Global Beta Carotene Market By Revenue Is Projected To Reach US$ 572.78 Million By 2022

In terms of revenue in 2014 the global market for beta carotene Market was calculated to be USD 436.67 million and is projected to reach USD 572.78 million by 2022, growing at a CAGR of 3.5% from 2015 to 2022. In 2014, in terms of volume the market demand market was 343.72 tons and is projected to reach 423.65 tons by 2022 at a CAGR of 2.7% for the same forecast period.

Browse full report (Global Beta Carotene Market – Growth, Share, Opportunities & Competitive Analysis, 2015 -2022)@ http://www.credenceresearch.com/report/beta-carotene-market

The global market for beta carotene is witnessing growth on the back of rising health concerns and increased importance of intake of beta carotene.  Moreover, shift in trends towards natural ingredients is another factor driving towards the market growth. But, due to excessive intake of beta carotene can cause various effects on the human body therefore it is recommend to consumed in prescribed quantities only. However, rising demand from emerging economies and increasing demand of algae based carotene in functional and heath food provide huge market opportunities.

By product type, in 2014, synthetic beta carotene accounted for more than 70% of the market share in terms of volume as well as revenue. But growth is projected to highest from the natural segment which is projected to grow at a rate of 4.6% in terms of revenue during the forecast period from 2015 to 2022.

By applications type, food and beverages segment accounted for 40% share of the pie in terms of volume and revenue in 2014 and is projected to maintain their dominance during the forecast period. Growth is projected to be highest from the supplements segment which is projected to grow at a CAGR of 3.1% in terms of volume from 2015 to 2022 owing to rising health concerns, increasing awareness about the benefits of beta carotene and busy life style. Cosmetics segment is another major application which will witness potential growth during the forecast period.

In 2014, by region, Europe and North America together accounted for more than 70% share of the pie in terms of volume and revenue. Europe is projected to grow at a rate of 3.1% in terms of volume from 2015 to 2022. The U.S. is the largest market globally for beta carotene and is projected to grow at a rate of 3.5% in terms of revenue. Asia Pacific region is projected to witness fastest growth rate in both, volume and revenue. The Asia Pacific market in terms of revenue is projected to grow at a CAGR of 3.7% from 2015 to 2022. In, Asia Pacific, Japan accounted for the largest share but growth is projected to highest from India and China.  RoW is also projected to witness moderate growth rate.  Economies such as Brazil, Turkey, Saudi Arabia offers huge untapped market opportunities.

The market for beta carotene is highly competitive, two major players BASF and DSM accounts for 55% share of the market. These European players are facing stiff competition from the Indian and Chinese players. More than 20% share of the pie is accounted by Chinese and Indian Players. There are many other larger, medium, and small manufacturers and private label players present in the market but only accounts for major market share. Some of the other players in this market are Allied Biotech Corp. Ltd, Naturex SA, Chr. Hansen, and Zhejiang NHU Co.,Ltd  among others.

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New AI algorithm monitors sleep with radio waves

More than 50 million Americans suffer from sleep disorders, and diseases including Parkinson’s and Alzheimer’s can also disrupt sleep. Diagnosing and monitoring these conditions usually requires attaching electrodes and a variety of other sensors to patients, which can further disrupt their sleep.

To make it easier to diagnose and study sleep problems, researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep stages without sensors attached to the body. Their device uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM).

“Imagine if your Wi-Fi router knows when you are dreaming, and can monitor whether you are having enough deep sleep, which is necessary for memory consolidation,” says Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, who led the study. “Our vision is developing health sensors that will disappear into the background and capture physiological signals and important health metrics, without asking the user to change her behavior in any way.”

Katabi worked on the study with Matt Bianchi, chief of the Division of Sleep Medicine at MGH, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science and a member of the Institute for Data, Systems, and Society at MIT. Mingmin Zhao, an MIT graduate student, is the paper’s first author, and Shichao Yue, another MIT graduate student, is also a co-author.

The researchers will present their paper at the International Conference on Machine Learning on Aug. 9.

Remote sensing

Katabi and members of her group in MIT’s Computer Science and Artificial Intelligence Laboratory have previously developed radio-based sensors that enable them to remotely measure vital signs and behaviors that can be indicators of health. These sensors consist of a wireless device, about the size of a laptop computer, that emits low-power radio frequency (RF) signals. As the radio waves reflect off of the body, any slight movement of the body alters the frequency of the reflected waves. Analyzing those waves can reveal vital signs such as pulse and breathing rate.

“It’s a smart Wi-Fi-like box that sits in the home and analyzes these reflections and discovers all of these changes in the body, through a signature that the body leaves on the RF signal,” Katabi says.

Katabi and her students have also used this approach to create a sensor called WiGait that can measure walking speed using wireless signals, which could help doctors predict cognitive decline, falls, certain cardiac or pulmonary diseases, or other health problems.

After developing those sensors, Katabi thought that a similar approach could also be useful for monitoring sleep, which is currently done while patients spend the night in a sleep lab hooked up to monitors such as electroencephalography (EEG) machines.

“The opportunity is very big because we don’t understand sleep well, and a high fraction of the population has sleep problems,” says Zhao. “We have this technology that, if we can make it work, can move us from a world where we do sleep studies once every few months in the sleep lab to continuous sleep studies in the home.”

To achieve that, the researchers had to come up with a way to translate their measurements of pulse, breathing rate, and movement into sleep stages. Recent advances in artificial intelligence have made it possible to train computer algorithms known as deep neural networks to extract and analyze information from complex datasets, such as the radio signals obtained from the researchers’ sensor. However, these signals have a great deal of information that is irrelevant to sleep and can be confusing to existing algorithms. The MIT researchers had to come up with a new AI algorithm based on deep neural networks, which eliminates the irrelevant information.

“The surrounding conditions introduce a lot of unwanted variation in what you measure. The novelty lies in preserving the sleep signal while removing the rest,” says Jaakkola. Their algorithm can be used in different locations and with different people, without any calibration.

Using this approach in tests of 25 healthy volunteers, the researchers found that their technique was about 80 percent accurate, which is comparable to the accuracy of ratings determined by sleep specialists based on EEG measurements.

“Our device allows you not only to remove all of these sensors that you put on the person, and make it a much better experience that can be done at home, it also makes the job of the doctor and the sleep technologist much easier,” Katabi says. “They don’t have to go through the data and manually label it.”

Sleep deficiencies

Other researchers have tried to use radio signals to monitor sleep, but these systems are accurate only 65 percent of the time and mainly determine whether a person is awake or asleep, not what sleep stage they are in. Katabi and her colleagues were able to improve on that by training their algorithm to ignore wireless signals that bounce off of other objects in the room and include only data reflected from the sleeping person.

The researchers now plan to use this technology to study how Parkinson’s disease affects sleep.

“When you think about Parkinson’s, you think about it as a movement disorder, but the disease is also associated with very complex sleep deficiencies, which are not very well understood,” Katabi says.

The sensor could also be used to learn more about sleep changes produced by Alzheimer’s disease, as well as sleep disorders such as insomnia and sleep apnea. It may also be useful for studying epileptic seizures that happen during sleep, which are usually difficult to detect.

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