Percent Relative Deviation Research Articles

Feasibility of Nondestructive Soluble Sugar Monitoring in Tomato: Quantified and Sorted through ATR-FTIR Coupled with Chemometrics

As a fast detection method, Fourier transform infrared attenuated total reflection (ATR-FTIR) spectroscopy is seldom used for monitoring soluble sugars in crops. This study aimed to demonstrate the feasibility of leveraging ATR-FTIR coupled with chemometrics to quantify and sort the contents of soluble sugar in tomatoes. Firstly, 192 tomato samples were scanned using ATR-FTIR; subsequently, a quantitative model was developed using PLSR with selected wavelength variables as inputs. Finally, a classification model was estimated through probabilistic neural network (PNN) to determine the samples. The results indicated that ATR-FTIR had successfully captured the spectra from the cellular layers of tomatoes, resulting in a robust PLSR model created by 468 selected variables with a R² value of 0.86, a RMSEP of 0.71%, a ratio of performance to relative percent deviation (RPD) of 1.87, and a ratio of prediction to interquartile range (RPIQ) of 2.1. Meanwhile, the PNN model demonstrated a high rate correct (RC) of 92.17% in identifying whether the samples with a higher soluble sugar content than the limit of detection (LOD at 2.1%). Overall, ATR-FTIR coupled with chemometrics has proven effective for non-destructive determination of soluble sugars in tomatoes, offering new insights into internal monitoring techniques for crop quality assurance.

Quantification of soluble protein content and characterization of protein secondary structure by Raman spectroscopy combined with chemometrics

In the large-scale industrialized production of soymilk, the accurate monitoring of protein content and its secondary structure is crucial to guarantee product quality and enhance nutritional value, while traditional protein detection methods have significant limitations. In this study, Raman spectroscopy was used to characterize the changes in the relative content of protein secondary structures during boiling, in which the α-helix structure was converted to other unstable secondary structures, indicating that the protein was thermally denatured. The study also accurately predicted the soluble protein content of soymilk for the first time by combining Raman spectroscopy and chemometrics based on algorithms such as Partial Least Squares (PLS), Least Squares Support Vector Machines (LSSVM), and Artificial Neural Networks (ANN). The results showed that the relative percentage deviation (RPD) values of all models were greater than 2, indicating that the models all possessed a certain degree of robustness; The PLS-based regression prediction model (with an average RPD value of 3.5592 and an average R2p of 0.9597) had the best overall prediction. The study's results can guide the soymilk production industry into online monitoring of soymilk quality.

Deep reinforcement learning driven trajectory-based meta-heuristic for distributed heterogeneous flexible job shop scheduling problem

As the production environment evolves, distributed manufacturing exhibits heterogeneous characteristics, including diverse machines, workers, and production processes. This paper examines a distributed heterogeneous flexible job shop scheduling problem (DHFJSP) with varying processing times. A mixed integer linear programming (MILP) model of the DHFJSP is formulated with the objective of minimizing the makespan. To solve the DHFJSP, we propose a deep Q network-aided automatic design of a variable neighborhood search algorithm (DQN-VNS). By analyzing schedules, sixty-one types of scheduling features are extracted. These features, along with six shaking strategies, are used as states and actions. A DHFJSP environment simulator is developed to train the deep Q network. The well-trained DQN then generates the shaking procedure for VNS. Additionally, a greedy initialization method is proposed to enhance the quality of the initial solution. Seven efficient critical path-based neighborhood structures with three-vector encoding scheme are introduced to improve local search. Numerical experiments on various scales of instances validate the effectiveness of the MILP model and the DQN-VNS algorithm. The results show that the DQN-VNS algorithm achieves an average relative percentage deviation (ARPD) of 3.2%, which represents an approximately 88.45% reduction compared to the best-performing algorithm among the six compared, with an ARPD of 27.7%. This significant reduction in ARPD highlights the superior stability and performance of the proposed DQN-VNS algorithm.

Vapor-liquid equilibria for the CO2 + trimethoxymethylsilane and CO2 + triethoxymethylsilane systems under high-pressure conditions

New binary isotherms are crucial for designing chemical separation processes within supercritical carbon dioxide (CO2) + trialkoxysilane systems. Vapor-liquid equilibria (VLE) were investigated for two-component systems, trimethoxymethylsilane + CO2 and triethoxymethylsilane + CO2, at five temperatures (313.2, 333.2, 353.2, 373.2, and 393.2 K) and pressures up to 14.07 MPa using a synthetic high-pressure phase equilibria apparatus. The pressure-temperature (P-T) plot indicates that the critical mixture curve lies between the critical points of CO2 and the trialkoxysilane compounds. The solubility of trimethoxymethylsilane and triethoxymethylsilane in CO2 increased with increasing temperature at constant pressure, following a type-I phase behavior characteristic. The experimentally observed VLE values of the CO2 + trialkoxysilane systems were correlated using the Peng-Robinson equation of state with binary parameters (kij and ηij) in the conventional mixing rule. The model accuracy was validated by calculating the average relative deviation percentage for the pressure of the binary systems, resulting in values of 4.98% for the trimethoxymethylsilane + CO2 system and 3.64% for the triethoxymethylsilane + CO2 system. The estimated variables fell within reasonable limits and showed no significant differences between the predicted and observed VLE data for both systems.

Estimation of gross calorific value of coal based on the cubist regression model.

The gross calorific value (GCV) of coal is an important parameter for evaluating coal quality, and regression analysis methods can be used to predict GCV. In this study, we proposed a GCV prediction model based on cubist regression. To develop a good regression model, feature selection of input variables was performed using a correlation analysis and a recursive feature elimination algorithm. Thus, in this study, we determined three sets of variables as the optimal combination for regression models: proximate analysis variables (Set 1: moisture, standard ash, and volatile matter), element analysis variables (Set 2: carbon, sulfur, and oxygen), and comprehensive index variables (Set 3: carbon, volatile matter, standard ash, sulfur, moisture, and hydrogen). Results for comparison with multiple linear regression, random forest regression, and numerous previous prediction models, such as gradient boosting regression tree, support vector regression (SVR), backpropagation neural networks, and particle swarm optimization-artificial neural network (PSO-ANN), indicate that these seven regression models have the best fitting effect on the comprehensive index variables among the three sets of input variables. The cubist model showed higher prediction accuracy and lower error than most other models (R2, mean absolute error, root mean square error, and average absolute relative deviation percentage values are 0.990, 0.476, 0.668, and 0.086% for the proximate analysis variables; 0.992, 0.381, 0.596, and 0.140% for element analysis variables; and 0.999, 0.161, 0.219, and 0.087% for comprehensive index variables, respectively). The cubist model combines the advantages of decision tree and linear regression, which not only enables it to perform well in terms of accuracy but also makes the model highly interpretable because it is based on multiple sublinear equations. In addition, the cubist model shows obvious advantages in terms of running speed, especially compared with SVR and PSO-ANN, which require complex parameter optimization. In summary, the cubist model considers the prediction accuracy, model interpretability, and computational efficiency as well as provides a new and effective method for GCV prediction.

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog − partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with RP, RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.

A Bi-Population Competition Adaptive Interior Search Algorithm Based on Reinforcement Learning for Flexible Job Shop Scheduling Problem

In this paper, a bi-population competition adaptive interior search algorithm (BCAISA) based on a reinforcement learning strategy is proposed for the classical flexible job shop scheduling problem (FJSP) to optimize the makespan. First, the scheduling solution is represented using a machine-job-based two-segment integer encoding method, and various heuristic rules are then applied to generate the initial population. Secondly, a bi-population mechanism is introduced to partition the population into two distinct sub-populations. These sub-populations are specifically tailored for machine assignment and operation permutation, employing different search strategies respectively, aiming to facilitate an efficient implementation of parallel search. A competition mechanism is introduced to facilitate the information exchange between the two sub-populations. Thirdly, the ISA is adapted for the discrete scheduling problem by discretizing a series of search operators, which include composition optimization, mirror search, and random walk. A Q-learning-based approach is proposed to dynamically adjust a key parameter, aiming to strike a balance between the capacity for global exploration and local exploitation. Finally, extensive experiments are conducted based on 10 well-known benchmark instances of the FJSP. The design of the experiment (DOE) method is employed to determine the algorithm’s parameters. Based on the computational results, the effectiveness of four improvement strategies is first validated. The BCAISA is then compared with fifteen published algorithms. The comparative data demonstrate that our algorithm outperforms other algorithms in 50% of benchmark instances. Additionally, according to the relative percentage deviation (RPD) from the state-of-the-art results, the BCAISA also exhibits superior performance. This highlights the effectiveness of our algorithm for solving the classical FJSP. To enhance the practical application, the scope of the ISA will be broadened in future work to more complex problems in real-world scenarios.

Solubility determination, mathematical modeling, and thermodynamic analysis of naproxen in binary solvent mixtures of (1-propanol/2-propanol) and ethylene glycol at different temperatures

This study investigates the solubility behavior of Naproxen (NAP) in binary solvent mixtures of 1-propanol (1-PrOH) and 2-propanol (2-PrOH) with ethylene glycol (EG) across a range of temperatures. The solubility of NAP was experimentally determined at five different temperatures (293.15 to 313.15 K), and the data were correlated using various thermodynamic models, including the van’t Hoff, Jouyban-Acree, modified Wilson, mixture response surface, Jouyban-Acree-van’t Hoff. The results demonstrated that NAP’s solubility increases with temperature in both solvent systems. Notably, NAP exhibited higher solubility in mixtures with 1-PrOH compared to 2-PrOH, despite the lower polarity of 2-PrOH. This unexpected trend is attributed to the distinct molecular interactions, including hydrogen bonding, influenced by the structural differences between 1-PrOH and 2-PrOH. The X-ray diffraction analysis confirmed that no polymorphic transformation occurred in NAP during dissolution, maintaining its crystalline structure. The solubility data were well-correlated by the applied models, with overall MRDs% (mean relative deviation percentage) below 6.1.

Effect of ZnO particle size on the radiation shielding efficiency of B2O3–BaO–ZnO glass system

Abstract This study analyzes the ZnO particle size’s effect on glass samples’ radiation shielding ability. Four glass samples with differing micro and nanoparticle ZnO content were investigated at four energies, 0.060, 0.662, 1.173, and 1.333 MeV. The investigated glasses are a B2O3–BaO–ZnO glass system and are composed of 30 % micro ZnO (30 M), 20 % micro ZnO and 10 % nano ZnO (20 M−10 N), 10 % micro ZnO and 20 % nano ZnO (10 M−20 N), and lastly 30 % nano ZnO (30 N). The theoretical XCOM software was employed to validate the experimental LAC values of the glasses, revealing that for at all energies, the values obtained from the two methods agreed with each other well. The glasses’ HVL, MFP, and RSE were then compared. The HVL values at all energies decreased as more nano ZnO was introduced into the glass system, reaching a minimum of 1.947 cm at 0.662 MeV for the 30 N sample. This sample also had the lowest MFP at all energies, while the 30 M glass had the highest, such as 0.088 and 0.070 for 30 M and 30 N respectively at 0.060 MeV. The RSE of a 1 cm thick sample of each of the glasses was tested and found that the 30 N sample exhibited the greatest RSE. The relative percent deviation between the 30 N and 30 M glasses was also analyzed, which highlighted the difference between 30 N’s greater LAC values compared to 30 M at all energies.

Choline chloride and amino acid solutions taste and hydration behavior with experimental thermodynamic properties and COSMO-PC-SAFT calculation

Aqueous amino acid solutions have been introduced as dietary supplements for both animals and humans. This study investigates the physicochemical properties of the solutions containing amino acids (l-glycine, d,l-alanine, l-proline), choline chloride, and water at temperature range of 288.15 to 318.15 K. The results show that increasing concentrations of amino acids and choline chloride lead to higher solution densities. Analysis of apparent molar volume (Vφ) and apparent molar isentropic compressibility (κφ) reveals that Vφ values increase with choline chloride concentration and temperature, indicating enhanced solute–solvent interactions, while κφ values decrease, suggesting increased solution compression. Thermodynamic analysis using the Redlich-Mayer model and COSMO-based modeling provides insights into molecular interactions. However, COSMO-based parameters show high average relative deviation percentage (ARD %) values, indicating poor predictive performance for the density of these systems. In contrast, the ePC-SAFT equation of state effectively predicts the densities, particularly for l-proline-based solutions, which show very low ARD % values, indicating high accuracy. The ePC-SAFT model also performs reasonably well for l-glycine solutions but shows poorer results for d,l-alanine-based solutions. The study also examines the sweetness and saltiness criteria (ASV and ASIC) of these solutions. The ASV values, which serve as a sweetness criterion, are higher than the ideal range of 0.5 < ASV < 0.7, suggesting an overly sweet taste. The ASIC values follow a similar trend, indicating increased saltiness. To achieve an appropriate grade of sweetness and saltiness, dilution to lower concentrations of the solution is recommended. Furthermore, the use of choline chloride is found to increase salt intake and enhance the taste of salt, which can be beneficial in amino acid supplements used in animal food.

Validación de un método de cromatografía líquida (HPLC-UV/Vis) para la cuantificación de aldehídos en agua de lluvia y aire

Aldehydes are compounds in the atmosphere formed by the photochemical degradation of other organic compounds in the troposphere and can be emitted by natural or anthropogenic sources. [Objective] An analytical method was implemented to quantify aldehyde samples in matrices such as air and rainwater. [Methodology] Aldehyde sampling and analysis were conducted using method TO-11 from the United States Environmental Protection Agency (EPA). Sampling was carried out by using adsorption cartridges coated with a 2,4-DNPH solution, forming a hydrazone with the aldehydes present in the rainwater and the air, which were tested using a liquid chromatograph coupled to a UV-visible detector (HPLC-UV/Vis). To validate the analysis technique and the analytical quality of the results, the following was determined: linearity, sensitivity, quantification limits, detection limits, repeatability, reproducibility, and recovery percentage. In the case of repeatability, the comparison of the Horwitz coefficient of variation was used with the percentages of relative standard deviation (% RSD) of the samples. [Results] The detection and quantification limits obtained range between 0.18 μg/m3 and 3.20 μg/m3 for acetaldehyde and acrolein, respectively, while quantification limits are between 0.62 μg/m3 for acetaldehyde and 4.70 μg/m3 for heptanal, data that characterizes the method’s analytical quality. [Conclusions] In general, the analysis method for aldehydes showed linearity, with R2 values equal to or greater than 0.9991 for each calibration curve, and relative deviation percentage values less than 2.25 %, indicating good precision in the analysis.

Optimizing the effects of potato size and shape on near-infrared prediction models of potato quality using a linear-nonlinear algorithm

The potato size and shape can affect the accuracy of predicting potato quality using near-infrared (NIR) spectroscopy. This study used NIR spectroscopy and a linear-nonlinear algorithm to eliminate the influence of potato size and shape on the accuracy of the prediction model for potato starch and moisture content. Savitzky-Golay (SG) filtering and four dimensionality reduction algorithms (iterative variable subset optimization (IVSO), variable combination population analysis- iteratively retaining informative variables (VCPA-IRIV), bootstrapping soft shrinkage (BOSS), and principal component analysis (PCA)) were used to optimize the NIR spectrum and extract spectral data. Partial least squares (PLS) linear regression and a nonlinear model (convolutional neural network–bi-directional long short-term memory (CNN-BiLSTM)) were used to establish and compare 52 quantitative prediction models. The optimum prediction model was the SG-IVSO-PLS-CNN-BiLSTM. Its correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and relative percent deviation (RPD) were 0.949, 1.350 %, and 3.172 for predicting the moisture content and 0.937, 1.110 %, and 2.863 for predicting the starch content. The SG-IVSO-PLS-CNN-BiLSTM eliminated the influence of potato size and shape on the accuracy of the prediction model. This method is suitable for predicting potato quality in the potato processing industry.

Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

Potential of two-dimensional correlation-based dual-band visible/near infrared spectroscopy to predict total volatile basic nitrogen content in meat

In the meat industry, it is essential to monitor the total volatile basic nitrogen (TVB-N) content due to its impact on human diets safety. In the present study, two-dimensional correlation spectroscopy (2D-COS) was combined with dual-band visible/near-infrared spectroscopy to identify the characteristic wavelengths of TVB-N in pork meat during storage. Reflectance spectra of pork longissimus dorsi muscles at 350–1100 nm and 1000–2500 nm were obtained during a 13-day storage period. The dual-band spectra were merged via parabolic fitting, and subjected to pre-treatments using absorbance conversion, first derivation (FD), and standard normal variate transformation (SNV). Then, the storage day was used as the external perturbation factor, and 2D-COS was performed on the dynamic spectra to identify TVB-N-related wavebands. Twenty-five characteristic wavelengths were selected, and a simplified partial least square regression (PLSR) model was established with a correlation coefficient (Rp) of 0.9591, root mean square error on prediction (RMSEP) of 1.8341 mg/100 g, and relative percent deviation (RPD) of 3.2518. These results outperformed those of the model based on full wavebands data (Rp = 0.9215, RMSEP = 2.8191 mg/100 g, RPD = 2.1114). This study accurately predicted TVB-N values in pork meat, and the integration of 2D-COS and dual-band spectra had great potential for real-time meat freshness evaluation.

Just-in-time scheduling of unrelated parallel machines with family setups and soft time window constraints

ABSTRACT This study addresses an unrelated parallel machine scheduling problem with family setups and soft time windows, which includes considerations for machine eligibility. The objective is to minimize the total weighted tardiness and earliness, and to maximize the number of Just-in-Time jobs. Four different metaheuristic algorithms are compared to determine which type – single-point (simulated annealing), population-based evolutionary (artificial immune system, genetic algorithm), or swarm intelligence algorithms (ant colony optimization) – is more effective in solving this problem. Two strategies, namely penalty and repair, are proposed to handle precedence constraints. Experimental results demonstrate that ant colony optimization with local search which employs the repair strategy, as a swarm intelligence-based algorithm outperforms its rivals in terms of objective function values and robustness. The performance of this algorithm has shown an improvement in objective function values ranging from 2.12% to 8.52% on average, with a relative percentage deviation of 10.35.

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.

A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more closely compared to other algorithms, thereby substantiating the effectiveness of the proposed algorithm.

Rapid pH Value Detection in Secondary Fermentation of Maize Silage Using Hyperspectral Imaging

As pH is a key factor affecting the quality of maize silage, its accurate detection is essential to ensuring product quality. Although traditional methods for testing the pH of maize silage feed are widely used, the procedures are often complex and time-consuming and may damage the sample. This study presents a non-destructive hyperspectral imaging (HSI) technology that provides a more efficient and cost-effective method of monitoring pH by capturing the spectral information of samples and analyzing their chemical and physical properties rapidly and without contact. We applied four spectral preprocessing methods, among which the multiplicative scatter correction (MSC) preprocessing method yielded the best results. To minimize model redundancy and enhance predictive performance, we utilized six feature extraction methods for characteristic wavelength extraction, integrating these with partial least squares (PLS), non-linear support vector machine regression (SVR), and extreme learning machine (ELM) algorithms to construct a quantitative pH value prediction model. The results showed that the model based on the bootstrapping soft shrinkage (BOSS) feature wavelength extraction method outperformed the other feature extraction methods, selecting 20 pH value-related feature wavelengths from 256 bands and building a stable BOSS–ELM model with prediction set determination coefficient (RP2), root-mean-square error of prediction (RMSEP), and relative percentage deviation (RPD) values of 0.9241, 0.4372, and 3.6565, respectively. To further optimize the model for precisely predicting pH at each pixel in hyperspectral images, we employed three algorithms: the genetic algorithm (GA), whale optimization algorithm (WOA), and bald eagle search (BES). These algorithms optimized and compared the BOSS–ELM model to obtain the best model for predicting maize silage pH: the BOSS–BES–ELM model. This model achieved a determination coefficient (RP2) of 0.9598, an RMSEP of 0.3216, and an RPD of 5.1448. We generated a visualized distribution map of pH value variation in maize silage using the BOSS–BES–ELM model. This study provides strong technical support and a reference for the rapid, non-destructive detection of maize silage pH from an image, an advancement of great significance to ensuring the quality of maize silage.

Response Surface Optimization of Steam Blanching Conditions of Shrimp (Metapenaeus dobsoni) for Drying in Solar-Electrical Hybrid Dryer

ABSTRACT The study was done to optimize the steam blanching of shrimp (Metapenaeus dobsoni) for drying purposes. Response surface methodology was used to study the interaction effects of salt concentration and blanching time on drying rate, shrinkage, rehydration ratio, redness index, chewiness, and salt content of shrimp. The optimized steam blanching conditions were salt concentration: 0.5% and blanching time: 2 min. The experimental values were validated with the predicted values using the mean relative percent deviation modulus (%P) for drying rate, shrinkage, rehydration ratio, redness index, chewiness, and salt content of shrimp and was found to be 0.06, 2.62, 2.25, 8.55, 5.45, and 1.72, respectively.

Comments Regarding “Solubility determination and thermodynamic model analysis of L-α-glyceryl phosphorylcholine in different organic solvents of 278.15–323.15 K”

A polemic is given regarding several of the calculated curve-fit parameters that Zhou and coworkers reported in their published paper. The calculated curve-fit parameters for the Combined Nearly Ideal Binary Solvent/Redlich-Kister, Jouyban-Acree-van’t Hoff, Sun and modified Apelblat models were found to give mole fraction solubilities that exceeded unity. Our analysis also found that the mean relative absolute percent deviations provided by the authors were significantly underestimated.