Text Transcript with Description of Visuals
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| [ Music ] | Views of bluberry bushes in rows on a farm, and harvested blueberries in a bowl. On screen text. Washington State University. College of Agricultural, Human, and Natural Resource Sciences. Views of equipment in a lab. On screen text. And N C State present Vac Cap. Improving Fruit Quality. Texture Analysis of Blueberry Fruits. |
| Hello. My name is Heeduk Oh. I'm a PhD student at North Carolina State University working with Dr. Massimo Iorizzo and Dr. Penelope Perkins-Veazie. Texture is a very important trait for consumers. | A man stands in the lab. On screen text reads, Heeduk Oh PH.D Student North Carolina State University |
| Multiple studies have shown that firmer berries or crisp berries are preferable for consumers compared to soft or mushy berries. | Packaged pints of blueberries in a grocery store. |
| Also, texture can be a very important trait for machine harvesting. | A machine moves through a row of blueberry bushes on a farm. |
| Machine harvesting saves a lot of labor and time compared to harvesting by hand. | A person harvests blueberries by hand. |
| Firmer berries are generally considered more suitable for machine harvesting because they are less susceptible to internal bruising. | The machine pulls in the bush. People on a platform on top of the machine collect the pulled blueberries in crates. |
| A texture analyzer is one of the instruments that allow you to measure multiple texture components in a very accurate way. There are various instruments available for measuring fruit texture, but not all of them can measure more than one mechanical parameter at a time. A texture analyzer like this one is one of the instruments that are capable of assessing multiple parameters. | The texture analizer is a tabletop machine with a platform and an armature. A blueberry rests on a small green ring on the platform. A metal probe in the armature is directly above the blueberry. |
| How it works is that it has a probe that slowly goes down. You could have it to compress the fruit a little bit or you could have it to puncture the fruit depending on the method that you're using, and then the probe goes back up. | The probe compresses the blueberry, then pierces the skin, continuing to press down until it contacts the platform below the blueberry. It then reverses and lifts the blueberry from the platform. |
| The force or the resistance that the probe is receiving from the fruit is recorded along the way in the form of a graph like you see here. The force goes up at first. It's at maximum force right when the skin breaks, and then the force goes down rapidly. After that, it's the internal part of the berry. | Line graph on a computer screen. |
| Different types of probes can be used to collect different types of data. This is an example of a 2-millimeter flat probe. From this probe, you can collect parameters like the maximum force, distance to maximum force, mean internal force, and Young's modulus, and so on. | A view of the probe without a blueberry on the platform. |
| This is a compression probe that's basically a wide plate. It's usually used for compression tests like the texture profile analysis, or the TPA. | A person holds a metal disk and a cable. Text written on the disk reads, T A X T COMP PLATE P P V |
| And from this method, you can get parameters like hardness or resilience. | The round flat plate presses down on a blueberry. |
| We've been also using a needle probe which has a very sharp tip. It's only penetrating the berry by 2 millimeters, and we're trying to measure the texture components at the very outer layers like the skin. | The short needle probe enters, then picks up the blueberry. |
| When you're running texture analysis, the first thing you need to do is calibrate the instrument. The first thing to calibrate is the force using a calibration weight. | A person places a solid metal weight on top of the armature. |
| And then you calibrate the probe height so it knows how high up the probe is above the platform. It goes down slowly, touches the platform, and then goes back up. | The probe lowers until it contacts the platform and then raises again. |
| After calibration, to measure the texture of a sample, you need to run something called a macro. A macro is a sequence of instructions for the texture analyzer, which is set up before your experiments. The macro includes instructions on what kind of information or data to collect from each sample. You can also integrate different devices like a barcode reader, a digital caliper, or a scale. And also you can put in different prompts like, "What's the name of the sample or the rep? What's the date? And are there any signs of decay?" Or any other information you want to collect about a sample. | Rows of data on a computer screen. |
| To analyze the data from the texture analyzer, you need to use the output file. The output file is a simple Excel file with the parameters that you're interested in. You can change the type of parameters to be included in the resulting Excel sheet. For downstream analysis, you can just use the output Excel sheet like any other dataset you have. | A view of Microsoft Excel files on a computer screen. |
| You can run analyses on Excel itself, or you can use other statistical softwares as well. The first column of the Excel sheet will have the information of the sample that you put in, like the sample name, the cultivar name, the harvest date, or the experiment name. And the following columns will have the data for each of the parameters that you set up for the macro. And this includes the data that was put in from the devices that you added on, like the digital caliper or the scale. | Example: R Studio. Then, view of an Excel spreadsheet. |
| Our recommendation is to use an instrument that can measure multiple texture components and not just one. Blueberry texture cannot be understood by just one component, so it's important to collect data for different components to evaluate different aspects of texture, like the sensorial firmness that you feel in your mouth, or how the texture changes during storage, and so on. | Color coded data in a chart. |
| Depending on the program and number of seedlings to evaluate each year, if it's not high throughput enough for early-stage selection, this could be used at advanced selection stages as well. | Scenes from blueberry fields. |
| There seems to be mainly four components that contribute to blueberry texture. The first one includes force at 1 millimeter, which is the force recorded at 1 millimeter depth, and Young's modulus. | Text on screen reads, Four components contribute to blueberry texture. 1. F 1 m m and Young Modulus 2. F M, A F L D 3. MIF (mean internal force) 4. D F M, L D F M, B u S t r, A F M, - D F M represents the deformation before skin break (ductility) |
| These parameters measure the stiffness or resistance to the deformation of the berry. | Line graph. Force at One M M, F One M M. Force at One M M depth. N. Old name: Firmness. Giongo et al, 2022, N/A. |
| The second component is maximum force, or FM. | Text on screen reads, Four components contribute to blueberry texture. 1. F 1 m m and Young Modulus 2. F M, A F L D 3. M I F (mean internal force) 4. D F M, L D F M, B u S t r, A F M, - D F M represents the deformation before skin break (ductility) |
| And this is the force recorded at the maximum peak where the skin breaks. | Line Graph. Maximum Force (F M). Force at point of skin puncture, N. Old name: Skin Strength. Giongo et al, 2022: Maximum force (F M). |
| The third component is the mean internal force, or the MIF. | Text on screen reads, Four components contribute to blueberry texture. 1. F 1 m m and Young Modulus 2. F M, A F L D 3. MIF (mean internal force) 4. D F M, L D F M, B u S t r, A F M, - D F M represents the deformation before skin break (ductility) |
| This measures the force while the probe is inside the berry going through the pulp. | Line Graph. Mean Internal force (M I F): Mean force between first peak after minimum force and eighty percent strain, N. Old name: Mean Internal Firmness. Giongo et al, 2022, N/A. |
| The fourth component is DFM, or the distance to maximum force, | Text on screen reads, Four components contribute to blueberry texture. 1. F 1 m m and Young Modulus 2. F M, A F L D 3. MIF (mean internal force) 4. D F M, L D F M, B u S t r, A F M, - D F M represents the deformation before skin break (ductility) |
| which represents the deformation before skin break, or the ductility. | Line graph. Distance to maximum force (D F M): Distance between point of contact and skin puncture (M M). Old name: Skin Elasticity. Giongo et al, 2022: Deformation to maximum force (d F M). |
| These four components could be used to predict both the sensory firmness and the post-storage texture. | Text on screen reads, Four components contribute to blueberry texture. 1. F 1 m m and Young Modulus 2. F M, A F L D 3. MIF (mean internal force) 4. D F M, L D F M, B u S t r, A F M, - D F M represents the deformation before skin break (ductility) |
| Maximum force, or FM, is associated with sensory hardness, springiness, or crispness that you feel in your mouth when you're biting into a berry. And FM was also associated with consumer's preference or willingness to pay. | A scatter plot graph titled Maximum force (F M) and hardness (sensory. A line graph titled Maximum force (F M) and willingness to pay (W T P). Source: Oh et al, 2024. |
| Thinking about how quickly the texture of a blueberry changes during storage, we wanted to also identify parameters that can help us understand the rapid softening during shelf life. | Blueberries |
| Initial firmness drives the post-storage texture, and we've found some parameters that can explain the variation between harvest and post-storage, like the Young's modulus, F1 millimeter, and DFM. DFM is a rather independent component from Young's modulus or F1 millimeter, but these parameters all contribute collectively to the change of texture during storage. | A graph showing Texture change during storage, Y M, F i m m, D F M from Harvest to Post storage for a large selection of blueberry varieties. |
| Texture is a complex trait. It has moderate to high heritability, but it's genetically complex. So phenotyping will remain the most effective way to select for this trait, compared to DNA-informed selection. We won't have any molecular markers available for this trait anytime soon, because it's a very complex trait. | Graphics of G W A S texture showing Heritability of texture parameters and phenotype date. Source: Ferrao et al, 2024. |
| When you set up the macro, you can have it to include a barcode or a QR code scanner like this. You can print out stickers or labels to put on cups with your blueberry samples in it that contains information about the experiment, the sample name, the set, or the rep, or the date that it was harvested, et cetera. And the QR code would have all that information in it. And when you're running the macro, you can simply scan this barcode into the barcode scanner, and it will record all the information that's included in this code. | A small scanner attached to a computer. A small plastic cup containing blueberries sits near the scanner. It has a white sticker applied to it. The sticker has text which is too small to read. It also has a square Q R code. A person picks up the cup and scans the Q R code on the sticker. |
| You can also integrate devices like a digital caliper to measure the stem scar diameter of a berry. And the data that you collect from the caliper will be automatically sent to the laptop or the macro. You won't need to measure the diameter of the whole berry because the texture analyzer will automatically measure the diameter. | Holding a blueberry under a magnifying glass, he uses a caliper to measure the small round stem scar. A digital reading appears on the caliper screen. |
| We can also attach a scale to the macro so that you can send the weight of the berry to the macro, and the resulting Excel sheet would have the weight along with all the texture parameters that you measure for each sample. | Sets a blueberry on a scale. Digital output reads one point five three grams. |
| Compared to Firm Tech, the biggest advantage of using an instrument like the texture analyzer is that you can collect multiple texture components of a berry. The texture of a blueberry is a very complex trait which cannot be understood by one texture component. So being able to collect multiple parameters using a texture analyzer can help us understand the texture in depth. | Heeduk Oh speaks to the camera. |
| Based on recent findings through collaborative research, four texture components were identified as the main contributors to blueberry fruit texture, and post-harvest texture was found to be related to the initial texture. Some possible parameters to select for in breeding programs, maximum force, or FM, was the best predictor for sensory attributes of hardness, springiness, and crispness. High DFM led to less overall texture change. High Young's modulus 20 burst strain was related to lower bruising ratio. And larger size at harvest led to less water loss and wrinkling. The macro that we developed to measure these texture parameters is available for anyone, so feel free to contact us if you're interested in using these methods. | Text on screen reads, Four texture components contribute to blueberry fruit texture (Y M, D F M, F M, M I F) Postharvest fruit texture is related to initial texture (positively correlated) Possible parameters to select for: High F M (best predictor for sensorial hardness/springiness/crispness) High D F M (less overall texture change) High Y M 20_B u S t r (lower bruising ratio) Larger size/diameter (less water loss and wrinkle) The macro developed through this project to measure these texture parameters is available for those interested to use this method. Please contact Massimo Massimo Lorizzo. M I O R I Z Z I at N C S U dot E D U. or Heeduk Oh. H E E D U K 2000 at gmail dot com. |
| [ Music ] | Text on screen reads, Produced by C A H N R S Communications Washington State University Executive Producers Lisa Wasko DeVetter Washington State University Massimo Iorizzo N C State University Thanks to Heeduk Oh for his assistance and expertise Funding provided by United States Department of Agriculture National Institute of Food and Agriculture Award #: 2019-51181-30015 N C S U footage provided by Island Sound and Video Raleigh, N C Collaborating Institutions USDA Agricultural Research Service United States Department of Agriculture National Institute of Food and Agriculture Award #: 2019-51181-30015 CEDAR LAKE RESEARCH GROUP LLC UNIVERSITY of FLORIDA FONDAZIONE EDMUND MACH UNIVERSITY OF GEORGIA MICHIGAN STATE UNIVERSITY® MISSISSIPPI STATE UNIVERSITY NC STATE UNIVERSITY NRSP10 CROP DATABASE RESOURCES Plant & Food RESEARCH RANGAHAU AHUMARA KAI RUTGERS WASHINGTON STATE UNIVERSITY WISCONSIN UNIVERSITY OF WISCONSIN-MADISON |
| [ Music ] | Text on screen reads, VacCap Improving Fruit Quality |
| [ Music ] | Washington State University logo College of Agricultural, Human, and Natural Resource Sciences Washington State University |