MATLAB GUI工具箱：二阶多元校准方法的图形化实施
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MATLAB GUI for Calibration: A Comprehensive Guide
MATLAB是一种广泛使用的高级编程语言，尤其在科学研究和工程领域，其强大的数值计算能力深受青睐。GUI（Graphical User Interface）是MATLAB中的一个重要组成部分，它提供了用户友好的界面，使得复杂的数据分析和模型构建过程变得直观易用。在本文中，我们聚焦于"MVC2：MATLAB图形用户界面工具箱用于二次多元校准"这一主题。
二次多元校准（Second-order multivariate calibration）是一种统计方法，用于建立多个变量之间的复杂关系模型，常用于化学、生物、环境科学等领域的数据分析。MVC2是一个专为此目的设计的MATLAB工具箱，它简化了高级校准技术的实施过程。这个工具箱的核心优势在于其支持多种输入数据格式，无论是矩阵还是向量，都能够轻松处理。用户可以方便地导入实验数据，如光谱、色谱或质谱数据，然后通过GUI进行预处理、模型构建和结果可视化。
MVC2工具箱的设计遵循Model-View-Controller (MVC)架构，这是一种软件设计模式，将应用程序分为三个独立的部分：模型（Model）、视图（View）和控制器（Controller）。在MATLAB GUI中，模型负责数据处理和算法执行，视图负责显示结果，而控制器则负责协调两者间的交互。这种分离的结构有助于代码的维护和扩展，使得开发者能够专注于核心算法的优化，同时提供直观的用户界面。
使用MVC2时，用户可以直观地设置校准参数、选择合适的校准方法（如多元线性回归、偏最小二乘法或多级多元回归等），并实时监控模型性能。工具箱还提供了各种评价指标（Figures of Merit），如决定系数（R²）、残差平方和（RSS）等，帮助用户评估校准模型的精度和适用性。此外，MVC2允许用户创建交互式图形，展示校准曲线、三维散点图等，以便于深入理解变量之间的关系和模型的预测能力。
总结来说，MVC2是MATLAB中一个强大的工具，专为那些需要执行二次多元校准任务的研究人员和工程师提供便捷的图形化解决方案。它通过简化数据处理流程，提升工作效率，并通过MVC架构确保代码的可维护性和扩展性。对于想要利用MATLAB进行复杂数据分析的用户来说，掌握MVC2是提高科研成果质量和用户体验的关键一步。
MVC2: A MATLAB graphical interface toolbox for second-order
multivariate calibration
Alejandro C. Olivieri
a,
⁎
, Hai-Long Wu
b
, Ru-Qin Yu
b
a
Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, and Instituto de Química Rosario (IQUIR),
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Suipacha 531, Rosario S2002LRK, Argentina
b
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
abstractarticle info
Article history:
Received 10 November 2008
Received in revised form 11 February 2009
Accepted 12 February 2009
Available online 23 February 2009
Keywords:
Second-order multivariate calibration
MATLAB program
Graphical interface
Figures of merit
This work reports the release of Multivariate Calibration 2 (MVC2), a MATLAB graphical interface toolbox for
implementing several second-order multivariate calibration methodologies. The toolbox accepts a variety of
input data formats, arranged in either matrices or vectors (i.e., unfolded matrices), and contained in ASCII
files. It allows one to manually select working sensor regions and plot landscapes for selected samples. The
development of each model and its subsequent application to unknown samples is straightforward.
Prediction results are produced along with analytical fi gures of merit and standard concentration errors, as
calculated by modern concepts of uncertainty propagation.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
MVC2, an integrated chemom etric toolbox for MATLAB was
developed in order to manage several different second-order multi-
variate calibration algorithms in an easy-to-use graphical interface
environment. The toolbox can be applied to any type of data which are
structured in matrix form for each sample, allowing for the efficient
extraction of information concerning certain properties or analytes of
interest from the multi-component space. The toolbox is a sequel of
the already described MVC1 MATLAB interface [1] and the MULTIVAR
Visual Basic program [2], both developed for handling first-order
multivariate calibration methods.
The calibration techniques included in the MVC2 toolbox can be
divided in two relevant groups, namely those based on: 1) trilinear
decomposition (TLD) or 2) residual bilinearization (RBL). The former
group include parallel factor analysis (PARAFAC) [3], alternating
trilinear decomposition (ATLD) [4], alternating penalty TLD (APTLD)
[5], and self-weighted ATLD (SWATLD) [6]. For a review of the
properties and applicability of the different algorithms, see ref. [7,8].
The second group of methods, based on residual bilinearization,
comprise: 1) bilinear least-squares followed by RBL (BLLS/RBL) [9,10],
2) unfolded partial least-squares/RBL (U-PLS/RBL) [11,12], 3) multi-
dimensional p artial least-squares/RBL (N-PLS/RBL) [13,14],and
unfolded principal component analysis/RBL (U-PCA/RBL) [15]. The
latter methodology has been devised in order to produce suitably pre-
processed data from non-linear instrumental data, for further analysis
using artificial neural networks [16,17]. In the U-PLS and N-PLS
methods, the calibration models can be optimized by leave-one out
cross-validation [18]. Reference [7] does also discuss im portant
characteristics of this group of RBL algorithms.
Finally, the popular second-order multivariate methodology
multivariate curve resolution-alternating least-squares (MCR-ALS)
[19,20] is available in the internet including a useful MATLAB graphical
interface (http://www.ub.es/gesq/mcr/mcr.htm). The presently pro-
posed software allows one to generate the required augmented
matrices to be processed by MCR-ALS. Readers interested in this latter
methodology are advised to consult ref. [19].
Calculations and graphical outputs are conveniently managed in
MVC2 through graphical user interface (GUI) shells. The software does
not require a highly experienced user, but a basic knowledge on the
underlying methods is advisable in order to successfully interpret the
results.
The software is freely available, along with examples and manual,
and will be provided on request via e-mail. Please contact the
corresponding author or download the program (including manual
and examples) from www.chemometry.com. In the present report, the
toolbox versatility and performance is evidenced through the analysis
of a simulated example.
2. Software
The software runs under MATLAB version 5 (or higher) [21]. The
files do only need to be copied into a folder declared in the MATLAB
path. Please refer to the document named ‘MVC2_manual.doc’ which
is provided with the software.
Chemometrics and Intelligent Laboratory Systems 96 (2009) 246–251
⁎ Corresponding author. Tel./fax: +54 3414372704.
E-mail address: olivieri@iquir-conicet.gov.ar (A.C. Olivieri).
0169-7439/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.chemolab.2009.02.005
Contents lists available at ScienceDirect
Chemometrics and Intelligent Laboratory Systems
journal homepage: www.elsevier.com/locate/chemolab
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