We cannot speak about Mediterranean diet without the presence of olive oil in our dishes. This fruit juice plays an essential role in the gastronomy of our country.
Extra virgin olive oil, virgin olive oil, olive oil, olive-pomace oil…all of these products are obtained from olives, each one of them has their characteristics, but what differentiates one from another and how we can choose it?
The process of olive oil production, physico-chemical parameters and in the case of virgin oils, the sensory quality (evaluated with an expert taster panel) are used to classify the olive oil.
If we speak about tasting or sensory analysis of a product, it can think and not without reason, in a subjective process and under many errors in their implementation, ambiguous or subject to interpretation expressions. But sensory analysis is a scientific discipline used to evaluate the organoleptic characteristics of food, and it has been used for many years like a method to measure, analyze and understandhuman reactions to the organoleptic characteristics of food by the senses. Data from a sensory analysis are evaluated by a panel of tasters trained for it and are statistically treated in order to minimize errors and make objective results.
In the case of tasting olive oil for classification as extra virgin olive oil, virgin oil or lamp oil, it is carried out by a panel of experts, which will also be authorized by bodies of the member states, to carry out official control of the country.
The tasting test is carried out under a specific regulation developed by the International Olive Oil Council, in which the tasters follow a profile sheet with positive attributes and some negative attributes that are valued on a scale of 10 cm. The tasting test is carried out in a glass for oil specific and the oil samples shall be kept in the glasses at 28ºC±2ºC throughout the test, this temperature has been chosen because it makes it easier to observe organoleptic differences than at ambient temperature.
In the method for the organoleptic assessment are detailed the number of samples, amount of oil, explanation of vocabulary, etc.. to assessing the virgin olive oil.
The positive attributes that are valued in oil are fruity, bitter and pungent, these attributes will depend on the variety of olive, the degree of maturity of the same, and the time they have been harvested.
The negative attributes are determined by the following attributes:
1. Fusty/muddy sediment Characteristic flavour of oil obtained from olives piled or stored in such conditions as to have undergone an advanced stage of anaerobic fermentation, or of oil which has been left in contact with the sediment that settles in underground tanks and vats and which has also undergone a process of anaerobic fermentation.
2. Musty-humid-earthy Characteristic flavour of oils obtained from fruit in which large numbers of fungi and yeasts have developed as a result of its being stored in humid conditions for several days or of oil obtained from olives that have been collected with earth or mud on them and which have not been washed.
3. Winey-vinegary. Characteristic flavour of certain oils reminiscent of wine or vinegar.
4.Acid-sour. This flavour is mainly due to a process of aerobic fermentation in the olives or in olive paste left on pressing mats which have not been properly cleaned.
5. Rancid Flavour of oils which have undergone an intense process of oxidation.
6. Frostbitten olives. (wet wood) Characteristic flavour of oils extracted from olives which have been injured by frost while on the tree.
On the same tab tasting the taster may indicate other negative attributes such as a heated or burnt, hay-wood, rough, greasy, vegetable water, brine, metallic, esparto, grubby and/or cucumber.
To be considered extra virgin, the oil shall not have any defect and the fruity attribute must be greater than 0; if the oil has a defect (less than 3.5 on the scale) and the median of the fruity attribute is above 0, would become a virgin oil; and if the oil was very defective in sensory quality is classified as lampante virgin olive oil and should be refined for consumption.
In most cases as consumers, we will not be able to distinguish all of these attributes, but it’s all about training your palate, have good sensory memory and taste, taste and taste different oils. And whenever we want to enjoy quality oil, choose an extra virgin olive oil, where we can appreciate the variety of olive, the time of harvest the fruit, nuances of smells and flavors of the harvested area. Not all olive oils are the same … taste, let’s try and enjoy the liquid gold.
The first post about Industry 4.0 indicated the need for key technologies that would make possible the 4th industrial revolution. These key tehcnologies have been called “digital enablers“. Each industrial revolution has had its “enablers”. The first one was made possible by inventions like the steam engine or mechanical loom. The second came started with breakthroughs like electricity or the car assembly line. In the third, disruptive technologies such as robotics, microelectronics and computer networks made their debut.
This post is intended as a shopping list to review those technologies considered highly relevant and key for this fourth revolution. Each brief description is linked to an extended information covered inside our Blog. In next posts we will complete the descriptions to have an overview of the full range of technologies:
Virtual / Augmented Reality: provides information to the operator adapted to the context (e.g. during a maintenance operation) and merged with their field of view.
IoT: internet for virtually any object, in this case, the ones we can find in a factory: a workpiece, a motor, a tool…
Traceability: seeks the monitoring of manufacturing operations (automatic and manual), products as well as the conditions that were used to create them (temperature, production speed…)
Predictive maintenance: an optimized way to perform maintenance in order to avoid unexpected stops and unnecessary waste because of periodic maintenance operations.
Artificial vision: provides the production process visual context information for quality control or assistance in manufacturing (e.g. automatic positioning of a robot to take a piece).
Big Data: generates knowledge and value from manufacturing data as well as other context data (e.g. demand for similar or related products)
3D Printing recreates of a three-dimensional copy of: existing parts, spare parts or prototypes with the same or different scale for review or testing.
Cloud Computing leverages on internet computing resources to undertake storage and processing of large data sets (e.g. Big Data) without the need of investment in own IT infrastructure.
Cybersecurity as physical and logical security measures used to protect infrastructure (manufacturing in this context) from various threats (e.g. a hacker, sabotage, etc).
Cyber-Physical Systems as any complex system consisting of a combination of any of the above technologies seeking improved performance, in this case, of manufacturing.
The strength of these digital enablers is not in their individual features but in their ability to come together. We as engineers love to look for the latest technology trend and then found a problem or area for its application. But to succeed in this revolution, it is necessary to face real challenges within the factories, using innovative solutions, and why not, combining several of the digital enablers shown above. Moreover, this terminology creates a common framework that facilitates a dialogue between technologists and manufacturers for undertaking successful projects seeking to optimize the factory.
If we think, for example, to optimize maintenance operations in a factory, the “predictive maintenance” will be one of the first enablers that comes to our mind. Also, this technology solution will benefit from a connection to a “Cloud computing” system where sensors’ data coming from different factories will be analyzed generating better diagnosis and predictions of the production assets under monitoring. In this type of cloud solutions, however, the security of information transmitted must be ensured via appropriate “Cybersecurity” mechanisms. We will, therefore, generate an Industry 4.0 cybersecure, multi-site, predictive maintenance solution.
The list of presented technologies doesn’t intend to be final. Also, technological evolution is continuous and incredibly fast. Like we have mentioned, the combination of different digital enablers generates a wide range of industry 4.0 solutions. In next posts we will discuss more scenarios where digital enablers can answer to different challenges in manufacturing.
We could read some time ago, as a headline in a national newspaper, that the Chairman of Repsol, Antonio Brufau, literally stated that “It is false that the electric car has zero emissions” and “(…) emissions must take into account not only CO2 emitted by the vehicle, but those produced during manufacture“. With this headline and without realizing, the Chairman of REPSOL was advocating for considering the life cycle of a product to make environmental self-declarations. The fact is that this generalized doubt about the relationship between the electric car and the CO2 cannot lead us to think that it is not one of the most environmental mobility options because it is, what happens is that we should be meticulous when it comes to talk about the environmental performance of products.
One of the first examples of using life-cycle thinking assesment (LCA) in the late 60s, in the USA, when Coca-Cola® decided to explore alternative containers besides the glass bottle through this approach. And this concept arose from a very logical way, due to the emerging companies’ demand for distributing environmental loads, nobody liked being the most pollutant. Companies began to ask about an extended responsibility in this regard and through methodologies such as Life Cycle Assessment (LCA), one of the most internationally recognized and accepted methods to investigate the environmental performance of products throughout their life cycle, it could be verified that the associated environmental impacts with the manufacturing stage were not the most relevant in some cases.
Let’s see an example to summarize this issue. Imagine a conversation between Mary Ecological and Mary Nosy:
Mary Ecological: “Have you seen the garlands I have placed for the party? they are made of recycled paper because I am an ecological woman, you know“ Mary Nosy: “They are lovely, where did you buy them?” Mary Ecological: “In a Chinese online shop, extremely cheap”
A Life Cycle Assessment (LCA) applied to these garlands would probably have confirmed us that Mary Ecological is attributing to herself a label that is not true. To buy a product in China can cause that an item made of recycled paper may have a hidden environmental price that is “disguised” using a more environmentally friendly raw material within the manufacturing process. And from the moment we are free to choose what we buy, as consumers, we are sharing the environmental responsibility with the industry, let´s keep this in mind.
When a company asks for what is the environmental profile of its product and / or process, CARTIF always advises to apply this methodology because the obtained results are a detailed environmental picture of the life cycle of its process, product or service (suppliers included), with the consequent opportunity to identify critical points and reduce costs, both environmental and economic. We have been able to check it many times in many of our projects. It doesn’t matter if we are a consumer or a product manager, to take a life-cycle approach to the environmental impact of the products we are acquiring, producing or selling, is essential to make decisions and to put in clear terms our environmental performance.
For this reason, the Chairman of Repsol said the principle of only considering the stage of use in an electric car to confirm that it does not emit CO2 is incorrect. Although it is perhaps the most significant phase (in fuel-consuming vehicles too), the assessment must be extended to its life cycle which, obviously, includes CO2 emissions from electricity production. Strictly speaking, we should either clarify that the electric vehicle does not emit CO2 during the stage of use or apply the LCA considering its life cycle (CARTIF has already done it) so that, based on the results, to generate environmental headlines.
We love the environmental assessments well done and undertaking rigorous environmental claims. Ask us and we’ll tell you how to do this!
We are used to see how new technologies help people with physical disabilities: automatic wheel chairs, revolutionary prosthesis and even image or voice sensors directly connected to the brain through electrodes. But what about people with a mental disability? Let’s think on those persons that suffer schizophrenia. This is a chronic condition characterized by certain behaviors that are abnormal for the community. In particular, many people with schizophrenia have difficulty recognizing emotions in the facial expressions of other people, which seriously affects social behavior. Furthermore, this difficulty is not limited to schizophrenia, but is also observed in cases of mania, dementia, brain damage, autism etc.
Here come into play social robotics technologies. A social robot is a robot that interacts and communicates with people (or other robots) following social behaviors and rules. Furthermore, traditionally a robot is assumed to be materialized in the form of physical device. However, the same interaction skills designed for a physical robot can be integrated into a virtual character represented in a computer. From this viewpoint, an Avatar may be considered to be a robot, in line with the new technological paradigm in which the boundary between the physical and the virtual reality is progressively diluted.
Now, what advantages does the use of Avatars in psychological and psychiatric therapies have? In my opinion, these advantages are innumerable. An avatar can reach an expressiveness level comparable (if not superior) to that of a physical robot, and even a real personal. Not even a hyper-realistic human appearance is needed: a simple cartoon can be extremely expressive. (Let’s think of the coyote when, in pursuit of the roadrunner, exceeds the limit of the cliff). In addition, unlike a real person, the expressiveness of an avatar can be controlled to the millimeter by a therapist. This way, the virtual avatar can display emotions in varying degrees, from emerging to very marked, randomly or in progression, even depending on the user behavior.
Another great aspect involved is sensorization. Here, the Computer Vision technologies play a decisive role. We are used to our mobile phone camera that detects and tracks faces, identifies which faces correspond to people in our family or social environment and determine when they open their eyes and smile. Obviously, this technology can be put at the service of perceiving the user’s attitude during interaction: whether the user smiles or is sad, if he/she is calm or nervous or feels anxious. In addition, certainly voice analysis can supplement this information. The words used by the person say a lot about his/her mood. In addition, the tone and rhythm also provide crucial information: an angry person talks fast and loud, while someone who is bored speaks slowly, in a slurred speech. Certainly, nowadays the voice analysis goes a step behind the image analysis, probably because it is very close to artificial intelligence that still represents a challenge (although increasingly affordable by technology).
Where does this lead us?To a virtual (or physical) avatar that tracks the user’s face with its eyes, interprets user emotions and reacts accordingly to them, talk friendly and can be supervised by a therapist, with the advantage of being available 24 hours day. A companion, ultimately, that serves as a personal trainer to improve the perception of human emotions. This is not the future. This is the present.
Surely many of you have as childhood memory go shopping with your parents to buy fresh milkfrom farm or the nearest village, or even remembers the van of milkman that went selling the milk in jugs by the streets. This flavour, the cream was left on the surface after boiling at home and what good this cream was to prepare delicious pastries!
In Spain the direct supply by the producer of small quantities of raw milk to the final consumer or to local retail establishments that supply directly to consumers is prohibited, according to Royal Decree 640/2006.
But meanwhile the AECOSAN (Spanish Agency of Consumer Affairs, Food Security and Nutrition) is considering the possibility of amending Royal Decree 640/2006, so that by 2015 it requested the Scientific Committee to report on the microbiological risks associated with the consumption of milk raw and processed dairy products made from raw milk. The report by the Scientific Committee gathered concretely aspects:
The sale of raw milk and cream
The production of cheese more than 60 days with raw milk that does not meet the criteria somatic cell and total germs
The applicable requirements colostrum.
However, the sale of raw milk and cream in Spain intended for direct human consumption is not limited or prohibited, if all the requirements of Regulation (EC) No 852/2004 and Regulation (EC) No 853/2004 are achieved.
Therefore, currently in Spain we can find places to buy raw milk, in fact the trend for “natural is healthier” and other trends have increased the sale of this product. In Spain 42 tons of raw milk were consumed in 2013 (1.2% of all milk consumed), according to the Ministry of Agriculture, Food and Environment. In the United States these trends are much higher, and there are lobby groups that promote the consumption of raw milk and dairy products, but is it safe drinking raw milk?
According to European Union (EU) legislation, “raw milk” is defined as milk produced by the secretion of the mammary gland of farmed animals that has not been heated to more than 40 °C or undergone any treatment that has an equivalent effect. Therefore, when it comes to raw milk consumption we refer to milk without any treatment, not even if we purchased raw milk and it is boiled by us in our home, and by the way, it is made at the discretion of each one.
Milk is a rich in nutrients, high water activity and with proper pHfor growth of microorganisms, both microorganisms beneficial (species of the genera Lactobacillus, Streptococcus, Enterococcus) as pathogens (the most common organisms Salmonellaspp., Campylobacter spp., Escherichia coli, Yersinia enterocolitica, Listeria monocytogenes and Staphylococcus aureus, but also viruses, parasites and food toxins) and this is where it runs a risk when consuming raw milk or products made from it.
Raw milk, contrary to what many people think, is not sterile, and may be the vehicle for the transmission of various diseases, some of them very serious depending on the state of health of the affected person or the moment of the life (children, pregnant women, ageing people, immunocompromised persons, etc.). Potential pathogens are not eliminated because does not exist a heat treatment, and may have reached the milk by a systemic infection of animals, or mastitis thereof (udder infection), addition during milking and subsequent distribution there is a risk contamination and deterioration thereof.
Treatments like pasteurization (heating for a specified time at temperatures below 100 ºC) or sterilization (higher than 100 ºC for a given time), allow us to have in our homes safe milk for our consumption, killing vegetative forms in first case and vegetative and spore forms the second.
The movements and groups advocating the consumption of raw milk, recommend its use without any heat treatment, even for children, arguing that the milk itself is safe, and they rely on the control of livestock and good practices. They also believe that raw milk is able to prevent allergies and intolerances. Neither pasteurization or sterilization determine allergy or intolerance to milk, milk either raw, pasteurized or sterilized is not suitable for people who have intolerance to lactose.
Processing operations of milk have influence in their organoleptic quality, and someflavors and tastes that have raw milk are lost during these operations, mainly due to the process of homogenization of fat, rather than processes heat to make it safe for its consumption.
The technology of food processes has allowed over the years to provide safe and affordable food to consumers. In the case of milk may we yearn for the taste of milk, their original taste, but if we do a real analysis of the risks of drinking raw milk without any treatment not worth playing roulette with a glass of raw milk… even if it is yummy.
In the 20th Century 80’s decade there was reborn interest in neural networks, both in academia and industry. A neural network is an algorithm that mimics the neural connections present in the neocortex. The interest was motivated by the rediscovering of algorithms to train the networks. Through training, a neural network can learn to do something. And since neural networks are implemented in computers, we have computers that can learn. This is an intellectual ability that people share with monkeys among other animals with neocortex. For this reason, neural networks are the backbone of machine learning, which according to some is part of artificial intelligence.
Neural networks can learn to classify objects and also to reproduce the behaviour of complex systems. They learn by examples. When we want to teach a neural network to differentiate between apples and oranges we have to present it examples of both fruits with a label indicating if it is an orange or an apple. The point is the neural network will be able to correctly classify oranges and apples different to the ones used during training. This is because a neural network does not perform a mere memorisation, but they are able to generalise. This is the key for learning.
But the interest in neural networks that raised up during the eighties faded as the following decade started because more promising machine learning methods appeared. However, a group of indomitable Canadian researchers managed to persevere and transformed neural networks into deep learning.
Deep learning is an algorithm family similar to neural networks, with the same aim and better performance. The number of neurons and connections is higher, but the main difference is the abstraction capacity. When we train a neural network to differentiate between apples and oranges we cannot present the items as they are, we have to extract some features that describe the oranges and apples, as the colour, shape, size, etc. To do this is what in this context we call abstraction. In contrast to neural networks, deep learning is able to do abstraction by itself. This is the reason why deep learning is thought to be able to understand what they see and heard and it is, therefore, a bridge between machine learning and artificial intelligence.
As it happened with neural networks, deep learning has gained huge interest among companies. In 2013, Facebook failed to buy company DeepMind, but Google succeeded one year later when it paid 500 million dollars for it. In case some body missed this irruption of deep learning in the media, it became mainstream in early 2016 when Google DeepMind software AlphaGo beated Lee Sedol, the go champion. This was an unprecedented technical success because go is much harder than chess. When IBM’s Deep Blue won Garry Kasparov in 1996, it used a strategy based on figuring out all the possible short-term movements. However, this strategy is not possible in go because the possibilities are infinite in comparison to chess. For this reason, Google DeepMind’s AlphaGo is not programmed to play go, it is able to learn to play by itself. The machine learns by playing many times against a human player, improving in every game until it becomes unbeatable.
Deep learning is not a secret arcane, anybody who wants to learn it can do it. There are free available tools, like Theano, TensorFlow and H2O, that allows any person with programming knowledge and the concepts in mind to try it. The company OpenAI has freely released its first algorithm, which has been made around the reinforced learning paradigm. There also companies offering commercial products onto which applications can be build. These are the cases of the Spanish Artelnics and the Californian Numenta. Deep learning is being successfully used for face recognition and verbal command interpretation.
Deep learning, besides other machine learning paradigms, could be an important innovation opportunity. It could be one of the tools to unleash the value hidden in the big data repositories. Moreover, in the industrial practice it could be used to detect and classify faults or defects, to model complex systems to be used in control schemes, and in novelty detection.
