Uscrambler使用说明书

2.Table of contents 目录
1. Combine DA 7200 data with reference data in Simplicity 在Simplicity中合并DA参考数据
2. Export data from Simplicity 从Simplicity中导出数据
3. Import data into the Unscrambler 将数据导入Unscrambler
4. Develop PLS calibrations for each parameter separately 分别对每个变量建立PLS校正表
5. Define a CDF file in Simplicity (according to the DA manual) 在Simplicity中定义CDF文件（依DA手册）
6. MSC pretreatment example in the Unscrambler 在Unscrambler中MSC预处理实例
7. Reduced wavelength range in Unscrambler calibrations 在Unscrambler定标中优化波长范围
3.Prepare data with Simplicity 用Simplicity准备数据
4.Data collection on the DA 7200 and at the LAB 在实验室用DA 7200收集数据
Data collection on the DA 7200 and at the LAB 在实验室用DA 7200收集数据
Wheat_collection.db
Wheat_collection.spc
化学参考值表
Reference values wheat.xls
bine reference values with DA 7200 data 合并参考值与DA近戏外光谱数据
1. Open the Excel file with reference values
Reference values wheat.xls
用Excel打开化学值参考表文件
Reference values wheat.xls
2. Make sure the parameters (protein & moisture) are in the
same order as in the wheat_collection.db file.
确认参数与收集的DB文件中同序，如蛋白、水分
3. Make sure the sample IDs in the Excel file are written
exactly the same way as in the DA 7200 instrument during
data collection.
确认Excel表中样品ID号书写与DA收集期间完全一致
4. Save the Excel file as a *.txt file。

用*.txt形式来保存Excel文件
5. Click OK to the questions about text files not
supporting multiple sheets.
点击OK
（不支持其他工作表）
bine reference values with DA 7200 data 用DA合并参考数据
1. Click on Tools/Utilities/Import/Merge with concentrations
点击工具/实用功能/导入/与浓度合并
bine reference values with DA 7200 data
1. Select the text file with reference values
选择化学参考值text文件
2. Select the Wheat_collection.db file
选择方法的DB 文件
3. The reference data and DA 7200 data are combined in the
*.db file
参考值数据和DA数据合并与*.db文件中
4. Click on View/Spectrum database and select the *.db file to
verify that the reference values were imported properly.
点击视图/光谱数据库，选择该*.db文件，确认参考值被正
确地导入。

5. Click on Tag/Untag all to mark all sample IDs. Individual samples may be marked by double clicking in the marker column.
点击Tag/Untag all来标记所有的样品ID，（特殊的样品可能需要在标记时点击两次。

）
图中显示为
工具/实用功能/导出/
校正数据—JCAMP*.dx file
9.
1. Select the *.db file and click OK
选择*.db文件，点OK
2. Average the data and click on Export
选中Average（平均值），点导出
3.Enter a file name for the new *.dx file and click
OK
输入一个新的*.dx文件名，点OK
10.The Unscrambler
11.Import data into The Unscrambler 将数据导入Unscrambler中
1. Open The Unscrambler
打开Unscrambler化学计量软件
2. Click on File/Import/JCAMP-DX…
点击File/Import/JCAMP-DX…
12. Import data into The Unscrambler
1. Browse to find the directory
C:\pda7200\Data\Products where the
data is located
浏览查找目录，按C:\pda7200\Data\Products查找，
数据文件位于此。

2. Select the file and click OK
选择文件，点击OK
13.
Rows: Samples
Columns: Spectra and reference data
行为样品ID
列为光谱数据和参考数据
14.Plot the DA 7200 data (spectra)
Select all samples by marking the data with
the mouse or click Edit/Select samples
用鼠标选中所有样品或
点击工具栏Edit/Select samples来选。

Click on the Line plot icon or on Plot/Line
点击Plot/Line
Select NEAR INFRARED as variable set
在Line plot框中variable set项下，
选择NEAR INFRARED
15.
Close the spectra plot to get back to the
database. In case you would like to keep
the spectra plot, click on Window and
select the database.
关闭光谱图回到数据库处
若想保留光谱图，可点击窗口选择数据
库
16.Define parameters/reference data 定义参数或参考数据
Click on Modify/Edit set
点击Modify/Edit set
The spectral data is pre-defined as
141 columns (Interval 1-141)
光谱数据已被预先定义为141列。

To define which column that
includes reference data for a specific
parameter, click on Add to define a
new variable
为一特定参数定义包含参考值的列
时，点击Add按钮来定义新的变量。

17.
Enter the parameter name
键入参数名称
Enter which column that includes the reference data for that parameter or click on Select and mark the column
指定参数名的包含参考值的列，或选中标注列。

18.
Repeat the procedure to define all
parameters; both protein and moisture in
this example
重复以上过程来定义所有的参数，在这个
例子中有蛋白和水分两个参数。

19..Plot the reference data
Mark the protein column and click on the Histogram icon or on Plot/Histogram
标记蛋白列，点击直方图图标或Plot/Histogram
—The histogram shows that one sample has no reference value for protein. Most samples have protein contents between 11-14 %.该直方图显示，该一个样品没有蛋白的化学参考值，多数含蛋白的样品都落在11~14%的区间内。

20.Save the data in The Unscrambler 在Unscrambler中保存数据
To save the data in Unscrambler format, click on File/Save as
在Unscrambler版式下保存数据，点击File/Save as
The data now consists of 4 files located in the Explorer
新数据包含有以下四个文件。

在C:\pda7200\Data\Products下的
wheat_collection.00D
wheat_collection.00L
wheat_collection.00P
wheat_collection.00T
导数
微分
21.Pretreatments建模（预处理）
•Pretreatments are often used to reduce effects of e.g.
scatter caused by e.g. different particle sizes
建模通常是用来降低分散样本的影响，其影响归因于样本的不同颗粒大小。

•Common pretreatments are derivatives (1st or 2nd), SNV, MSC or combinations of these. When combinations of pretreatments are used, the order of pretreatments may be essential.
一般建模是进行（一阶或二阶）求导，标准正交变换（SNV），多元散射校正（MSC）或这些的组合。

–Some claim that SNV or MSC should be applied before derivatives and others claim the opposite.
一些不用求导而采用标准正交变换或多元散射校下而应用，而另一些则相反。

–For info: the Thermo GRAMS software for instance applies derivatives first and then SNV or MSC
–The 1st example will shown 1st derivative and SNV
第一个例子是一阶求导和SNV
–The 2nd example will show MSC and 1st derivative
第二个例子是MSC和一阶求导
校正均方根误差（RMSEC）预测均方根误差（RMSEP）
22．Pretreatments 1st derivative 预处理时先求导
•Click on Modify/Transform/Derivatives/S. Golay
过程是：点击Modify/Transform/Derivatives/S. Golay
•Plot the spectra (Mark samples and click on Line plot)
绘光谱图（标记样品，点plot/Line）
23．Pretreatments 1st derivative & SNV 预处理时一阶求导和SNV
•Click on Modify/Transform/SNV 点击Modify/Transform/SNV
•Plot the spectra (Mark samples and click on Line plot) 绘光谱图（标记样品，点plot/Line）24．Pretreatments 1st derivative & SNV 预处理时一阶求导和SNV
•The database now includes pretreated data.
现在数据库包含光谱数据。

•In order not to overwrite the raw data, save the database with
a new name (File/Save as)
若想保留原始数据库而不被覆盖，用新文件名保存数据库。

25．PLS regression for protein 对蛋白进行偏最小二乘法（PLS）回归•Click on Task/Regression 点Task/Regression
–Method: PLS1 选方法：PLS1
–Samples: All samples 样品：All samples
–X-variables: NEAR INFRARED SPEC X-variables栏中: NEAR INFRARED SPEC
–Y-variables: Protein Y-variables栏中: Protein
–Validation method: Cross validation Validation method项下: Cross validation 26．PLS regression for protein
27．PLS regression for protein对蛋白进行偏最小二乘法（PLS）回归
28．PLS regression for protein对蛋白进行偏最小二乘法（PLS）回归
4 plots of the PLS model are shown as default PLS模式四图以默认方式展现。

- also by Plot/Regression overview 也可通过点击Plot/Regression overview实现
- the green frame shows which plot that is active 绿框标示该图为激活的图框。

29．Exclude outliers 剔除异常值
Remove sample without reference value 删除在参考值外的样本。

•Mark the sample without reference value 标记超出参考值外的样本
Mark the sample by clicking on the icon Mark with Rectangle and select the sample in the plot
通过点击图标来标记样本，用拉长方形框选择图上的样本
30.Exclude outliers 剔除异常值
Remove sample without reference value删除在参考值外的样本
Click on Task/Recalculate without marked
点击Task/Recalculate对没有标记的进行重新计算。

Keep Out of Calculation: sample no. 28
排除在外的样本号为28
Click OK to recalculate without this sample
点击OK，对不包括该样本的其它数据进行重新计算。

31.Exclude outliers 剔除异常值
Remove sample without reference value删除在参考值外的样本
The calibration has been
recalculated without sample
no 28
这次校准是不包括28号样本
的重新估计。

32.
Determine number of PCs 确定过程控制的数目
Click on Plot/Variances and RMSEP
点击Plot/Variances and RMSEP
Select RMSE and tick the box Validation (RMSEP) in the
lower right corner
选RMSE（均方根误差）视窗中右下角的Validation (RMSEP)
复选框，
33. The RMSE is plotted as a function of the number of components in the model. The optimal number
of components is where the validation curve (i.e. RMSEP) reaches a minimum.
根据模型中成份的数目，均方根误差（RMSE）被绘制成图，成份的最佳数目是确认曲线的预测均方根误差（即RMSEP）估计值范围最小时的数目。

•Display as curve or bars depending on preference 显示的曲线或柱状图取决于个人偏爱。

–Right click/Edit options/Curve or Bars 右键单击/Edit /options/选Curve 或Bars
34.Predicted vs. Measured plot 预测与测量图对比
•The Predicted vs. Measured plot is 预测与测量图是
One of the 4 default plots; visible in the lower right corner or 四幅默认图其中之一，见右下角图。

或--Accessible via Plot/Predicted vs. Measured (select validation) 经Plot/Predicted vs. Measured进入。

--Make sure the plot is displayed with optimal number of PCs 确认用过程控制最挂数目展示的图。

35.Predicted vs. Measured plot 预测与测量图对比
•Compare RMSEP at different PCs to verify the optimal number of PCs.
在不同的预处理控制过程中比较RMSEP值，来校验最佳的建标样品数目•Click on the green arrows to easily change the number of PCs
点击绿色箭头，很容易地改变建标数目数。

36.Predicted vs. Measured plot预测与测量图对比
统计学原理被引入预测与测量图对比中。

The box with statistics is inserted/removed by clicking View/Plot statistics
通过点击View/Plot statistics，统计学被插入或移出。

RMSEP is the cross validation error commonly denoted as SECV (in e.g. the GRAMS software)
交叉验证普通错误的RMSEP值，用SECV（交叉验证标准误差）表示。

（在GRAMS软件的例子中）
37.Plot settings平面图设置
•Settings for changing between sample
IDs, sample numbers or symbols
样品ID，样品号或样品标识之间的转换，–Right click Edit/Options (Name, Number, or Symbol)
右键单击，Edit/Options (Name, Number, or Symbol)
38.Plot settings
•Examples of other plot settings其它图例设置
---Changing scales of axis: Right click
View/Scaling
坐标轴的缩放：右键单击，View/Scaling
----Trendlines: Right click View/Trendlines
趋势线：右键单击，View/ Trend lines
39．Scores评价
The plot gives information about patterns in the
samples. The score plot for (PC1,PC2) is especially
useful, since these two components summarize more
variation in the data than any other pair of
components. Also useful to check at other PCs.
图表给出了关于样品模型的信息。

为(PC1,PC2)的评价图尤其有效，自两种成份到数
据中更多参数调节乃至更多的其它成双成对成
份。

而且在其它控制过程的核对上也非常有用。

40．Influence plots影响因子
•Plot/Residuals/Influence Plot
依次点击Plot/Residuals/Influence Plot
•Check for outliers by viewing Influence plots for X- Y- and
X and Y-variables at the optimal number of PCs
通过观察影响因子，在建模的最佳数值中为变量X-Y ，X，
Y变量检查异常值。

41．Influence plots影响因子
•This is a plot of the residual X- and Y-variances vs. leverages. Look for samples with a high leverage and high residual X- or Y-variance.
这是一张X有残差，Y值与杠杆值有差异的图。

利用高杠杆值和高残差或Y的方差来寻找需剔除的样品。

-Sample no 46, ID 1.404.531 is detected as an outlier
46号样，ID 1.404.531被检测到为异常值。

42．Exclude outliers剔除异常值
•Mark the sample or several samples at the same time同时标注出一个或几个样品。

–Mark the sample by clicking on the icon Mark with Rectangle and select the sample in the plot
在图中通过上面有长方形框标记图标，标记样品。

若取消标记，再点图标则取消所有标记。

43.Exclude outliers剔除异常值
Click on Task/Recalculate without marked
点击Task/Recalculate，用未作标记的样本进行重新计算。

Keep Out of Calculation: sample no. 46 (and 28 without reference
value as was previously removed)
Keep Out of Calculation中的46和28被排除在外。

28号超出了参考值被原先方案排除在外。

Click OK to recalculate without this sample
点击OK，在排除样本以后进行重新计算。

44．Exclude outliers剔除异常值
•After recalculation, check all plots again重新计算后，再一次检查所胡坐标图
RMSEP in this example was only slightly reduced to 0.24 compared to 0.25 before the sample was excluded when comparing at 5 PCs.
例子中，RMSEP只是稍有变化为0.24，较之先前未剔除前0.25…………………
45．Calibration model completed建模完成
Activate pretreatments before the calibration is saved
When the calibration model is completed, the pretreatments need to be
activated before the model is saved. This will enable correct
predictions when the calibration is installed and used on the DA
instrument.
当建标完成后，模型需要保存并激活。

这将调整模型，当在DA仪器上安装并使用时。

Click on File/Properties/Transformations
点击File/Properties/Transformations等按钮。

0 out of 2 transformations/pretreatments are selected.
下面显示0 out of 2 transformations selected
Click on. select点击select
46．Activate pretreatments before calibration is saved模型需要保存并激活
•Select All and click OK. 选拔Select All，点击OK
•Click Yes to answer the question about automatic pretreatment variables
在弹出的对话框中，点击Yes
47．Activate pretreatments before calibration is saved
保存模型并激活
2 out of 2 transformations are now selected
下面显示2 out of 2 transformations selected
48．Set the number of PCs before calibration is saved
•Click on Model (still within File/Properties)
选Model工具框
•Enter the number of PCs that the calibration will be based
on. The software sets the recommended number of PCs as
default.
输入的数字将被定为基准。

软件设置推荐的数是PCs的默认值。

在Number of PCs in Classification $ Prediction项下，键入数值。

49．Save the calibration保存模型（校正表）
•Click on File/Save as
点击File/Save as
•Change the directory to c:\pda7200\Calibs
改变目录到c:\pda7200\Calibs
•Calibrations can be saved as Classic format with 5 sub files or
Merged format with 1 file in Unscrambler 9.7
模型用5个文件被经典的保存。

或保存在一个Unscrambler 9.7
合并式文件中。

50．Save the calibration保存模型
•The calibration consists of 5 files (Classic format) or 1 file if Unscrambler version 9.7 is used (Merged format) located in the Explorer C:\pda7200\Calibs
模型由5个文件（传统格式）组成或由一个Unscrambler 9.7文件组成。

这个文件在使用时，被定位在资源管理器C:\pda7200\Calibs下。

WheatProtein.41D
WheatProtein.41L Calibration saved as Classic format
WheatProtein.41P with Unscrambler versions 9.7 or WheatProtein.41T below
WheatProtein.41W
or
WheatProtein.41M Calibration saved as Merged format with Unscrambler version 9.7 51．Moisture calibration水分校正模型
•Each parameter is calibrated separately. 每个变量被各自校准。

• A moisture calibration was developed with the same pretreatments as for protein.
一个水分模型的开发就像蛋白的预处理一样。

（水分模型的开发与蛋白模型的处理过程相同）
52．Moisture calibration水分校正模型
The calibration was saved in the same directory as protein. 5 files were created if the calibration is saved as the Classic format or 1 file if Unscrambler version 9.7 is used and the calibration is saved as a Merged file 其保存与蛋白被保存在相同的目录下，模型保存时5个文件以传统格式被创建，或保存在一个Unscrambler 9.7合并式文件中。

WheatMoisture.41D
WheatMoisture.41L
WheatMoisture.41P
WheatMoisture.41T
WheatMoisture.41W
or
WheatMoisture.41M
53．Create CDF file in Simplicity 在Simplicity中创建CDF文件
54．Create CDF file创建CDF文件
•In Simplicity, calibrations models are defined in the CDF file (calibration definition file).
在Simplicity中，校准模型被在CDF文件中被定义（是模型的定义文件）。

•This file contains:文件包括
–The name/s of the actual calibration files from GRAMS or Unscrambler.
来自GRAMS或Unscrambler现行模型的名称
–The name/s of the parameter/s. 变量名称
–The number of decimals. 小数点位数
–The limits (minimum and maximum) for the range of the calibration.
定标的范围（最小值和最大值）
•Before a CDF file is created, the calibration files you wish to use must have been saved in or copied into pda7200\Calibs or a subdirectory of Calibs. The calibration files are created in GRAMS or Unscrambler.
在CDF文件创建前，希望使用的校准文件必须保存在中拷贝到pda7200\Calibs下或Calibs的子目录下。

校准文件由GRAMS或Unscrambler创建。

55．Create CDF file创建CDF文件
•The procedure for creating a CDF file for Unscrambler calibrations are available in the DA 7200 operation manual.
用Unscrambler建模创建CDF文件的过程，在DA 7200操作手册中提供。

56.The Unscrambler
Example with MSC pretreatment 以多元散射校正建标为例
57.Example with MSC pretreatment以多元散射校正建标为例
•The procedure of using MSC as pretreatment is also shown as an example as MSC is one of the most common pretreatments used when working with NIR data.
利用多元散射校正（MSC）预处理的过程已展示。

如MSC是众多使用近红外光谱数据建标常用方法的一种被采用。

•With the original database containing raw data (without any pretreatments yet applied), click on Modify/Transform/MSC EMSC
使用包含原始数据的原始资料库（未进行任何预处理已应用），点击Modify/Transform/MSC EMSC
58.
点击后出现的对话框如如图，选择项如下：
•Samples: All samples
•Variables: NEAR INFRAREF SPECTRA
•Compute and use new MSC: Full MSC (already pre-selected)
59.
•Save the MSC model (Click YES to confirm) 保存MSC模型（点YES确认）
•Save the MSC model in the directory c:\pda7200\Calibs. If the calibration will be saved as a Merged file with Unscrambler 9.7, it is not important where the MSC model is saved.
保存MSC模型到指定目录c:\pda7200\Calibs下，
若像Unscrambler 9.7以一个合并文件保存一样来保存，保存MSC模型则没必要。

60.
Plot the spectra (Mark samples and click on
Line plot)
绘制光谱图（标记所有样本，点plot /Line）
61.
1st derivate is often applied in combination with MSC
在MSC处理时一阶求导常常采用。

Click on Modify/Transform/Derivatives/S.Golay 点击Modify/Transform/Derivatives/S.Golay
Plot the spectra (Mark samples and click on Line plot 绘制光谱图（标记所有样本，点plot /Line）
62.
Save the pretreated database in the directory
c:\pda7200\Data\Products by clicking File\Save as
点击File\Save as，
保存预处理数据库到c:\pda7200\Data\Products
63.
•Perform PLS regression for protein in the same way as in the previous example
用与前面相同的方法为蛋白执行PLS回归。

•The sample without reference data was removed 与参考数值偏离的样本已被排除。

•RMSEP 0.23 for 5 PCs RMSEP 值为0.23，因子数为5。

64.
The Influence plots indicate that sample no 7 (ID B1.17.1.1) is an outlier. Note that this is not the same sample as was excluded when derivatives and SNV were applied. 在Influence图中，no 7样本(ID B1.17.1.1)是离群值。

注意当用SNV等其他方法进行处理时，排除样本是不同的。

65.
•The calibration was recalculated without sample no 7 (and without no 28 with no reference value)
排除7号和28号（无化学值）样品后，重新进行校准计算。

•RMSEP dropped to 0.19 for 5 PCs compared to 0.23 prior to excluding this sample. Also compare to calibration based on derivatives and SNV as pretreatments, RMSEP 0.24 for the same number of PCs. RMSEP值由先前0.23降至0.19（因子数5），同时对比SNV其他处理方法RMSEP 0.24，因子数相同。

66.
Before the calibration is saved, the pretreatments need to be activated 校正表保存后，模型须进行激活。

---Click on File/Properties/Transformations 点击File/Properties/Transformations ---There are 0 out of 2 transformations selected. Click on Select
0 out of 2 transformations selected右边Select键条上，点击Select
67.
Click Select All to mark both pretreatments 点击
Select All，即选两个方法的预处理结果。

68.
Click Set MSC Path. It is important that the path is set correctly for the MSC model if the calibration is saved as the Classic format. If not, the predictions on the DA instrument will not be correct with this calibration. If the calibration will be saved as Merged format this is not important.
点击Set MSC Path 按键。

为MSC模型设置正确的路径非常重要，如果模型以传统格式保存而没点击该键的话，则DA仪器预测时，就不能正确地调用该模型。

若是以合并格式保存，则不重要。

69.Example with MSC pretreatment
Now 2 out of 2 transformations are selected. Click OK.
70.
Click Browse and select the directory C:\pda7200\Calibs and click OK
点击Browse，选C:\pda7200\Calibs，点OK。

•Save the calibration in the C:\pda7200\Calibs directory 在C:\pda7200\Calibs目录下保存标线
•Create CDF file in Simplicity 在Simplicity中建CDF文件•Installation on the DA instrument 在DA仪器上安装。

–The MSC files and *.cdf & *.cdb files need to be stored in the C:\pda7200\Calibs directory (MSC files are not available if calibrations are stored as Merged files *.41M)
MSC文件、*.cdf、*.cdb 文件须保存在C:\pda7200\Calibs目录下
如果模型保存为合并文件*.41M ，MSC文件是不可用的。

–The calibration files may be stored in a subdirectory to C:\pda7200\Calibs if it is properly defined in the cdf file
如果在cdf 文件中给出了正确的定义，模型文件会保存在C:\pda7200\Calibs 目录下。

– If calibrations are saved as Merged format with Unscrambler 9.7 only 1file will be available
WheatProtein.41M which also will include the MSC files
如果模型在Unscrambler 9.7下仅用一个文件以合并格式被保存，则是可用的。

WheatProtein.41M 中包含MSC 文件。

MSCWheat.50D WheatProtein.41D Wheat_Collection.cdf MSCWheat.50L WheatProtein.41L Wheat_Collection.cdb MSCWheat.50P WheatProtein.41P MSCWheat.50T WheatProtein.41T WheatProtein.41W 71.
Using a reduced wavelength range 波长优化
• If it is desirable to use only a part of the wavelength range you can choose to reduce the number of
variables when you make the regression.
若有必要只使用波段的一部分，当进行回归时，你可选择减少变量值。

• Click in the ”Keep out of Calculation” box and choose Select. 点击Keep out of Calculation 框，选Select 。

72.Mark the variables that you wish to keep out. 标记（或选中）出你想排除的变量值
• The final calibration is saved in the same way as described above for MSC and derivatives. 最终的校正模型以前面描述的相同方式被保存，
4 MSC files Unscrambler *.cdf & *.cdb files Simplicity
5 calibration files Unscrambler
73.Note:You cannot use the method ”Modify/Edit Set” as this leads to the wrong number of variables in the final calibration and will result in the error message to the left.
注意：不能用Modify/Edit Set进行设置的方法，这引起最终完成校正时变量数目出错。

从而出现下面的出错信息。

eful plots
•Note that Unscrambler includes many useful plots that graphically visualises the data. There are several plots available for identification of outliers. This example only included a couple of different plots. There are also different kinds of plots of loadings and regression coefficients available.
注：
Unscrambler包含许多有用的图。

这些图行动地显现数据情况。

有几个图用于确认离群值。

这个例子中仅包括了一对不同的图。

还有不同种类的附图以及非常有用的相关变量的变化率和回归系数等。