INTERVIEW: Spectroscopy and Solanaceae – Rapid Phenotyping of Nightshades
February 19, 2021 at 12:29 am | Updated February 19, 2021 at 12:29 am | 10 min read
Alberto De Marcos and his team at Semillas Fitó are exploring the use of NIRS to breed better varieties of seven solanaceae (nightshade) and cucurbit (gourd) crops: tomato, pepper, eggplant, melon, watermelon, zucchini, and cucumber.
In our most recent interview, Application Scientist Galen George sits down with Alberto to chat about his plans to integrate the F-750 Produce Quality Meter to improve in-house testing with data-driven insights.
I hold a PhD in agronomy and environmental sciences and MSc in bioinformatics. During my PhD I worked using model species like Arabidopsis and I was focused on stomatal development, studying the genetic players of the process and their transcriptomic signatures. After my PhD I decided to move into the Ag industry and have been a scientist at Semillas Fitó, a multinational plant breeder company, since 2017. Here at Semillas Fitó, I am responsible of the R&D genetics projects in cucurbits species. I am always trying to improve our analytical skills and that is the reason I am now involved in a transversal project for the setup of NIR technology for reliable and high-throughput phenotyping.
If you want to reach out, please feel free to visit my Researchgate and LinkedIn profiles.
Galen: My name is Galen George and I’m an application scientist at Felix Instruments and CID Bio-Science, and my background’s in food science. So, I have Masters’ degree in Food Science. I am a certified food scientist, and so I do a lot of work now with the company helping with R&D of new products as well as helping with the model building and things of that nature, chemometrics work and stuff like that. So Alberto, if you could give us some information about yourself, your background and, you know, what you currently do at the company you currently work for or where you are currently [0:30] working, that information would be great.
Alberto: Okay. Well, my name is Alberto De Marcos. I’m from Spain, and I am working on a company here is Spain that is called Semillas Fitó, that is a plant breeding company mostly in the horticultural field. In the company I am one of the geneticists [1:00] of the company, working in my case with cucurbits. But also the company works with Solanaceae species. So, my background, well, I have a PhD in Genetics and Molecular Biology, and I’m one of the researchers here. I’m trying to understand – better understand – the [1:30] phenotype and genotype relationship to improve our varieties.
Galen: Okay. So tell me some more about the current work that you’re doing right now. So, I know that actually you and I have been in contact before when we were working on, you were working on cherry tomato phenotyping with the device, the F-750 I believe. And just tell me more about how, you know, the work that you’ve done to, you know, [2:00] do that – to do that model-building process – and then also work that you might be planning on the future for that.
Alberto: Okay. Well, it’s not a long time that we have the device, so we didn’t have enough time to span all the possibilities that the device gives us. But to start and to get in touch with modelling with the device – and [2:30] that is something that is new for us – we decided to start with… to start modelling something that… I don’t know if the word is easy, but at least is a commodity that has been very used in other [inaudible 2:53], or in other companies and so on. And not only the commodity but the parameter that we are modelling, [3:00] that is the brix, the solid soluble content, is also very studied, so for us was the best option to start with, just in case we didn’t manage to make the models well. So we know that this model has to work. It’s something that we know is doable. [3:30] To start, well, we have very good results with this model with, well, some problems at the beginning, but everything was solved. And I think we have a model that is quite good.
Galen: Yeah. So, and I know that some of that confusion stemmed from, when I was looking at your data I was confused about… I was assuming that you were using the second derivative spectra [4:00] the whole time. And then the Eureka moment. I was like, ‘What if it’s not the first, what if it’s not the second derivative? What if it might be the first derivative?’ And that happened to be the model that worked best for you guys, so that’s great. And yeah, the flexibility to be able to kind of choose. You know, the openness of the data is kind of something that, you know, that this instrument was designed for, is to be able to manipulate it in whatever way you need to to create the best possible model. But, so what kind of plans do you have for the future [4:30] with this device? Are you planning on looking at more solanaceous crops? Or are you looking at something totally, completely different? Or…?
Alberto: Well, I think we have more plans than the time we have to do it. So, we have to decide. I think the next step will be to continue use in cherry tomato, but for another parameter, [5:00] like acidity should be one that is very interesting for us, for the company. Even it’s more interesting than brix, because brix is something you can measure easily.
Alberto: Rapidly. So the advantage of the device is not so big. You know?
Alberto: But in terms of acidity [5:30], acidity is more difficult to measure. The analytical measure is more difficult or more time consuming. So here, with this parameter, I think then to create a model will be a big step for the company to improve the phenotyping.
Galen: I think that that sentiment about titratable acidity is shared across everyone that knows how to run that [6:00] experiment, because I used to work for a food analytical lab and we would, like all of the analysts always fight over who has to do titratable acidity. It’s just such a time-consuming, and it takes a lot of glassware and it just takes a lot of time to actually get that… to get those results. And so, yeah, I think with titratable acidity especially it’s a very valuable… the advantage like you said, it’s a lot more obvious at that stage. [6:30] I know that also I was at – I forget what the conference was – I think it might have been ASHS or something like that. But it might have been at that conference I saw somebody presenting on measuring anthocyanin in tomatoes as well. So that’s also something that, you know, if you were interested in you could always, because the instrument goes to the visible and the near-infrared, you have those options as well. [7:00] Cool. So, what about… so, your plant breeding company, what other… you know, so cherry tomatoes are your first endeavor. How big are, like, peppers? You know, if you’re looking at other solanaceous, is that another thing on the radar as far as the crop that you might be interested in? Or…?
Alberto: Yes. We are interested. Well, the company works with 7 crops. We have 7 main crops. That is, well, tomato, pepper and eggplant, in the case of Solanaceae. And in the case of cucurbits we work with melon, watermelon, zucchini and cucumber. So that’s the 4 cucurbits that we work. So our plan is to expand the.. to use the device in all our species, because in all the species [8:00], so for instance in melon it will be very useful if we can measure the brix. Even better if we can measure brix without cutting the fruit. But I think that could be a challenge because the peel is very strong. So, I know you told me that in in watermelon it’s not possible [8:30]. You have to cut the watermelon.
Alberto: But in melon we don’t know. So at least we will try, and let’s see. And the other commodities as well. We are interested maybe in dry matter or water content, or other… other parameters. Yes.
Galen: So, are you, in the intent of developing these models, are you just going to then use it as kind of a rapid phenotyping [9:00] tool that you can then use alongside your genotyping to, in order to help you selectively breed these crops? Or is it for – is there another use that you plan on, like, implementing it into your systems?
Alberto: Well it’s, the main… the main goal is to do a rapid phenotyping process, because as you can [9:30] stack several models for one measure, for one near measure, only with one measure you can give 5 maybe parameters in the future. So, brix, acidity, firmness or whatever. And this is our main goal; to speed up, to make it faster – all the phenotyping process [10:00]. When we will be able to stack several models. In the meantime, also it’s useful because, well, in the case of acidity it’s faster to do it with near than do it in the analytical way. So, this is the main [10:30] goal, to rapid phenotyping. For breeding this is very useful, because you can select plants more accurate as well, because if this data is very accurate you can select not only for maybe the taste but for other data, real data. So, well, this is what we see [11:00] that can be good for the company.
Galen: Yeah. Do you guys currently, for the testing that you do right now not with the device, is that all inhouse? Or do you outsource that testing to a third party lab?
Alberto: No. It’s inhouse, inhouse.
Alberto: Everything is inhouse.
Galen: Okay, yeah. So, also I guess it would free up time for your employees. Right? I mean you would have less lab manual work. Of course always probably good to still check, double check results and stuff like that. You know. But [11:30] yeah, that’s something that I think is a pretty under-looked advantage of implementing technology like this, is that you also free up a lot of time that was spent in the lab. Right? Doing all these kind of destructive tests. Okay, well yeah. That’s awesome. Is there any other, you know, interesting research that you guys are doing right now that you want to talk about or tell any of our viewers about?
Alberto: Well, one of the [12:00] utilities that we can, we can face maybe is not only to focus on the fruits that is, well, the main goal for this device. But also we are thinking that maybe this device can be used in leaves, for instance, to measure chlorofic content or maybe some other secondary metabolites [12:30] or whatever in the leaves. Not only in the… in the fruit, but in the leaves. I don’t know if you have any background or any experience on that.
Galen: So, actually, have you seen our CI-710s, the SpectraVue Leaf Spectrometer that we have?
Alberto: Yes. The other device. Yes.
Galen: So, it’s the exact same concept. It’s using a similar spectrometer as the 750. Same thing, just a different [13:00] form factor for exactly the purpose of looking at, you know, things like chlorophyll or anthocyanins or carotenoids, water content, things of that nature in the leaves. But yeah, that’s absolutely something you can do with the instrument, for sure.
Alberto: So your leaf spectrometer is the same technology but in a different format?
Galen: It’s pretty similar. It’s different in also it has a [13:30], like a colorful touchscreen display and, you know, it’s a little more advanced in that way. But essentially the technology behind it, yeah, it’s the same concept. It’s a similar – it’s not the same spectrometer, but it’s a similar one that has a pretty similar wavelength range. It goes from I think 300 to 1,100 nanometers.
Galen: And yeah, it’s pretty much, it’s the same kind of concept as… it’s just the form factor of it, the actual design of it, was it’s more accommodating towards leaves [14:00] and things like that, as opposed to the form factor of the other one. You know, this is more intended for fruit or yeah, down there. So, but yeah, that’s totally… I mean that, and incorporating not just, yeah, the fruit health, but also the plant health and, you know, the plant physiology parameters would be very interesting data for sure. And especially for your applications. I’m sure that’s very useful data. Right?
Alberto: Yes. Absolutely. [14:30] Because, well, the breeder is not only selecting the fruit but the plants. It’s something that you have to select. So, for instance we know that sometimes the breeder is selecting some kind of leaf color. Well, for the color you can use a colorimeter. You don’t need the near [15:00] spectra. But maybe for chlorophyll or some metabolite you need more details.
Galen: Yes. People oftentimes use it, I know that potassium, phosphorus, nitrogen are, it’s a very common use of the near-infrared spectrometers in the leaves at least, to look at those micronutrient levels, as well as other, you know, other micronutrients that are present in leaves as well. Yeah, that’s awesome. So, [15:30] is there any other, you know, before we go here, is there any other, you know, resources that you want people to know about? You know, websites or research publications that you want people to read about so that they can, you know, keep up with the work that you’re doing?
Alberto: Maybe. I don’t understand what you mean. Can you repeat the question?
Galen: Yeah. Like do you have, is there anything that you want the viewers of this video to know about [16:00] if they’re interested in more information about what you do or what your company does, or any research that you might have published or anything like that?
Alberto: No. I think everything is okay. I think this is, well, I mean we are in the early beginning of the process. We are not… we didn’t have enough time to do what we want, because our [16:30] plans are very big.
Alberto: So, in the future maybe we can talk again.
Alberto: And let’s see if we improve some other models.
Galen: Awesome. Alright, well thank you so much. I appreciate you taking the time to do this interview. I really do. And it was great talking to you and actually seeing your face and meeting you in person after talking over email for so long. But yeah, thank you so much, Alberto, for this. And yeah, I look forward to learning more about what you guys do, [17:00] and obviously if you have any more questions you can always reach out and I can help with whatever you need help with.
Alberto: Okay. Thank you, and I have the opportunity to say also thank you for helping me in the process of doing the modelling, because I was stuck in this second derivative equation and so on. And you helped me a lot. [17:30] So, thank you for that.
Galen: Yeah, of course.
Alberto: And it’s good to see you, see your face here.
Galen: Yeah, it’s always nice.
Alberto: So, perfect. So, for me it was a pleasure to talk to you.
Galen: Awesome. Well, thanks so much Alberto, and have a great rest of your day.
Alberto: Same to you.
Galen: Alright, bye.
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