gobakken ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Near Infrared ◽  
Prediction Models ◽  
Hyperspectral Image ◽  
Principal Component Regression ◽  
Principal Component ◽  
Transmission Mode ◽  
Infrared Wavelength ◽  
Robust Prediction ◽  
Wooden Surface

Untreated wooden surfaces degrade when exposed to natural weathering. In this study thin wood samples were studied for weather degradation effects utilising a hyperspectral camera in the near infrared wavelength range in transmission mode. Several sets of samples were exposed outdoors for time intervals from 0 days to 21 days, and one set of samples was exposed to ultraviolet (UV) radiation in a laboratory chamber. Spectra of earlywood and latewood were extracted from the hyperspectral image cubes using a principal component analysis-based masking algorithm. The degradation was modelled as a function of UV solar radiation with four regression techniques, partial least squares, principal component regression, Ridge regression and Tikhonov regression. It was found that all the techniques yielded robust prediction models on this dataset. The result from the study is a first step towards a weather dose model determined by temperature and moisture content on the wooden surface in addition to the solar radiation.

scholarly journals Assessment of small-scale integrated water vapour variability during HOPE

2014 ◽  
Vol 14 (16) ◽  
pp. 22837-22879 ◽  
Author(s):  
S. Steinke ◽  
S. Eikenberg ◽  
U. Löhnert ◽  
G. Dick ◽  
D. Klocke ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Water Vapour ◽  
Time Scales ◽  
Near Infrared ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
High Definition ◽  
Integrated Water Vapour ◽  
The Mean

Abstract. The spatio-temporal variability of integrated water vapour (IWV) on small-scales of less than 10 km and hours is assessed with data from the two months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sunphotometer, radiosondes, Raman Lidar, infrared and near infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of standard deviation (≤ 1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Non-hydrostatic modelling framework (ICON) which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities in the order of 4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

106 Assessment of fecal near infrared reflectance spectroscopy to detect and monitor the reproductive status of endangered Amur and Snow leopard females

2019 ◽  
Vol 31 (1) ◽  
pp. 179
Author(s):  
M. Santos-Rivera ◽  
L. Johnson-Ulrich ◽  
A. Graham ◽  
E. Willis ◽  
A. J. Kouba ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component ◽  
Component Analysis ◽  
Chemometric Analysis ◽  
Reproductive Status ◽  
Fecal Samples ◽  
Near Infrared Spectra

Feces from captive and wild carnivores can yield valuable information about an individuals’ physiological and reproductive status, diet, and ecology. Near infrared spectroscopy (NIRS) is a rapid, noninvasive, cost-efficient technique widely used in the agricultural, pharmaceutical, and chemical industries that has gained traction in diagnostic and ecological field applications for herbivore species, such as wild deer, antelope, and giant panda. The aim of this study was to test the transferability of NIRS to measuring reproductive status in feces from 2 endangered carnivore species, the Snow (Panthera uncia) and Amur (Panthera pardus orientalis) leopards. Fecal near infrared spectra analysed with multivariate statistics were used to generate prediction models for estrone-3-glucuronide (E1G) and progesterone (P4). In the E1G NIRS model, fecal samples (n=93) were obtained from 5 female leopards (3 Amur, 2 Snow) at 5 different zoo facilities, whereas for the P4 NIRS model fecal samples (n=51) from only 1 pregnant Amur leopard was available. The hormones were extracted with methanol and quantified by enzyme-linked immunosorbent assays (C. Munroe), where the sample range for E1G was 0.20-2.17 μg/g and the range for P4 was 0.06-61.89 μg/g. The near infrared spectra (350-2500nm) were acquired with an ASD FieldSpec®3 portable spectrometer (Malvern Panalytical, Malvern, UK), and the chemometric analysis was realised using the Unscrambler® X v.10.4 (CAMO Software AS, Oslo, Norway). Hormone reference values were log transformed before chemometric analysis to account for the heterogeneity of variance. Spectral pretreatment of standard normal variate was applied to the truncated wavelength range 700-240 0nm in order to remove interference from the visible region (350-700nm) due to individual diets that can confer colour variants that alter spectral signatures. Initial principal component analysis for the E1G and P4 datasets models showed >95% of the variation was explained by 4 factors, with no separation of principal component analysis scores between species or reproductive status. Quantitative prediction models using partial least-squares regression on selected wavelength ranges yielded a coefficient of determination for E1G and P4 of 0.10-0.04 and 0.35-0.19 for calibrations and validations, respectively. These near infrared models require further mathematical processing and consideration of sample variation due to diet complexity in carnivores in order to accurately assess hormone levels and monitor reproductive cycles in these species. This work was supported by USDA-ARS Biophotonics Initiative grant #58-6402-3-018.

scholarly journals 157 Spectroscopic analysis of tissues collected from male goats differing in genetic propensity to consume juniper

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 119-120
Author(s):  
Douglas R Tolleson ◽  
Nick Garza ◽  
Kaylee Hollingsworth ◽  
Jose M Diaz ◽  
Thomas H Welsh
Keyword(s):  
Near Infrared ◽  
Statistical Significance ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Breeding Value ◽  
Capra Hircus ◽  
Strongly Correlated ◽  
Least Squares Regression ◽  
Tissue Samples ◽  
Physico Chemical

Abstract Juniper (Juniperus spp.) foliage is consumed by free-ranging goats in the southwestern US. Junipers contain monoterpenes which have toxicological and pharmacological effects. We sampled 20 male Spanish goats (Capra hircus; 10 young [2-year-old] and 10 old [3-5-year-old]) from a herd selected for their propensity to consume a high (estimated breeding value [EBV] = 13.01 ± 0.20) or low (EBV = -14.76 ± 0.48) proportion of juniper in the diet to determine the ability of near infrared spectroscopy (NIRS) in detecting physico-chemical differences in animal tissues. Heart (ventricle; interior and exterior surfaces), liver (caudate lobe), and muscle (longissimus dorsi) samples as well as an entire kidney and testicle, were collected at harvest (n = 5 of each age [i.e. length of exposure to monoterpenes] and juniper EBV combination). Tissue samples were stored in whirl-pac bags at -20o C and later thawed to ~24o C for NIRS analysis. Spectra (400–2500 nm) were obtained with an ASD Field Spec using a contact probe directly through the whirl-pac sample bag. Principal component and partial least squares regression procedures were accomplished in SAS; P < 0.05 indicated statistical significance. Both age (RSQ, 0.2 to 0.6; P < 0.05) and juniper EBV (RSQ, 0.2 to 0.65; P < 0.05) were correlated with near infrared (NIR) spectral characteristics. Propensity to consume juniper was most strongly correlated with NIR spectra in testicular tissue (RSQ = 0.65, MSE = 0.25, P < 0.01), and least strongly correlated in the heart interior surface (RSQ = 0.21, MSE = 0.21, P < 0.05). Spectral correlations with propensity to consume juniper were stronger in tissues from old (RSQ ~ 0.68) than young (RSQ ~ 0.52) goats, especially in liver, kidney, muscle, and testicle. Physico-chemical differences in goat tissues were affected by genetic propensity to consume juniper, and these were detected by NIRS.

scholarly journals Fast And Simultaneous Prediction Of Agricultural Soil Nutrients Content Using Infrared Spectroscopy

2019 ◽  
Vol 12 (1) ◽  
pp. 61-66
Author(s):  
Devianti Devianti ◽  
Zulfahrizal Zulfahrizal ◽  
Sufardi Sufardi ◽  
Agus Arip Munawar
Keyword(s):  
Agricultural Soil ◽  
Near Infrared ◽  
Prediction Models ◽  
Agricultural Soils ◽  
Principal Component Regression ◽  
Principal Component ◽  
Nutrient Content ◽  
Laboratory Method ◽  
Near Infrared Reflectance ◽  
Simultaneous Prediction

Abstract. The functions soil depends on the balances of its structure, nutrients composition as well as other chemical and physical properties. Conventional methods, used to determine nutrients content on agricultural soil were time consuming, complicated sample processing and destructive in nature. Near infrared reflectance spectroscopy (NIRS) has become one of the most promising and used non-destructive methods of analysis in many field areas including in soil science. The main aim of this present study is to apply NIRS in predicting nutrients content of soils in form of total nitrogen (N). Transmittance spectra data were obtained from a total of 18 soil samples from 8 different sites followed by N measurement using standard laboratory method. Principal component regression (PCR) with full cross validation were used to develop and validate N prediction models. The results showed that N content can be predicted very well even with raw spectra data with coefficient correlation (r) and residual predictive deviation index (RPD) were 0.95 and 3.35 respectively. Furthermore, spectra correction clearly enhances and improve prediction accuracy with r = 0.96 and RPD = 3.51. It may conclude that NIRS can be used as fast and simultaneous method in determining nutrient content of agricultural soils.

scholarly journals Spectral Range and Wood Surface Impacts on Equilibrium Moisture Content Estimation in Thermally Modified Beech Wood by FT-NIR Spectroscopy

2021 ◽  
Vol 1208 (1) ◽  
pp. 012022
Author(s):  
Nebojša Todorović
Keyword(s):  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Cross Section ◽  
Near Infrared ◽  
Prediction Models ◽  
Beech Wood ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Least Squares Regression ◽  
The Cross

Abstract Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares regression (PLS-R) were tested for the possibility of equilibrium moisture content (EMC) prediction in thermally modified beech wood (Fagus moesiaca C.). The samples were modified for 4h at temperatures of 170, 190 and 210 °C. After thermal modification, the samples were kept in a climatic chamber until EMC was reached. FT-NIR spectra (100 scans and 4 cm-1) were collected on the cross-section and radial surfaces at four points. PLS – R models were developed for four spectral regions: the first overtone, the second overtone, the third overtone and the combination band region. Applied thermal treatment caused a decrease of EMC by 42 % at 170 °C, by 53 % at 190 °C, and by 62 % at 210 °C. Principal component analysis (PCA) indicated that there is a difference both between treatments and between wood surfaces. The results of the spectra taken from the radial surface were, in all models, better than the spectra of the cross-section. Related to chemical changes, the first and second overtone region play an important role in the calibrations. The best prediction models for EMC of thermally modified beech wood were obtained from radial surface spectra in the first (Rp2=0.86, RPD=2.69) and second overtone region (Rp2=0.87, RPD=2.70). The obtain results could contribute to the development of predictive models in monitoring of EMC which could significantly improve the quality of industrial production of thermally modified wood.

scholarly journals Rapid quantification of rice (Oryza sativa) qualities based on adaptive near infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012020
Author(s):  
R Hayati ◽  
A A Munawar ◽  
A Marliah
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component ◽  
Component Analysis ◽  
Partial Least Square ◽  
Rice Samples ◽  
Rapid Quantification

Abstract Determination of rice quality parameters is the key factor affecting sustainable agriculture practices. The main purpose of this present study is to develop prediction models based on adaptive near infrared spectroscopy (NIRS) for rapid quantification of rice qualities in form of protein content. Rice samples were obtained from several paddy field in Aceh province with different cultivars. Near infrared spectral data of rice samples were acquired and in wavelength range from 1000 to 2500 nm and recorded as diffuse reflectance spectrum. Prediction models were established using principal component analysis (PCA), principal component analysis (PCR) and partial least square regression (PLSR). The results showed that NIRS combined with PCA can classify rice samples based on their cultivars. Moreover, this approach with PCR and PLSR can also predicted and determined protein contents with satisfactory performance achieving maximum correlation coefficient (r) of 0.81 and ratio prediction to deviation (RPD) index of 2.84 for PCR and r of 0.90 and RPD of 3.19 for PLSR respectively. Based on achieved results, it may conclude that adaptive NIRS approach can be used to quantify rice qualities rapidly and non-destructively.

scholarly journals Assessment of small-scale integrated water vapour variability during HOPE

2015 ◽  
Vol 15 (5) ◽  
pp. 2675-2692 ◽  
Author(s):  
S. Steinke ◽  
S. Eikenberg ◽  
U. Löhnert ◽  
G. Dick ◽  
D. Klocke ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Water Vapour ◽  
Time Scales ◽  
Near Infrared ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
High Definition ◽  
Integrated Water Vapour ◽  
The Mean

Abstract. The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities on the order of 0.4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

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