Coefficient Of Determination In Prediction Research Articles

465 Comparative analysis of semantic segmentation and deep regression models with supervised pre-training for accurate prediction of pig body weight from video data: Insights from industry-scale datasets

Abstract Accurate pig body weight (BW) measurement is essential for producers as it is related to pig growth, health, and marketing, yet conventional manual weighing methods are time-consuming and may cause potential stress to the animals. Although there is a growing trend towards the adoption of three-dimensional cameras coupled with computer vision techniques for pig BW estimation, their validation using industry-scale data are still limited. Among the prevailing methodologies, semantic segmentation and supervised pre-training regression are prominent models currently used. Therefore, the objectives of this study were: 1) to estimate pig BW from repeatedly measured video data obtained from a commercial setting, 2) to compare the performance of the two image analysis methods: thresholding segmentation and deep regression approaches, and 3) to evaluate the predictive ability of BW estimation models. An Intel RealSense D435 camera was installed in the commercial farm to collect top-view videos of 540 pigs biweekly at five different time points over three months. At the same time, manually measured BW records were collected using a digital weighing system. We used an automated video conversion pipeline and fine-tuned YOLOv8 to pre-process the raw depth videos. Subsequently, we acquired a total of 151,756 depth images and depth map files. Adaptive thresholding was applied to segment the pig body from the background. Four image-derived biometric features, including dorsal length, abdominal width, height, and volume, were estimated from the segmented images and fitted using ordinary least squares and random forest models. We applied transfer learning by initializing the weights of five deep learning models, including ResNet50, Xception, EfficientNetV2S, ConvNeXtBase, and Vision Transformer, with pre-trained weights from ImageNet. We then fine-tuned these models on the pig depth images. The last layer of each model was adapted to linear regression, enabling direct estimation of BW for each image without the need for additional image pre-processing steps. We employed random repeated subsampling cross-validation, dividing 80% of the pigs for training and 20% for testing, to evaluate prediction performance at each time point. The best prediction coefficients of determination and mean absolute percentage error for each time point were 0.76, 0.86, 0.90, 0.83, 0.90, and 4.57%, 3.76%, 3.01%, 3.41%, 4.84%, respectively. On average, the Xception model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.90 and 3.01%. Our results suggest that deep learning-based supervised learning models improve the prediction performance of pig BW from industry-scale depth video data.

Improving the quality of Glycyrrhiza stems and leaves through solid-state fermentation: flavonoid content, antioxidant activity, metabolic profile, and release mechanism

Glycyrrhiza stems and leaves (GSL) are waste products of Glycyrrhiza; however, it has been considered a potential source of flavonoid compounds. In this study, GSL was processed by solid-state fermentation (SSF) to improve the flavonoid content and antioxidant activity. Firstly, a near-infrared (NIR) prediction model for flavonoid content in fermented Glycyrrhiza stems and leaves (FGSL) was established. Next, the effects of SSF on the antioxidant activity and metabolite profile of GSL were investigated. Finally, the possible mechanism of ferment release of flavonoids was explored based on enzyme activity, thermogravimetric analysis, and FTIR spectroscopy. The results revealed that NIR spectroscopy can efficiently analyze flavonoid contents in GSL, with predicted determination coefficient (Rp2) and root mean square error (RMSEP) of 0.9874 and 0.125, respectively. SSF significantly increased the levels of flavonoids, and enhanced the scavenging activities of DPPH radical and hydroxyl radical and reducing power of FGSL. Widely targeted metabolomic analysis showed the detection of 461 differential metabolites were identified after SSF, with 141 metabolites remarkably up-regulated and 320 metabolites of FGSL down-regulated during fermentation. The main types of differential metabolites were phenolic acids and flavonoids, and the destruction of cellulose by SFF was crucial to the release of flavonoids. In conclusion, our study revealed that SSF remarkably improved the phytochemical components of FGSL by increasing enzyme activity and destroying cellulose structure, thereby contributing to the enhancement of antioxidant activity. This study provided a scientific basis for the production of high-value flavonoids from plant materials and offered a novel approach to elucidate the release and conversion of flavonoids.Graphical

Influence of the peel on online detecting soluble solids content of pomelo using Vis-NIR spectroscopy coupled with chemometric analysis

The application of Visible-near infrared spectroscopy (Vis-NIRs) to internal quality detection of pomelo with large sizes and thick peels is still challenging. Considering that the peel has a large mass ratio in pomelo and a great added value in industrial processing, a postharvest processing mode of grading after peeling without affecting consumption of pomelos may be an approach to avoid adverse influence of peel on soluble solids content (SSC) evaluation. In this study, we investigated the performance variation of Vis-NIRs for SSC online determination of pomelos with and without peel for the first time. An online detection system for pomelo spectra acquisition was developed, and the spectral characteristic difference of the intact and peeled pomelos was analyzed. Subsequently, the performance of calibration models was gradually improved and comparatively analyzed by various spectral processing and wavelength selection methods. Furthermore, convolutional neural network (CNN) was utilized to explore its ability for feature extraction. The results showed that combined with standard normal variate (SNV) and second order detrending and changeable-size moving window algorithms, the partial least squares regression (PLSR) model using spectra of peeled pomelos achieved the best prediction results. Specifically, the model attained a determination coefficient of prediction (Rp2) of 0.88, a root mean square error of prediction (RMSEP) of 0.294%, and a residual predictive deviation (RPD) value of 2.57. This study demonstrates that the peel has a significantly negative effect on the prediction performance and the CNN could be an alternative to conventional PLSR method. Our work may open new avenues for the internal quality assessment of agro-products with complex tissue structure.

Prediction of protein content in paddy rice (Oryza sativa L.) combining near-infrared spectroscopy and deep-learning algorithm.

Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice's protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, Rp 2=0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with Rp 2=0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice.

Rapid Determination of Soil Organic Carbon and Permanganate Oxidizable Carbon: Comparison of Individual and Data Fusion Performance Using Fourier Transform Mid-Infrared Photoacoustic Spectroscopy (FTIR-PAS) and Laser-Induced Breakdown Spectroscopy (LIBS)

Rapid and accurate prediction of soil organic carbon (SOC) and the carbon fractions that are sensitive to management are vital for evaluating soil fertility and SOC turnover from both long-term and short-term perspectives. This study used 396 soil samples to investigate the individual and combined use of FTIR-PAS and LIBS for both SOC and permanganate oxidizable carbon (POXC) prediction coupled with partial least squares regression (PLSR). The results showed that LIBS from 205 to 899 nm (LIBS-H) was suitable for predicting SOC and POXC, with a coefficient of determination in prediction ( R P 2 ) of 0.68 and 0.73, and a root mean square error in prediction (RMSEP) of 0.57% and 169 mg kg−1, respectively. FTIR-PAS also exhibited good potential for the prediction of SOC and POXC with a slightly poorer performance ( R P 2 of 0.66 and RMSEP of 0.59% for SOC; R P 2 of 0.72 and RMSEP of 171 mg kg−1 for POXC). The prediction ability of SOC was improved by low-level data fusion based on direct concatenation of FTIR-PAS spectra and LIBS spectra at the range of 184–205 nm (LIBS-L), resulting in an R P 2 of 0.72 and an RMSEP of 0.53%. In general, there is good potential for combining FTIR-PAS and LIBS-L to improve the SOC prediction. Both FTIR-PAS and LIBS-H can be applied for individual prediction of SOC and POXC. In addition, FTIR-PAS offers the advantages of reduced complexity in the modeling and easier sample preparation.

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.

Deep learning model based on molecular spectra to determine chlorpyrifos residues in corn oil

This study presents a novel molecular spectroscopy depth fusion strategy for rapid detection of residual chlorpyrifos in corn oil. Raman and Fourier transform near-infrared (FT-NIR) spectrometers were employed to collect contaminated corn oil sample spectra. The multi-spectral fusion enhances spectral features, improving data reliability and outcomes. The research employed low and mid-level fusion strategies alongside a one-dimensional convolutional neural network (1D-CNN) for modeling. The mid-level data fusion model outperformed the low-level model, achieving a relative percent deviation (RPD) of 11.6517 and a prediction coefficient of determination (RP2) of 0.9874. The results indicate that the mid-level fusion strategy is suitable for multivariate model calibration of Raman and FT-NIR, providing a rapid detection method for pesticide residues in edible oils. This study offers a method reference for fusion strategies in molecular spectroscopy and expands the application scope of deep learning in spectroscopy.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonseed Based on Machine Learning Algorithms.

Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

Near-infrared spectroscopy in resin transfer molding—determination of the degree of cure

Near-infrared (NIR) spectroscopy is employed to directly monitor the degree of cure of composite material during the curing phase in the resin transfer molding (RTM) process. The composite material consists of natural fibers and an epoxy-amine resin system. The quantitative determination of the degree of cure, α, from the NIR spectra is realized by partial least square (PLS) regression in conjunction with various pre-treatments of the spectral data. As a reference, the degree of cure is determined by isothermal differential scanning calorimetry (DSC) experiments. The best PLS model that could be obtained for α is characterized by a determination coefficient of prediction (R²P) of 0.980 and root mean square error of prediction (RMSEP) of 4.4 %. While the spectra change during curing according to the literature for each RTM trial, significant differences can be observed between the spectra of different RTM trials. The reasons for this are discussed in detail. The findings show the potential of inline NIR spectroscopy for monitoring α in the RTM process.

Innovative solar distillation system with prismatic absorber basin: Experimental analysis and LSTM machine learning modeling coupled with great wall construction algorithm

Enhancing the vaporization surface area of the solar distillers through the implementation of a novel configurations based solar distiller represents a cost-efficient strategy for maximizing the distilled water output of conventional solar stills. The study introduces a pioneering wicked prismatic-shaped solar distiller equipped with wick materials and feed spaying nozzles, aimed at augmenting vaporization rates inside the still trough and, consequently, increasing the yield of freshwater. Two solar distillers with double slope covers are constructed and tested include a modified solar still with a prismatic basin, two incline covers, and spraying nozzles (MSS) and a reference double slope solar still (RSS). Furthermore, we have constructed a hybrid artificial intelligence framework, integrating a long short-term memory (LSTM) neural network fine-tuned through the utilization of great wall construction algorithm (GWCA). This model has been designed for the purpose of forecasting both the saltwater temperature and the associated freshwater product within the two examined solar distillers, whereas, the time, solar flux, wind velocity, and ambient temperature are considered as inputs. GWCA is effectively employed to optimize the LSTM model by determining the optimal parameter values to enhance its performance. The experimental results revealed that the daily freshwater production for the MSS reached 7.94 kg/m²/day, while the RSS achieved 5.31 kg/m²/day. This represents a substantial 49.53% improvement when compared to the RSS. Additionally, the daily energy efficiency of the MSS and RSS was assessed at 57.40% and 39.80%, respectively, whereas the daily exergy efficiency was 3.80% and 2.20%, respectively, signifying a notable 44.23% and 72.74% increase the energetic and exergetic efficiencies over RSS. Furthermore, the prediction findings demonstrated that during the testing phase, the coefficient of determination for saltwater temperature prediction of the MSS was calculated at 0.996 for LSTM-GWCA and 0.963 for LSTM. In the case of freshwater product prediction, these values were 0.983 for LSTM-GWCA and 0.922 for LSTM, respectively.

Influences of season on cattle slurry compositions predicted by micro-NIR spectrometry

In this study, we used a low-cost micro-near-infrared (NIR) spectrometer to analyze the composition of cattle slurries and examined the influence of the season (spring vs. winter) and characteristic wavelength on the performance of the corresponding quantitative models. One hundred and twenty-seven cattle slurry samples collected from five farms in spring and winter (Heilongjiang, China) were characterized in terms of total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), available potassium (AK), organic matter (OM), and pH using reference methods. The partial least squares (PLS) method was used to establish seasonal models that involved winter and a mixed model to quantify cattle slurry composition. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The results showed that the winter model successfully predicted the TP content using the full band. The coefficient of determination for prediction (R2p) and ratio of prediction to deviation (RPD) were 0.846 and 3.077, respectively. This model was useful for predicting TK and AK contents and pH (R2p = 0.551–0.773, RPD = 2.251–2.727) and moderately useful for predicting TN and OM contents (R2p = 0.517 and 0.498, respectively; RPD = 1.892 and 2.094, respectively). For all compositions, the prediction performance was better for the winter models than for the mixed models. Although the use of characteristic wavelengths improved the efficiency of model construction, it had no positive effect on the performance of the mixed models. This study enables the setup of a rapid testing method for safely returning slurry to the field.

Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R2C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSECV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R2P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSEP) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R2C increased by 0.15%, 0.41%, and 0.34%, RMSECV decreased by 0.53%, 0.32%, and 0.24%, R2P increased by 0.21%, 0.63%, and 0.35%, RMSEP decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.

Study on rapid prediction of low concentration o-nitrotoluene in mononitrotoluene mixture by near infrared spectroscopy combined with novel calibration strategies

The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). However, the analytical techniques traditionally used for its content determination are tedious and time consuming. Therefore, we explored the analysis of spectral data based on near-infrared spectroscopy (NIRS) and chemometrics, and extracted the spectral features of the o-MNT based on the interval selection algorithm. The calibration models for the o-MNT content based on samples with different concentration ranges were developed by PLS. Among them, the calibration model based on samples with 0.01–0.5 % concentration range has the best prediction performance. The calibration model was established with the determination coefficient of prediction (R2) of 0.959, root mean squared error of prediction (RMSEP) of 0.011 and ratio of standard deviation of the calibration set to standard error of prediction (RPD) of 4.899 for o-MNT. It is sufficient to meet the fast detection needs of the o-MNT content for process control in chemical industry. In addition, in order to reduce the demand of the model on samples and corresponding reference values, we explored the virtual sample generation method based on background difference compensation. The calibration model based on virtual samples was established with R2 of 0.74, RMSEP of 0.028 and RPD of 1.951. This study shows that the method based on NIRS and chemometrics has strong prediction performance for o-MNT in separation process of MNT, which is a guideline for controlling product purity of m-MNT and p-MNT. And the virtual sample generation method proposed in this study can significantly reduce the sample demand of calibration model.

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost.

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.

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.

Comparison of optimization algorithms for variable selection to enhance the predictive performance of PLS regression model in determining the concentration of heavy metal Cd in peanut oil

Peanut oil is a widely loved edible vegetable oil in the world. The raw material of peanut oil may be contaminated by heavy metals in the process of planting, processing, and transportation. In this study, the heavy metal Cd content in peanut oil was rapidly determined by FT-NIRS combined with a machine learning model. Three variable optimization algorithms were used for FT-NIRS obtained by experiments, namely, the variable combination population analysis (VCPA), the iteratively variable subset optimization (IVSO), and the multi-feature spaces ensemble strategy by least absolute shrinkage and selection operator (MFE-LASSO). A partial least squares (PLS) regression model was used to determine Cd content in peanut oil. The experimental findings demonstrate that the three variable selection optimization algorithms are effective in identifying key combinations of variables. Among them, the MFE-LASSO-PLS model has strong robustness and generalization performance. Its root mean square error of prediction (RMSEP) was 3.7753 mg·kg-1, prediction coefficient of determination (RP2) was 0.9675, and the relative prediction deviation (RPD) was 5.5485. The research findings demonstrate that the rapid and accurate determination of Cd in peanut oil can be achieved through the integration of FT-NIRS technology with a machine learning model.