699_Noodle
Michael Jiayang Chen
4/21/2020
Background and Limitation
For our Noodle experiemental research study, here we have a manually collected dataset of 26 observations with 31 variables. These 26 observations or trials are conducted over two month periods, due to the current COVID-19 pandemic and the grocery shortage. This experiment took significantly longer to acquire needed ingredients, as flour had became an emergency household stock to buy. There is a shortage of flour in experimenter’s vicinity, and online ordering was significantly back-ordered. The original proposal planned for a variety mix types of flours including high-protein bread flour, white wheat flour, high mountain flour, cake flour as well as blended wheat flour. In the end, the experiment was reduced to 2 types of flour with varying protein content.
EDA
## # A tibble: 6 x 31
## Trial MakeDate Temperature_C Humidity ProteinContent
## <dbl> <dttm> <dbl> <dbl> <dbl>
## 1 1 2020-04-18 00:00:00 20 0.3 0.104
## 2 2 2020-04-18 00:00:00 20 0.3 0.104
## 3 3 2020-04-18 00:00:00 20 0.3 0.104
## 4 4 2020-04-20 00:00:00 19 0.31 0.104
## 5 5 2020-04-20 00:00:00 19 0.28 0.104
## 6 6 2020-04-21 00:00:00 22 0.3 0.104
## # … with 26 more variables: ActiveTime_Hour <dbl>, InactiveTime_Hour <dbl>,
## # Lamian <dbl>, DoughHydration <dbl>, SaltPercent <dbl>,
## # pH_PreAutolyse <dbl>, pH_PostAutolyse <dbl>, FlourWeight <dbl>, Oil <dbl>,
## # DoughColor_YScale <dbl>, PreKneadMins <dbl>, NumAutolyseHours <dbl>,
## # Yeast_Glutathione <dbl>, Yratio <dbl>, Kansui <dbl>, Kratio <dbl>,
## # BakingSoda_SodiumBicarbonate <dbl>, BSRatio <dbl>,
## # BakedBS_SodiumCarbonate <dbl>, BakedBSRatio <dbl>, Extensibility <dbl>,
## # KneadMins <dbl>, CookingTime <dbl>, Score <dbl>, Area <chr>, Notes <chr>## Trial MakeDate Temperature_C Humidity
## Min. : 1.00 Min. :2020-04-18 00:00:00 Min. :19.00 Min. :0.1800
## 1st Qu.: 7.25 1st Qu.:2020-04-22 18:00:00 1st Qu.:20.50 1st Qu.:0.2000
## Median :13.50 Median :2020-05-04 00:00:00 Median :23.00 Median :0.2150
## Mean :13.46 Mean :2020-05-06 12:00:00 Mean :24.12 Mean :0.2315
## 3rd Qu.:19.75 3rd Qu.:2020-05-17 12:00:00 3rd Qu.:28.00 3rd Qu.:0.2475
## Max. :26.00 Max. :2020-06-06 00:00:00 Max. :30.00 Max. :0.3100
## ProteinContent ActiveTime_Hour InactiveTime_Hour Lamian
## Min. :0.1040 Min. :0.1667 Min. : 0.500 Min. :0.0000
## 1st Qu.:0.1040 1st Qu.:0.3333 1st Qu.: 1.000 1st Qu.:0.0000
## Median :0.1040 Median :0.3333 Median : 2.000 Median :1.0000
## Mean :0.1061 Mean :0.4006 Mean : 3.269 Mean :0.6154
## 3rd Qu.:0.1100 3rd Qu.:0.5000 3rd Qu.: 2.750 3rd Qu.:1.0000
## Max. :0.1100 Max. :0.8333 Max. :24.000 Max. :1.0000
## DoughHydration SaltPercent pH_PreAutolyse pH_PostAutolyse
## Min. :0.5000 Min. :0.00000 Min. :5.500 Min. :5.000
## 1st Qu.:0.6500 1st Qu.:0.01000 1st Qu.:6.000 1st Qu.:6.400
## Median :0.6500 Median :0.01000 Median :6.100 Median :6.750
## Mean :0.6308 Mean :0.01423 Mean :6.158 Mean :6.662
## 3rd Qu.:0.6500 3rd Qu.:0.01000 3rd Qu.:6.300 3rd Qu.:7.000
## Max. :0.6500 Max. :0.10000 Max. :6.700 Max. :7.800
## FlourWeight Oil DoughColor_YScale PreKneadMins
## Min. :100.0 Min. :0.0000 Min. :1.000 Min. : 5.000
## 1st Qu.:100.0 1st Qu.:1.0000 1st Qu.:2.000 1st Qu.: 5.000
## Median :100.0 Median :1.0000 Median :3.000 Median :10.000
## Mean :107.7 Mean :0.7692 Mean :2.692 Mean : 9.423
## 3rd Qu.:100.0 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:10.000
## Max. :200.0 Max. :1.0000 Max. :5.000 Max. :20.000
## NumAutolyseHours Yeast_Glutathione Yratio Kansui
## Min. : 0.500 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 2.000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean : 3.269 Mean :0.2692 Mean :0.0250 Mean :0.3846
## 3rd Qu.: 2.750 3rd Qu.:0.7500 3rd Qu.:0.0375 3rd Qu.:1.0000
## Max. :24.000 Max. :1.0000 Max. :0.1000 Max. :1.0000
## Kratio BakingSoda_SodiumBicarbonate BSRatio
## Min. :0.00000 Min. :0.0000 Min. :0.000000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000000
## Median :0.00000 Median :0.0000 Median :0.000000
## Mean :0.02038 Mean :0.1154 Mean :0.004615
## 3rd Qu.:0.02000 3rd Qu.:0.0000 3rd Qu.:0.000000
## Max. :0.10000 Max. :1.0000 Max. :0.050000
## BakedBS_SodiumCarbonate BakedBSRatio Extensibility KneadMins
## Min. :0.00000 Min. :0.000000 Min. :0.1000 Min. :10.00
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.3000 1st Qu.:20.00
## Median :0.00000 Median :0.000000 Median :0.4000 Median :20.00
## Mean :0.07692 Mean :0.003846 Mean :0.4269 Mean :24.04
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.5750 3rd Qu.:30.00
## Max. :1.00000 Max. :0.050000 Max. :0.8000 Max. :50.00
## CookingTime Score Area Notes
## Min. :0.000 Min. :0.000 Length:26 Length:26
## 1st Qu.:2.000 1st Qu.:1.000 Class :character Class :character
## Median :3.000 Median :2.500 Mode :character Mode :character
## Mean :2.346 Mean :2.615
## 3rd Qu.:3.000 3rd Qu.:4.750
## Max. :5.000 Max. :6.000## Rows: 26
## Columns: 31
## $ Trial <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
## $ MakeDate <dttm> 2020-04-18, 2020-04-18, 2020-04-18, 202…
## $ Temperature_C <dbl> 20, 20, 20, 19, 19, 22, 23, 28, 28, 26, …
## $ Humidity <dbl> 0.30, 0.30, 0.30, 0.31, 0.28, 0.30, 0.21…
## $ ProteinContent <dbl> 0.104, 0.104, 0.104, 0.104, 0.104, 0.104…
## $ ActiveTime_Hour <dbl> 0.4166667, 0.5833333, 0.8333333, 0.33333…
## $ InactiveTime_Hour <dbl> 1.0, 12.0, 24.0, 1.0, 1.0, 2.0, 6.0, 2.0…
## $ Lamian <fct> 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1…
## $ DoughHydration <dbl> 0.50, 0.50, 0.50, 0.60, 0.65, 0.65, 0.65…
## $ SaltPercent <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01…
## $ pH_PreAutolyse <dbl> 6.7, 6.7, 6.7, 6.7, 6.7, 5.5, 5.5, 6.0, …
## $ pH_PostAutolyse <dbl> 7.0, 7.2, 7.3, 7.0, 7.0, 6.7, 7.0, 5.3, …
## $ FlourWeight <dbl> 100, 100, 100, 100, 100, 100, 100, 100, …
## $ Oil <fct> 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1…
## $ DoughColor_YScale <fct> 1, 3, 4, 2, 3, 2, 1, 2, 3, 2, 2, 2, 3, 4…
## $ PreKneadMins <dbl> 5, 5, 5, 5, 5, 15, 15, 10, 10, 15, 10, 1…
## $ NumAutolyseHours <dbl> 1.0, 12.0, 24.0, 1.0, 1.0, 2.0, 6.0, 2.0…
## $ Yeast_Glutathione <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Yratio <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00…
## $ Kansui <fct> 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1…
## $ Kratio <dbl> 0.00, 0.10, 0.10, 0.05, 0.05, 0.00, 0.00…
## $ BakingSoda_SodiumBicarbonate <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ BSRatio <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00…
## $ BakedBS_SodiumCarbonate <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ BakedBSRatio <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00…
## $ Extensibility <fct> 0.2, 0.4, 0.5, 0.5, 0.5, 0.3, 0.2, 0.4, …
## $ KneadMins <dbl> 25, 35, 50, 20, 40, 35, 50, 20, 20, 25, …
## $ CookingTime <dbl> 3, 3, 3, 2, 2, 5, 5, 4, 4, 3, 3, 0, 3, 3…
## $ Score <dbl> 3, 4, 4, 3, 3, 2, 1, 2, 2, 3, 2, 0, 1, 1…
## $ Area <fct> Control, Autolyse, Autolyse, Hydration, …
## $ Notes <chr> "This is the control noodle, it is not p…In total, there are 31 variables that I am tracking. The variable of experimental study interest is Noodle Tasting Score, the scale is from 1 to 7, and 5 is the optimal score being the noodle is not too bland and soft, nor is it too bitey and slick that does not create ideal mouthfeel or pick up broth flavor. This is a composite score that is based on overall noodle making experience, taste tests, mouthfeels, flavor carry ability and broth absorption.
Visualization
Univariate
Skewness
## [1] 0.139698Kurtosis
## [1] 1.69196The variable of study interest is the Noodle Tasting Score (“Score”), the distribution of the score from the experiemt appears to be normally distributed, with a skewness of 0.14, and kurtosis of 1.69. The kurtosis score indicates that the score is rather peaked. It is understandable, since the experiment goal was to understand noodle making process, and make good, delicious noodles. Therefore, it is expected to have a series of mediocre tasting noodles and eventually through trials become great noodles, hence the peakness that was shown in the kurtosis value.
Bivariate
In this graph, we are reviewing the relationship between the number of Autolyse hours and its impact on the resulting noodle tasting score. Autolyse is a process that water and flour are blended, then let rest for an extended period of time to allow gluten formation and relaxations.
The original hypothesis was that the longer time flour-water blend gets to relax, the easier it would be to form a stretchable dough with great extensibility and elasticity. Based on this hypothesis, I experimented with long autolyse of up to 24 hours to truly understand how that would affect the texture. As the graph shown, that the longer autolyse hours produced mediocre noodle. Surpisingly that shorter autolyse hours actually produced ideal noodle.
As we can see from the above graph, longer kneading time does not necessary lead to better tasting noodle. The reason I experimented with long kneading time is that dough with high hydration tend to be sticky, however, extended kneading can alleviate the stickiness and encourage the gluten bond formation. Stronger gluten bond will tend to result in better chew that is common in the chinese-style Lamian. Based on this notion, I wanted to find out whether or not this make sense. As it turns out, longer kneading time resulted in noodle that is too chewy and inedible during some trials. In hindsight, I understood that when I am kneading it, the gluten bond is strecthed in all directions, rather than linearly aligned which is what noodles are known for. Noodles are extended in one axis where as bread are extended in all directions. Therefore, longer kneading time is not ideal for noodle making.
Multivariate
Here we have a multivariate plot with a third variable of Inactive Yeast Addtion. We can see that with the addition of inactive yeast, which is glutathione, the noodle quality taste better, and the kneading time was also the shortest amongst all trials.
Regression
Data Preparation
## Temperature_C Humidity ProteinContent ActiveTime_Hour
## Min. :-5.115 Min. :-0.05154 Min. :-0.002077 Min. :-0.23397
## 1st Qu.:-3.615 1st Qu.:-0.03154 1st Qu.:-0.002077 1st Qu.:-0.06731
## Median :-1.115 Median :-0.01654 Median :-0.002077 Median :-0.06731
## Mean : 0.000 Mean : 0.00000 Mean : 0.000000 Mean : 0.00000
## 3rd Qu.: 3.885 3rd Qu.: 0.01596 3rd Qu.: 0.003923 3rd Qu.: 0.09936
## Max. : 5.885 Max. : 0.07846 Max. : 0.003923 Max. : 0.43269
## InactiveTime_Hour DoughHydration SaltPercent pH_PreAutolyse
## Min. :-2.7692 Min. :-0.13077 Min. :-0.014231 Min. :-0.65769
## 1st Qu.:-2.2692 1st Qu.: 0.01923 1st Qu.:-0.004231 1st Qu.:-0.15769
## Median :-1.2692 Median : 0.01923 Median :-0.004231 Median :-0.05769
## Mean : 0.0000 Mean : 0.00000 Mean : 0.000000 Mean : 0.00000
## 3rd Qu.:-0.5192 3rd Qu.: 0.01923 3rd Qu.:-0.004231 3rd Qu.: 0.14231
## Max. :20.7308 Max. : 0.01923 Max. : 0.085769 Max. : 0.54231
## pH_PostAutolyse FlourWeight PreKneadMins NumAutolyseHours
## Min. :-1.66154 Min. :-7.692 Min. :-4.4231 Min. :-2.7692
## 1st Qu.:-0.26154 1st Qu.:-7.692 1st Qu.:-4.4231 1st Qu.:-2.2692
## Median : 0.08846 Median :-7.692 Median : 0.5769 Median :-1.2692
## Mean : 0.00000 Mean : 0.000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.33846 3rd Qu.:-7.692 3rd Qu.: 0.5769 3rd Qu.:-0.5192
## Max. : 1.13846 Max. :92.308 Max. :10.5769 Max. :20.7308
## Yratio Kratio BSRatio BakedBSRatio
## Min. :-0.0250 Min. :-0.0203846 Min. :-0.004615 Min. :-0.003846
## 1st Qu.:-0.0250 1st Qu.:-0.0203846 1st Qu.:-0.004615 1st Qu.:-0.003846
## Median :-0.0250 Median :-0.0203846 Median :-0.004615 Median :-0.003846
## Mean : 0.0000 Mean : 0.0000000 Mean : 0.000000 Mean : 0.000000
## 3rd Qu.: 0.0125 3rd Qu.:-0.0003846 3rd Qu.:-0.004615 3rd Qu.:-0.003846
## Max. : 0.0750 Max. : 0.0796154 Max. : 0.045385 Max. : 0.046154
## KneadMins CookingTime Score
## Min. :-14.038 Min. :-2.3462 Min. :-2.6154
## 1st Qu.: -4.038 1st Qu.:-0.3462 1st Qu.:-1.6154
## Median : -4.038 Median : 0.6538 Median :-0.1154
## Mean : 0.000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 5.962 3rd Qu.: 0.6538 3rd Qu.: 2.1346
## Max. : 25.962 Max. : 2.6538 Max. : 3.3846We apply the normalization technique here, to ensure that all the numerical data have a mean value of 0, this will help us in developing a regression
MLR Model
Model 1
Variable: Temperature, Humidity, ProteinContent
##
## Call:
## lm(formula = Score ~ Temperature_C + Humidity + ProteinContent,
## data = noodle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9781 -0.8420 -0.2988 0.8851 3.7157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.0962 16.1143 2.612 0.0159 *
## Temperature_C -0.2108 0.1249 -1.688 0.1055
## Humidity -12.3465 11.6120 -1.063 0.2992
## ProteinContent -297.3191 138.4903 -2.147 0.0431 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.873 on 22 degrees of freedom
## Multiple R-squared: 0.273, Adjusted R-squared: 0.1739
## F-statistic: 2.754 on 3 and 22 DF, p-value: 0.06676This model was constructed using three variables: room temperature, humidity and protein content as predicting variables. The reason that I chose these three are because these are environmental variables, and uncontrollable by me through experimentations. However, it appears that these variables do not necessarily correlates to noodle tasting score, protein content is the only one that has little relevance.
Model 2
Variable: Dough Hydration, NumAutolyseHours, Yeast_Glutathion, ProteinContent
mlr_model2 <- lm(Score~DoughHydration+NumAutolyseHours+Yeast_Glutathione+ProteinContent,data = noodle)
summary(mlr_model2)##
## Call:
## lm(formula = Score ~ DoughHydration + NumAutolyseHours + Yeast_Glutathione +
## ProteinContent, data = noodle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6721 -0.5967 0.2381 0.4226 1.5065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.962e+01 6.253e+00 4.737 0.000112 ***
## DoughHydration -1.348e+01 4.662e+00 -2.892 0.008727 **
## NumAutolyseHours 3.982e-03 4.513e-02 0.088 0.930533
## Yeast_Glutathione1 3.930e+00 3.886e-01 10.113 1.59e-09 ***
## ProteinContent -1.845e+02 6.036e+01 -3.057 0.005991 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8278 on 21 degrees of freedom
## Multiple R-squared: 0.8644, Adjusted R-squared: 0.8386
## F-statistic: 33.47 on 4 and 21 DF, p-value: 7.785e-09This time, I have added another variable. Protein Content was included from the previous model. These four variables are I believe to be important predictor for determining noodle quality. The summary output reflected my hypothesis, among the chosen variables, Yeast_Glutathione is the one with highest predicatability, and both Dough Hydration and Protein Content are significant as well. The adjusted R-Squared result is 0.84
Model Evaluation
Between Model 1 and Model 2, it is obvious that Model 2 has better performance. The adjusted R-Squared for Model 2 is 0.84, compared to model 1’s result of 0.17. From a practical perspective, this result indicated that environmental variables do not play a significant role when it comes to making ideal noodle product, meaning that this result is not subject to any location, or ambiance limitation. Model 2 variables show that Yeast is a significant attribute of making good noodle.
Factor Analysis
Data Description
## [1] 26 31## tibble [26 × 31] (S3: tbl_df/tbl/data.frame)
## $ Trial : num [1:26] 1 2 3 4 5 6 7 8 9 10 ...
## $ MakeDate : POSIXct[1:26], format: "2020-04-18" "2020-04-18" ...
## $ Temperature_C : num [1:26] 20 20 20 19 19 22 23 28 28 26 ...
## $ Humidity : num [1:26] 0.3 0.3 0.3 0.31 0.28 0.3 0.21 0.2 0.2 0.22 ...
## $ ProteinContent : num [1:26] 0.104 0.104 0.104 0.104 0.104 0.104 0.104 0.104 0.104 0.104 ...
## $ ActiveTime_Hour : num [1:26] 0.417 0.583 0.833 0.333 0.667 ...
## $ InactiveTime_Hour : num [1:26] 1 12 24 1 1 2 6 2 6 1 ...
## $ Lamian : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 2 2 2 ...
## $ DoughHydration : num [1:26] 0.5 0.5 0.5 0.6 0.65 0.65 0.65 0.65 0.65 0.65 ...
## $ SaltPercent : num [1:26] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 ...
## $ pH_PreAutolyse : num [1:26] 6.7 6.7 6.7 6.7 6.7 5.5 5.5 6 6 6.3 ...
## $ pH_PostAutolyse : num [1:26] 7 7.2 7.3 7 7 6.7 7 5.3 5 6.5 ...
## $ FlourWeight : num [1:26] 100 100 100 100 100 100 100 100 100 100 ...
## $ Oil : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 1 2 2 2 ...
## $ DoughColor_YScale : Factor w/ 5 levels "1","2","3","4",..: 1 3 4 2 3 2 1 2 3 2 ...
## $ PreKneadMins : num [1:26] 5 5 5 5 5 15 15 10 10 15 ...
## $ NumAutolyseHours : num [1:26] 1 12 24 1 1 2 6 2 6 1 ...
## $ Yeast_Glutathione : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Yratio : num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
## $ Kansui : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 1 1 1 ...
## $ Kratio : num [1:26] 0 0.1 0.1 0.05 0.05 0 0 0 0 0 ...
## $ BakingSoda_SodiumBicarbonate: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ BSRatio : num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
## $ BakedBS_SodiumCarbonate : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ BakedBSRatio : num [1:26] 0 0 0 0 0 0 0 0 0 0 ...
## $ Extensibility : Factor w/ 8 levels "0.1","0.2","0.3",..: 2 4 5 5 5 3 2 4 5 4 ...
## $ KneadMins : num [1:26] 25 35 50 20 40 35 50 20 20 25 ...
## $ CookingTime : num [1:26] 3 3 3 2 2 5 5 4 4 3 ...
## $ Score : num [1:26] 3 4 4 3 3 2 1 2 2 3 ...
## $ Area : Factor w/ 12 levels "Autolyse","BakedBS",..: 4 1 1 7 7 9 9 6 6 10 ...
## $ Notes : chr [1:26] "This is the control noodle, it is not pulled, instead it is rolled out using rolling pin and cut to noodle stri"| __truncated__ "12hr Autolyse trial, noodle extended quite a bit, easier to pull, but still break apart after a few pull" "24hr extended autolyse trial, dough feel very slimy, like playdough, but has great extensibility" "60% dough hydration test to see if extensibility is improved" ...## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(num_col)` instead of `num_col` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## # A tibble: 6 x 19
## Temperature_C Humidity ProteinContent ActiveTime_Hour InactiveTime_Ho…
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 20 0.3 0.104 0.417 1
## 2 20 0.3 0.104 0.583 12
## 3 20 0.3 0.104 0.833 24
## 4 19 0.31 0.104 0.333 1
## 5 19 0.28 0.104 0.667 1
## 6 22 0.3 0.104 0.583 2
## # … with 14 more variables: DoughHydration <dbl>, SaltPercent <dbl>,
## # pH_PreAutolyse <dbl>, pH_PostAutolyse <dbl>, FlourWeight <dbl>,
## # PreKneadMins <dbl>, NumAutolyseHours <dbl>, Yratio <dbl>, Kratio <dbl>,
## # BSRatio <dbl>, BakedBSRatio <dbl>, KneadMins <dbl>, CookingTime <dbl>,
## # Score <dbl>Correlation Matrix
Initial Corrplot
noodlematrix <- cor(noodle_X)
corrplot(noodlematrix,order = 'hclust', type='upper',tl.srt = 45, tl.cex = 0.7)
Observations pH_PreAutolyse and Humidity are highly correlated Humidity and Kansui Ratio are highly correlated PreKneadMins and Baking Soda Ratio are highly correlated NumAutolyseHours and Kansui Ratio are highly correlated
Updated Corrplot
## Warning in sqrt(1 - h * h): NaNs produced## Temperature_C Humidity ProteinContent ActiveTime_Hour
## Temperature_C 1.00000000 -0.57821523 0.24554691 -0.22218711
## Humidity -0.57821523 1.00000000 -0.36745539 0.53449371
## ProteinContent 0.24554691 -0.36745539 1.00000000 -0.16198650
## ActiveTime_Hour -0.22218711 0.53449371 -0.16198650 1.00000000
## InactiveTime_Hour -0.19834903 0.33398031 -0.11535156 0.61698838
## DoughHydration 0.46608599 -0.68526786 0.29034675 -0.41057886
## SaltPercent 0.13835585 -0.05337261 -0.20404010 -0.02733468
## pH_PreAutolyse -0.52214877 0.56821462 -0.22238602 0.11281236
## pH_PostAutolyse -0.25739749 0.35556222 0.42020202 0.43376072
## FlourWeight -0.08921599 -0.15371627 -0.21004201 -0.38039412
## PreKneadMins -0.06760293 -0.42182121 0.18490260 -0.13320193
## NumAutolyseHours -0.19834903 0.33398031 -0.11535156 0.61698838
## Yratio -0.04421852 -0.32690476 -0.14376274 -0.58848454
## Kratio -0.22650178 0.60484824 -0.03298921 0.49897953
## BSRatio 0.53560534 -0.41681828 0.34618847 -0.15459301
## BakedBSRatio -0.32917624 -0.15371627 0.39674602 -0.10942844
## KneadMins -0.22218711 0.53449371 -0.16198650 1.00000000
## CookingTime -0.03538037 0.26073864 -0.59211740 0.32983179
## Score -0.33673547 0.12488693 -0.42164825 -0.29411744
## InactiveTime_Hour DoughHydration SaltPercent pH_PreAutolyse
## Temperature_C -0.19834903 0.4660860 0.13835585 -0.52214877
## Humidity 0.33398031 -0.6852679 -0.05337261 0.56821462
## ProteinContent -0.11535156 0.2903468 -0.20404010 -0.22238602
## ActiveTime_Hour 0.61698838 -0.4105789 -0.02733468 0.11281236
## InactiveTime_Hour 1.00000000 -0.6522371 -0.10178998 0.27269544
## DoughHydration -0.65223713 1.0000000 0.08862660 -0.64758172
## SaltPercent -0.10178998 0.0886266 1.00000000 0.11270632
## pH_PreAutolyse 0.27269544 -0.6475817 0.11270632 1.00000000
## pH_PostAutolyse 0.27946896 -0.3120939 -0.18029373 0.21887390
## FlourWeight -0.16475498 0.1151939 -0.06411406 0.03654747
## PreKneadMins -0.15290337 0.3960599 0.11936710 -0.37796606
## NumAutolyseHours 1.00000000 -0.6522371 -0.10178998 0.27269544
## Yratio -0.29601113 0.2365326 -0.17952019 -0.22513346
## Kratio 0.56401372 -0.5396694 -0.13723643 0.53875394
## BSRatio 0.03930285 0.1349013 -0.07508270 -0.27755163
## BakedBSRatio -0.01601785 0.1151939 -0.06411406 0.07973994
## KneadMins 0.61698838 -0.4105789 -0.02733468 0.11281236
## CookingTime 0.11269775 -0.1456258 0.14890179 -0.28205737
## Score -0.01101451 -0.1944490 -0.04765609 0.12399871
## pH_PostAutolyse FlourWeight PreKneadMins NumAutolyseHours
## Temperature_C -0.25739749 -0.089215989 -0.06760293 -0.19834903
## Humidity 0.35556222 -0.153716271 -0.42182121 0.33398031
## ProteinContent 0.42020202 -0.210042013 0.18490260 -0.11535156
## ActiveTime_Hour 0.43376072 -0.380394118 -0.13320193 0.61698838
## InactiveTime_Hour 0.27946896 -0.164754980 -0.15290337 1.00000000
## DoughHydration -0.31209392 0.115193919 0.39605990 -0.65223713
## SaltPercent -0.18029373 -0.064114061 0.11936710 -0.10178998
## pH_PreAutolyse 0.21887390 0.036547473 -0.37796606 0.27269544
## pH_PostAutolyse 1.00000000 -0.120887026 -0.09088671 0.27946896
## FlourWeight -0.12088703 1.000000000 0.03737175 -0.16475498
## PreKneadMins -0.09088671 0.037371755 1.00000000 -0.15290337
## NumAutolyseHours 0.27946896 -0.164754980 -0.15290337 1.00000000
## Yratio -0.26282382 0.513335673 -0.12789503 -0.29601113
## Kratio 0.50660515 -0.003365573 -0.56096704 0.56401372
## BSRatio 0.24635676 -0.097590007 0.04376532 0.03930285
## BakedBSRatio 0.22577430 -0.083333333 0.68514884 -0.01601785
## KneadMins 0.43376072 -0.380394118 -0.13320193 0.61698838
## CookingTime -0.42419598 -0.065842692 -0.14107766 0.11269775
## Score -0.28602019 0.340679911 -0.34499087 -0.01101451
## Yratio Kratio BSRatio BakedBSRatio KneadMins
## Temperature_C -0.04421852 -0.226501775 0.53560534 -0.32917624 -0.22218711
## Humidity -0.32690476 0.604848238 -0.41681828 -0.15371627 0.53449371
## ProteinContent -0.14376274 -0.032989213 0.34618847 0.39674602 -0.16198650
## ActiveTime_Hour -0.58848454 0.498979532 -0.15459301 -0.10942844 1.00000000
## InactiveTime_Hour -0.29601113 0.564013722 0.03930285 -0.01601785 0.61698838
## DoughHydration 0.23653259 -0.539669395 0.13490131 0.11519392 -0.41057886
## SaltPercent -0.17952019 -0.137236427 -0.07508270 -0.06411406 -0.02733468
## pH_PreAutolyse -0.22513346 0.538753940 -0.27755163 0.07973994 0.11281236
## pH_PostAutolyse -0.26282382 0.506605148 0.24635676 0.22577430 0.43376072
## FlourWeight 0.51333567 -0.003365573 -0.09759001 -0.08333333 -0.38039412
## PreKneadMins -0.12789503 -0.560967042 0.04376532 0.68514884 -0.13320193
## NumAutolyseHours -0.29601113 0.564013722 0.03930285 -0.01601785 0.61698838
## Yratio 1.00000000 -0.200409560 -0.06679524 -0.17111189 -0.58848454
## Kratio -0.20040956 1.000000000 -0.20889184 -0.17837536 0.49897953
## BSRatio -0.06679524 -0.208891835 1.00000000 -0.09759001 -0.15459301
## BakedBSRatio -0.17111189 -0.178375362 -0.09759001 1.00000000 -0.10942844
## KneadMins -0.58848454 0.498979532 -0.15459301 -0.10942844 1.00000000
## CookingTime 0.10515370 -0.071797875 -0.37411206 -0.44626714 0.32983179
## Score 0.78979488 0.059918212 -0.29815011 -0.37364893 -0.29411744
## CookingTime Score
## Temperature_C -0.03538037 -0.33673547
## Humidity 0.26073864 0.12488693
## ProteinContent -0.59211740 -0.42164825
## ActiveTime_Hour 0.32983179 -0.29411744
## InactiveTime_Hour 0.11269775 -0.01101451
## DoughHydration -0.14562581 -0.19444895
## SaltPercent 0.14890179 -0.04765609
## pH_PreAutolyse -0.28205737 0.12399871
## pH_PostAutolyse -0.42419598 -0.28602019
## FlourWeight -0.06584269 0.34067991
## PreKneadMins -0.14107766 -0.34499087
## NumAutolyseHours 0.11269775 -0.01101451
## Yratio 0.10515370 0.78979488
## Kratio -0.07179787 0.05991821
## BSRatio -0.37411206 -0.29815011
## BakedBSRatio -0.44626714 -0.37364893
## KneadMins 0.32983179 -0.29411744
## CookingTime 1.00000000 0.40713984
## Score 0.40713984 1.00000000## Temperature_C Humidity ProteinContent ActiveTime_Hour
## Temperature_C NA 0.0019748879 0.226628589 0.2753058175
## Humidity 0.001974888 NA 0.064787160 0.0049092278
## ProteinContent 0.226628589 0.0647871596 NA 0.4291818672
## ActiveTime_Hour 0.275305817 0.0049092278 0.429181867 NA
## InactiveTime_Hour 0.331378394 0.0954166639 0.574708116 0.0007870606
## DoughHydration 0.016399592 0.0001120733 0.150183627 0.0372087291
## SaltPercent 0.500288418 0.7956755117 0.317404660 0.8945495729
## pH_PreAutolyse 0.006215549 0.0024591050 0.274865173 0.5832123654
## pH_PostAutolyse 0.204282349 0.0746472233 0.032572705 0.0268380374
## FlourWeight 0.664719157 0.4534191570 0.303065902 0.0552356187
## PreKneadMins 0.742812511 0.0318402750 0.365844188 0.5165348857
## NumAutolyseHours 0.331378394 0.0954166639 0.574708116 0.0007870606
## Yratio 0.830167518 0.1030892116 0.483521646 0.0015651656
## Kratio 0.265848490 0.0010631340 0.872894532 0.0094616732
## BSRatio 0.004803957 0.0341469000 0.083195637 0.4508160002
## BakedBSRatio 0.100577712 0.4534191570 0.044777074 0.5946301895
## KneadMins 0.275305817 0.0049092278 0.429181867 NaN
## CookingTime 0.863763031 0.1982633673 0.001438914 0.0998614411
## Score 0.092547760 0.5432738355 0.031917873 0.1447198309
## InactiveTime_Hour DoughHydration SaltPercent pH_PreAutolyse
## Temperature_C 0.3313783940 0.0163995922 0.5002884 0.0062155495
## Humidity 0.0954166639 0.0001120733 0.7956755 0.0024591050
## ProteinContent 0.5747081160 0.1501836271 0.3174047 0.2748651730
## ActiveTime_Hour 0.0007870606 0.0372087291 0.8945496 0.5832123654
## InactiveTime_Hour NA 0.0003053762 0.6207476 0.1777217747
## DoughHydration 0.0003053762 NA 0.6668085 0.0003484022
## SaltPercent 0.6207475770 0.6668084588 NA 0.5835687088
## pH_PreAutolyse 0.1777217747 0.0003484022 0.5835687 NA
## pH_PostAutolyse 0.1667671641 0.1206216249 0.3781218 0.2827120444
## FlourWeight 0.4212289523 0.5752344769 0.7556783 0.8593121030
## PreKneadMins 0.4558398293 0.0451818957 0.5613727 0.0569387250
## NumAutolyseHours 0.0000000000 0.0003053762 0.6207476 0.1777217747
## Yratio 0.1420302631 0.2446801874 0.3802055 0.2688247485
## Kratio 0.0026909195 0.0044351904 0.5037954 0.0045160929
## BSRatio 0.8488208943 0.5111499035 0.7154589 0.1698185622
## BakedBSRatio 0.9380959964 0.5752344769 0.7556783 0.6985961158
## KneadMins 0.0007870606 0.0372087291 0.8945496 0.5832123654
## CookingTime 0.5835975065 0.4778111856 0.4678544 0.1627092088
## Score 0.9574112472 0.3411660227 0.8171755 0.5461677270
## pH_PostAutolyse FlourWeight PreKneadMins NumAutolyseHours
## Temperature_C 0.204282349 0.664719157 0.7428125109 0.3313783940
## Humidity 0.074647223 0.453419157 0.0318402750 0.0954166639
## ProteinContent 0.032572705 0.303065902 0.3658441885 0.5747081160
## ActiveTime_Hour 0.026838037 0.055235619 0.5165348857 0.0007870606
## InactiveTime_Hour 0.166767164 0.421228952 0.4558398293 0.0000000000
## DoughHydration 0.120621625 0.575234477 0.0451818957 0.0003053762
## SaltPercent 0.378121834 0.755678333 0.5613726736 0.6207475770
## pH_PreAutolyse 0.282712044 0.859312103 0.0569387250 0.1777217747
## pH_PostAutolyse NA 0.556361907 0.6588099724 0.1667671641
## FlourWeight 0.556361907 NA 0.8561710996 0.4212289523
## PreKneadMins 0.658809972 0.856171100 NA 0.4558398293
## NumAutolyseHours 0.166767164 0.421228952 0.4558398293 NA
## Yratio 0.194569105 0.007317516 0.5335265798 0.1420302631
## Kratio 0.008265535 0.986981407 0.0028704958 0.0026909195
## BSRatio 0.225051588 0.635303828 0.8318836791 0.8488208943
## BakedBSRatio 0.267428185 0.685679250 0.0001125044 0.9380959964
## KneadMins 0.026838037 0.055235619 0.5165348857 0.0007870606
## CookingTime 0.030790006 0.749295534 0.4918121909 0.5835975065
## Score 0.156632389 0.088553916 0.0843391093 0.9574112472
## Yratio Kratio BSRatio BakedBSRatio
## Temperature_C 8.301675e-01 0.265848490 0.004803957 0.1005777124
## Humidity 1.030892e-01 0.001063134 0.034146900 0.4534191570
## ProteinContent 4.835216e-01 0.872894532 0.083195637 0.0447770738
## ActiveTime_Hour 1.565166e-03 0.009461673 0.450816000 0.5946301895
## InactiveTime_Hour 1.420303e-01 0.002690919 0.848820894 0.9380959964
## DoughHydration 2.446802e-01 0.004435190 0.511149904 0.5752344769
## SaltPercent 3.802055e-01 0.503795408 0.715458938 0.7556783330
## pH_PreAutolyse 2.688247e-01 0.004516093 0.169818562 0.6985961158
## pH_PostAutolyse 1.945691e-01 0.008265535 0.225051588 0.2674281853
## FlourWeight 7.317516e-03 0.986981407 0.635303828 0.6856792504
## PreKneadMins 5.335266e-01 0.002870496 0.831883679 0.0001125044
## NumAutolyseHours 1.420303e-01 0.002690919 0.848820894 0.9380959964
## Yratio NA 0.326276661 0.745785162 0.4032774816
## Kratio 3.262767e-01 NA 0.305781981 0.3833013621
## BSRatio 7.457852e-01 0.305781981 NA 0.6353038276
## BakedBSRatio 4.032775e-01 0.383301362 0.635303828 NA
## KneadMins 1.565166e-03 0.009461673 0.450816000 0.5946301895
## CookingTime 6.091887e-01 0.727431743 0.059725618 0.0222993285
## Score 1.605349e-06 0.771235402 0.139035728 0.0600675100
## KneadMins CookingTime Score
## Temperature_C 0.2753058175 0.863763031 9.254776e-02
## Humidity 0.0049092278 0.198263367 5.432738e-01
## ProteinContent 0.4291818672 0.001438914 3.191787e-02
## ActiveTime_Hour NaN 0.099861441 1.447198e-01
## InactiveTime_Hour 0.0007870606 0.583597507 9.574112e-01
## DoughHydration 0.0372087291 0.477811186 3.411660e-01
## SaltPercent 0.8945495729 0.467854406 8.171755e-01
## pH_PreAutolyse 0.5832123654 0.162709209 5.461677e-01
## pH_PostAutolyse 0.0268380374 0.030790006 1.566324e-01
## FlourWeight 0.0552356187 0.749295534 8.855392e-02
## PreKneadMins 0.5165348857 0.491812191 8.433911e-02
## NumAutolyseHours 0.0007870606 0.583597507 9.574112e-01
## Yratio 0.0015651656 0.609188680 1.605349e-06
## Kratio 0.0094616732 0.727431743 7.712354e-01
## BSRatio 0.4508160002 0.059725618 1.390357e-01
## BakedBSRatio 0.5946301895 0.022299328 6.006751e-02
## KneadMins NA 0.099861441 1.447198e-01
## CookingTime 0.0998614411 NA 3.898749e-02
## Score 0.1447198309 0.038987495 NAcorrplot(res2$r,type = 'upper', order = 'hclust',p.mat = res2$P, sig.level = 0.01, insig = 'blank', tl.srt = 45,tl.cex = 0.7)
KMO
## Error in solve.default(r) :
## system is computationally singular: reciprocal condition number = 3.89955e-32## matrix is not invertible, image not found## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = datamatrix)
## Overall MSA = 0.5
## MSA for each item =
## Temperature_C Humidity ProteinContent ActiveTime_Hour
## 0.5 0.5 0.5 0.5
## InactiveTime_Hour DoughHydration SaltPercent pH_PreAutolyse
## 0.5 0.5 0.5 0.5
## pH_PostAutolyse FlourWeight PreKneadMins NumAutolyseHours
## 0.5 0.5 0.5 0.5
## Yratio Kratio BSRatio BakedBSRatio
## 0.5 0.5 0.5 0.5
## KneadMins CookingTime
## 0.5 0.5Number of Factors
## [1] 5.747511e+00 2.789818e+00 2.223570e+00 1.888014e+00 1.329005e+00
## [6] 1.147362e+00 7.918106e-01 6.395797e-01 5.083986e-01 3.044951e-01
## [11] 2.221834e-01 1.541481e-01 1.077138e-01 8.468951e-02 4.041151e-02
## [16] 2.128848e-02 -1.531197e-16 -1.961984e-16
Run Analysis
Cluster Analysis
Optimal Cluster (Elbow)
Using the elbow method, we can see the bend at around 3 clusters.
K-Means Cluster
k2 <- kmeans(norm,centers = 2, nstart = 25)
p2 <- fviz_cluster(k2, data = norm)+ggtitle('K = 2')
k3 <- kmeans(norm,centers = 3, nstart = 25)
p3 <- fviz_cluster(k3, data = norm) + ggtitle('K = 3')
k4 <- kmeans(norm,centers = 4, nstart = 25)
p4 <- fviz_cluster(k4, data = norm) + ggtitle('K = 4')
k5 <- kmeans(norm,centers = 5, nstart = 25)
p5 <- fviz_cluster(k5, data = norm) + ggtitle('K = 5')
grid.arrange(p2,p3,p4,p5,nrow=2)
Here plotted noodle data with 2,3,4 and 5 clusters, 3 cluster was the ideal number based on the elbow method.
Hierachical Analysis
d <- dist(norm,method = 'euclidean')
hier <- hclust(d,method = 'complete')
plot(hier,cex=0.6, hang = -1)
rect.hclust(hier,k=4, border = 2:5)
hier2 <- hclust(d,method = 'ward.D2')
sub_grp <- cutree(hier2, k =3)
fviz_cluster(list(data = norm, cluster = sub_grp))
Evaluation
From both the K-Means and Hierachical analysis, we see that 3 clusters are ideal for our data. From observing the cluster pattern, we can see the last observation was the single cluster, it is understandable because this was good noodle. The second cluster, has most of the early experiemts, this is when I am trying to explore the environmental variables. The first cluster has some of the later stage experiments, which include additives that I had added based on research to alter the noodle texture and gluten formation.
Dimension Reduction (PCA)
Visualization
## List of 2
## $ values : num [1:19] 761 122.8 25.3 20.5 12.9 ...
## $ vectors: num [1:19, 1:19] 9.36e-03 2.84e-04 2.02e-05 2.93e-03 3.88e-02 ...
## - attr(*, "class")= chr "eigen"## [,1] [,2]
## [1,] 9.360277e-03 1.052237e-01
## [2,] 2.843096e-04 -1.906533e-03
## [3,] 2.017734e-05 7.285331e-05
## [4,] 2.931642e-03 -1.416346e-02
## [5,] 3.876970e-02 -3.372028e-01
## [6,] -2.647884e-04 2.289098e-03
## [7,] 4.146067e-05 1.560003e-04
## [8,] -2.992494e-04 -6.751756e-03
## [9,] 3.492374e-03 -2.320913e-02
## [10,] -9.824281e-01 -1.790340e-01
## [11,] -7.955104e-03 6.819841e-02
## [12,] 3.876970e-02 -3.372028e-01
## [13,] -8.468391e-04 1.362244e-03
## [14,] 5.614122e-05 -1.845671e-03
## [15,] 4.309960e-05 2.271457e-04
## [16,] 3.554354e-05 1.734079e-04
## [17,] 1.758985e-01 -8.498077e-01
## [18,] 4.922025e-03 -4.035623e-02
## [19,] -2.630012e-02 1.454068e-02## PC1 PC2
## Temperature_C -9.360277e-03 -1.052237e-01
## Humidity -2.843096e-04 1.906533e-03
## ProteinContent -2.017734e-05 -7.285331e-05
## ActiveTime_Hour -2.931642e-03 1.416346e-02
## InactiveTime_Hour -3.876970e-02 3.372028e-01
## DoughHydration 2.647884e-04 -2.289098e-03
## SaltPercent -4.146067e-05 -1.560003e-04
## pH_PreAutolyse 2.992494e-04 6.751756e-03
## pH_PostAutolyse -3.492374e-03 2.320913e-02
## FlourWeight 9.824281e-01 1.790340e-01
## PreKneadMins 7.955104e-03 -6.819841e-02
## NumAutolyseHours -3.876970e-02 3.372028e-01
## Yratio 8.468391e-04 -1.362244e-03
## Kratio -5.614122e-05 1.845671e-03
## BSRatio -4.309960e-05 -2.271457e-04
## BakedBSRatio -3.554354e-05 -1.734079e-04
## KneadMins -1.758985e-01 8.498077e-01
## CookingTime -4.922025e-03 4.035623e-02
## Score 2.630012e-02 -1.454068e-02Before we get to the visualization for PC1 and PC2, and determining the useful variables for our analysis. We first caluculated the eigenvalues and eigenvectors. As we can see from the above graph, we can assume that PC1 corresponds to overall noodle making process since it included variables such as FlourWeight, PreKneadMins, and YeastRatio that have higher values. While PC2 appears to be for the method of extended dough relaxation using Autolyse, variables that have high values in PC2 include KneadMins, pH_PreAutolyse, NumAutolyseHours, InactiveTime_Hour, CookingTime.
## Trial PC1 PC2
## 1 1 -7.541226 -1.322794
## 2 2 -10.128037 14.586391
## 3 3 -13.698070 35.432236
## 4 4 -6.647185 -5.508263
## 5 5 -10.166112 11.492440
## 6 6 -9.352824 7.039462
We can see from this above 2-dimensional graph, the PC1 represents the experiment success and overall noodle making process, with the last trial as the most desirable noodle. While the y-axis which PC2 represent another method that had experienced success in trial, but takes significant longer to replicate desirable result, that is to utilize the characteristics of dough to deform and realign gluten structures through this process called Autolyse.
Scree Plot/ Optimal Components
## [1] 0.804 0.130 0.027 0.022 0.014 0.004 0.001 0.000 0.000 0.000 0.000 0.000
## [13] 0.000 0.000 0.000 0.000 0.000 0.000 0.000## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 row(s) containing missing values (geom_path).
We constructed the Scree Plot for better and easier gauging of variables choice. As we gather from our first and second principal components, the PC1 explains 80.4% of variability, and PC2 explains 13.0% variability. These two combined contribute 93% of noodle trial data variability. The scree plot also show that the first two components are significant in terms of determining data variability.
Reflection
Both PC1 and PC2 are proven to be helpful indeterminng useful variables, while reduced overall dimension of noodle trial data. The PC1 and PC2 represents two methods that I have found useful for making ideal Lamian-style noodle. The three variables of importance in PC1 are FlourWeight, YRatio (Inactive Yeast Ratio), PreKneadMins. These three are important variables to keep in mind when trying to replicate the noodle making process. The volume of flour determines the baseline measurements for all other variables such as hydration level, salt percentage and so on. Inactive Yeast have proven to be an useful additive that contribute to the overall success, and is demonstrated to be significant previously in cluster, regression and pca. PreKneadMins gives you an idea of the ease of overall noodle making process. PC2 listed variables that are of importance when utilizing the Autolyse method. Since this method only utilize base ingredients of noodle with no additives added, it utilize extended time to relax and let flour, water, salt to work their magics and become an uniformed ball that is not only stretchable, but also have great extensibility and elasticity.
Based on the results from PC1 and PC2, it overuled my previous notion that was found through cluster analysis of recommending one lamian making method using inactive yeast. Now I planned to provide two methods, one with additives and one without additives.