scholarly journals Design and Development of a Shortwave near Infrared Spectroscopy using NIR LEDs and Regression Model

2017 ◽  
Vol 7 (6) ◽  
pp. 3070
Author(s):  
Kim Seng Chia ◽  
Yit Peng Tan
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Linear Array ◽  
Mean Squared Error ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Financial Barriers ◽  
Transmission Grating ◽  
Array Sensor ◽  
The Cross

<span>Near infrared (NIR) spectroscopic technology has been getting more attention in various fields. The development of a low cost NIR spectroscopy is crucial to reduce the financial barriers so that more NIR spectroscopic applications will be investigated and developed by means of the NIR spectroscopic technology. This study proposes an alternative to measure shortwave NIR spectrum using one collimating lens, two slits, one NIR transmission grating, one linear array sensor, and one microcontroller. Five high precision narrow bands NIR light emitting diodes (LEDs) were used to calibrate the proposed spectroscopy. The effects of the proposed two slits design, the distance between the grating and linear array sensor, and three different regression models were investigated. The accuracy of the proposed design was cross-validated using leave-one-out cross-validation. Results show that the proposed two slits design was able to eliminate unwanted signals substantially, and the cross-validation was able to estimate the best model with root mean squared error of cross-validation of 3.8932nm. Findings indicate that the cross-validation approach is a good approach to estimate the final model without over-fitting, and the proposed shortwave NIR spectroscopy was able to estimate the peak value of the acquired spectrum from NIR LEDs with RMSE of 1.1616nm.</span>

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

2002 ◽  
Vol 10 (3) ◽  
pp. 203-214 ◽  
Author(s):  
N. Gierlinger ◽  
M. Schwanninger ◽  
B. Hinterstoisser ◽  
R. Wimmer
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Hot Water ◽  
Extractive Content ◽  
Fast Method ◽  
Wood Powder ◽  
Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

&lt;p&gt;Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.&lt;/p&gt;&lt;p&gt;The study is performed in Mediterranean Croatia (44&amp;#176; 05&amp;#8217; N; 15&amp;#176; 22&amp;#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed &lt;em&gt;Quercus ssp.&lt;/em&gt; and &lt;em&gt;Juniperus ssp.&lt;/em&gt; forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22&lt;sup&gt;nd&lt;/sup&gt; 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31&lt;sup&gt;st&lt;/sup&gt; and September 5&lt;sup&gt;th &lt;/sup&gt;2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE&lt;sub&gt;p&lt;/sub&gt;) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.&lt;/p&gt;&lt;p&gt;The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE&lt;sub&gt;p &lt;/sub&gt;(Spectroscopy dataset RMSE&lt;sub&gt;p&lt;/sub&gt; = 1.91; Sentinel-2 dataset RMSE&lt;sub&gt;p&lt;/sub&gt; = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; remote sensing, soil spectroscopy, wildfires, soil organic matter&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgment: &lt;/strong&gt;This work was supported by the Croatian Science Foundation through the project &quot;Soil erosion and degradation in Croatia&quot; (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&amp;#269;in is acknowledged for her cooperation during the laboratory work.&lt;/p&gt;

scholarly journals Prediction of Texture of Raw Poultry Meat by Visible and Near–Infrared Reflectance Spetroscopy

2013 ◽  
Vol 64 (5) ◽  
Author(s):  
Herlina Abdul Rahim ◽  
Rashidah Ghazali ◽  
Shafishuhaza Sahlan ◽  
Mashitah Shikh Maidin
Keyword(s):  
Prediction Accuracy ◽  
Broiler Chickens ◽  
Near Infrared ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Food Composition ◽  
Poultry Meat ◽  
Near Infrared Reflectance ◽  
Village Chickens ◽  
Raw Poultry

Near-infrared (NIR) spectroscopy is a non-destructive, low cost and fast measurement technique that is required to improve the meat texture quality prediction. In this research, visible/NIR spectroscopy has been used for the prediction of raw chicken meat texture from different types of chickens by referring to the reference data obtained from destructive measurement using a Volodkevich Bite Jaws texture analyser. The Partial Least Squares analysis shows that the prediction accuracy is higher for the Az-Zain village organic chickens (85–95%) than for village chickens (42–68%) and broiler chickens (42–44%). The high prediction accuracy and low absorbance spectra of Az-Zain village organic chickens compared to broiler and village chickens could be correlated with the food composition of the chicken meal.

Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky–Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

2020 ◽  
Vol 74 (4) ◽  
pp. 417-426 ◽  
Author(s):  
Zhenzhen Xia ◽  
Jie Yang ◽  
Jing Wang ◽  
Shengpeng Wang ◽  
Yan Liu
Keyword(s):  
Fractional Order ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Predictive Ability ◽  
Selection Method ◽  
Wavelength Selection ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Accuracy And Stability

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.

True Accuracy of near Infrared Spectroscopy and its Dependence on Precision of Reference Data

2002 ◽  
Vol 10 (1) ◽  
pp. 15-25 ◽  
Author(s):  
L.K. Sørensen
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reference Data ◽  
Nir Spectroscopy ◽  
Limited Resources ◽  
Standard Errors ◽  
Calibration Data ◽  
Pls Regression ◽  
Calibration Equations ◽  
Very High

A more precise estimate of the accuracy of near infrared (NIR) spectroscopy is obtained when the measured standard errors of cross validation ( SECV) and prediction ( SEP) are corrected for imprecision of the reference data. The significance of correction increases with increasing imprecision of reference data. Very high precision of reference data obtained through replicate analyses under reproducibility conditions may not be the optimal goal for the development of calibration equations. In a situation of limited resources, the precision of the reference data should be related to the obtainable accuracy of the spectroscopic system. Investigation of several routine applications based on the partial least-squares (PLS) regression technique showed that increased precision of calibration data only resulted in marginal improvements in true accuracy if the total standard error of reference results from the beginning was less than the estimated true accuracy of the corresponding NIR calibration.

scholarly journals Toward a Reliable Evaluation of Forecasting Systems for Plant Diseases: A Case Study Using Fusarium Head Blight of Wheat

Plant Disease ◽  
2012 ◽  
Vol 96 (6) ◽  
pp. 889-896 ◽  
Author(s):  
S. Landschoot ◽  
W. Waegeman ◽  
K. Audenaert ◽  
J. Vandepitte ◽  
G. Haesaert ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Plant Diseases ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Head Blight ◽  
Evaluation Strategies ◽  
The Cross ◽  
Validation Strategy

Despite great efforts to forecast plant diseases, many of the existing systems often fall short in providing farmers with accurate predictions. One of the main problems arises from the existence of year and location effects, so that more advanced procedures are required for evaluating existing systems in an unbiased manner. This paper illustrates the case of Fusarium head blight of winter wheat in Belgium. We present a new cross-validation strategy that enables the evaluation of the predictive performance of a forecasting system for years and locations that are different from the years and locations on which the forecast was developed. Four different cross-validation strategies and five regression techniques are used. The results demonstrated that traditional evaluation strategies are too optimistic in their predictions, whereas the cross-year cross-location validation strategy yielded more realistic outcomes. Using this procedure, the mean squared error increased and the coefficient of determination decreased in predicting disease severity and deoxynivalenol content, suggesting that existing evaluation strategies may generate a substantial optimistic bias. The strongest discrepancies between the cross-validation strategies were observed for multiple linear regression models.

Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

2006 ◽  
Vol 82 (1) ◽  
pp. 111-116 ◽  
Author(s):  
N. Barlocco ◽  
A. Vadell ◽  
F. Ballesteros ◽  
G. Galietta ◽  
D. Cozzolino
Keyword(s):  
Shear Force ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Intramuscular Fat ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Intact Muscle

AbstractPartial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

scholarly journals NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6892
Author(s):  
Isabel Revilla ◽  
Ana M. Vivar-Quintana ◽  
María Inmaculada González-Martín ◽  
Miriam Hernández-Jiménez ◽  
Iván Martínez-Martín ◽  
...  
Keyword(s):  
Single Neuron ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Sensory Profile ◽  
Squared Error ◽  
Sensory Parameters ◽  
Hidden Layer ◽  
Reflectance Data

For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat “cecina de León”, a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.

The Possibility of near Infrared Spectroscopy for Evaluation of Microbial Nitrogen Content of in Sacco Feed Residues and Duodenal Digesta of Sheep

1998 ◽  
Vol 6 (1) ◽  
pp. 167-174 ◽  
Author(s):  
S. Atanassova ◽  
N. Todorov ◽  
D. Djouvinov ◽  
R. Tsenkova ◽  
K. Toyoda
Keyword(s):  
Nitrogen Content ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Maize Silage ◽  
Total N ◽  
Alfalfa Hay ◽  
Microbial Nitrogen ◽  
Mn Content ◽  
In Sacco

This study aimed to estimate by near infrared (NIR) spectroscopy the microbial nitrogen content (MN) of feed residues from in sacco degradability trails and duodenal digesta of sheep. NIR spectra from 50 samples of duodenal digesta, and from in sacco residues—110 samples of alfalfa hay and 38 samples of maize silage were obtained using an NIRSystems 4250 spectrophotometer. The microbial nitrogen (MN) content of part of the alfalfa hay in sacco residues (78 samples) was calculated from the percentage of 15N enrichment compared to enrichment in the original samples; for the rest of the alfalfa samples and samples of maize silage residues were determined by diaminopimelic acid (DAPA) as a bacterial marker, and MN of duodenal digesta samples by the purine N (RNA equivalent) content as a microbial marker. The calibration equations were developed by modified least squares as the calibration method. The microbial content of all kinds of samples was accurately calibrated and cross-validated. A standard error of cross validation ( SECV) of 0.418 g microbial N kg−1 DM, a coefficient of determination for the cross validation of 0.925 and a ratio of standard deviation of population and the SECV of 3.88 were obtained for the alfalfa 15N labelled hay residues. For maize silage residues, the corresponding values were 0.832, 0.938 and 3.90, and for duodenal digesta samples the values were 1.05, 0.962 and 5.19, respectively. Prediction of MN as percentage of total N of the samples gave approximately the same level of accuracy. For example, the SECV was 2.35% units, cross-validation R2 was 0.953, SD/SECV was 4.60 for alfalfa 15N labelled hay residues. Despite the different origin of the analysed samples (feed residues and duodenal digesta), the NIR spectroscopy determination of MN content of all samples was based on spectral data at very similar wavelengths. The study indicated that NIR spectroscopy has the potential to predict microbial nitrogen content and to distinguish MN from total N content of in sacco feed residues and duodenal digesta.

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