http://www.camo.com/rt/Products/Unscrambler/assets/The-Unscrambler-X-10.5-Datasheet.pdf
來自挪威的多變數分析，使用多變量統計分析和交互式視覺化進行建模、預測和最佳化的行業領先工具。 通過比以往更輕鬆地分析大型複雜數據集，更快地開發產品、提高質量並最佳化流程。25,000 名科學家、研究人員和工程師的首選，是最受歡迎的多變數分析軟體，主要內容如下：
主成份分析(Principal Component Analysis)、迴歸分析(PLS1、 PLS2、 PCR、 MLR)、預測及validation、分類(SIMCA)、資料處理、變異數分析ANOVA、實驗設計(Fractional and factorial design)、 Placket-Burmann、 Box Behnken、 Central Composite，Response Surface Analysis、Chemometrics、PCA、PCR、PLS，即時預測您的迴歸模式。
Unscrambler 本軟體實際應用資訊 應用國內相關論文
摻偽蜂蜜理學辦別指標之建立
運用物件導向影像處理方法於崩塌製圖研究台灣白石集水區的個案
近紅外線光譜用於肉品分類與新鮮度之鑑別
利用近紅外光譜技術進行台灣烏龍茶快速分析與鑑別之研究
台灣特色茶感官特性與電子舌及電子鼻分析之相關性
利用感官品評與電子鼻及電子舌儀器分析檢測台灣特色茶之感官特性
應用反應曲面法探討鳳梨酒之發酵條件
菠菜與四季豆質地測定方法之探討
健康老年人與原發性高血壓或第2型糖尿病老年人味覺敏銳度之探討
甘藍與毛豆質地感官與儀器分析相關性之探討
本軟體的使用者 請您參考 (含詢價)
國立中興大學／食品科學系
中臺科技大學／食品科技研究所
國立中興大學／食品暨應用生物科技學系所
國立臺灣大學／生物產業機電工程學研究所
國立臺灣大學／地理環境資源學研究所
衛生福利部食品藥物管理署
行政院農業委員會林業試驗所
農業委員會 農業試驗所
長庚科技大學
雲陽科技有限公司
臺灣國際商業機器股份有限公司
美和科技大學
國立臺灣海洋大學
國立中山大學
國立臺灣科技大學
工研院 工業技術研究院
永昕生物醫藥股份有限公司
大同大學....
The following briefly describe the new features in version 10.3.
Design of Experiments
- A completely new response surface plotting module with high resolution, fast graphics rendering and improved plotting controls for graphical optimization.
- A new D-optimal design module with option to augment design with space-filling points (more robust).
- Re-introduction of PLS-DoE and more design information displayed in ‘Tasks – Analyze – Analyze Design Matrix’ to help you find the best method for your data.
New methods
- Basic ATR correction of absorbance transformed spectra included under ´Tasks – Transform – Spectroscopic…´
- Introduced Double Kennard-Stone sample selection for PLSR, PCR and PCA
Plotting
- Plot settings in ´Tools – Options – Viewer´ can be used to change the default appearance of plots.
- New plots and plot layouts for Residuals and Influence plots in PCA, PCR, PLSR and Projection, including F-residuals with limits.
- Point labeling using value of any matching variable (Sample Grouping)
General
- ASCII file import with default list separator based on system settings.
- New Alarms tab in analysis dialogs of PCA, MLR, PCR and PLSR and right-click option for setting alarm limits in the project navigator (these limits are applied for online prediction using some of our prediction engines).
- New dialog for assigning Scalar/Vector tags as well as units (´Edit – Scalar and Vector´ in editor mode or right-click option in project navigator). This information is used for collecting data from various sources during online monitoring of processes.
- General enhancements and bugfixes.
http://www.youtube.com/watch?feature=player_embedded&v=KhA_PCMPZZo&list=UUEE8oXFqfI7LMOHyHQU4kKg
Unscrambler tutorials
01 Setup Demo
10 Tutorial A
10 Tutorial A1
10 Tutorial A2
11 Tutorial B 1
11 Tutorial B help
12 Tutorial C help
12 Tutorial C
13 Tutorial E 1
14 Tutorial F 1
15 Tutorial H 1
16 Tutorial I 1
17 Tutorial J 1
18 Tutorial K 1
19 Tutorial L 1
20 Tutorial M 1
21 Cluster quick start 1
22 LDA quick start 1
24 MBM quick start 1
25 MCR quick start 1
26 MLR quick start 1
27 PCA quick start 1
28 PCR quick start 1
285 Quartz Pole
29 PLS quick start 1
53 Plot Properties
54 Transform Smoothing
55 Transform Spectroscopic
57 Transform Interaction
58 Transform Compute
59 View Insert
60 Plot
62 Analyze1 Descriptive
63 Analyze2 Moving Block
64 Analyze3 SPC
65 Analyze4 PCA
66 Analyze5 MCR
67 Cluster1
80 Sample Alignment
82 Statistical Process Control
100 PCA help
101 MLR help
102 PCR help
103 PLSR help
104 L PLS help
105 SVR help
106 MCR help
107 Cluster Analysis help
108 Projection help
109 SIMCA help
110 LDA help
111 SVC help
112 Moving Block help
113 Prediction help
Introduction to Unscrambler
Analysing spectral data with Unscrambler
Data import
Data handling
Diagnostics and plotting
Preprocessing and transforms
Building a PCA model
Interpreting a PCA model
Building a PLS model
Interpreting a PLS model
Python tutorials
Installing Python
Installing the PyCamo package
Running a simple Python script
Installing Python packages
Data Import and preparation for Python scripting
Getting Python scripts from Camo Community
Running advanced Python scripts in Unscrambler
Visualising Python script results in Unscrambler
