Squared Error Of Prediction Research Articles

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.

Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue

This study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (Di) and burnout index (Df), the comprehensive combustion performance (Si) and flammability index (Ci) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm−1. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R2P), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for Df, obtained through GA-PLSR using the first derivative (D1), and Si, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R2P, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻4, and 2.5; and 0.8808, 0.1566 wt.%2 min⁻2 °C⁻3, and 3.1. The remaining models (Di in chip and ground, Df, and Si in chip, and Ci in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior.

Predicting Silicate Glass Geochemistry Using Raman Spectroscopy and Supervised Machine Learning: Partial Least Square Applications to Amorphous Raman Spectra.

Here, Raman spectroscopy is used to develop a univariate partial least squares (PLS) calibration capable of quantifying geochemistry in synthetic and natural silicate glass samples. The calibration yields eight oxide-specific models that allow predictions of silicon dioxide (SiO2), sodium oxide (Na2O), potassium oxide (K2O), calcium oxide (CaO), titanium dioxide (TiO2), aluminum oxide (Al2O3), ferrous oxide (FeOT), and magnesium oxide (MgO) (wt%) in glasses spanning a wide range of compositions, while also providing correlation-coefficient matrices that highlight the importance of specific Raman channels in the regression of a particular oxide. The PLS suite is trained on 48 of the 69 total glasses, and tested against 21 validation samples (i.e., held out of training). Trends in root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP) model accuracy metrics are investigated to uncover the efficacy of utilizing multivariate analysis for such Raman data and are contextualized against recently produced strategies. The technique yields an average root mean of calibration (∼2.4 wt%), cross-validation (∼ 2.9 wt%), prediction (∼ 2.6 wt%), and normalized variance (∼ 28%). Raman band positional shifts are also mapped against underlying chemical variations; with major influences arising primarily as a function of overall oxidation state and silica concentration: via ferric cation (Fe3+)/ferrous cation (Fe2+) ratios and SiO2 (wt%). The algorithm is further validated preliminarily against a separate external set of 11 natural basaltic glasses to unravel the limitations of the synthetic models on natural samples, and to determine the suitability of "universal" Raman-model applications in scenarios where prior chemical contextualization of the target sample is possible. This study represents the first time Raman spectra of amorphous silicates have been paired with PLS, offering a foundation for future improvements utilizing these systems.

Spider chart, greenness and whiteness assessment of experimentally designed multivariate models for simultaneous determination of three drugs used as a combinatory antibiotic regimen in critical care units: Comparative study

In this study, five earth-friendly spectrophotometric methods using multivariate techniques were developed to analyze levofloxacin, linezolid, and meropenem, which are utilized in critical care units as combination therapies. These techniques were used to determine the mentioned medications in laboratory-prepared mixtures, pharmaceutical products and spiked human plasma that had not been separated before handling. These methods were named classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm partial least squares (GA-PLS), and artificial neural network (ANN). The methods used a five-level, three-factor experimental design to make different concentrations of the antibiotics mentioned (based on how much of them are found in the plasma of critical care patients and their linearity ranges). The approaches used for levofloxacin, linezolid, and meropenem were in the ranges of 3–15, 8–20, and 5–25 µg/mL, respectively. Several analytical tools were used to test the proposed methods' performance. These included the root mean square error of prediction, the root mean square error of cross-validation, percentage recoveries, standard deviations, and correlation coefficients. The outcome was highly satisfactory. The study found that the root mean square errors of prediction for levofloxacin were 0.090, 0.079, 0.065, 0.027, and 0.001 for the CLS, PCR, PLS, GA-PLS, and ANN models, respectively. The corresponding values for linezolid were 0.127, 0.122, 0.108, 0.05, and 0.114, respectively. For meropenem, the values were 0.230, 0.222, 0.179, 0.097, and 0.099 for the same models, respectively. These results indicate that the developed models were highly accurate and precise. This study compared the efficiency of artificial neural networks and classical chemometric models in enhancing spectral data selectivity for quickly identifying three antimicrobials. The results from these five models were subjected to statistical analysis and compared with each other and with the previously published ones. Finally, the whiteness of the methods was assessed by the recently published white analytical chemistry (WAC) RGB 12, and the greenness of the proposed methods was assessed using AGREE, GAPI, NEMI, Raynie and Driver, and eco-scale, which showed that the suggested approaches had the least negative environmental impact. Furthermore, to demonstrate solvent sustainability, a greenness index using a spider chart methodology was employed.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

Application of smart chemometric models for spectra resolution and determination of challenging multi-action quaternary mixture: statistical comparison with greenness assessment

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

Research on the upgrade and maintenance method of apple soluble solids content models

AbstractIn this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients (Rp) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the Rp of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile.Practical applicationsNIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust.

Monitoring the quality changes and freshness assessment of eggs based on chemometrics method

AbstractDespite their low cost, egg mislabeling is a prevalent problem in the market, resulting in the sale of stale eggs as fresh. In this study, unlike the classical egg freshness parameters such as Haugh Unit and air cell height, UV–Visible (UV‐Vis) spectra, fluorescence spectroscopy, and pH + color properties combined chemometrics methods were used to monitor the eggs' ages. It is known that these methods are able to detect furosine formation and chemical changes during storage. To do this, the qualities of eggs were observed for 15–24 days at three different temperatures: 4°C (refrigeration temperature), 25°C (room temperature), and 35°C (abuse temperature). After conducting a pattern recognition analysis using principal component analysis (PCA), a correlation between storage days and UV–Vis spectra, fluorescence spectra, and pH + color parameters at various temperatures was created using the partial least‐squares (PLS) method. Results showed that PLS models were able to predict eggs' ages successfully. The most reliable age assessment models for eggs were obtained for UV–Vis spectra and fluorescence spectra (ex: 320 nm) of egg samples at 35°C. Real and predicted ages of eggs were presented in validation graphs of UV–Vis spectra and fluorescence spectra (ex: 320 nm) with excellent performance of R2P of 0.96 and 0.97, and root mean square error of prediction (RMSEP) of 1.08 and 0.87, respectively. This method could help food safety organizations use UV–Vis and fluorescence spectrometers at various levels of the egg supply chain as an alternative method.

Determination of zearalenone content in wheat by modified one-dimensional convolutional neural network-based Raman spectra

In this study, a Raman spectral model trained based on Transformer-improved one-dimensional convolutional neural network (1D-CNN) was proposed for the quantitative analysis of zearalenone in wheat. Raman spectra of wheat flour samples with varying levels of mold contamination were acquired using a portable Raman spectrometer. The 1D-CNN was introduced to extract local features of Raman spectra using three convolutional layers, and then the Transformer module was employed to encode and decode these local features for obtaining global information. This approach ensured that the network could better learn complex input spectra data, thereby improving the detection accuracy and precision of the model. The results showed that the improved 1D-CNN model had better generalization performance compared to the 1D-CNN model, with a root mean square error of prediction (RMSEP) of 2.5759 μg/kg, a coefficient of determination (RP2) of 0.9837, and a relative prediction deviation (RPD) of 7.9516. The study demonstrated that the 1D-CNN model improved by Transformer could achieve better feature self-learning and multivariate model correction of Raman spectroscopy data. This research introduces a novel reference method for chemometric analysis of Raman spectra.

Exploration of Global and Specialized Near-Infrared Calibrations for the Quantification of Nutritional Content in Grapevine Organs, Berry Phenological Stages, and Shoot Lignification.

Current infrared spectroscopy applications in the field of viticulture are moving toward direct in-field measuring techniques. However, limited research is available on quantitative applications using direct measurement of fresh tissue. The few studies conducted have combined the spectral data from various cultivars, growing regions, grapevine organs, and phenological stages during model development. The spectral data from these heterogeneous samples are combined into a single data set and analyzed jointly during quantitative analysis. Combining the spectral information of these diverse samples into a global data set could be an unsuitable approach and could yield less accurate prediction results. Spectral differences among samples could be overlooked during model development and quantitative analysis. The development of specialized calibrations should be considered and could lead to more accurate quantitative analyses. This study explored a model optimization strategy attempting global and specialized calibrations. Global calibrations, containing data from multiple organs, berry phenological, and shoot lignification stages, were compared to specialized calibrations per organ or stage. The global calibration for organs contained data from shoots, leaves, and berries and produced moderately accurate prediction results for nitrogen, carbon, and hydrogen. The specialized calibrations per organ yielded more accurate calibrations with a coefficient of determination in validation (R2val) at 90.65% and a root mean square error of prediction (RMSEP) at 0.32% dry matter (DM) for the berries' carbon calibrations. The leaves and shoots carbon calibrations had R2val and RMSEP at 84.99%, 0.34% DM, and 90.06%, 0.37% DM, respectively. The specialized calibrations for nitrogen and hydrogen showed similar improvements in prediction accuracy per organ. Specialized calibrations per phenological and lignification stage were also explored. Not all stages showed improvement, however, most stages had comparable or improved results for the specialized calibrations compared to the global calibrations containing all phenological or lignification stages. The results indicated that both global and specialized calibrations should be considered during model development to optimize prediction accuracy.

Quantitative detection of crude protein in brown rice by near-infrared spectroscopy based on hybrid feature selection

It is important for rice breeding and quality evaluation to predict the protein content of brown rice rapidly and easily. In this study, near-infrared spectroscopy (NIRS) was utilized to establish a model for detecting crude protein content in brown rice based on 349 samples prepared from three kinds of brown rice, and the performance of the model was evaluated. Improved interval partial least squares (iPLS) was used to divide and screen different feature intervals after spectral preprocessing in this research. On this basis, competitive adaptive reweighted sampling (CARS) optimized the selected feature intervals, and finally 14 effective spectral features concentrated in 1160 nm–1338 nm were selected from 1050 features. The above hybrid feature selection has more advantages than the single selection. Support vector regression (SVR) calibration model was established, and partial least squares regression (PLSR) model commonly used in similar studies was selected as a comparison. The optimal spectral preprocessing method was selected according to the model prediction effect. The coefficient of determination (R2), R2 of cross-validation set (Rcv2), root mean square error of prediction (RMSEP), and relative percent difference (RPD) evaluated for the prediction model reached 0.9185, 0.8876, 0.2040% and 3.5194, respectively. The results showed that the designed method can be used for the rapid determination of the crude protein content of brown rice, providing a convenient, efficient and non-destructive method for related detection.

Near-infrared spectral interval screening based on hierarchical variables clustering and group SCAD in multivariate calibration

Spectral interval screening is a critical step in multivariate calibration, which can improve the model predictive performance and data interpretation. In this study, a novel method for interval selection is proposed based on a hierarchical variables clustering and group smoothly clipped absolute deviation(group SCAD) in combination with partial least squares(VCG-PLS). The proposed method makes use of hierarchical variables clustering to yield a variables partitioning into groups at each level, and these groups of variables from different clustering levels are then used as input for group SCAD. The method is designed to select informative wavelength intervals for near-infrared(NIR) spectroscopic data analysis. The proposed method mainly consists of three steps. Firstly, an effective hierarchical clustering is employed to cluster wavelengths(variables), which generates a partition of variables into groups at each hierarchy level and obtains all possible wavelength intervals. Then, the series of group variables obtained from various hierarchy levels are given as input to group-SCAD, and group-SCAD can generate potential group variables corresponding to each regularization parameter value. Finally, a collection of PLS models is constructed recursively by employing all wavelength intervals except one, until the optimal wavelength intervals are obtained. The optimal intervals correspond to the lowest root mean square error of prediction. The VCG-PLS integrates the advantages of hierarchical variable clustering and group SCAD, which is an efficient technique to enhance the performance of PLS in interval selection. The performance of VCG-PLS was tested on three real NIR datasets. The results demonstrate that VCG-PLS can improve prediction performance with fewer variables and may be a good wavelength interval selection strategy.

Comparative study of Vis/NIR reflectance and transmittance method for on-line detection of strawberry SSC

Strawberry, as a fragile and vulnerable fruit, the realization of automatic sorting is conducive to improve the intelligent level of strawberry industry and improve the ability of product quality management. An on-line soluble solids content (SSC) detection prototype which can protect the strawberry from mechanical damage was researched and developed. The reflectance and transmittance of visible and near infrared (Vis/NIR) spectra were acquired by the prototype respectively, and the performances of the two spectra on the SSC detection performance of strawberry were compared. Four feature selection algorithms like competitive adaptive reweighted sampling (CARS) ware used for reflectance and transmittance spectra to reduce the spectra complexity, improve the strawberry SSC detection accuracy and optimize the running time of the prototype. The comparison showed that the transmittance spectra can reflect the internal SSC information of strawberry better. Then the results of feature variable selection showed that strawberry transmittance spectra combined with CARS algorithm achieved the best result of SSC prediction, and the prediction correlation coefficient (Rp) was 0.928, the root mean square error of prediction (RMSEP) was 0.412 °Brix, and the residual predictive deviation (RPD) value was 2.670. The CARS-PLS model for reflectance spectra also obtained the optimization result in the reflectance group, but its Rp, RMSEP and RPD value was 0.812, 0.587 °Brix and 1.670 respectively, which still did not meet the reliability of application. The results demonstrated that the Vis/NIR transmittance spectra have great application potential in strawberry on-line internal quality detection.

Fuel forensics: Recent advancements in profiling of adulterated fuels by ATR-FTIR spectroscopy and chemometric approaches

Gasoline and diesel are the main petroleum products used for road transportation in India. Due to this reason, adulteration can be done by fraudsters using different miscible substances such as kerosene, turpentine, thinner, ethanol etc. In this work, Fourier transform infrared spectroscopy (FTIR) coupled with principal component analysis (PCA) and partial least square regression (PLSR) were used to investigate adulteration in petroleum products and to design an adulterant profiling. ATR-FTIR has an advantage over other traditional methods as it is less time-consuming and needs no extraction procedure. The samples used for the study were prepared by adding different volume of adulterant (0–20%) to standard diesel and gasoline samples. According to the results obtained from this study, ATR-FTIR spectroscopy proved to be the most comprehensible method for the detection of adulteration in diesel and gasoline fuels. Furthermore, the use of FTIR spectroscopy combined with PCA got best segregation of adulterated samples. The predictive model achieved a root mean square error of prediction of 0.477% and 0.592% for diesel and gasoline respectively.

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

The precise measurement of Al, Mg, Ca, and Zn composition in copper slag is crucial for effective process control of copper pyrometallurgy. In this study, a remote laser-induced breakdown spectroscopy (LIBS) system was utilized for the spectral analysis of copper slag samples at a distance of 2.5 m. The composition of copper slag was then analyzed using both the calibration curve (CC) method and the partial least squares regression (PLSR) analysis method based on the characteristic spectral intensity ratio. The performance of the two analysis methods was gauged through the determination coefficient (R 2), average relative error (ARE), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). The results demonstrate that the PLSR method significantly improved both R 2 for the calibration and test sets while reducing ARE, RMSEC, and RMSEP by 50% compared to the CC method. The results suggest that the combination of LIBS and PLSR is a viable approach for effectively detecting the elemental concentration in copper slag and holds potential for online detection of the elemental composition of high-temperature molten copper slag.

Comparison of Generalized Linear Model between Gamma and Tweedie Compound Response for Rainfall Prediction in Lampung Province

Rainfall plays a pivotal role in influencing agricultural production in Lampung province. The precision of rainfall predictions holds significant importance for enhancing agricultural yields in the region. One effective approach for modeling rainfall is Statistical Downscaling (SD), which employs statistical models to examine the correlation between large-scale (global) climatological data and small-scale (local) data. SD addresses the limitation of global scale data, such as the General Circulation Model (GCM), which lacks the resolution to directly forecast localized climate conditions like rainfall. Rainfall can be broadly categorized into continuous and discrete components. The continuous component delineates the intensity of rainfall, while the discrete component describes the occurrence of rain. Both components are integral to accurate rainfall predictions. The mixed Tweedie distribution, combining Gamma and Poisson distributions, is proficient in handling both continuous and discrete components of rainfall. GCMs commonly encounter multicollinearity issues in SD modeling, which can be mitigated through Principal Component Analysis. This study seeks to compare two regression models: the generalized linear model with a gamma response and the Tweedie compound response. Rainfall data from three distinct regions in Lampung province, representing high, medium, and lowlands, is utilized. The research findings indicate that, for high and lowlands, the Tweedie compound exhibits a smaller Root Mean Square Error of Prediction (RMSEP) compared to gamma. Conversely, in medium lands, gamma-GLM demonstrates a smaller RMSEP value than the Tweedie compound. Thus, the distribution of the Tweedie compound is better suited for use than Gamma-GLM, especially for high and lowland areas.

Real-time and non-destructive control of the freshness and viability of live mussels through portable near-infrared spectroscopy

This study introduces the application of a novel, rapid, and non-destructive method employing portable near-infrared (NIR) spectroscopy to assess the freshness and viability of live mussels throughout their shelf-life. NIR spectra ranging from 908 to 1676 nm were collected from 150 Mediterranean mussels (Mytilus galloprovincialis, L.) over a 10-day storage period. Simultaneously, measurements of weight loss, intravalvular liquid pH, and nonprotein nitrogen levels were taken. A predictive model was established by correlating the multivariate information derived from NIR spectral data with days of storage under refrigerated conditions using orthogonal partial least squares regression (OPLSR) analysis. While physical and chemical parameters (except for gradual weight loss due to water leakage) showed no distinct trends throughout storage, the fourth derivative-preprocessed NIR spectra enabled the construction of an OPLSR model that, following cross-validation, exhibited a correlation coefficient of 0.86 and, following external validation with new mussel samples, an average accuracy (root mean square error of prediction) of just 1.3 days. The predictive power of the model was primarily attributed to specific NIR wavelengths associated with key chemical features, including unsaturated fatty acids, nitrogen compounds, water content, glycerol, and ATP-related compounds that, collectively, constituted a distinctive fingerprint for forecasting mussel storage duration. The performance of the tool, along with its environmentally friendly, non-invasive, real-time, and cost-effective characteristics, align with industry control procedures and inspection needs, allowing effective freshness and viability assessment of live mussels across the food supply chain.

Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil

The quality and safety of edible vegetable oils are closely related to human life and health, meaning it is of great significance to explore the rapid detection methods of pesticide residues in edible vegetable oils. This study explored the applicability potential of substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion in soybean oils. First, we explored the impact of laser energy, delay time, and average oil film thickness on the spectral signals to identify the best experimental parameters. Afterward, we quantitatively analyzed soybean oil samples using these optimized conditions and developed a full-spectrum extreme learning machine (ELM) model. The model achieved a prediction correlation coefficient (RP2) of 0.8417, a root mean square error of prediction (RMSEP) of 167.2986, and a mean absolute percentage error of prediction (MAPEP) of 26.46%. In order to enhance the prediction performance of the model, a modeling method using the Boruta algorithm combined with the ELM was proposed. The Boruta algorithm was employed to identify the feature variables that exhibit a strong correlation with the fenthion content. These selected variables were utilized as inputs for the ELM model, with the RP2, RMSEP, and MAPEP of Boruta-ELM being 0.9631, 71.4423, and 10.06%, respectively. Then, the genetic algorithm (GA) was used to optimize the parameters of the Boruta-ELM model, with the RP2, RMSEP, and MAPEP of GA-Boruta-ELM being 0.9962, 11.005, and 1.66%, respectively. The findings demonstrate that the GA-Boruta-ELM model exhibits excellent prediction capability and effectively predicts the fenthion contents in soybean oil samples. It will be valuable for the LIBS quantitative detection and analysis of pesticide residues in edible vegetable oils.

Development of FTIR Spectroscopy and multivariate calibration for authentication beef meatballs from pork meat

In several countries, especially Indonesia, meatballs are one of the favorite foods made from meat. The high price of beef is a reason for adulterated beef meat with pork to gain a significant profit. All food products must not contain pig derivatives such as pork in Muslim societies. This study aimed to develop Fourier Transform Infrared (FTIR) spectroscopy in combination with linear discriminant analysis (LDA) for classifying pork in beef meatballs and multivariate calibration for qualitative and quantitative analysis methods using partial least square regression (PLSR) and principal component regression (PCR). The lipid component from meatballs containing pork and beef in different concentrations was obtained by employing three lipid extraction methods, namely BlighDyer, Folch, and Soxhlet methods, and subjected to attenuated total reflectance (ATR) spectral measured at wavenumbers of 4000-650 cm-1 . LDA at 3800-800 cm-1 was able to differentiate between beef meatballs and meatballs containing pork meat (PM). Additionally, PLSR using second derivative FTIR spectra at wavenumbers of 3100-900 cm-1 delivered great region for predicting pork levels in beef meatballs extracted using Folch methods with coefficient determination (R2 ) values was > 0.99 and 0.02% for root mean square error of calibration (RSMEC) and root mean square error of prediction (RSMEP) values, respectively. PCR using absorbance values of 1st derivative FTIR spectra at wavenumbers 1400-800 cm-1 for Bligh-Dyer method and 3800-800 cm-1 for Soxhlet was offered the R2 value was> 0.99 with low RSMEC and RSMEP values for prediction of pork fat in beef meatballs, respectively. It can be concluded that FTIR spectroscopy on the Savitzy-Golay derivatization method in combination with chemometrics was an accurate and quick approach for authenticating pork adulteration in beef meatballs.