Effect of different gelators on the physicochemical properties and microstructure of coconut oleogels.

BACKGROUND: The inherent properties of coconut oil (CO), including its elevated saturated fatty acid content and low melting point, make it suitable for application in plastic fat processing. The present study explores the physicochemical characteristics, micromorphology and oxidative stability of oleogels produced from CO using various gelators [ethylcellulose (EC), ß-sitosterol/γ-oryzanol (PS) and glyceryl monostearate (MG)] to elucidate the formation mechanisms of coconut oleogels (EC-COO, PS-COO and MG-COO). RESULTS: Three oleogel systems exhibited a solid-like behavior, with the formation of crystalline forms dominated by ß and ß'. Among them, PS-COO exhibited enhanced capability with respect to immobilizing liquid oils, resulting in solidification with high oil-binding capacity, moderate hardness and good elasticity. By contrast, MG-COO demonstrated inferior stability compared to PS-COO and EC-COO. Furthermore, MG-COO and PS-COO demonstrated antioxidant properties against CO oxidation, whereas EC-COO exhibited the opposite effect. PS-COO and EC-COO exhibited superior thermodynamic behavior compared to MG-COO. CONCLUSION: Three oleogels based on CO were successfully prepared. The mechanical strength, storage modulus and thermodynamic stability of the CO oleogel exhibited concentration dependence with increasing gelling agent addition. PS-COO demonstrated relatively robust oil-binding capacity and oxidative stability, particularly with a 15% PS addition. This information contributes to a deeper understanding of CO-based oleogels and offers theoretical insights for their application in food products. © 2024 Society of Chemical Industry.

BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study.

BACKGROUND: With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS: We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION: BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

A physicochemical double-cross-linked gelatin hydrogel with enhanced antibacterial and anti-inflammatory capabilities for improving wound healing.

BACKGROUND: Skin tissue is vital in protecting the body from injuries and bacterial infections. Wound infection caused by bacterial colonization is one of the main factors hindering wound healing. Wound infection caused by colonization of a large number of bacteria can cause the wound to enter a continuous stage of inflammation, which delays wound healing. Hydrogel wound dressing is composed of natural and synthetic polymers, which can absorb tissue fluid, improve the local microenvironment of wound, and promote wound healing. However, in the preparation process of hydrogel, the complex preparation process and poor biological efficacy limit the application of hydrogel wound dressing in complex wound environment. Therefore, it is particularly important to develop and prepare hydrogel dressings with simple technology, good physical properties and biological effects by using natural polymers. RESULTS: In this study, a gelatin-based (Tsg-THA&Fe) hydrogel was created by mixing trivalent iron (Fe3+) and 2,3,4-trihydroxybenzaldehyde (THA) to form a complex (THA&Fe), followed by a simple Schiff base reaction with tilapia skin gelatin (Tsg). The gel time and rheological properties of the hydrogels were adjusted by controlling the number of complexes. The dynamic cross-linking of the coordination bonds (o-phthalmictriol-Fe3+) and Schiff base bonds allows hydrogels to have good self-healing and injectable properties. In vitro experiments confirmed that the hydrogel had good biocompatibility and biodegradability as well as adhesion, hemostasis, and antibacterial properties. The feasibility of Tsg-THA&Fe hydrogel was studied by treating rat skin trauma model. The results showed that compared with Comfeel® Plus Transparent dressing, the Tsg-THA&Fe hydrogel could obvious reduce the number of microorganisms, prevent bacterial colonization, reduce inflammation and accelerate wound healing. Local distribution of the Tsg-THA&Fe hydrogel in the skin tissue did not cause organ toxicity. CONCLUSIONS: In summary, the preparation process of Tsg-THA&Fe hydrogel is simple, with excellent performance in physical properties and biological efficacy. It can effectively relieve inflammation and control the colonization of wound microbes, and can be used as a multi-functional dressing to improve wound healing.

Determination of Microbial Diversity and Community Composition in Unfermented and Fermented Washing Rice Water by High-Throughput Sequencing.

Washing rice water (WRW) refers to the sewage produced by rice washing in China and other parts of Asia people's daily life. As in the WRW is rich a variety of nutrients, microorganisms are prone to multiply and pollute the environment. In this article, high-throughput sequencing is used to describe the microbial diversity in different fermentation time WRW. The results showed that the sequencing depth effectively covered the microbial species in the samples, and the bacterial community structure in the samples of WRW at different fermentation periods was rich in diversity. Preominant taxa included Proteobacteria (62%), Firmicutes (28%), approximately Cyanobacteria (10%) and Bacteroidetes (0.5%). The core WRW microbiome comprises Trabulsiella, Pseudomonas, Serratia, Lactobacillus, Erwinia, Enterobacter, Clostridium and Acinetobacter, some of which are potential beneficial microbes. The change of microbial community composition with the change of habitat was assessed. It was found that environmental factors had significant influence on the assembly structure of microbial community.

Antibacterial Activity and Mechanism of Linalool against Shewanella putrefaciens.

The demand for reduced chemical preservative usage is currently growing, and natural preservatives are being developed to protect seafood. With its excellent antibacterial properties, linalool has been utilized widely in industries. However, its antibacterial mechanisms remain poorly studied. Here, untargeted metabolomics was applied to explore the mechanism of Shewanella putrefaciens cells treated with linalool. Results showed that linalool exhibited remarkable antibacterial activity against S. putrefaciens, with 1.5 µL/mL minimum inhibitory concentration (MIC). The growth of S. putrefaciens was suppressed completely at 1/2 MIC and 1 MIC levels. Linalool treatment reduced the membrane potential (MP); caused the leakage of alkaline phosphatase (AKP); and released the DNA, RNA, and proteins of S. putrefaciens, thus destroying the cell structure and expelling the cytoplasmic content. A total of 170 differential metabolites (DMs) were screened using metabolomics analysis, among which 81 species were upregulated and 89 species were downregulated after linalool treatment. These DMs are closely related to the tricarboxylic acid (TCA) cycle, glycolysis, amino acid metabolism, pantothenate and CoA biosynthesis, aminoacyl-tRNA biosynthesis, and glycerophospholipid metabolism. In addition, linalool substantially affected the activity of key enzymes, such as succinate dehydrogenase (SDH), pyruvate kinase (PK), ATPase, and respiratory chain dehydrogenase. The results provided some insights into the antibacterial mechanism of linalool against S. putrefaciens and are important for the development and application of linalool in seafood preservation.

Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.

Lipophilicity, as evaluated by the n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In this study, we developed several quantitative structure-property relationship (QSPR) models to predict log D7.4 based on a large and structurally diverse data set. Eight popular machine learning algorithms were employed to build the prediction models with 43 molecular descriptors selected by a wrapper feature selection method. The results demonstrated that XGBoost yielded better prediction performance than any other single model (RT2 = 0.906 and RMSET = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance (RT2 = 0.922 and RMSET = 0.359). The robustness, reliability, and generalization ability of the models were strictly evaluated by the Y-randomization test and applicability domain analysis. Moreover, the group contribution model based on 110 atom types and the local models for different ionization states were also established and compared to the global models. The results demonstrated that the descriptor-based consensus model is superior to the group contribution method, and the local models have no advantage over the global models. Finally, matched molecular pair (MMP) analysis and descriptor importance analysis were performed to extract transformation rules and give some explanations related to log D7.4. In conclusion, we believe that the consensus model developed in this study can be used as a reliable and promising tool to evaluate log D7.4 in drug discovery.

Preparation and Characterization of Octenyl Succinate ß-Cyclodextrin and Vitamin E Inclusion Complex and Its Application in Emulsion.

Vitamin E (VE) and ß-cyclodextrin (ß-CD) can form an inclusion complex; however, the inclusion rate is low because of the weak interaction between VE and ß-CD. The results of a molecular docking study showed that the oxygen atom in the five-membered ring of octenyl succinic anhydride (OSA) formed a strong hydrogen bond interaction (1.89 Å) with the hydrogen atom in the hydroxyl group of C-6. Therefore, ß-CD was modified using OSA to produce octenyl succinic-ß-cyclodextrin (OCD). The inclusion complexes were then prepared using OCD with VE. The properties of the inclusion complex were investigated by Fourier-transform infrared spectroscopy (FT-IR), 13C CP/MAS NMR, scanning electron microscopy (SEM), and atomic force microscopy (AFM). The results demonstrated that VE had been embedded into the cavity of OCD. Furthermore, the emulsifying properties (particle size distribution, ζ-potential, and creaming index) of the OCD/VE inclusion-complex-stabilized emulsion were compared with that stabilized by ß-CD, OCD, and an OCD/VE physical mixture. The results showed that the introduction of the OS group and VE could improve the physical stability of the emulsion. In addition, the OCD/VE inclusion complex showed the strongest ability to protect the oil in the emulsion from oxidation. OCD/VE inclusion complex was able to improve the physical and oxidative stability of the emulsion, which is of great significance to the food industry.

Effects of Molecular Weight and Guluronic Acid/Mannuronic Acid Ratio on the Rheological Behavior and Stabilizing Property of Sodium Alginate.

The aim of this study was to prepare sodium alginates (SAs) with different molecular weight and G/M ratio, and characterize their rheological behaviors and emulsifying properties. The result of Fourier transform infrared (FTIR) showed that the chemical bonds among the ß-d-mannuronic acid- (M-), α-l-guluronic acid- (G-), and MG-sequential blocks in the SA chains were not changed significantly by acid treatment. Meanwhile, the molecular weight and G/M ratio of the SA exhibited drastic variation after acid modification. The result of rheological analysis suggesting that the apparent viscosity of SA reduced from 30 to 16.4 mPa.s with the increase of shear rate, reveals that SA solution belongs to pseudoplastic liquid. Also, the apparent viscosity of acid-modified SA solution dropped rapidly with the decrease of the molecular weight. The properties of emulsions stabilized by SA, SA-Ms, and commercial SAs were evaluated via the interface tensiometry and determination of the zeta potential, droplet size, creaming index (CI), and Turbiscan stability index (TSI). Compared with the SA-stabilized emulsion, the interfacial tension of the emulsion stabilized by SA-M increased with the decrease of the molecular weight reduced at the similar M/G ratio. The decrease in zeta potential and the increase in TSI of the emulsion were observed with the decrease of molecular weight, indicating that molecular weight plays an important role on the emulsifying ability of SA. In addition, the SA with low G/M ratio can form emulsions with stable and fine droplets.

Antimicrobial Activity and Proposed Action Mechanism of 3-Carene against Brochothrix thermosphacta and Pseudomonas fluorescens.

3-Carene is an antimicrobial monoterpene that occurs naturally in a variety of plants and has an ambiguous antibacterial mechanism against food-borne germs. The antibacterial effects and action mechanism of 3-carene against Gram-positive Brochothrix thermosphacta ACCC 03870 and Gram-negative Pseudomonas fluorescens ATCC 13525 were studied. Scanning electron microscopy (SEM) examination and leakage of alkaline phosphatase (AKP) verified that 3-carene caused more obvious damage to the morphology and wall structure of B. thermosphacta than P. fluorescens. The release of potassium ions and proteins, the reduction in membrane potential (MP), and fluorescein diacetate (FDA) staining further confirmed that the loss of the barrier function of the cell membrane and the leakage of cytoplasmic contents were due to the 3-carene treatment. Furthermore, the disorder of succinate dehydrogenase (SDH), malate dehydrogenase (MDH), pyruvate kinase (PK), and ATP content indicated that 3-carene could lead to metabolic dysfunction and inhibit energy synthesis. In addition, the results from the fluorescence analysis revealed that 3-carene could probably bind to bacterial DNA and affect the conformation and structure of genomic DNA. These results revealed that 3-carene had strong antibacterial activity against B. thermosphacta and P. fluorescens via membrane damage, bacterial metabolic perturbations, and genomic DNA structure disruption, interfering in cellular functions and even causing cell death.

Nanobody-Alkaline Phosphatase Fusion Protein-Based Enzyme-Linked Immunosorbent Assay for One-Step Detection of Ochratoxin A in Rice.

Ochratoxin A (OTA) has become one a focus of public concern because of its multiple toxic effects and widespread contamination. To monitor OTA in rice, a sensitive, selective, and one-step enzyme-linked immunosorbent assay (ELISA) using a nanobody-alkaline phosphatase fusion protein (Nb28-AP) was developed. The Nb28-AP was produced by auto-induction expression and retained an intact antigen-binding capacity and enzymatic activity. It exhibited high thermal stability and organic solvent tolerance. Under the optimal conditions, the developed assay for OTA could be finished in 20 min with a half maximal inhibitory concentration of 0.57 ng mL-1 and a limit of detection of 0.059 ng mL-1, which was 1.1 times and 2.7 times lower than that of the unfused Nb28-based ELISA. The Nb28-AP exhibited a low cross-reactivity (CR) with ochratoxin B (0.92%) and ochratoxin C (6.2%), and an ignorable CR (<0.10%) with other mycotoxins. The developed Nb-AP-based one-step ELISA was validated and compared with a liquid chromatography-tandem mass spectrometry method. The results show the reliability of Nb-AP-based one-step ELISA for the detection of OTA in rice.

Deep-Learning-Based Drug-Target Interaction Prediction.

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis.

Variable selection and outlier detection are important processes in chemical modeling. Usually, they affect each other. Their performing orders also strongly affect the modeling results. Currently, many studies perform these processes separately and in different orders. In this study, we examined the interaction between outliers and variables and compared the modeling procedures performed with different orders of variable selection and outlier detection. Because the order of outlier detection and variable selection can affect the interpretation of the model, it is difficult to decide which order is preferable when the predictabilities (prediction error) of the different orders are relatively close. To address this problem, a simultaneous variable selection and outlier detection approach called Model Adaptive Space Shrinkage (MASS) was developed. This proposed approach is based on model population analysis (MPA). Through weighted binary matrix sampling (WBMS) from model space, a large number of partial least square (PLS) regression models were built, and the elite parts of the models were selected to statistically reassign the weight of each variable and sample. Then, the whole process was repeated until the weights of the variables and samples converged. Finally, MASS adaptively found a high performance model which consisted of the optimized variable subset and sample subset. The combination of these two subsets could be considered as the cleaned dataset used for chemical modeling. In the proposed approach, the problem of the order of variable selection and outlier detection is avoided. One near infrared spectroscopy (NIR) dataset and one quantitative structure-activity relationship (QSAR) dataset were used to test this approach. The result demonstrated that MASS is a useful method for data cleaning before building a predictive model.

A new method for wavelength interval selection that intelligently optimizes the locations, widths and combinations of the intervals.

In this study, a new algorithm for wavelength interval selection, known as interval variable iterative space shrinkage approach (iVISSA), is proposed based on the VISSA algorithm. It combines global and local searches to iteratively and intelligently optimize the locations, widths and combinations of the spectral intervals. In the global search procedure, it inherits the merit of soft shrinkage from VISSA to search the locations and combinations of informative wavelengths, whereas in the local search procedure, it utilizes the information of continuity in spectroscopic data to determine the widths of wavelength intervals. The global and local search procedures are carried out alternatively to realize wavelength interval selection. This method was tested using three near infrared (NIR) datasets. Some high-performing wavelength selection methods, such as synergy interval partial least squares (siPLS), moving window partial least squares (MW-PLS), competitive adaptive reweighted sampling (CARS), genetic algorithm PLS (GA-PLS) and interval random frog (iRF), were used for comparison. The results show that the proposed method is very promising with good results both on prediction capability and stability. The MATLAB codes for implementing iVISSA are freely available on the website: .

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

Separation of nine compounds from Salvia plebeia R.Br. using two-step high-speed counter-current chromatography with different elution modes.

Nine compounds were successfully separated from Salvia plebeia R.Br. using two-step high-speed counter-current chromatography with three elution modes. Elution-extrusion counter-current chromatography was applied in the first step, while classical counter-current chromatography and recycling counter-current chromatography were used in the second step. Three solvent systems, n-hexane/ethyl acetate/ethanol/water (4:6.5:3:7, v/v), methyl tert-butyl ether/ethyl acetate/n-butanol/methanol/water (6:4:1:2:8, v/v) and n-hexane/ethyl acetate/methanol/water (5:5.5:5:5, v/v) were screened and optimized for the two-step separation. The separation yielded nine compounds, including caffeic acid (1), 6-hydroxyluteuolin-7-glucoside (2), 5,7,3',4'-tetrahydroxy-6-methoxyflavanone-7-glucoside (3), nepitrin (4), rosmarinic acid (5), homoplantaginin (6), nepetin (7), hispidulin (8), and 5,6,7,4'-tertrahydroxyflavone (9). To the best of our knowledge, 5,7,3',4'-tetrahydroxy-6-methoxyflavanone-7-glucoside and 5,6,7,4'-tertrahydroxyflavone have been separated from Salvia plebeia R.Br. for the first time. The purities and structures of these compounds were identified by high-performance liquid chromatography, electrospray ionization mass spectrometry, (1)H and (13)C NMR spectroscopy. This study demonstrates that high-speed counter-current chromatography is a useful and flexible tool for the separation of components from a complex sample.

Novel hydrocolloids synthesized by polyphenols grafted onto chitosan: A promising coating to inhibit PhIP formation during pan-frying of golden pompano fillets.

Hydrocolloids synthesized by gallic acid (GA) and ferulic acid (FA) grafting onto chitosan (CS) were characterized, and their effects on PhIP formation in pan-fried golden pompano were investigated. Spectrograms including nuclear magnetic resonance, Fourier transform infrared spectroscopy and ultraviolet-visible confirmed that GA and FA were successfully grafted onto CS via covalent bonds, with grafting degree of 97.06 ± 2.56 mg GA/g and 93.56 ± 2.76 mg FA/g, respectively. The CS-g-GA and CS-g-FA exerted better solubility and antioxidant activities than CS. For the 8-min pan-fried golden pompano fillets, CS-g-GA and CS-g-FA (0.5 %, m/v) significantly reduced the PhIP formation by 61.71 % and 81.64 %, respectively. Chemical models revealed that CS-g-GA and CS-g-FA inhibited PhIP formation mainly by decreasing the phenylacetaldehyde contents from Maillard reaction and competing with creatinine to react with phenylacetaldehyde. Therefore, it was suggested that CS-g-phenolic acids emerge as novel coating for aquatic products during processing and inhibit heterocyclic amines generation.

Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics.

Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.

Comparative key aroma compounds and sensory correlations of aromatic coconut water varieties: Insights from GC × GC-O-TOF-MS, E-nose, and sensory analysis.

Aroma is a key criterion in evaluating aromatic coconut water. A comparison regarding key aroma compounds and sensory correlations was made between Thailand Aromatic Green Dwarf (THD) and Cocos nucifera L. cv. Wenye No. 4 coconut water using E-nose and GC × GC-O-TOF-MS combined with chemometrics. Twenty-one volatile components of coconut water were identified by GC × GC-O-TOF-MS, and 5 key aroma compounds were analyzed by relative odor activity value and aroma extract dilution analysis. Moreover, the combination of the E-nose with orthogonal partial least squares was highly effective in discriminating between the two coconut water samples and screened the key sensors responsible for this differentiation. Additionally, the correlation between volatile compounds and sensory properties was established using partial least squares. The key aroma compounds of coconut water exhibited positive correlations with the corresponding sensory properties.

A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems.

Classical calibration and inverse calibration are two kinds of multivariate calibration in chemical modeling. They use strategies of modeling in component spectral space and in measured variable space, respectively. However, the intrinsic difference between these two calibration models is not fully investigated. Besides, in the case of complex analytical systems, the net analyte signal (NAS) cannot be well defined in inverse calibration due to the existence of uninformative and/or interfering variables. Therefore, application of the NAS cannot improve the predictive performance for this kind of calibration, since it is essentially a technique based on the full-spectrum. From our perspective, variable selection can significantly improve the predictive performance through removing uninformative and/or interfering variables. Although the need for variable selection in the inverse calibration model has already been experimentally demonstrated, it has not aroused so much attention. In this study, we first clarify the intrinsic difference between these two calibration models and then use a new perspective to intrinsically prove the importance of variable selection in the inverse calibration model for complex analytical systems. In addition, we have experimentally validated our viewpoint through the use of one UV dataset and two generated near infrared (NIR) datasets.