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Chemometrics Toolbox 提供了 70 多種用于化學計量校準的專用 MATLAB 函數(shù)。它使您能夠使用方法（例如潛在變量中的多元線性回歸、主成分回歸和偏小二乘法）執(zhí)行定量和定性分析。化學計量學將數(shù)據(jù)組織成矩形，以便對齊進行處理以創(chuàng)建校準或提取有用信息。這使得MATLAB成為化學計量學的理想環(huán)境。
Chemometrics Toolbox 提供化學家、生物學家、科學家和工程師開發(fā)和驗證與復雜過程分析儀和實驗室儀器一起使用的校準所需的全面功能。Chemometrics Toolbox 旨在提高生產(chǎn)力，提供的工具使用戶能夠快速輕松地將化學計量技術應用于數(shù)據(jù)，使他們能夠對其應用進行定量和定性分析，同時保持對數(shù)學的信心。
為了簡化校準的工業(yè)部署并減少與版本相關的維護問題，Chemometrics Toolbox與在平臺上運行的 MATLAB 版本保持兼容。有些人成功地將Chemometrics Toolbox與 Octave 結合使用，這是一個開源 MATLAB 克隆，可使用。
特征
超過 70 種專門的化學計量學功能
經(jīng)典小二乘法 (K-matrix) 多元線性回歸
逆小二乘法 (P-matrix) 多元線性回歸
主成分分析（PCA 或因子分析）
主成分回歸 (PCR)
偏小二乘法 (PLS)
聚類分析
用于基于因子的方法的模型制定的指標函數(shù)
繪圖和可視化功能
逐鍵入門教程
重點介紹
多元線性回歸 (MLR) 技術?；瘜W計量學工具箱包括用于生成定量校準的核心程序。主要功能包括：經(jīng)典小二乘法（CLS 或 K 矩陣）、逆小二乘法（ILS 或 P 矩陣）和 Q 矩陣方法。
基于因素的技術。該工具箱包括基于因子的方法，用于生成定量校準和獲得對數(shù)據(jù)的定性洞察。主要功能包括：主成分分析（PCA 或因子分析）、主成分回歸 (PCR)、偏小二乘法 (PLS) 和 PLS 回歸矩陣（無需迭代 PLS 預測）。
指標功能。 包含指標函數(shù)系列，以幫助在使用基于因子的技術時制定正確的模型：減少特征值指標、減少特征值的雙向 F 檢驗、擬合訓練數(shù)據(jù)、擬合驗證數(shù)據(jù)、分數(shù)指標函數(shù)、交叉驗證和預測殘差平方和 (PRESS)。
繪圖。多個繪圖命令可以輕松查看數(shù)據(jù)和發(fā)現(xiàn)模式，例如在單個圖形中繪制多達四個統(tǒng)計指標函數(shù)，以及繪制分數(shù)與分數(shù)以使用基于因子的技術可視化數(shù)據(jù)集群。
【英文介紹】
The Chemometrics Toolbox provides more than 70 specialized MATLAB functions for chemometric calibration. It enables you to perform quantitative and qualitative analysis using powerful methods such as Multiple Linear Regression, Principal Component Regression, and Partial Least-Squares in latent variables. Chemometrics organizes chemical data into matrices so that it can be processed to create calibrations or extract useful information. This makes MATLAB an ideal environment for chemometrics.
The Chemometrics Toolbox provides a comprehensive array of the functions needed by chemists, biologists, scientists, and engineers to develop and validate calibrations used with sophisticated process analyzers and laboratory instruments. Designed to enhance productivity, the Chemometrics Toolbox provides the tools that enable users to quickly and easily apply chemometric techniques to data, allowing them to perform quantitative and qualitative analysis on their applications while maintaining confidence in the math.
In order to simplify industrial deployment of calibrations and to eliminate version-related maintenance issues, the Chemometrics Toolbox remains compatible with all versions of MATLAB running on all platforms. Some people are successfully using the Chemometrics Toolbox with Octave, an Open Source MATLAB clone which is available at no charge.
Features
More than 70 specialized chemometrics functions
Classical Least-Squares (K-matrix) multiple linear regression
Inverse Least-Squares (P-matrix) multiple linear regression
Principal component analysis (PCA or factor analysis)
Principal component regression (PCR)
Partial least-squares (PLS)
Cluster Analysis
Indicator functions for model formulation for use with factor-based methods
Plotting and visualization functions
Keystroke-by-keystroke introductory Tutorial
Highlights
Multiple linear regression (MLR) techniques. The Chemometrics Toolbox includes core routines for producing quantitative calibrations. Primary capabilities include: classical least-squares (CLS or K-matrix), inverse least-squares (ILS or P-matrix), and Q-matrix methods.
Factor-based techniques. The toolbox includes factor-based methods for producing quantitative calibrations and for gaining qualitative insight into data. Key functions include: principal component analysis (PCA or factor analysis), principal component regression (PCR), partial least-squares (PLS), and PLS regression matrix (eliminating the need for iterative PLS prediction).
Indicator functions.A complete family of indicator functions are included to assist in formulating the correct models when using factor-based techniques: reduced eigenvalues indicator, two-way F-test for reduced eigenvalues, fit to training data, fit to validation data, fractional indicator function, cross-validation, and predicted residual error sum-of-squares (PRESS).
Plotting.Several plotting commands make it easy to view data and spot patterns, such as plotting up to four statistical indicator functions in a single figure, and plotting scores vs. scores to visualize data clusters with factor-based techniques.
Tutorial. The introductory tutorial included in the user's guide quickly teaches you how to use the toolbox by leading you keystroke-by- keystroke through the toolbox functions.
