Soluble Model Research Articles

Extensive data analysis and modelling of carbon dioxide solubility in ionic liquids using chemical structure-based ensemble learning approaches

The significant rise in carbon dioxide (CO2) emission due to industrial growth is a major global challenge. As a result, there is a need to implement various techniques to reduce and regulate this phenomenon. One such technique involves the utilization of ionic liquids (ILs) as solvents in CO2 capturing and separation processes, which is already commonly practiced. In this study four advanced intelligent models, Extreme Gradient Boosting (XGBoost), Gradient Boosting (GBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) have been proposed to predict the solubility of CO2 in 160 different ILs based on factors such as temperature, pressure, and the chemical structure of the ILs. Findings indicate that the XGBoost model is the most accurate among the four models, with the root mean square error (RMSE) and coefficient of determination (R2) values of 0.014 and 0.9967, respectively. Moreover, the results reveal that increasing pressure, decreasing temperature, and lengthening the alkyl chain all increase the solubility of CO2 in ILs. Furthermore, pressure and the number of –CH2 substructure in ILs have the most significant impact on the CO2 solubility in ILs, respectively. To ensure the XGBoost model's reliability, the model's data has been assessed using the leverage technique. The results show that 92.44 % of the data fell within the valid area, which is a substantial percentage and indicates the model's high reliability. The findings of this study will assist in designing and fine-tuning the chemical structure of ionic liquids specifically for CO2 capture purposes.

Bethe M-layer construction on the Ising model

In statistical physics, one of the standard methods to study second order phase transitions is the renormalization group that usually leads to an expansion around the corresponding fully connected solution. Unfortunately, often in disordered models, some important finite dimensional second-order phase transitions are qualitatively different or absent in the corresponding fully connected model: in such cases the standard expansion fails. Recently, a new method, the M-layer one, has been introduced that performs an expansion around a different soluble mean field model: the Bethe lattice one. This new method has been already used to compute the upper critical dimension DU of different disordered systems such as the Random Field Ising model or the Spin glass model with field. If then one wants to go beyond and construct an expansion around DU to understand how critical quantities get renormalized, the actual computation of all the numerical factors is needed. This next step has still not been performed, being technically more involved. In this paper we perform this computation for the ferromagnetic Ising model without quenched disorder, in finite dimensions: we show that, at one-loop order inside the M-layer approach, we recover the continuum quartic field theory and we are able to identify the coupling constant g and the other parameters of the theory, as a function of macroscopic and microscopic details of the model such as the lattice spacing, the physical lattice dimension and the temperature. This is a fundamental step that will help in applying in the future the same techniques to more complicated systems, for which the standard field theoretical approach is impracticable.

The black hole interior from non-isometric codes and complexity

Quantum error correction has given us a natural language for the emergence of spacetime, but the black hole interior poses a challenge for this framework: at late times the apparent number of interior degrees of freedom in effective field theory can vastly exceed the true number of fundamental degrees of freedom, so there can be no isometric (i.e. inner-product preserving) encoding of the former into the latter. In this paper we explain how quantum error correction nonetheless can be used to explain the emergence of the black hole interior, via the idea of “non-isometric codes protected by computational complexity”. We show that many previous ideas, such as the existence of a large number of “null states”, a breakdown of effective field theory for operations of exponential complexity, the quantum extremal surface calculation of the Page curve, post-selection, “state-dependent/state-specific” operator reconstruction, and the “simple entropy” approach to complexity coarse-graining, all fit naturally into this framework, and we illustrate all of these phenomena simultaneously in a soluble model.

The thermal equation of state of xenon: Implications for noble gas incorporation in serpentine minerals and their transport to depth

Isotopic signatures of heavy noble gases in the Earth's mantle contain a major component recycled by subduction. The experimental and field studies reported in the literature show increasing evidence that serpentine minerals can hold large quantities of noble gases, potentially serving as their primary vectors to depth. However, at present, their retention mechanism in these minerals is not fully understood. Additionally, noble gas solubilities from field and experimental studies show large differences in terms of elemental concentrations. Here, we performed crystal chemical modeling to evaluate the incorporation mechanism of noble gases and their solubilities in serpentine minerals along subduction zone geotherms. To this end, we determined the thermal equation of state of xenon using in situ X-ray diffraction and absorption up to 60 GPa and 728 K. In this range, the xenon equation of state is well-adjusted using the Mie-Grüneisen-Debye formalism with relevant fitting parameters. We show that the experimentally observed solubility trend, which follows the order Ne < He < Ar < Kr < Xe, can be explained by the incorporation of noble gases at two distinct crystallographic sites. The light noble gases He and Ne are most likely retained at the van der Waals hydrogen-oxygen bond position between the layers, while the heavy and larger noble gases enter the voids between the six-membered SiO4 rings. It should be noted that octahedral sites can potentially host xenon, but cannot accommodate argon and krypton. Indeed, this would require unrealistic flexibility of the crystal lattice. Our models extended to mantle wedge conditions predict decreasing solubilities, particularly for light noble gases, in agreement with observations from natural samples. Compared to the noble gas concentrations determined experimentally in serpentine, natural concentrations are much higher and very variable. Our solubility model confirms that equilibrium processes cannot explain these observations. We therefore suggest that the high and variable noble gas concentrations found in natural samples must be due to hybrid hydration processes in ultramafic rocks that involve different degrees of water activities.

Solubility Measurement, Model Correlation, and Solvent Effect Analysis of Spectinomycin Dihydrochloride Pentahydrate in Three Binary Solvents

Solubility Measurement, Model Correlation, and Solvent Effect Analysis of Spectinomycin Dihydrochloride Pentahydrate in Three Binary Solvents

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BackgroundProtein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.ResultsIn this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.ConclusionsGATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol.

Optical measurements of solid nitrogen solubility in hydrogen at cryogenic temperatures

The solubility of impurities in hydrogen at cryogenic temperatures is essential information for the design of liquid hydrogen plants. However, large data scatter and inconsistent predictions of impurity solubility by thermodynamic models mean that expensive engineering design margins are required to avoid blockages in cryogenic equipment. Here we report the combination of a stereomicroscope, commercial cryocooler and pressure cell to directly visualise solid impurity freeze-out and melting in stirred hydrogen at low temperatures and elevated pressures. Scattered literature data for nitrogen's solubility in hydrogen are compared to new solid-fluid equilibrium (SFE) measurements for a 1000 ppm N2 in H2 mixture at (1.8–5) MPa, and (42–52) K. These results were used to improve solubility models implemented in the ThermoFAST software package, achieving a three-fold decrease in the root-mean-square deviations between the SFE predictions and the data, helping lower the risk of impurity freeze-out in hydrogen liquefaction.

Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation

A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure–property relationship (QSPR) models, different solvent types are identified by molecular descriptors, which, in this study, are considered as UNIFAC subgroups. To overcome the potential lack of UNIFAC subgroups for the complex Active Pharmaceutical Ingredients (APIs) currently developed in the pharmaceutical industry, the API molecule is considered as a unique entity in the proposed modelling approach. Therefore, API solubility is predicted as a function of temperature, functional subgroups of the solvents and composition of the solvent mixture; in turn, regressors’ correlation is handled through Partial Least-Squares (PLS) regression. The method is developed and tested with experimental data of a real API and 14 organic solvents that are industrially employed for crystallisation. Solubility predictions are accurate and precise for single solvents, binary mixtures and ternary mixtures of organic solvents at different compositions and temperatures, with a determination coefficient R2 ≥ 0.90.To further test the applicability of the model, the proposed approach is applied to 9 literature organic solubility datasets of drugs and drug-like compounds and compared to benchmark solubility models in the literature. Results show that the proposed approach provides satisfactory predictions: the majority of validation and calibration data have R2 = 0.95–0.99; the ratio between RMSE (root mean squared error) of the proposed method and the range of measured solubility values is from 1 to 3 orders of magnitude smaller than the RMSE ratio obtained by the benchmark models.

Relative Bioavailability Assessment of Solid Forms by An Artificial Stomach and Duodenum Apparatus

In this work, the ability of the artificial stomach and duodenum (ASD) model to predict bioavailability in rats was investigated using a poorly soluble model compound, BI-639667. A solution and four suspensions of different solid forms of BI-639667 were tested both in an ASD and rats. Rank order of the bioavailability estimated from an ASD apparatus is consistent with that of in vivo result in rats, i.e., solution > salicylic acid cocrystal > malate salt > maleate salt > monohydrate, which correlates with the ability of the different solid forms to maintain supersaturation with respect to the stable form in aqueous solution. The results support the use of an ASD for characterizing dissolution performance of solid forms to aid their selection for tablet formulation development.

Separation and recovery of sulfur from direct leach residue of zinc oxygen pressure leaching by dissolution in n-decane and recrystallization

The effective recovery and utilization of elemental sulfur in the direct leach residue (DLR) from zinc oxygen pressure leaching poses a significant challenge. This study analyzes the distribution characteristics of sulfur in DLR and determines its solubility in n-decane at various temperatures. Results indicate a gradual increase in sulfur solubility with temperature, reaching a maximum of 6.84 g/100 mL at 150 °C Utilizing the Apelblat model, a fitting equation of lnX = 88.3–7155.9/T − 12.0lnT is derived. Under conditions of 130 °C, a liquid–solid ratio of 8:1, a reaction time of 3 min, and a stirring speed of 300 rpm, 99.2% of sulfur in the residue can be dissolved in n-decane. Additionally, this separation process naturally enriches other valuable elements in the residue. The cooling rate significantly influences sulfur purity, with elemental sulfur forming high-quality crystals exhibiting a positive octahedral rhombic morphology at a cooling rate of 0.018 °C/s. Finally, the dissolution mechanism of sulfur in n-decane involves sulfur complexation, and the reliability of the sulfur solubility model is verified.

Calculations of solubilities of pure noble gases and He–CO2/CH4/N2 gas mixtures in aqueous solutions with accurate thermodynamic models

Determining solubilities of pure noble gases and He–CO2/CH4/N2 gas mixtures in aqueous solutions is essential for various applications of noble gases in geological processes and for understanding the migration and accumulation of helium. This study developed two thermodynamic models for calculations of solubilities of five pure noble gases (i.e. He, Ne, Ar, Kr, and Xe) and He–CO2/CH4/N2 gas mixtures in water and in aqueous NaCl solution. The first model is constructed by extending the improved SAFT-LJ equation of state (EOS) to five binary H2O-noble gas systems and four ternary systems (i.e. H2O–He–NaCl, H2O–He–CO2, H2O–He–CH4, and H2O–He–N2). Both the non-ideality of aqueous phase and that of vapor phase of these systems were described using the improved SAFT-LJ EOS (φ-φ model). The second model is developed by modifying the gas solubility model proposed by Duan and his coworkers (one type of γ-φ model). The Pitzer theory was used to describe the non-ideality of aqueous phase and the improved SAFT-LJ EOS was used to describe the non-ideality of vapor phase. Both binary adjustable parameters of the improved SAFT-LJ EOS and parameters of the modified solubility model for pure gas were evaluated from experimental solubility data for pure gas. Both models are predictive for solubilities of gas mixtures since there is no need to evaluate their parameters from solubility data of gas mixtures.Comparisons of calculations of two models with experimental data show that both models can well represent solubilities and Henry's coefficients of five noble gases in pure water, as well as the solubility of He in aqueous NaCl solutions over a wide P-T range (273–600 K, 1–2000 bar for the solubility of He in water). The absolute average deviation of the improved SAFT-LJ EOS from experimental solubility data for five noble gases in pure water is 3.14% and that of the modified solubility model is 2.21%. Predictions of the improved SAFT-LJ EOS for Henry's coefficients for He in the He–CO2–H2O system are consistent with available experimental data. According to predictions of the improved SAFT-LJ EOS, Henry's coefficient for He in the He–CO2–H2O system is always lower than that in the He–H2O system at the same depth. Within 0–5 km depth, He solubility of He–CO2 gas mixture is much greater than that of pure He at the same depth and He partial pressure, He solubility of He–N2 mixture is a little greater than that of pure He, and He solubility of He–CH4 mixture is between that of He–CO2 mixture and that of He–N2 mixture. A computer program for calculations of solubilities of pure noble gases and He–CO2/CH4/N2 gas mixtures in pure water and in aqueous NaCl solutions can be obtained from the corresponding author via email.

Porous hollow nanospheres nickel phosphide and its high catalytic hydrogenation performance on naomaohu coal soluble portion and model compounds

The catalytic conversion of oxygen-containing aromatic ring in lignite under mild conditions is crucial to obtain clean liquid fuel. Here, hollow nanosphere Ni3P-500–24 catalysts were prepared at a calcination temperature of 500 °C and acid-etching time of 24 h. The porous hollow nanospheres structure could expose more catalytic sites, and the electron-rich nickel exhibited a good catalytic activity. The conversion rate of benzyl phenyl ether (BPE) was 100 % (140 ℃, 1 MPa H2, 2 h), and the main products were methylcyclohexane and cyclohexanol, indicating that the catalyst had good catalytic hydrocracking performance. In addition, the catalyst exhibited certain deoxygenation activity and high product selectivity for other oxygen-containing model compounds. For the catalytic hydrogenation of Naomaohu coal (NMHC) soluble portion, the content of alkanes increased from 19 % to 47 %, while the content of oxygen-containing compounds decreased from 56 % to 21 %, which further demonstrate that the catalyst have good catalytic hydrogenation and deoxygenation performance. Density Functional Theory (DFT) calculations showed that the properties of the Ni3P-500–24 catalyst was similar to noble metal, which could activate H2 to produce two kind of active hydrogen: H radical and diatomic hydrogen. The synergistic pathways for the transfer of active hydrogen during the catalytic hydroconversion (CHC) process were analyzed. Above results could provide theoretical guidance for the conversion of lignite into high-quality clean liquid fuels.

ZN lattice gauge theories with matter fields

Motivated by the exotic phenomenology of certain quantum materials and recent advances in programmable quantum emulators, we here study fermions and bosons in ZN lattice gauge theories. We introduce a family of exactly soluble models, and characterize their orthogonal (semi)metallic ground states, the excitation spectrum, and the correlation functions. We further study integrability-breaking perturbations using an appropriately derived set of Feynman diagrammatic rules and borrowing physics associated to Anderson's orthogonality catastrophe. In the context of the ground states, we revisit Luttinger's theorem following Oshikawa's flux insertion argument and furthermore demonstrate the existence of a Luttinger surface of zeros in the fermionic Green's function. Upon inclusion of perturbations, we address the transition from the orthogonal metal to the normal state by condensation of certain excitations in the gauge sectors, so-called “e particles.” We furthermore discuss the effect of dynamics in the dual “m-particle” excitations, which ultimately leads to the formation of charge-neutral hadronic N-particle bound states. We present analytical arguments for the most important phases and estimates for phase boundaries of the model. Specifically, and in sharp distinction to quasi-one-dimensional ZN lattice gauge theories, renormalization group arguments imply that the phase diagram does not include an emergent deconfining U(1) phase in the limit of large number of fermion flavors. Therefore, in regards to lattice QED problems, ZN quantum emulators with N&lt;∞ can at best be used for approximate solutions at intermediate length scales. Published by the American Physical Society 2024

Exaggerated Kawasaki disease-associated vasculitis in C-C Motif Chemokine Receptor 5 (CCR5) deficient mice is driven by Granulocyte Colony Stimulating Factor (G-CSF) overproduction and neutrophil formation and infiltration

Kawasaki disease (KD) is a rare but serious illness that primarily affects children. It is characterized by inflammation of the blood vessels throughout the body, including the coronary arteries (CA). Polymorphisms in CCR5 influence KD susceptibility and cardiovascular outcome, but the mechanisms are enigmatic yet. Thus, we aimed to explore the mechanisms by which polymorphisms in CCR5 aggravate the vascular injury in KD by using the Candida albicans water soluble (CAWS) fraction model. Male (6-10-weeks-old) CCR5 deficient mice (CCR5−/−) and their counterpart (CCR5+/+) were used. Cardiac single-cell transcriptomics (scRNAseq) analysis of both control and KD mice revealed an inflammatory landscape characterized by an abundance of macrophages, monocytes, and neutrophils. Additionally, our scRNAseq data indicated that CCR5 expression is predominantly observed in macrophages and monocytes in KD mice, whereas its ligands, CCL3 and CCL4, are primarily expressed in neutrophils. Our CellChat analysis uncovered robust intercommunication between neutrophils and macrophages and monocytes. This interaction involves the production of CCL3 and CCL4 by neutrophils, which act on CCR5 receptors in macrophages and monocytes. To validate this scRNAseq data, we analyzed the vasculitis levels in CA and immune cells content in CCR5+/+ and CCR5−/− mice with KD via histology and flow cytometry. CCR5−/− mice with KD exhibited aggravated vasculitis, increased neutrophil accumulation [(% in relation to 10^6 leukocytes) CCR5+/+: 2.5±1.7 n=4 vs. CCR5+/+ KD: 16.1±3.2 n=4], and CCR5−/− KD: 30.2±7.4 n=4), alongside cardiac senescence (3-fold rise in p21 expression) and a 2-fold increase in Caspase-3, indicating increased cell death compared to CCR5+/+ with KD. Mechanistically, our plasma proteome profile cytokine assay revealed that KD increases circulating IL-6 and circulating neutrophil, interestingly CCR5−/− mice with KD showed further circulating IL-6 and neutrophil along with G-CSF, which plays a critical role in stimulating the production and release of granulocytes, particularly neutrophils, from the bone marrow into the bloodstream. Thus, deficiency in CCR5 triggers a worse vasculitis in KD by regulating an over production of neutrophil by bone marrow via G-CSF, which in turn will lead to an exaggerated IL-6 production, further accumulation in CA, and aggravated cardiac damage. FAPESP (2022/06639-2), NHLBI-R00 (R00HL14013903), AHA (CDA857268 and 23DIVSUP1069230), Vascular Medicine Institute, the Hemophilia Center of Western Pennsylvania Vitalant, and Children's Hospital of Pittsburgh of the UPMC Health System. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Non-inertial interpretation of the Dirac oscillator

Non-inertial physics is seldom considered in quantum mechanics and this contrasts with the ubiquity of non-inertial reference frames. Here, we show an application to the Dirac oscillator which provides a fundamental model of relativistic quantum mechanics. The model emerges from a term linearly dependent on spatial coordinates added to the momentum of the free-particle Dirac Hamiltonian. The definition generates peculiar features (mutating vacuum energy, non-Hermitian momentum, accidental degeneracies of the spectrum, etc). We interpret these anomalies in terms of inertial effects. The demonstration is based on the decoupling of the Dirac equation from the stereographic projection that maps the 3D geometry of the dynamical problem to the complex plane. The decoupling shows that the fundamental mechanical model underpinning the Dirac oscillator reduces to the representation of the oscillator in the rotating reference frame attached to the orbital angular momentum. The resulting Coriolis-like contribution to the Hamiltonian accounts for the peculiarities of the model (mutating vacuum energy, form of the non-minimal correction to the momentum, classical intrinsic spin and gain of its quantum value, accidental degeneracies of the energy spectrum, supersymmetric potential). The suggested interpretation has an interdisciplinary character where stereographic geometry, classical physics of the Coriolis effect and quantum physics of Dirac particles contribute to the definition of one of the few exactly soluble models of relativistic quantum mechanics.

Measurement and Modeling of Zolpidem Solubility in Supercritical Carbon Dioxide: Effect of Two Cosolvents

Measurement and Modeling of Zolpidem Solubility in Supercritical Carbon Dioxide: Effect of Two Cosolvents

Theoretical models of solubility for organic solvents. A conceptual density functional theory approach

We present a simple semi-empirical model for solubility in organic solvents, based on the conceptual density functional theory (CDFT) and inspired on the well-known empirical linear solvation energy relationships (LSER) proposed by Kamlet and Taft, where a multiparametric correlation between solubility and empirical descriptors including the hydrogen bond acidity (HBA) and basicity (HBB) plus an additional term describing the dipole polarizability of the solvents was presented. Herein, we set up an analogue model that describes HBA, HBB and dipole polarizability with electronic descriptors derived from the conceptual density functional theory. The main relevant results are as follows: (i) as expected, HBA and HBB properties are not absolute but regional properties, thereby suggesting that any solvent can display both properties with different weights (bifunctional solvents); (ii) while HBA can nicely be represented by a regional electrophilicity index, the HBB property can be accounted for by a regional nucleophilicity. The dipole polarizability is consistently represented by the global softness of the solvent molecule; (iii) the regionalization of these properties is achieved by defining electrophilic and nucleophilic basins within a given solvent molecule by using the electrophilic and nucleophilic Fukui Functions integrated within these basins. The resulting model is validated against the experimental solubility of vanillin and hesperidin in different organic solvents. Further perspectives of the present study should be of special interest in theoretical studies concerning ionic liquids and deep eutectic solvents.

Solubility Determination and Model Evaluation of Triethylamine Hydrochloride in Three Binary Mixed Solvents

Solubility Determination and Model Evaluation of Triethylamine Hydrochloride in Three Binary Mixed Solvents

New solubility model to correlate solubility of anticancer drugs in supercritical carbon dioxide and evaluation with Kruskal–Wallis test

Anti-cancer drugs are the result of human endeavor in search of a treatment for a deadly disease like cancer. The advent of the ideology of sustainability and eco-friendliness has propelled the manipulation of supercritical fluid technology in numerous fields. Beyond green technology, they account for the production of nanoparticles, due to which they are highly exploited for the production of anti-cancer drugs, making the existing technologies obsolete. Production of anticancer drugs through supercritical fluid technology requires the knowledge of anticancer drug as well as an efficient model to correlate its solubility in the supercritical fluid. Thus, in this work, a new model for correlating the solubility of anticancer drugs in supercritical carbon dioxide based on thermodynamic frame work has been proposed. Over eighteen important anticancer drugs are considered for evaluation. Further, five successful six-parameter models are considered to compare the performance of the new thermodynamic model. Various statistical parameters and Kruskal-Wallis test have been used to compare and evaluate the significance of the difference between the models in order to test the efficacy of the proposed model.

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion.

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.