Good morning or good afternoon for all of you. I'm Margaux Renaud and today we will talk about the mixing plan of wine blending and the testing of these modalities to validate the receipt of a wine.
First of all, I would like to present my company. I'm working for Chêne Company, which is a group of cooperage. I t owns a French cooperage, Taransaud, which is make barrels and vats from French oak. An American cooperage, Canton, and Kádár Cooperage, and [inaudible 00:00:42] , which make oak wood sticks and chips, XtraChên.
The French cooperage and the R&D department are based in the Bordeaux area in France. In our R&D department, we have 8% with different background. We have a PhD in chemistry and enology, engineer, agronomist, enologist, technician. W ith all these various skills, we have a lot of different trials.
From the forest, for example, we can do trials about DNA of the oak in the forests. On the aging wood, we thought to do the barrel or the vats in relation with the climate change. Two, the analysis on our client wine in our barrel directly in the wine.
Today, I would like to talk with you about a mixing plan for a wine blending. It's a client trial and I will present you the problematic of the client. In this case, the client has different wine and one of them, you want to keep in the same wine style, but you want to optimize the ingredients.
In the wine industry what we call ingredients it's very different, very diverse. It can be different variety of wine. In Bordeaux area, we used to mix Merlot and Cabernet, for example. It can be different quality of wine, different type of aging. If the wine is aging in barrel or in tanks with oak chips or without.
I n this case, the client has four different ingredient to mix. Our team follow the different ingredient during all the wine aging, and at the end, we have created a mixing plan and we taste it.
Just before to go on JMP to present you the way to process data, I just want to talk a bit about the wine testing. An important thing in the wine industry is all the wine recipe is decided by testing. We do a lot of analysis, but it's not the last point of a recipe. It's always the tasting.
The usual way to taste wine it's a quantitative tasting. We do a profile with grade on different descriptor. It could be bitterness, for example, or the fruity notes or the woody notes. All the taster are writing the intensity of the descriptor.
Then we process the data with an ANOVA, a two- factor ANOVA. The first factor is the modalities, the different modalities in the trial, and the tester. To have really significant results you need to have a large and trained panel for your testing.
In our case, when we do a trial with a client or in our group, we have different different type of taster. Most of the time you have the winery team, some part of the commercial team, and some part of the R&D team. A ll this taster doesn't taste the wine in the same way. They don't have the same target when they taste the wine.
The target for the client is not the same as the commercial part and it's not the same for us. Most of the time we are not trained to taste the wine in the same way. When we analyzed the data, there is a really big effect of the taster. In fact, the taster have not the same feeling about the profile asking.
For us, it's complicated to using the profile, so we decide to use another type of testing, the free sorting. The free sorting it's a testing when I asked my taster to test the different modalities and to make group groups inside them. I put a little example on the PowerPoint.
In this case, I asked the taster to make groups if the wine is similar and if there is difference between the two modalities, they put them in two different groups. In this case, for example, there is 11 samples, and the taster decides to make four groups. A first one with four samples, a second one with three, another one with three of the sample, and the last one with an only glass of wine.
I ask them after making group to describe a bit the group. In this case, the taster decide to put together for sample because they have some chestnut not present in the other samples. I n this case, we don't need to have a trained panel, so if there is enough big difference between my modalities, normally all the taster will put together the wine, the wine really close and put separately the other wine.
This type of tasting is really easy to use for us because we don't need a trained panel. We can have a small panel too. It can be used in different language. It doesn't matter if we have a French panel or an Italian panel, for example. They just have to do groups.
The other thing, thanks to JMP, it's easy to present the result right after the testing. When you do a profile, you have to process data making the ANOVA test, and send the result to the client. Most of the time it takes a few days or a few weeks if you are really late.
With the free sorting, we can , and thanks to JMP, present the result right after. This type of testing will create a distance matrix between all the sample. In fact, if you put samples in the same group, there is no distance between them. If you put them in two other group, there is a distance of one between them. At the end, you can make a matrix distance between all the samples. It's what I do with JMP. I will show you just after.
Okay, I will switch on JMP. To process this data, I'm using a project. I'm using several data tables and it's easier for me to put them in the same place. Before to go on the testing result, I just want to talk a bit about my mixing plan.
I told you that my client has four ingredients. Unfortunately, I didn't make the mixing table with JMP. Because when I began to work on the mixing plan, I was not really confident enough with JMP to do it on it. The client gave us a lot of rules in this mixing plan, a bit complicated. So we decided to make it by hand and to treat the rest of the result with JMP.
Just to show you, this is my mixing plan. I have a code for all of my samples and the ingredients one, two, three, four, and the proportion of each one in these samples. There is just few information. For my ingredients, there is a minimum and maximum proportion.
The important thing is the ingredients works two by two. The ingredients one and two are working together. In fact, the ingredient one plus the ingredient two is always equal to 14 % of the blending. Exactly the same for three and four. The addition of these two is always equal to 86 % of the sample.
That's few words given by the client. T hanks to that we did a mixing plan with 16 samples and a target. The target is the historical recipe of the winery, the typical wine. The client wants the other ingredients to be closer than the historical wine.
You can see here the mixing plan. It's why I explained just earlier, they're working to pay two. Okay, this is the mixing plan. We created, we're blending the samples, and we did the tasting with the client.
This is my results data table. It's in fact, very easy. I have a first column with my sample in the wine testing. Most of the time you have to test without knowing which is the modality in your glass. To do that, to recreate a random number, sorry, a random number of three digits like that you can't know which sample is it.
I put it on my first column and after that I have one column by tester. In this case, I have five testers. On each column, I put the group where the sample has been put. Just to show you with the distribution we can see for the tester one in the group three, for example, just for the tester one. He put in the group three the sample 4 74, 486 and 910. It's the same for all the samples.
I'm not sure I said... Yes, I told you that at the beginning. I asked to my tester to describe the group with few words. When I do a testing with my clients, I don't write on my result data table, group six, group one. I'm writing directly the descriptor, the term used by the tester to describe the proof. I will explain you why a bit later.
I have this data table. To have it, most of the time, I ask to my tester to put the result on Excel file on a tablet like that. He put directly all the results on the file and I just have to open it after with JMP.
I need another the data table, which is called NUMMOD. You can see that the first column is my random number and the second one is the modalities. Y ou can see what modality is behind the number given. Then the other column is the description of each modalities. In this case, it's the proportion of each ingredient.
I need these two data table and I need a script. To process the data directly after the testing, I have created a script. For this script, I have to thank a lot the JMP communities because they helped me a lot to do this really complicated part.
In fact, this script helped me to create the distance matrix just with the data result I show you earlier. In this case, this, I will not explain all the line because it's a bit complicated, but I will show you how I'm using it. I'm just checking I am on the right data table and I'm running the script.
I can save the results. Thanks to the project, I can save the result directly inside the folder result. Yes, the folder result. Directly, I can have my distance matrix. You can see I have still my sample number in the first column. Then all the samples in column and the distance with all the other samples, so for the 001, it's the same sample, so it's 0. Then you have the distance with the other samples.
In the script, I have also joined the information from my data table in the map, so I can add the modalities and the ingredient proportion in the same data table. The best way to show the result is to create a map. To show the map, I'm using a multivariate method and precisely the multi dimensional scaling.
In this case, I will put in column my distance matrix. I didn't show you, but I have grouped directly all my matrix, it's also in the script. Like that I just have to select this group of columns to put inside the process. I add my distance matrix on it. I'm running it and I can have this map.
I can see all my sample, the 16 plus the target. I don't know which one is it. What we can see is some samples are really close. For example, the 246 and the 592 are really close. They look really similar for all the taster. Not the same because they are not on the same point. There's a little distance between them, but really close. At the opposite, the 246 and the 661 are really far away from each other. They look really different.
At this point when I present the results to my panel, I begin to show which sample is it. We can talk about if all the tester are agree with the map. I f they say, okay, I can find my group on this one. We can talk about that and I show which sample is it.
For that, I have just to label the modality. I go back on my map and you can see there is the code of each sample of the mixing plan and most important, the target. You can see the original recipe is here. W e can say that there is some sample really close from this one.
I think this one should be interesting to use with all the ingredients to keeping the same wine style of the target. To be sure of that, I will do clustering to ask to JMP to show me which sample are really close from each other. For that, I'm doing a clustering and more precisely a [inaudible 00:19:04] cluster.
A s I did for the multi dimensional scaling, I'm using the distance matrix as [inaudible 00:19:15], sorry. I'm running it. Usually, I'm testing three, four, or five cluster because I know in my testing it's more or less the number of group usually. In this one, I already know that three cluster is the best way. I'm testing three and I'm saving the cluster in the data table like that.
I can put in legend the row state of the cluster and the map will be colored with the different cluster. You can see we have three cluster really well separate. One looks very interesting, the green one. You have the target and four sample really close of the target.
I can start the process now. I can say to the client, okay, you can use one of these four samples from the mixing plan to keep the same quality or the same type of wine. They are really close. Maybe you can choose this one, it's the closer one.
But if I want to give more information to the client about where it can play inside the mixing plan, I did another treatment. I would like to know the distance between each sample from the target. For that, I saved the coordinates of each sample. You can see they are right here, the dimension one and the dimension two. I have just calculated the distance between the target and all the others in sample.
To go a bit faster, I have already created a script with just adding a new column and a formula to calculate the distance between the target and the sample. I will just running it. You can see here the new column with the distance.
To represent the best part of the mixing plan, I will do a graph builder. A s I said, the sample is working two by two. I can represent it in two dimensions. For that, I will put the ingredients three here and the ingredient one here. As they're working two by two, we know that the complement of the ingredient one is the ingredient two, and the complement of the ingredient three is the four.
We don't need to show the target, so I will hide and exclude it. I have my 16 sample right here. I will put the distance in color and I will represent it with the contour and the points.
To be easier, I'm just changing the color. I will take this one, the green, yellow, red. Like that the sample is close from the target with the shorter distance from the target are in green and the other one are in red. I don't really know how to change the color of the points. We don't see them very well.
You can have this type of mode. That is really interesting for the client. You can see there is different spots in green and different spots in the in red. In fact, we know that it's not interesting for the client to playing with the mixing plan in this area. It doesn't look like historical wine. It's the same for this area.
But there is two other green area. This one there's in fact, only one sample really close from the target. If you look the point around, then doesn't really look like for the historical wine, so it's not really interesting to play in this area.
In this one, it's really more interesting because you have three sample really close from the target and two other one a bit far away, but still close. W e can say to the client that, okay, if you want to keep the same type of wine, you can add between 4 and 10 % of your ingredient one and between 20 % and 60 % of your ingredients three. The most interesting is to keep in this area above 50 % of your ingredient 3 and around 7 % of your ingredient one.
With this information our clients in relation with the age is volume tank, is what aging you want to do. It can play a bit, but in the way to be sure to keep the same quality and the same type of wine. This helps really a lot the clients.
We can do another treatment. I will explain you quickly because it's a long treatment to do. But in this case, I only use the group. It doesn't matter if it's called group one or if it's called Fruity, Woody, it doesn't matter. It's just the group.
But I asked my panel to describe the group. In this case, I do another treatment. From the data table result, this one, I'm doing a text explorer with a classic JMP Pro . I can have this type of data table with my samples and descriptor. In fact, I ask him to count how many times each descriptor are written for each sample. Like that, I can do another type of map with a multi word method, but this one, a multiple correspondence analysis.
In this case, I will put in response the descriptor, and in factor, the modalities. I will add in the count in the frequency. Just after running that, I just will show you with the script because we'll see it's well, there is a better presentation in this way.
Okay. You can have this type of map with in blue all the modalities, all the samples, and in red, all descriptor used. It's a complementary map from the first one, from this one. From this one because in this one, you have the sample close or far away from each other, but you don't know why. You don't know why this sample are together, or why this sample are on the right of the map, and why this one are on the left, why they are separated.
With this process, we try to explain a bit why the sample are separated. It's not always exactly the same map because it's not the same treatment. This one needs a process a bit longer than the first one because when sometimes you don't have exactly the same way to write a word, whereas in French we have accent, so sometimes you have to check the result of the data table before to do the process.
Some words are more or less the same sense, so you have to put them together. So it's a bit longer, so I can't do it right after the testing, but I do it after. We can explain a bit better why the sample are located on this way on the map.
In this case, you can see some sample are really high, coconut, some vanilla nuts, other one more toasty, spicy on this one. Unfortunately, some are really negative descriptor, so you can explain a bit better, always working all the samples. That is really good complementary information of the mixing plan to explain. If you choose to go on that side of the mixing plan, all your wine will be described. That's it.
I just now conclude. I hope it was not too speedy. For us, the testing, it's a difficult exercise to modelize and to represent with the panel we used because it's not trend, it's not a big one, and we don't have the same target when we begin testing.
It's why we decided to use a descriptive testing, not quantitative, the free sorting. This type of testing can be only thanks to JMP, thanks to the script. I can do all the process really quickly, really show about the significance of the results, and I can show it right after the testing.
Like that, we can talk with all the taster about the results. When we leave the testing, we are all clear with the wine we have tasted and the result. It's really more powerful than just testing with some weight. W e can use that type of testing with a small and untrained panel.
Just to finish, in this trial, the client was really happy with this mixing plan and it can adjust the recipe. I know the recipe is working since two years with the four ingredients and it can play a bit each year, but the recipe is fixed and he's really happy with that. Thank you very much for your attention. Have a good day.