scholarly journals Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

2021 ◽  
Vol 23 (1) ◽  
pp. 220
Author(s):  
Soo-In Sohn ◽  
Subramani Pandian ◽  
John-Lewis Zinia Zaukuu ◽  
Young-Ju Oh ◽  
Soo-Yun Park ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Near Infrared ◽  
Rapid Development ◽  
Nir Spectroscopy ◽  
Gm Crops ◽  
F1 Hybrids ◽  
Support Vector ◽  
Composition Analysis ◽  
Least Squares Regression ◽  
Brassica Napus L

In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking 

2021 ◽  
Author(s):  
Dongxue Zhao ◽  
Maryem Arshad ◽  
Jie Wang ◽  
John Triantafilis
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
High Rate ◽  
Support Vector ◽  
Least Squares Regression ◽  
Spectral Library ◽  
Calibration Models ◽  
Development Data ◽  
Different Depths ◽  
Spectral Libraries

<p>Due to high rate of nutrient removal by cotton plants, the productive cotton-growing soils of Australia is becoming depleted of exchangeable (exch.) cations. For long-term development, data on exch. calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) throughout the soil profile is required. However, traditional laboratory analysis is tedious. The visible-near-infrared (Vis-NIR) spectroscopy is an alternative; whereby, spectral libraries are built which couple soil data and Vis-NIR spectra using models. While various models have been used to predict exch. cations, their performance was seldom systematically compared. Moreover, most previous studies have focused on prediction of topsoil (0–0.3 m) exch. cations while the effects of depth on applicability of topsoil spectral libraries are rarely investigated. Our first aim was to determine which model (i.e. partial least squares regression (PLSR), Cubist, random forest (RF), or support vector machine regression (SVMR)) produces the best prediction of topsoil exch. Ca, Mg, K and Na. The second aim was to evaluate if the best topsoil model can be used to predict subsurface (0.3–0.6 m) and subsoil (0.9–1.2 m) cations. The third aim was to explore the effect of spiking on the prediction in subsurface and subsoil. The fourth aim was to see if combining all depths to build a profile spectral library improved prediction. Based on independent validation, PLSR was superior for topsoil exch. cations prediction, while Cubist outperformed PLSR in some cases when spiking was applied, and the profile spectral library was considered. Topsoil PLSR could be applied to predict exch. Ca and Mg in the subsurface and subsoil, while spiking improved prediction. Moreover, a profile spectral library achieved equivalent results with when topsoil samples coupled with spiking were considered. We, therefore, recommended to predict exch. Ca and Mg throughout the profile using topsoil spectral library coupled with spiking approach.</p>

scholarly journals Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Da Wang ◽  
Wenwen Wei ◽  
Yanhua Lai ◽  
Xiangzheng Yang ◽  
Shaojia Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Absolute Difference ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Successive Projections Algorithm ◽  
Mid Infrared ◽  
Mir Spectra

The quality of strawberry powder depends on the freshness of the fruit that produces the powder. Therefore, identifying whether the strawberry powder is made from freshly available, short-term stored, or long-term stored strawberries is important to provide consumers with quality-assured strawberry powder. Nevertheless, such identification is difficult by naked eyes, as the powder colours are very close. In this work, based on the measurement of near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectra of strawberry powered, good classification results of 100.00% correct rates to distinguish whether the strawberry powder was made from freshly available or stored fruit was obtained. Furthermore, partial least squares regression and least squares support vector machines (LS-SVM) models were established based on NIR, MIR, and combination of NIR and MIR data with full variables or optimal variables of strawberry powder to predict the storage days of strawberries that produced the powder. Optimal variables were selected by successive projections algorithm (SPA), uninformation variable elimination, and competitive adaptive reweighted sampling, respectively. The best model was determined as the SPA-LS-SVM model based on MIR spectra, which had the residual prediction deviation (RPD) value of 11.198 and the absolute difference between root-mean-square error of calibration and prediction (AB_RMSE) value of 0.505. The results of this work confirmed the feasibility of using NIR and MIR spectroscopic techniques for rapid identification of strawberry powder made from freshly available and stored strawberry.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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

<p>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.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 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<sup>st</sup> and September 5<sup>th </sup>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<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>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<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 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.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

scholarly journals Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sylvio Barbon ◽  
Ana Paula Ayub da Costa Barbon ◽  
Rafael Gomes Mantovani ◽  
Douglas Fernandes Barbin
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Chicken Meat ◽  
Poultry Meat ◽  
Spectral Information ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

<p>Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40°C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec®3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation ∆SOC<sub>obs</sub><sub></sub>obtained from observed values in 2013 and 2018 (∆SOC<sub>obs</sub> = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R²= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed ∆SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which ∆SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.</p>

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

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