SN/T 5774-2025 in English
VALIDOrthogonal Experimental Design and Statistical Analysis of Results in a Chemical Analysis Laboratory
- Issued on:2025-07-25
- Implemented on:2026-02-01
- File Format:PDF
- Delivery:RFQ
| Standard No: | SN/T 5774-2025 |
| Document status: | VALID |
| Title in English: | Orthogonal Experimental Design and Statistical Analysis of Results in a Chemical Analysis Laboratory |
| Title in Chinese: | 化学分析实验室正交试验设计及结果统计分析方法 |
| Language: | English |
| File Format: | Electronic (PDF) |
| Delivery: | RFQ |
| Issued on: | 2025-07-25 |
| Implemented on: | 2026-02-01 |
| ICS Classification: | 71.040.99-Other standards related to analytical chemistry |
| Chinese Classification: | G04-Basic standards and general methods |
| Professional Classification: | SN-Import&Export Inspection |
| Related Keywords: | chemical analysis laboratory introduction in-depth analysis
data analysis phase combining range analysis chemical analysis statistical analysis chemical analysis laboratories |
Introduction
In-depth Analysis of the Standard Technical Framework
SN/T 5774-2025, "Orthogonal Experiment Design and Statistical Analysis Methods for Chemical Analysis Laboratories," is a key industry standard issued by the General Administration of Customs. It provides a systematic experimental design framework for the development of new methods in chemical analysis laboratories. Based on the GB/T 3358 series of statistical standards, this standard establishes a complete technical system from experimental design to results analysis.
Key Technical Points of Orthogonal Experimental Design
| Design Steps | Technical Requirements | Implementation Points | Common Misunderstandings |
|---|---|---|---|
| Determination of Experimental Indicators | Quantitative indicators are prioritized, and qualitative indicators require quantitative scoring | Weighted comprehensive scoring method is used for multiple indicators | Ignore the weight differences between indicators |
| Factor Level Selection | 3-7 controllable factors, 2-4 levels | The number of levels for important factors can be appropriately increased | The level spacing is too large or too small |
| Orthogonal array selection | Number of factors ≤ number of columns, number of levels matched | Give priority to orthogonal arrays with fewer experiments | Ignore interaction arrangement requirements |
| Header design | Difficult level factors should be arranged first | Avoid confounding of interactions with factors | Incorrect arrangement of interaction columns |
Comparison and application of statistical analysis methods
The standard specifies two statistical techniques, range analysis and variance analysis, which are suitable for different experimental scenarios and analysis needs respectively.
| Analysis Method | Computational Complexity | Applicable Scenarios | Advantages and Characteristics | Limitations |
|---|---|---|---|---|
| Range Analysis Method | Simple and Intuitive | Preliminary Screening, Rapid Optimization | Small Calculation Amount, Intuitive Results | Unable to Estimate Experimental Error |
| Variance Analysis Method | Complex System | Precise Analysis, Significance Test | Distinguishing Factor Effects from Random Errors | Complex Calculation Process |
Analysis of Technological Evolution of Standard Implementation
SN/T 5774-2025 integrates the actual needs of modern chemical analysis laboratories based on traditional orthogonal experiment theory, which is mainly reflected in the following technological evolution:
Technological Evolution Characteristics
- Computer-aided Analysis Integration: The standard appendix details the application of office software such as WPS spreadsheets in orthogonal experiment data processing, greatly improving analysis efficiency
- Application of Mixed-Level Orthogonal Arrays: For the common unequal-level factor combinations in chemical analysis, a systematic mixed-level orthogonal array design method is provided
- Standardization of Interaction Processing: The principles for investigating interactions in chemical analysis experiments are clarified to avoid over-complication of experimental design
In-depth Analysis of Typical Application Scenarios
Pretreatment Condition Optimization Case
In the example of optimizing the extraction of triphenyltin residues in textiles, the standard demonstrates a complete orthogonal experimental design process:
| Optimization factors | Level setting | Range analysis results | ANOVA conclusion | Optimal level determination |
|---|---|---|---|---|
| Oscillating time | 20/25/30min | Range 9.9, small impact | F=7.4<F0.05, not significant | 25min (taking efficiency into account) |
| Ultrasonic extraction temperature | Room temperature/40/50℃ | Range 18.7, largest impact | F=28.1>F0.05, significant | 50℃ |
| Derivatization time | 2/5/10min | Range 16.6, second most influential | F=23.3>F0.05, significant | 5min |
Instrument Analysis Parameter Optimization Case
An example of the development of a detection method for surfactant cationic bactericides in disinfectant products, demonstrating the practical application of a mixed-level orthogonal array:
Through the L8(4¹×2⁴) mixed-level orthogonal array, four pre-processing parameters with different numbers of levels were simultaneously optimized, achieving the goal of obtaining reliable optimization results within a limited number of trials. The results of variance analysis showed that the ammonia content in the eluent had the most significant influence (F=314.5), providing a clear direction for subsequent method optimization.
Key recommendations for standard implementation
Recommendations for the experimental design phase
- Factor screening priority: Before the formal orthogonal experiment, it is recommended to conduct a single-factor experiment to preliminarily determine the reasonable level range of each factor
- Level spacing optimization: The level spacing of important factors should be appropriately narrowed to improve optimization accuracy; the level range of secondary factors can be appropriately expanded
- Blank column setting: Reserve blank columns in the orthogonal table to provide a basis for error estimation for variance analysis
Recommendations for the data analysis phase
- Combining range analysis with variance analysis: First use range analysis to quickly determine the primary and secondary order of factors, and then use variance analysis for significance test
- Computer-assisted verification: Use tools such as WPS spreadsheets to perform formula calculations to reduce human calculation errors
- Result visualization: Draw a graph of the factor level averages to intuitively display the changing trends of each factor
Strategy for determining the optimal solution
- Priority for significant factors: For the factors with significant influence determined by variance analysis, the optimal level is determined strictly according to the statistical results
- Flexible handling of insignificant factors: For factors with insignificant influence, the appropriate level can be determined by comprehensively considering factors such as economy, environmental protection, and operational convenience
- Validation test necessary: The optimal solution determined by statistics should be confirmed through verification test to confirm its actual effect
Technical innovation value of the standard
The release and implementation of SN/T 5774-2025 provides chemical analysis laboratories with a standardized experimental design methodology, which has important technological innovation value:
| Innovation points | Technical value | Application benefits |
|---|---|---|
| Systematic orthogonal experiment process | Establish a complete technical system from design to analysis | Improve method development efficiency and reliability |
| Computer-aided analysis integration | Combining modern office software with statistical methods | Lowering the technical threshold and improving popularity |
| Application specification of mixed-level orthogonal array | Solving the design problem of unequal-level factor combinations | Enhancing the practicality and adaptability of the method |
The implementation of this standard will significantly enhance the method development capabilities of my country's chemical analysis laboratories, provide more reliable technical support for inspection and quarantine work, and also help promote the improvement of laboratory quality management systems and the improvement of data analysis capabilities.
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