Ratio Of Performance To Deviation Research Articles

Dapsone determination in tablets to leprosy treatment using a portable NIR spectrometer

Leprosy is a neglected disease with limited technological advancements in the development of new formulations and analytical methods. The pharmaceutical industry offers several analytical alternatives for drug quality control; however, they are expensive, environmentally harmful, and labor-intensive. To address this, the United States Pharmacopoeia recommends the use of high-performance liquid chromatography (HPLC). In such a context, there arises a need for eco-friendly analytical approaches, such as near-infrared (NIR) spectroscopy, which offers the advantages of being non-destructive and eliminating the need for chemicals and solvents. Thus, in this study, we initially developed and validated an HPLC method for the quantification of dapsone in tablets. The method demonstrated selectivity, linearity, precision, accuracy, and very low limits of detection and quantification. Subsequently, the dapsone contents determined by HPLC in manipulated and industrial tablets were used as reference values to construct chemometric models based on NIR spectra acquired using a portable device. Partial Least Squares (PLS) and the Successive Projections Algorithm for interval selection coupled with PLS (iSPA-PLS) were then employed. Only the models constructed with linear baseline-corrected (LBC) spectra yielded significant results, with a ratio of performance to deviation (RPD) higher than 3. LBC/iSPA-PLS was the best model, selecting 3 out of 5 divided intervals, incorporated analytical information related to the aromatic rings and amino groups present in the dapsone molecule. This model achieved a relative prediction error of only 2.78 and 2.54 % for both the manufactured and industrialized tablets, respectively, and a limit of detection of 5.38 mg per 100 g of tablet. Therefore, the proposed methodology can be hereafter applied to the quality control of dapsone tablets, even in the presence of non-modeled excipients.

Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: A Meta-Analysis

The research papers assessing the content of soil organic carbon with the help of Vis-NIR spectroscopy approaches are systematically analyzed and subject to meta-analysis. This meta-analysis included 134 studies published in 1986–2022 with a total sample of 709 values of quantitative metrics. The papers have been searched for in databases of scientific periodicals (RSCI, Science Direct, Scopus, and Google Scholar) by the key word combination “Vis-NIR spectroscopy AND soil organic carbon”. The meta-analysis using the nonparametric one-sided Kruskal–Wallis variance analysis in conjunction with nonparametric pairwise method shows the presence of a statistically significant difference between the median values of the accepted quantitative metrics of the predictive power of the models, namely, coefficient of determination (R2cv/val), root mean square error (RMSE), and the ratio of performance to deviation (RPD). The best performance of the preprocessing method for spectral curves is demonstrated and the estimates of soil organic carbon content obtained by laboratory and field spectroscopies are compared.

Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: Meta-Analysis

In this study, a systematic review and meta-analysis of scientific researches devoted to the assessment of the soil organic carbon content using Vis-NIR spectroscopy approaches was carried out. The meta-analysis included 134 studies published between 1986 and 2022 with a total sample of 709 values of quantitative metrics. The articles were searched in databases of scientific periodicals: RSCI, Science Direct, Scopus, Google Scholar by the key words: “Vis-NIR spectroscopy AND soil organic carbon”. In the process of meta-analysis, using the nonparametric one-sided Kraskel-Wallis variance analysis in conjunction with the nonparametric pairwise method, the presence of a statistically significant difference between the median values of the accepted quantitative metrics of the predictive power of the models (coefficient of determination (R2cv/val), root mean square error (RMSE) and the ratio of performance to deviation (RPD) comparisons. As a result, the best efficiency (from the point of view of comparing these metrics) was revealed for the method of preprocessing spectral curves, for various multidimensional data analysis approaches used, and the results of assessing the organic carbon content of soils were compared between spectroscopy in the laboratory and directly in the field.

Near-infrared spectroscopy (NIRS) for monitoring the nutritional composition of black soldier fly larvae (BSFL) and frass.

The demand for protein obtained from animal sources is growing rapidly, as is the necessity for sustainable animal feeds. The use of black soldier fly larvae (BSFL) reared on organic side streams as sustainable animal feed has been receiving attention lately. This study assessed the ability of near-infrared spectroscopy (NIRS) combined with chemometrics to evaluate the nutritional profile of BSFL instars (fifth and sixth) and frass obtained from two different diets, namely soy waste and customised bread-vegetable diet. Partial least squares (PLS) regression with leave one out cross-validation was used to develop models between the NIR spectral data and the reference analytical methods. Calibration models with good [coefficient of determination in calibration (Rcal 2 ): 0.90; ratio of performance to deviation (RPD) value: 3.6] and moderate (Rcal 2 : 0.76; RPD value: 2.1) prediction accuracy was observed for acid detergent fibre (ADF) and total carbon (TC), respectively. However, calibration models with moderate accuracy were observed for the prediction of crude protein (CP) (Rcal 2 : 0.63; RPD value: 1.4), crude fat (CF) (Rcal 2 : 0.70; RPD value: 1.6), neutral detergent fibre (NDF) (Rcal 2 : 0.60; RPD value: 1.6), starch (Rcal 2 : 0.52; RPD value: 1.4), and sugars (Rcal 2 : 0.52; RPD value: 1.4) owing to the narrow or uneven distribution of data over the range evaluated. The near-infrared (NIR) calibration models showed a good to moderate prediction accuracy for the prediction of ADF and TC content for two different BSFL instars and frass reared on two different diets. However, calibration models developed for predicting CP, CF, starch, sugars and NDF resulted in models with limited prediction accuracy. © 2023 Society of Chemical Industry.

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400–1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.

Total Soluble Solids in Grape Must Estimation Using VIS-NIR-SWIR Reflectance Measured in Fresh Berries

Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo (white), Mencía, and Tempranillo (red) grape varieties based on diffuse spectroscopy measurements. To identify the most suitable spectral range for TSS prediction, the regression models were calibrated for four datasets that included the following spectral ranges: 400–700 nm (visible), 701–1000 nm (near infrared), 1001–2500 nm (short wave infrared) and 400–2500 nm (the entire spectral range). We also tested the standard normal variate transformation technique. Leave-one-out cross-validation was implemented to evaluate the regression models, using the root mean square error (RMSE), coefficient of determination (R2), ratio of performance to deviation (RPD), and the number of factors (F) as evaluation metrics. The regression models for the red varieties were generally more accurate than the models of those for the white varieties. The best regression model was obtained for Mencía (red): R2 = 0.72, RMSE = 0.55 °Brix, RPD = 1.87, and factors n = 7. For white grapes, the best result was achieved for Godello: R2 = 0.75, RMSE = 0.98 °Brix, RPD = 1.97, and factors n = 7. The methodology used and the results obtained show that it is possible to estimate TSS content in grapes using diffuse spectroscopy and regression models that use reflectance values as predictor variables. Spectroscopy is a non-invasive and efficient technique for determining optimal grape maturity for harvest.

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy

Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.

Performance of a Handheld MicroNIR Instrument for Determining Protein Levels in Sorghum Grain Samples.

Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R2) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R2 = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R2 = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R2 = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.

Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy

Turmeric is widely used as a health supplement and foodstuff in South East Asian countries because of its medicinal benefits. Like several other plants and peppers, turmeric is prone to exploitation because of its economic value, rising consumer need, and essential food element that adds colour and flavour. Due to this, quick and comprehensive testing processes are needed to detect adulterants in turmeric. In this study, pure turmeric powders were mixed with starch in proportions ranging from 0 to 50% with a 1% variation to obtain different combinations. Reflectance spectra of pure turmeric and starch mixed samples were recorded using a JASCO-V770 spectrometer from 400 to 2050 nm. The recorded spectra were pre-processed using a Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV). The Savitzky-Golay (SG) filter was initially applied to these original (X), MSC, and SNV-corrected spectra. Secondly, the Extra Tree Regressor (ETR) feature selection method was employed to select the best features. Finally, principal component analysis (PCA) was used to reduce the dimension of the selected features. The stacked generalization method was applied to improve the performance of this work. Both regressors and classifier stacking techniques have been tested with different classification and regression methods. The K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF) models were used as base learners, and Logistic Regression (LRC) was used as a meta-model for classification and Linear Regression (LR) for regression analysis. The proposed method achieved the best regression performance with r 2 of 0.999, Root Mean Square Error (RMSE) of 0.206, Ratio of Performance to Deviation (RPD) of 73.73, and Range Error Ratio (RER) of 480.58, whereas 100% F1 score and Matthew’s Correlation Coefficient (MCC) classification performance.

Detecting the temporal trend of cultivated soil organic carbon content using visible near infrared spectroscopy

Monitoring changes in soil properties is essential to ensure ecosystem function and agricultural productivity. This study evaluated the ability of visible near infrared (Vis-NIR) spectroscopy to detect the temporal trend in soil organic carbon (SOC) content after 5 years in a 12 km2 agricultural catchment in western France. Partial least squares regression models were developed using soil samples from a local dataset collected in 2013 at two depths (198 samples at 0–15 cm and 196 samples at 15–25 cm) to predict SOC content of 111 new samples collected in 2018 at the same locations and at similar depths (0–15 cm and 15–25 cm). Two approaches, which differed in whether or not they considered the SOC content variability that can result from collecting soil samples at two depths, were applied. For both approaches, the potential benefit of “temporal spiking” was evaluated by adding 10% of 2018 samples to the 2013 dataset. The results showed that removing outliers and stratifying the calibration dataset by depth yielded the highest accuracy, with SOC RMSEP of 4.1 and 2.7 g.kg−1 for 0–15 and 15–25 cm, respectively. Moreover, temporal spiking improved five of eight predictions (stratifying or not the calibration dataset by depth, removing or not poorly predicted outliers), with increases in the ratio of performance to deviation of 0.10–0.44. Furthermore, comparing observed and predicted changes in SOC content showed that Vis-NIR spectroscopy estimated its trend over time in most cases.

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Estimation of key potentially toxic elements in arid agricultural soils using Vis-NIR spectroscopy with variable selection and PLSR algorithms

Potentially toxic elements (PTEs) pose a significant threat to soil and the environment. Therefore, the fast quantification of PTEs is crucial for better management of contaminated sites. Versatile technique such as Visible near-infrared spectroscopy (Vis–NIRS) (350–2,500 nm) has attracted tremendous attention for assessing PTEs and has achieved promising results combined with successful multivariate analysis. This research investigated the potential of Vis–NIRS combined with partial least squares regression (PLSR) and variable selection methods to assess key PTEs (Cd, Co, Cu, Cr, Pb, and Zn) in agricultural soils under arid conditions. The soil samples (80) were collected from a polluted area around Al-Moheet drainage, Minya Governorate–upper Egypt. The samples were scanned using an ASD FieldSpec-4 spectroradiometer. Simulated annealing (SA) and uninformative variable elimination (UVE) were used to select the effective wavelengths in predicting PTEs. PLSR was used to develop the spectral models using the full range (FR-PLS) and feature-selected spectra techniques SA (SA-PLS) and UVE (UVE-PLS). The results indicated that UVE-PLS models performed better than FR-PLS and SA-PLS models in predicting the key PTEs. The obtained coefficient of determination (R2) and the ratio of performance to deviation (RPD) were 0.74 and 2.48 (Cr), 0.72 and 2.03 (Pb), 0.62 and 1.86 (Cd), 0.59 and 1.78 (Cu), 0.52 and 1.68 (Co), and 0.46 and 1.41 (Zn), respectively. The results suggested that the UVE-PLS spectral model is promising for predicting Cr, Pb, and Cd, and can be improved for predicting Cu, Co, and Zn elements in agricultural soils.

Development of Hungarian spectral library: Prediction of soil properties and applications

Updating soil information systems (SIS) requires advanced technologies to support the time and cost-effective and environment-friendly soil data. The use of mid- infrared (MIR) Spectroscopy as alternative to wet chemistry has been tested. The MIR spectral library is a useful technique for predicting soil attributes with high accuracy, efficiency, and low cost. The Hungarian MIR spectral library contained data on 2200 soil samples from 10 counties representing the first Soil Information and Mentoring System (SIMS) survey. Archived soil samples were prepared and scanned based on Diffuse Reflectance Infrared spectroscopy (DRIFT) technique and spectra data were saved in the fourier transform infrared (FTIR) spectrometer OPUS software. Preprocessed filtering methods, outlier detection methods and calibration sample selection methods were applied for spectral library. MIR calibration models were built for soil attributes using Partial Least Square Regression (PLSR) method. Coefficient determination (R2), The Root Mean Squared Error (RMSE) and Ratio of Performance to Deviation (RPD) were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to significantly estimate soil properties such as SOC, CaCO3, sand, clay and silt through various scale models (national, county and soil type). The findings showed that our spectral library soil estimations are precise enough to provide information on national, county and soil type levels enabling a wide range of soil applications that demand huge amounts of data such as soil survey, precision agriculture and digital soil mapping.

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na+, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R2 values for Na+, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na+, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

Qualitative and quantitative studies of plasticizers in extra virgin olive oil by surface-enhanced Raman spectroscopy combined with chemometrics

Plasticizers are widely used as significant adjuvants in the plastics processing industry and have received widespread attention from the international community because of their reproductive toxicity. This study used a combination of surface-enhanced Raman spectroscopy (SERS) technology and chemometrics to conduct qualitative and quantitative analyses of plasticizers in extra virgin olive oil (EVOO). Among three established classification models, namely, partial least squares discriminant analysis (PLS-DA), k-nearest neighbours (KNN), and support vector machine (SVM), SVM was found to be the best classification model, with a correct classification rate (CCR) of 100 %. For the plasticizer concentration prediction model established by partial least squares (PLS), the absolute coefficient Rp2 was higher than 0.97, the root mean square error of prediction (RMSEP) was less than 3.632, and the ratio of performance to deviation (RPD) values were all greater than 5. Furthermore, the proposed method was validated by a standard gas chromatography-mass spectrometry (GC-MS) method using t-test with no significant difference. These results indicate that SERS combined with chemometrics can achieve the identification and quantification of plasticizers in EVOO and has application value for the analysis of plasticizers in edible oils.

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics

Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.

Modeling to Correct the Effect of Soil Moisture for Predicting Soil Total Nitrogen by Near-Infrared Spectroscopy

Near-infrared (NIR) spectroscopy can improve the efficiency of soil property prediction, such as that of soil total nitrogen (TN) content. However, soil spectra are very sensitive to soil moisture content, which is a crucial factor affecting the accuracy of soil nutrient composition prediction. In response to this issue, the goal of this study is to identify the best model to correct the effect of soil moisture for predicting soil total nitrogen by near-infrared spectroscopy. The 107 collected soil samples were divided into six different water content (0%, 5%, 10%, 15%, 20%, and 25%) sample groups. Then, five correction methods, including direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), spectral space transformation (SST), and slope/bias (S/B), were executed. Finally, partial least squares regression (PLSR) models were established to forecast TN content. The results showed that SST could minimize the influence of moisture. Furthermore, SST–PLSR had the best TN content prediction accuracy: Rp2 (the coefficient of determination of the prediction set) in the range of 0.81–0.82, RMSEP (the root mean square error of the prediction set) in the range of 0.09–0.10 g/kg, and RPD (ratio of performance to deviation) in the range of 2.32–2.40. Therefore, the dry soil prediction model is competent for wet soil samples and could achieve preciseness in TN content prediction. The use of SST can effectively eliminate the influence of moisture and achieve high-precision TN prediction in wet soil samples. Additionally, the introduction of SST expands the application scope of soil nutrient prediction models and increases model robustness.

Construction of a hyperspectral estimation model for total nitrogen content in Shajiang black soil

AbstractBackgroundThe real‐time non‐destructive estimation of soil total nitrogen content via hyperspectral remote sensing is important for crop fertilization and precision agriculture development.AimsThe study aimed to use hyperspectral technology for the construction of an estimation model using different methods for assessing total nitrogen content in Shajiang black soil.MethodsIn our study, soil samples were obtained from Shangshui County, Henan Province, China. Using hyperspectral data of soil samples, the original spectral reflectance was transformed into logarithmic, reciprocal, and first derivative spectra. Hyperspectral estimation models for assessing the total nitrogen content were built with spectral indices, competitive adaptive reweighted sampling (CARS) algorithms, and a combination of machine learning methods, including the partial least square regression, support vector machine (SVM), and random forest methods.ResultsThe results showed that after the first derivative transformation, the performance of normalized indices constructed with a combination of two bands at 1401 and 776 nm was better. The coefficient of determination (R2), root mean square error (RMSE), ratio of performance to deviation (RPD), and ratio of performance to the inter‐quartile range (RPIQ) values of the model were 0.84, 0.10 g kg–1, 2.25, and 4.04, respectively. Validation was performed using data independent of the modeling samples, and validation R2, RMSE, RPD, and RPIQ values were 0.92, 0.07 g kg–1, 2.56, and 4.19, respectively. The same sample was analyzed using a CARS algorithm for screening feature bands, and the 61 reciprocal reflectance bands selected as the input for the SVM exhibited the best performance. A modeling R2 value of 0.92, RMSE of 0.07 g kg–1, RPD of 3.09, RPIQ of 5.97, and validation R2 of 0.96, RMSE of 0.06 g kg–1, RPD of 3.24, and RPIQ of 8.47 were observed.ConclusionsThe estimation models constructed using the two methods exhibited good ability to determine the total nitrogen content. The accuracy of the SVM model was slightly higher than that of the indices model, but both models could facilitate the rapid estimation of total nitrogen content in Shajiang black soil. The findings can provide a technical reference for the estimation of the levels of soil total nitrogen and other nutrients.

Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases

One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.