Subflow

subflow

Blent.ai has been using n8n for increasing customer engagement and improving customer experience. We talked with , co-founder of to learn more. Maxime Jumelle Blent.ai Can you tell me a bit about yourself? Hi, I am Maxime, co-founder and CTO of Blent.ai and former Data Scientist. At Blent, we provide online training in Data Science and Data Engineering. We are 100% committed to propose a fully configured development environment for our customers. What made you decide that n8n would be the most suitable tool for you? So far, we have used Zapier. It’s a very good tool that has a lot of integrated apps. However, the workflows were too “simple” in our opinion, and some actions were not automatically triggered. As both our engineering and marketing teams are using it, I was looking for a powerful yet simple-to-use tool. After a bit of research, I discovered n8n, which perfectly fulfilled our needs. What kind of workflows are you building with n8n? We currently have two types of workflows: Customer engagement, which sends emails and pushes content to warm leads. Customer experience, so that we can automatically release new courses depending on their progression. Which integrations have you been using the most? As our infrastructure is mainly on AWS, we mostly use AWS integrated nodes. But we also have several nodes for Stripe and Google since we are using G-Suite. What’s the most useful feature of n8n for you? As a developer, the most useful feature for me is the workflows’ structure. The fact that you can create conditional paths with loops is essential when it comes to creating complex workflows. Have you automated things with n8n for personal use as well? Not at the moment. However, since I love Arduino and Raspberry, I am tempted to integrate n8n in my IoT projects. What advice do you have for people looking to incorporate n8n in their teams or projects? My advice is that it requires low effort in terms of configuration, and the time saved worth it. In my opinion, the best option is to install n8n first locally, explore its capabilities, and then deploy on a remote server where multiple users can create and share their workflows. How do you envision using n8n in the near future? As our team grows, I will invite all my future colleagues to learn and use n8n, so they don’t lose time on tasks that can be automated. What’s a feature that you are looking forward to or would love to see in n8n? An interesting feature would be subflows, which are a sub-part of the workflow which you can wrap into a single group and insert in any workflows, triggering it as a single node. 💌 n8n users come from a wide range of backgrounds, experience levels, and interests. We have been looking to highlight different users and their projects in our blog posts. If you’re working with n8n and would like to inspire the community, email me at tanay@n8n.io to tell your story Previously published at https://medium.com/n8n-io/maxime-jumelle-talks-about-using-n8n-at-blent-ai-c47d3fca6141

Interview with Maxime Jumelle About Using n8n at Blent.ai 🚀

How and What to Develop with Corda

Redux saga is a middleware between an application and redux store, that is handled by redux actions. This means, it can listen to actions, intercept actions it is interested in and replace them with other actions. This may be useful for plenty of things. In particular, using sagas you can keep your components as simple as possible and move all the logic to sagas. When you start working with Redux saga, it seems quite natural to write sagas that describe behavior of a single component or a single page. And it works fine in simple applications that have two or three pages and everything is loaded synchronously. But when a project grows, this may lead you to a mess in sagas department, where sagas don’t have clear purposes and duplicate each other in many ways. It will be difficult to manage, debug and make even small changes. The thing is that sagas’ work should be coordinated. After some time of struggling with this mess and finally reading the documentation we in DashBouquet came up with a new approach, which has always been there. Describe a full flow in a saga. This is what the documentation implies, you should just read carefully. Let’s see, what it looks like. It looks like a flow, isn’t it? Sure, describing full flow in saga doesn’t mean that there should be only one saga. The point is that everything is handled from one place. Now, let’s take a look at an actual example. Say, in our app a user is able to fill up a job application and send it to the server for further processing. We want to create a saga that will describe full flow of this process. Everything starts on route when a user clicks an “Apply for a job” button and is redirected to the application form. /create_application_page {take, call, put, select, fork, cancel, race} 'redux-saga/effects'; { , } 'react-router-redux'; {takeEvery} "redux-saga"; * api './api'; import from import LOCATION_CHANGE push from import from import as from createApplication(data) { put( (`/application_form`)); function* yield push { saveApplication } = yield race({saveApplication: take(SAVE_APPLICATION),cancelApplication: take(CANCEL_APPLICATION),}); const (saveApplication) { { data } = saveApplication; api.saveApplication(data); put( (`/application_saved`));} else {yield put( (`/application_cancaled`));}} watchCreateApplication() { createApplicationTask = takeEvery(CREATE_APPLICATION, createApplication); if const yield yield push push function* const { {payload: {pathname}} = yield take( ); (pathname.search(/^\/create_application_page\/?$/) !== -1) { cancel(createApplicationTask); }}} while (true) const LOCATION_CHANGE if yield break; Here is an action dispatched when a user clicks “Apply for a job”. Then he is redirected to another page with a form. After the user fills up the form and clicks Submit or Cancel, he is redirected further depending on which action was dispatched. And data is sent to the server only if the user clicks Submit. CREATE_APPLICATION Also the user may change his mind in the middle of filling up the form and just go by another link without clicking “Cancel”. In this case if he later comes back to where the saga is injected asynchronously, it will be injected one more time, and the data on the server will be created twice, while the user intended to do it only once. We certainly don’t want that, so we need to cancel the task on LOCATION_CHANGE dispatched by redux-router, hence checking the pathname in . /create_application_page watchCreateApplication And just to make things a bit more complicated, we assume, that in the middle of filling up the form, the user can call a dialog with another form, in which he can choose, say, a country and a city he is applying from and submit this data to the server. We can do this by simply forking another saga that would be quite similar to saga: createApplication chooseLocation() { put(showDialog()); { saveLocation } = yield race({saveLocation: take(SAVE_LOCATION),cancelLocation: take(CANCEL_LOCATION),}); (saveLocation) { { data } = saveLocation; api.saveLocation(data);} put(closeDialog());} function* yield const if const yield yield createApplication() { put( (`/application_form`)); createLocationTask = takeEvery(CREATE_LOCATION, createLocation) function* yield push const ... cancel(createLocationTask); yield } Here we handle the dialog with and sagas. In two words, they should set some value in the store to tell a page, if the dialog has to be shown or hidden at the moment. In more detail it is a subject for another article. showDialog closeDialog It is important not to forget to cancel the forked tasks. If at some point you find an avalanche of requests going to the server when it was supposed to be only one or two, you probably missed this somewhere. It is a quite simple example. A flow may be much more complicated, with any amount of subflows (though it might be a bad idea from UX point of view). But anyway, what this approach gives is that you can build your flow step by step. You can make changes later quite easily and they will not lead to unexpected behavior from other sagas. You can make it more complicated, if you need to, but no matter, how many forks your flow has, you never get lost in it. _After applying few simple manipulations to the result of Normalizr’s work we get data that we can keep in store_hackernoon.com Using Normalizr to organize data in stores — practical guide _Why reselect is so good_hackernoon.com Usage of Reselect in a React-Redux Application _The second part of the article about how to use Normalizr to organize data in stores._hackernoon.com Using Normalizr to organize data in store. Part 2 _Javascript has two major ways of dealing with asynchronous tasks — callbacks and Promises. In general Promises are…_hackernoon.com How to Stop Using Callbacks and Start Living Written by Ilya Bohaslauchyk

Missing part of Redux Saga Experience

Hello, today I'd like to explain briefly how we use artificial intelligence to count sunflower seeds in a photo taken with a mobile device. Agenda: 1. Business needs 2. Data preparation 3. Model structure 4. Used libs and tools 5. Results 6. Error analysis 7. Fails/Hypotheses 8. Conclusion 9. References 1. Business needs Fortunately for me, I am working at . Where I am developing Computer Vision (CV) and other models to solve business problems and challenges. One of them is to count seeds on sunflower. Kernel Kernel – the world’s leading and the largest in Ukraine producer and exporter of sunflower oil, and major supplier of agricultural products from the Black Sea region to world markets. Kernel exports its products into more than 80 countries of the world. It is a common task for agronomist to count seeds on sunflower and corn, as these calculations will be used to predict "biologycal harvest". Separately, agronomists calculate weight of 1000 kernels, which will further be used to estimate the overall yield per area of field. Moreover, yield prediction is the primary function of agro analytics. The faster and more precise they make a forecast, the more money the company will earn. These inspections should be carried out for every filed. And this is how it works now: 1. Agronomists go to the field 2. Take about 10 sunflowers from different parts of the field 3. Split every sunflower into 4 quarters 4. Count the seeds and multiply them by 4 to find the total number of seeds on every sunflower 5. Take random 1000 seeds and weight them 5. We get the density of plants on the field per hectare from other inspections (or use planned density), btw we also will use AI to solve this task, and this could be topic for next post 6. And calculate (density per ha * avg seed quantity on sunflower * weight of 1000) / 1000 = yield weight per ha Here is an example of a common sunflower photo White (unformed) and black (formed) seeds can be seen, however only black seeds will be used to produce sunflower oil. One disadvantage of this calculation is that agronomists must count black, formed seeds on sunflower, which is very inaccurate and inconvenient. Furthermore, each agronomist may count a different number of seeds on the same sunflower because some seeds may be unformed (white, non-pollinated by bees) and it is difficult to determine which kernel is formed and which is not. 2. Data preparation To solve any Machine Learning problem, we need to get data and train the model. And for every specific task you cant find any free available labeled dataset. In our case, the dataset should contain hundreds of sunflower photos with label for every seed related to right class (black or white, for easy understanding), so we gathered our own dataset. Our agronomists have made about 1000 photos of different sunflower hybrids from different regions of Ukraine. And subsequently, another team labeled photos in free tool for data labeling. There should be for each image, as well as , and CVAT bounding_box (purple box) black seeds (green points in the picture below) white seeds (red points in the example below). Following this, we can check distribution of kernels in sunflowers looks normal with mean value - 1271. And correlation between number of black and white seeds. Weak dependence exists, but it can not be used further. 3. Model structure For business needs, all calculations should be done on a mobile device. That's why I used with encoder pretrained on What does this all means? U-net [1] mobilenet_v2 [2] imagenet . is Neural Net structure where NN takes an input image and returns a segmentation map (in our case heatmap), with downsampling and upsampling data. Our heatmap represents probability that pixel is related to some kernel. U-net Where up/down sampling blocks used weights from mobilenet_v2 Imagenet is a dataset with millions of images and tens thousands of classes. Frankly speaking, for this task I trained 2 NN and 1 algorithm. The first NN cut the sunflower from the image, like this: The second NN build 2 heatmaps, one per class (black and white). For training, input for this net is cropped image (from previous NN), and target is heatmap with builded gaussians on labeled points. For black kernels it looks like this: According to this heatmap, the lighter the pixel, the more model is sure that pixel is related to kernel. And the last algorithm (Laplacian of Gaussian approach) searches for centers of probability blobs (groups of bright pixels that are adjacent to each other), and on next image I used green points as the blob center above the cropped image. With white kernels prediction looks like this: 4. Used libs and tools For training, I used Python libs such as: and for training models - for image augmentation - to use pretrained models - to work with blobs on heatmap - to tune hyperparams for blob detection - to track training experiments PyTorch PyTorch Lightning albumentations segmentation_models_pytorch scikit_image hyperopt wandb 5. Results How do we validate the whole solution? To calculate mean absolute percentage error (MAPE) I made prediction for every image and compared the number of found seeds by model and number of seeds found by assessors. As a result, we got normal distribution with mean 0, which is extremely good. Blue bars - are for black kernels and MAPE is 3.6%, Orange bars - are for white kernels and MAPE is 8.8%. 6. Error analysis The biggest errors will have unusual sunflowers, that have with abnormally large number of kernels, or too many grey kernels, or with rounded edges, as it can be seen in the example below: Here, the model found 16% more than the assessor, and as we can see, the majority of extra seeds are on the edge of the sunflower. Or biggest error -257% for white seeds in this case: The total number of white seeds is 14, and the model found 50. As a  percentage, it is a really big number, but in absolute terms, it is not critical. 7. Fails/Hypotheses Once, I have found mathematically explained seeds positions in sunflowers and some other plants with fractal structure. Positions could be explained by which has an equation in polar coordinates Fermat's spiral where is index number of kernel and - constant, I guess for every sunflower it is different. As result, we should get the following positions n c But in practice it is doesn't work. I have modified that equations and added new constants a, b and the best result that I got was this: where blue - dot is point. So it is close in some way but not applicable for to real case. (0, 0) 8. Conclusion Currently, this task has been speeded up and standardized in our company in order to exclude human factor from counting. The MAPE is acceptable for business needs and this solution is working in mobile application now. For me, it is still unbelievable that we can count more than 1000 kernels, separate points into 2 different classes and all of this can be done via mobile device. Many thanks to for mentor ship in learning and support. Markiyan Kostiv 9. Referances 1. (2015) 2. (2019) U-Net: Convolutional Networks for BiomedicalImage Segmentation MobileNetV2: Inverted Residuals and Linear Bottlenecks

With AI, You Can Count 1000+ Sunflower Seeds In Seconds

The Quiet Crisis That Is Stealing Our Health (and How to Get It Back) - Insights From Dr. Eric Berg

ChatGPT 4 is required. In a Hurry?: See it in action, shared ChatGPT session: https://chat.openai.com/share/ab0ce967-68ec-4757-8b49-cd89a22c197a Introduction Discover how to swiftly incorporate a visually appealing "rating system" into your ChatGPT conversations. Use cases include: Add your own visually appealing rating system to any ChatGPT content. Using ChatGPT AI analysis to rate something you've written. Screenshot on iPhone ⏱️ Your 30 Second Guide Steps: Copy all the text below and paste it into any ChatGPT or ChatGPT Builder chat session: 1: AI Instructions for Rating System:

1. Convert text like [Rating: X/Y] to ★★★½ (X/Y).
2. Keep other text as is, including any Markdown tags.
3. Display without elaboration. Now, you can use prompts such as: 2: a) Please rate my essay (without making any changes to it) out of 5 using Rating System
b) Then, make improvements to it, and rate it again.

### History of Canada

Canada's history stretches back thousands of yeers, with Indigenous pepoles inhabiting the land long before European contat. First Nations, Inuit, and Métis populations developed rich cultures, languages and traditions intrinisically tied to there land. European explorers, begining with the Vikings around 1000 AD, and later French and British colonizers, dramatically altered the landskap. The fur trade feeled early economic activity, while competition between France and Britan lead to conflicts like the Seven Years' War, ultimately resulting in British dominance by the late 1700s You can also instruct ChatGPT by typing things like: Do that again, but use 🍅 as the rating system.

Do that again but use ⭐ and rate it out of 10.

I think the move "School of Rock" deserves a rating of [Rating: 4/5] Examples Rating can be out of anything you want, and display any symbol or Emoji that you want ★★★½ (3.5/5) 🍎🍎🍎½ (3.5/5) ⭐⭐⭐⭐ (4/4) Note: Since we are asking ChatGPT to do something it says it can't do, this technique may not be perfectly reliable within the existing chat interface. I have written OpenAI developers to ask that this feature using AImarkdown Script be added (with a more direct method). Making a Custom Version Say you want to use the tomato Emoji as your default 🍎 and always rate it out 100% rather than a scale. Just change the AI instructions to:
then AI Instructions for Rating System:

1. Convert text like [Rating: X%] to 🍎 X%.
2. Keep other text as is, including any Markdown tags.
3. Display without elaboration. Here is an example of the 🍎 X% style rating: https://chat.openai.com/share/a1f522e0-a6b6-4cb3-85fc-2690bcbab3e8 Use Basic Formatting (Markdown) In the custom version above, you will notice that you can apply some basic formatting in ChatGPT. This uses the . Markdown Language If you want to try out playing with - click here: Markdown Language https://markdownlivepreview.com/ Markdown Tip: If you want the raw Markdown version (just the plain text) of the ChatGPT response, just type: Can you give me that as Markdown, in a Markdown code window. Then, ChatGPT will give you the raw Markdown version you can work with. Sample Emojis You Could Use With the "Rating System." Food 🍎🍎🍎½ (3.5/5) _ (Apples, whole and half)_ 🍓🍓🍓½ (3.5/5) (Strawberries) 🍕🍕🍕½ (3.5/5) _ (Pizza slices)_ 🌮🌮🌮½ (3.5/5) _ (Tacos)_ 🍰🍰🍰½ (3.5/5) _ (Cake slices)_ Nature 🌻🌻🌻½ (3.5/5) (Sunflowers) 🌲🌲🌲½ (3.5/5) _ (Trees)_ 🦋🦋🦋½ (3.5/5) _ (Butterflies)_ 🐞🐞🐞½ (3.5/5) _ (Ladybugs)_ Celestial 🌕🌕🌕½ (3.5/5) _ (Moon phases)_ ☀️☀️☀️½ (3.5/5) _ (Sun and cloud)_ Miscellaneous 💎💎💎½ (3.5/5) _ (Diamonds)_ 💡💡💡½ (3.5/5) (Lightbulbs - representing ideas) 👍👍👍½ (3.5/5) _ (Thumbs up/down)_ 🏅🏅🏅½ (3.5/5) (Medals) 🚩🚩🚩½ (3.5/5) _ (Flags)_ What Is This Technique of Using Snippets Called? The AI Instruction snippets are part of the larger AImarkdown Script Language. For more info visit: https://aimarkdown.org For simple tasks like a "Rating System", short snippets are the better solution to controlling ChatGPT than more complex AImarkdown Script. Conclusion This simple technique adds a valuable layer of interactivity to ChatGPT conversations. Whether you're soliciting feedback on your writing, rating products or services, or just adding a touch of fun to your chats, the rating system provides a visually engaging way to interact. As ChatGPT and AImarkdown Script continue to evolve, we can expect even more creative and powerful ways to customize our interactions with AI. Appendix: Taking it Further AImarkdown is a simple scripting language that enhances ChatGPT's abilities. We'll use a tiny bit of it to unlock the image display. It is meant for programmers and non-programmers alike. Explore more AImarkdown Script Articles: Anyone Can Add Beautiful Interactive Images in ChatGPT 4 (in 30 Seconds): Here's How Finally! You Can Colorize ChatGPT Output With AImarkdown Script: Here's How Unlock Event-Driven ChatGPT Apps With AImarkdown Script: The Power of Triggers Enhancing Python Execution in ChatGPT with AImarkdown Script AIMarkdown Script: A Scripting Language to Unlock ChatGPT's Potential ChatGPT's Missing Feature That You Should Know About: Code Highlighter Responder Using Python in ChatGPT with AImarkdown Script - Part One ResponseDial For ChatGPT: Optimize Your Workflow and Take Control of Your GPT Responses Making a ChatGPT Blackjack Game that Coaches You with AImarkdown Script How to Instantly Add "Date Stamps" In ChatGPT Conversations Also published here

How to Transform ChatGPT Conversations With a Custom Rating System - 🍎🍎🍎½ (3.5/5)

214 Stories To Learn About Computer Vision

Vincent van Gogh, one of the most celebrated artists in history, is not only known for his mesmerizing and emotionally charged paintings but also for the tumultuous state of his mental health. This article delves into the intricate connection between van Gogh's artistic brilliance and the complexities of his mental well-being, exploring the profound impact of mental health on his life and work. The Artistic Genius: Vincent van Gogh's artistic genius is evident in his vibrant and emotive paintings that have left an indelible mark on the world of art. His distinctive use of color and expressive brushstrokes, exemplified in masterpieces like "Starry Night" and "Sunflowers," continues to captivate audiences worldwide. Van Gogh's ability to convey raw emotion and deep introspection through his art is a testament to his profound connection with the human experience. The Unraveling Mind: Behind the canvas, however, lies a tumultuous story of mental turmoil. Van Gogh's life was marred by periods of intense emotional instability, leading to strained relationships and isolation. He struggled with episodes of severe anxiety, depression, and psychosis, which significantly impacted his ability to navigate the challenges of daily life. Diagnoses and Mental Health: While a definitive diagnosis is challenging due to the historical context, contemporary experts speculate that van Gogh may have experienced various mental health conditions. Bipolar disorder, characterized by alternating periods of mania and depression, is a commonly suggested diagnosis. Van Gogh's frenetic periods of creative energy followed by deep depressive episodes align with the characteristic mood swings of bipolar disorder. Another possible diagnosis is borderline personality disorder, as evidenced by his intense interpersonal relationships, impulsivity, and emotional instability. Van Gogh's struggles with self-harm and suicidal thoughts further underscore the profound impact of mental health on his life. Art as a Coping Mechanism: Van Gogh's art is not only a reflection of his internal struggles but also a coping mechanism. Painting served as a therapeutic outlet, allowing him to channel his emotions onto the canvas. The act of creation provided a temporary respite from the torment of his mind, offering a glimpse into the intricate relationship between artistic expression and mental health. Legacy and Awareness: Vincent van Gogh's legacy extends beyond his artistic contributions; it serves as a poignant reminder of the intersection between creativity and mental health. His story prompts reflection on the challenges faced by individuals grappling with mental health issues, both in the past and present. As society continues to destigmatize mental health discussions, van Gogh's experiences offer a unique perspective on the complex interplay between brilliance and vulnerability. Vincent van Gogh's tumultuous journey through life, marked by artistic brilliance and mental health struggles, remains a compelling narrative that transcends time. By exploring the intricacies of his mental well-being, we gain a deeper understanding of the complex relationship between creativity and the human psyche. Van Gogh's enduring legacy prompts us to reflect on the importance of mental health awareness and compassion in both the art world and society at large.

Brushstrokes of Turmoil: Vincent van Gogh's Artistic Brilliance and Mental Struggles

Scientific American Supplement, No. 392, July 7, 1883 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . TESTING OLIVE OIL. here TESTING OLIVE OIL. By DR. O. BACH. There is no department in analytical chemistry in which so little success has been attained as in the testing of commercial fats and oils. All methods that have been proposed for distinguishing and recognizing the separate oils, alone or mixed, bear upon them the stamp of uncertainty. The facts observed by J. Koenig, and described by him in his excellent book entitled " " (p. 248), excited great expectations; viz., that the quantity of glycerine in vegetable fats was much less than the amount required to combine with all the fatty acids, and that the quantity of oleic acid in the oils that he examined exhibited essential differences. Koenig himself asserts that the fats have hitherto been too little investigated to found upon it a method for distinguishing them, but that nevertheless it may possibly do good service in some cases. Die Menschlichen Nahrungs und Genussmittel My own estimation of the amount of glycerine in different olive oils, by Koenig's method, has shown, unfortunately, that the percentage may vary from 1.6 to 4.68, according to the origin and quality of the oil. In like manner the estimation of the oleic acid, which was conducted essentially in the manner proposed by Koenig, showed that the amount of oleic acid in different olive oils varied from 45 to 54 per cent. But since cotton seed oil, for example, which is most frequently used to adulterate olive oil, contains 5 per cent. of glycerine, and 59.5 per cent. of oleic acid, it is easy to see an admixture of cotton seed oil cannot be detected by this method, which appeared to be so exact. The method of analysis that I am about to describe is based chiefly upon the determination of the melting point of the fatty acids contained in the oils, and upon their solubility in a mixture of alcohol and acetic acid. The oils employed in adulterating olive oil, and to which regard must be had in testing it, are the following: Cotton seed oil, sesame, peanut, sun flower, rape, and castor oils. The tests for the two last named have hitherto never presented any difficulty, as rape seed is easily detected, owing to the sulphur in it, by saponifying it in a silver dish, and castor oil by its solubility in alcohol. But in recent times another product has come into the market called sulphur oil or pulpa oil, obtained by extracting the pressed olive cake with sulphide of carbon. This also gives a sulphur reaction when saponified, while it resembles castor oil by its solubility in alcohol. When this oil is mixed with ordinary olive oil, it can easily deceive any one who uses the ordinary tests. My method of testing olive oil is as follows: First, the so-called elaidine test is made, and then the test with nitric acid. About 5 c. c. (a teaspoonful) of the oil is mixed in a test tube with its own volume of nitric acid, spec. gr. 1.30, and shaken violently for one minute. At the expiration of this time the oils will have acquired the following colors: Olive oil, pale green; cotton seed oil, yellowish brown; sesame, white; sun flower, dirty white; peanut, rape, and castor oils, pale pink or rose. As soon as the color has been observed, the test glass is put in a water bath at the full boiling temperature and left there five minutes. It was found that the action of nitric acid upon cotton seed and sesame oil was the most violent, sometimes so violent as to throw the oil out of the glass. At the end of another five minutes after the test tube is taken out of the water bath, the following colors are seen: olive and rape oils are red; castor oil is golden yellow; sun flower oil, reddish yellow; sesame and peanut, brownish yellow; cotton seed, reddish brown. After standing 12 to 18 hours at about 60° Fahr. the olive, rape, and peanut oils will have solidified; sun flower, castor, and cotton seed will be like salve (sticky), while sesame will remain perfectly liquid. Mixtures of olive oil with small quantities of cotton seed or sesame are distinguished by this characteristic--that, although the whole mass, which is darker in color than olive oil, solidifies at first, at the end of 24 or 36 hours a brown oil will be found floating upon the surface of the solid mass, while the lower strata exhibit the yellow color of pure olive oil. Oil of rosemary has no effect when shaken with cold nitric acid, and imparts to it only a slightly darker color on heating. Oils treated with lye act just like pure oils. Far the purpose of determining the melting point of the fatty acids, 10 grammes of oil were saponified with 5 grammes of caustic potash on the water bath; some water and alcohol being added. After all the alcohol had been expelled the soap was dissolved in hot water, and the fatty acids separated from the clear solution by adding hydrochloric acid. After prolonged heating these acids will swim on the salt solution as a perfectly clear oil, a portion of which is then put into a little, narrow, thin walled tube and allowed to solidify. The point at with it melts and solidifies is determined by putting this tube in a beaker glass filled with water and warming with a small flame. A thermometer is placed the fatty acids and moved gently about during the observation, and the point accurately observed at which the whole mass becomes perfectly clear, and also when the mercury bulb begins to be clouded. It was found that the acids from pure olive oil melt between 26½ and 28½° C. (= 80° to 83° Fahr.) and solidify at a point not lower than 22° C. (72° Fahr.). The melting point of the fatty acids in the oils used to adulterate olive oil differs considerably from this. The melting and solidifying points of the acids in cotton seed, sesame, and peanut oils lie considerably higher, those of sunflower, rape, and castor oils decidedly lower than those of olive oil. in The melting and solidifying points of these acids are as follows: Cotton seed melts at 38.0°C.  solidifies 35.0°C.
  Sesame       do.     35.0 do.    do.     32.5 do.
  Peanut       do.     33.0 do.    do.     31.0 do.
  Sunflower    do.     23.0 do.    do.     17.0 do.
  Rape         do.     20.7 do.    do.     15.0 do.
  Castor oil   do.     13.0 do.    do.      2.0 do. The above figures differ so much from those of olive oil, that adulteratious carried to the extent that they are in trade can easily be detected by the aid of an estimation of the melting point, for a Gallipoli olive oil, mixed with 20 per cent. of sunflower oil, melted at 24° C. and solidified at 18° C. (of course, the fatty acids are meant). A Nizza oil, mixed with 20 per cent. cotton seed oil, melted at 31½° C. and solidified at 28° C. A Gallipoli oil with 33-1/3 per cent. of rape oil melted at 23½° C. and solidified at 16½° C. When 0.50 per cent. of rape is added, it melts as low as 20° and solidifies at 13½° C., etc. In testing the solubility of the fatty acids in alcohol and acetic acid, I employ the method proposed by David (in , 1878, p. 1416) for estimating stearic acid. Comptes Rendus It depends upon the principle that when acetic acid is poured drop by drop into an alcoholic solution of oleic acid, there comes a time when all the oleic acid separates, but stearic acid, which is insoluble in a mixture of alcohol and acetic acid, remains insoluble if the mixture contains oleic acid. The following manipulations are adopted in testing olive oil: Equal parts of glacial acetic acid and water are mixed in a bottle. Then 1 c.c. of pure oleic acid, 3 c.c. of 95 per cent. alcohol, and 2 c.c. of acetic acid are put in a small tube graduated in tenths of cubic centimeters. The solution should remain clear; on adding another one-tenth c.c. of acetic acid it becomes turbid, and when 1 c.c. of oleic acid (or at first even more) floats on the mixture of acid and alcohol, the liquid is ready for use. If this is not the case, the proportions (of acetic acid and alcohol?) must be varied until the addition of one-tenth c.c. of the former will cause all the oleic acid to separate. The proportions having been ascertained from these preliminary experiments, the alcohol and acid are then mixed accordingly, e.g., 300 of alcohol to 225 of acid. One or two grammes of stearic acid are added to the alcoholic acetic acid, and the clear supernatant liquid used for the experiments. One cubic centimeter of the oil (acids) to be tested is put in the tube, and 15 c.c. of alcoholic acetic acid added, well shaken, and the whole left to stand quietly at 15° C. (60° Fahr). If the olive oil is pure, the acids dissolve to a clear solution that remains so. Cotton seed oil is insoluble, and the solution obtained by heating the solution solidifies at 60° Fahr. to a white jelly. Sesame and peanut oil react in a similar manner. Sunflower oil dissolves, but at 60° a granular precipitate falls. Rape oil is entirely insoluble and floats like oil on the surface. Castor oil on the contrary dissolves completely, just like olive oil, and hence cannot be detected therein by this method. To detect this oil we must take the melting point of the acids along with the solubility of the oil itself in alcohol. Olive oil when mixed with 25 per cent. of cotton seed oil yields a granular precipitate, and so does 25 per cent. of sesame. Smaller quantities cannot be detected by these methods. For rape oil the limit is 50 per cent., and in smaller quantities the oil does not collect on the alcoholic solution. The decided lowering of the melting point of the fatty acids in combination with the sulphur reaction, and the insolubility of the oil in alcohol, also furnish a method of detecting when present in smaller quantities in olive oil. Although I am well aware that I am making public a research that is by no means free from objections, I nevertheless believe that it may be of use to those who have to undertake the ticklish and intricate analyses of commercial fats.-- , p. 355. Translated from the Chemiker Zeitung Leipsic, Jan., 1883. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2005). Scientific American Supplement, No. 392, July 7, 1883. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/8742/pg8742-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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