Ratio Of Performance To Deviation Research Articles

Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Artificial neural networks in soil quality prediction: Significance for sustainable tea cultivation

In today's era artificial intelligence is quite popular, one of the most effective algorithms used is Artificial Neural Networks (ANN). In this study, the determination of soil quality using the Soil Management Assessment Framework (SMAF) model in areas where tea cultivation is carried out at the micro-watershed scale and the predictability of soil quality using ANN were evaluated. According to the results, the soil quality indices of tea-growing areas were generally classified as “medium” between 55 and 70 %. Among the evaluated features for determining soil quality, the highest relative importance value was for soil organic carbon content (13 %) and potential mineralizable nitrogen (13 %), whereas the lowest values were for exchangeable potassium (4 %) and sodium adsorption ratio (SAR) (4 %). In addition, when comparing the actual and predicted values for soil quality prediction using ANN, the Lin's concordance correlation coefficient (LCCC), ratio of performance to deviation (RPD), and R2 values were found to be 0.93, 2.95, and 0.89, respectively. Significant properties for the determined values within a 90 % predicted interval were found to be organic matter, microbial biomass carbon, bulk density, and aggregate stability of the soils. Moreover, the uncertainty values (standard deviation) in the model predictions were determined to be within the range of 1.01–4.56 %. Consequently, the Soil Quality Index (SQI) obtained from the SMAF model using 12 soil properties in tea-growing areas could be accurately predicted using ANN. As a result of this study, digital maps showing the spatial distribution of SQI and the predicted uncertainties can help monitor SQI levels in this area.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

A comparative study of acoustic and ultrasonic nondestructive testing for evaluating melon quality

Abstract Melon (Cucumis melo L.) is a high-value agricultural commodity known worldwide for its sweet taste and crisp flesh texture, which are important factors for quality and consumer acceptance. Unfortunately, quality testing and determining the optimal harvest time for achieving desired melon characteristics are traditionally performed through destructive methods. The aim of this study was to explore the potential of acoustic and ultrasonic tests for predicting the physicochemical properties of Honey Globe melons (Cucumis melo L. var. inodorus). A total of 100 melon samples were used in this study. For the nondestructive ultrasonic testing, attenuation values served as its variable, whereas in acoustic testing, variables included frequency, magnitude, short-term energy, and zero-moment. Fruit’s flesh firmness and total soluble solids (TSS) as physicochemical quality properties were determined using destructive tests. The calibration phase involved 80 melon samples, employing a K-Fold Cross Validation approach with ten folds, done on Partial Least Square Regression (PLS) modeling. Another 20 melon samples were used for blind testing. Reliability evaluation was done on key metrics, consisting of R2 values, RMSEC (Root Mean Square Error of Calibration), RMSECV (Root Mean Square Error of Cross-Validation), RMSEP (Root Mean Square Error of Prediction), and RPD (Ratio of Performance to Deviation). Analysis results on these metrics collectively support the conclusion that both ultrasonic and acoustic methods exhibit their potential to predict the firmness properties of melon fruits. The best evaluation result that has been conducted for the ultrasonic test uses attenuation, age, and density as predictors to predict fruit firmness, with R2 = 0.763 and RPD = 2.945, while the acoustic test achieved the best result with magnitude used as a predictor to predict fruit firmness with R2 = 0.718 and RPD = 2.230. However, evaluation metrics on the prediction of total soluble solids for both nondestructive tests were still not good enough for application with low R2 and RPD value.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Automatic non-destructive estimation of polyphenol oxidase and peroxidase enzyme activity levels in three bell pepper varieties by Vis/NIR spectroscopy imaging data based on machine learning methods

The browning process of food products if often formed upon cutting and damage during their processing, transport, and storage, amongst other potential sources and reasons. Enzymic browning can be mainly due to polyphenol oxidase (PPO) and peroxidase (POD) enzymes. Visible/near-infrared (Vis/NIR) imaging spectroscopy in the range of 350–1150 nm was used in this study for automatic and non-destructive evaluation of PPO and POD activity levels in three bell pepper varieties (red, yellow, orange; N = 30), with a total of 30 inputs samples in each variety. The spectral data were then modeled by the partial least squares regression (PLSR) throughout the whole spectral range, without using any subset of the most effective wavelength (EW) values. Regression determination coefficient (R2) values for the estimation (prediction) of POD enzyme activity levels were 0.794, 0.772, and 0.726 for red, yellow, and orange bell peppers, respectively, all over the validation set. At the same time, the activity levels of PPO enzyme over bell peppers showed R2 values of 0.901, 0.810, and 0.859, for red, yellow, and orange bell peppers, respectively, all over the validation set. In addition, a combination of support vector machine (SVM) with either genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), or imperialistic competitive algorithms (ICA) hybrid machine learning (ML) techniques were used to select the optimal (discriminant) spectral EW wavelength values, and regression performance was consistently improved, to judge from higher regression fit R2 values. Either 14 or 15 EWs were computed and selected in order of their discriminative power using previously mentioned ML techniques. The hybrid SVM-PSO method resulted the best one in the process of selecting the most effective wavelength values (nm). On the other hand, three regression methods comprising PLSR, multiple least regression (MLR), and neural network (NN), were employed to model the SVM-PSO selected EWs. The ratio of performance to deviation (RPD), the R2 and the root mean square error (RMSE), over the test set, for the non-linear NN regression method exhibited better results as compared to the other two regression methods, being closely followed by PLSR, and therefore NN regression method was selected as the best approach for modeling the most effective spectral wavelength values in this study.

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

PurposeMachine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples.Materials and methodsA spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique.Results and discussionsThe generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance.ConclusionsThis study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

Machine learning based technique to predict the water adulterant in milk using portable near infrared spectroscopy

Milk adulteration is a significant problem globally, as it is the most widely consumed and essential food product. Due to this, monitoring milk quality is necessary for sustaining human health. A Machine Learning (ML) based non-destructive system was developed to identify water adulteration in milk using Near Infrared (NIR) Spectroscopy. A database was created by mixing water in milk in varying proportions (0–40 %) and capturing spectra using compact TI DLP NIR scan Nano spectroscopy in the 900–1700 nm range. The captured spectra were pre-processed with the Savitzky-Golay (SG) filter, Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV) method. The most informative wavelength points were selected using the wavelength/feature selection technique, and the dimensions of these wavelengths were reduced using Principal Component Analysis (PCA). Various ML models were employed to predict the water concentration in milk. Both classification and regression methods were applied to check the system's performance. In the regression analysis, the k-Nearest Neighbour (KNN) achieved the best R2, Root Mean Square Error (RMSE), Standard Error of Prediction (SEP), Mean Absolute Error (MAE), Ratio of Performance to Deviation (RPD), Leave One Out Cross-Validation (LOOCV)-R2, and LOOCV-RMSE of 0.999, 0.399 mL ( % v/v), 0.096 mL ( % v/v), 0.227 mL ( % v/v), 33.005, 0.999, and 0.353 mL ( % v/v), respectively, while for classification analysis, the Random Forest (RF) achieved 100 % accuracy and Matthew's Correlation Coefficient (MCC). The impact of the proposed portable system, which has the potential to reshape practices and set new standards for food quality assurance, transformative, and offers solutions to critical challenges in the dairy industry.

Predictive Modeling of Fire Incidence Using Deep Neural Networks

To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model’s predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning.

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Abstract. Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg−1 for SOC, between 0.03 and 0.07 g kg−1 for POXC, between 0.09 and 0.14 g kg−1 for total N, between 1.29 and 2.63 g kg−1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation.

Non-destructive prediction of ready-to-eat kiwifruit firmness based on Fourier transform near-infrared spectroscopy

There is a growing demand for ready-to-eat kiwifruit in the world. However, ready-to-eat kiwifruit has a rather narrow range of firmness (e.g. 10–30 N), and it remains challenging to predict this firmness in a non-destructive manner. Here, we report a strategy for non-destructive prediction of kiwifruit firmness based on Fourier transform near-infrared (FT-NIR) spectroscopy. The radial basis function (RBF) model displayed superior performance, with a coefficient of determination (Rc2) of 0.83, a cross-validation coefficient of determination (Rp2) of 0.73, a root mean square error of calibration (RMSEC) of 0.58, a root mean square error of prediction (RMSEP) of 0.72, and a ratio of performance to deviation (RPD) of 1.92. To enhance the accuracy of kiwifruit firmness prediction, we optimized the FT-NIR algorithm through data preprocessing, feature selection, and dimensionality reduction. The results showed that the FD-CARS-SVR (RBF) algorithm exhibited the best performance in predicting kiwifruit firmness during the shelf life with impressive values of Rc2 (0.99), Rp2 (0.92), RMSEC (0.15), RMSEP (0.40), and RPD (3.48). To further evaluate the applicability of the FT-NIR model, we compared the data predicted by the model and acquired from the Kiwifirm™ and penetrometer GY-4. The results revealed pronounced superiority of the FT-NIR model for the firmness ranging from 10 to 40 N to replace Kiwifirm™, providing a new non-destructive model for the prediction of the firmness of ready-to-eat kiwifruit.

Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences.

In the evolving field of cannabis research, scholars are exploring innovative methods to quantify cannabinoids rapidly and non-destructively. This study evaluates the effectiveness of a hand-held near-infrared (NIR) device for quantifying total cannabidiol (total CBD), total delta-9-tetrahydrocannabinol (total THC), and total cannabigerol (total CBG) in whole cannabis inflorescences. Employing pre-processing techniques, including standard normal variate (SNV) and Savitzky-Golay (SG) smoothing, we aim to optimize the portable NIR technology for rapid and non-destructive cannabinoid analysis. A partial least-squares regression (PLSR) model was utilized to predict cannabinoid concentration based on NIR spectra. The results indicated that SNV pre-processing exhibited superior performance in predicting total CBD concentration, yielding the lowest root mean square error of prediction (RMSEP) of 2.228 and the highest coefficient of determination for prediction (R2P) of 0.792. The ratio of performance to deviation (RPD) for total CBD was highest (2.195) with SNV. In contrast, raw data exhibited the least accurate predictions for total THC, with an R2P of 0.812, an RPD of 2.306, and an RMSEP of 1.651. Notably, total CBG prediction showed unique characteristics, with raw data yielding the highest R2P of 0.806. SNV pre-processing emerges as a robust method for precise total CBD quantification, offering valuable insights into the optimization of a hand-held NIR device for the rapid and non-destructive analysis of cannabinoid in whole inflorescence samples. These findings contribute to ongoing efforts in developing portable and efficient technologies for cannabinoid analysis, addressing the increasing demand for quick and accurate assessment methods in cannabis cultivation, pharmaceuticals, and regulatory compliance.

Rapid and Non-Destructive Determination of Fatty Acid Profile and Oil Content in Diverse Brassica carinata Germplasm Using Fourier-Transform Near-Infrared Spectroscopy

Brassica carinata is one of the oilseeds in the Brassicaceae family, possessing seed quality traits such as oil with various fatty acid profiles suitable for many industrial applications. Determination of such quality traits using conventional methods is often expensive, time-consuming, and destructive. In contrast, the Near-Infrared Spectroscopic (NIRS) technique has been proven fast, cost-effective, and non-destructive for the determination of seed compositions. This study aimed to demonstrate that NIRS is a rapid and non-destructive method for determining the fatty acid profile and oil content in diverse germplasms of B. carinata. A total of 96 genetically diverse B. carinata germplasms that include accessions, advanced breeding lines, and varieties were used in this study. Reference data sets were generated using gas chromatography and the Soxhlet oil extraction method for fatty acid profile and oil content, respectively. Spectra data were taken from the wavenumber range of 11,500 to 4000 cm−1 using the Fourier-transform near-infrared (FT-NIR) method. NIRS calibration equations were developed using partial least square (PLS) regression with OPUS software, version 7.5.1. Higher coefficient of determination (R2val) and ratio of performance to deviation (RPD) > 3 were obtained for oleic acid (R2val = 0.92, RPD = 3.6), linoleic acid (R2val = 0.89, RPD = 3.2), linolenic acid (R2val = 0.93, RPD = 3.8), erucic acid (R2val = 0.92, RPD = 3.5), and oil content (R2val = 0.93, RPD = 3.6). Thus, the NIRS calibration models for the aforementioned fatty acids and oil content were found to be strong enough for prediction. However, the calibration models for palmitic acid (R2val = 0.78, RPD = 2.1) and stearic acid (R2val = 0.75, RPD = 2.0) showed relatively smaller R2val and thus became weaker in their prediction capacity. Despite their relatively lower R2, the calibration equations for palmitic and stearic acids could be used for approximate estimation and rough screening purposes. In conclusion, the calibration models that we have developed will be useful in applying NIRS as a high-throughput, non-destructive method for the screening of large germplasms in terms of their fatty acid profiles and oil content during the oil quality breeding efforts conducted on B. carinata.

Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression

This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil.

Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory

Organic carbon and total nitrogen are essential nutrients for plant growth. The presence of these nutrients at acceptable levels can create an optimal environment for the development of crops of interest. The application of spectroscopic techniques and the use of machine learning algorithms have made it possible to calibrate models capable of predicting the number of elements present in the soil. One of these techniques is hyperspectral imaging, which captures portions of the electromagnetic spectrum where the materials present in the soil can be differentiated due to the vibrations of chemical bonds. The objective of this research is to use statistical models to predict OC and N in soils from hyperspectral images. Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination (R2), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. For OC, the values found for the RF model were an R2 of 0.87, an RMSEP of 0.10, and an RPD of 6.74; the SVM model presented an R2 of 0.92, an RMSEP of 0.20, and an RPD of 3.56. For the variable N, the values found for the RF model were an R2 of 0.79, an RMSEP of 0.03, and an RPD of 5.44; for the SVM model, they were an R2 of 0.87, an RMSEP of 0.08, and an RPD of 2.76. The RF model showed a better fit for both variables. The SVM model also produced acceptable results. The results show that machine learning models are a good alternative for analysing soil-related variables.

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.