Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning

 Yan, Hui, Wenhui Fan, Xu Chen, Hanqi Wang, Chong Qin, and Xiaoqiang Jiang. "Component spectra extraction and quantitative analysis for preservative mixtures by combining Terahertz spectroscopy and machine learning." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy (2022): 120908.

for full paper see 

https://www.sciencedirect.com/science/article/abs/pii/S1386142522000567

Abstract

Preservatives are universally used in synergistic combination to enhance antimicrobial effect. Identify compositions and quantify components of preservatives are crucial steps in quality monitoring to guarantee merchandise safety. In the work, three most common preservatives, sorbic acid, potassium sorbate and sodium benzoate, are deliberately mixed in pairs with different mass ratios, which are supposed to be the “unknown” multicomponent systems and measured by terahertz (THz) time-domain spectroscopy. Subsequently, three major challenges have been accomplished by machine learning methods in this work. The singular value decomposition (SVD) effectively obtains the number of components in mixed preservatives. Then, the component spectra are successfully extracted by non-negative matrix factorization (NMF) and self-modeling mixture analysis (SMMA), which match well with the measured THz spectra of pure reagents. Moreover, the support vector machine for regression (SVR) designed an underlying model to the target components and simultaneously identify contents of each individual component in validation mixtures with decision coefficient R2 = 0.989. By taking advantages of the fingerprint-based THz technique and machine learning methods, our approach has been demonstrated the great potential to be served as a useful strategy for detecting preservative mixtures in practical applications.

… THz spectra of samples were obtained by TPS system (Teraview Ltd., UK) with resolution of 1 cm -1 (0.03 THz). Measurements were operated under purged dry-air (relative humidity < 1%) to minimize the impact of ambient water vapor. All the …

The 9th International Conference on Optical Terahertz Science and Technology (OTST 2022)

The 9th International Conference on Optical Terahertz Science and Technology (OTST 2022) will be held in the heart of Central Europe, in Budapest, the capital city of Hungary between 19–24 June 2022 (Sunday to Friday).

The conference webpage is available at: https://www.otst2022.hu/

The abstract submission is open at: https://www.otst2022.hu/registration-and-submission/abstract-submission.html

The deadline for the abstract submission is 14 March 2022.

The conference will be organized as an on-site event and personal attendance is strongly encouraged. However, the possibility of online participation will also be given. They hope that OTST 2022 will give our scientific community a new impulse and the possibility to meet each other in person after a long time.

2022 47th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

The 2022 47th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz), which will be held in Delft, The Netherlands, from 28 August to 2 September 2022.

See https://www.irmmw-thz2022.tudelft.nl/index.html for more information.

Key dates

Abstract submission: 25 March 2022

Author notification: 25 May 2022

Final paper submission including live/online registration: 30 June 2022

Conference: 28 August - 2 September 2022

TeraView is seeking a Technical Support Engineer

 Job description

TeraView Ltd is the world’s first and leading provider of terahertz solutions to Fortune 500 companies, in a variety of industries. TeraView was created in 2001 from a relationship between the Toshiba Corporation and the Cavendish Laboratory at the University of Cambridge. TeraView’s vision is to establish terahertz as the premier imaging and inspection tool for the 21st century.

An opportunity has arisen for a technical support engineer to join TeraView’s Semiconductor Group to service and support TeraView’s THz based time domain reflectometry (TDR) systems, both in the field and in house. You will have a minimum of 3 years experience of working in a field service or technical support role, gained within a scientific instrumentation/equipment environment, and be familiar with the set up and alignment of optical instrumentation. Qualified to a minimum of HNC level or above in electronics or other relevant technical discipline, you will be a self-starter with a positive attitude and a proven ability to work in small multi-disciplinary teams. The role entails a significant level of overseas travel, sometimes with little notice. Experience of working within a formal quality system, such as ISO9001 is important.

KEY TASKS AND RESPONSIBILITIES

·        Servicing and maintaining instruments both in the field and in-house.

·        Alignment of newly built instruments.

·        Factory Acceptance testing.

·        Assisting in the build of instruments.

·        Installation and Site acceptance testing.

PROFILE, SKILLS, EXPERIENCE & CHARACTERISTICS REQUIRED

The ideal candidate will possess:

·        HNC or above in electronics or similar technical discipline.

· Previous experience of working in service/support of scientific instrumentation.

·        Knowledge of lasers and alignment.

·        Knowledge of electronics.

·        Excellent customer facing skills.

·        Excellent technical communication skills both written and oral.

·        The ability to work alone and on own initiative.

·        Ability to travel abroad and at short notice.

·        Own transport and a clean driving licence.

·   Experience in the any of the following areas is desirable but is not required: semiconductor or semiconductor equipment industry, semiconductor failure analysis, GHz TDR, and terahertz systems. 

FURTHER INFORMATION

The job location is Cambridge, UK but the successful candidate will be expected to spend up to 50% of their time at customer locations around the world. To apply for this vacancy, please send your CV together with a covering letter which should give details of your current remuneration and examples of where and how you have applied your skills in previous working environments. The letter should also highlight how your skills could be used within TeraView.

These should be sent via LinkedIn 

TeraView offers an excellent salary and benefits package, along with the opportunity for highly-motivated staff to work together in a friendly, intellectually stimulating and challenging environment where there is plenty of scope to influence and shape the development of products in one of the newest, and most exciting, areas of technology.

We are an equal opportunities employer and pride ourselves on our diverse workforce. For further information and background on the company, please visit our website on: www.teraview.com

Deep Learning Classification of Breast Cancer Tissue from Terahertz Imaging Through Wavelet Synchro-Squeezed Transformation and Transfer Learning

 Liu, Haoyan, Nagma Vohra, Keith Bailey, Magda El-Shenawee, and Alexander H. Nelson. "Deep learning classification of breast cancer tissue from terahertz imaging through wavelet synchro-squeezed transformation and transfer learning." Journal of Infrared, Millimeter, and Terahertz Waves (2022): 1-23.

For full paper see https://link.springer.com/article/10.1007/s10762-021-00839-x

Abstract

Terahertz imaging and spectroscopy is an exciting technology that has the potential to provide insights in medical imaging. Prior research has leveraged statistical inference to classify tissue regions from terahertz images. To date, these approaches have shown that the segmentation problem is challenging for images of fresh tissue and for tumors that have invaded muscular regions. Artificial intelligence, particularly machine learning and deep learning, has been shown to improve performance in some medical imaging challenges. This paper builds on that literature by modifying a set of deep learning approaches to the challenge of classifying tissue regions of images captured by terahertz imaging and spectroscopy of freshly excised murine xenograft tissue. Our approach is to preprocess the images through a wavelet synchronous-squeezed transformation (WSST) to convert time-sequential terahertz data of each THz pixel to a spectrogram. Spectrograms are used as input tensors to a deep convolution neural network for pixel-wise classification. Based on the classification result of each pixel, a cancer tissue segmentation map is achieved. In experimentation, we adopt leave-one-sample-out cross-validation strategy, and evaluate our chosen networks and results using multiple metrics such as accuracy, precision, intersection, and size. The results from this experimentation demonstrate improvement in classification accuracy compared to statistical methods, an improvement to segmentation between muscle and cancerous regions in xenograft tumors, and identify areas to improve the imaging and classification methodology.

… For the imaging process, a pulsed TPS Spectra 3000 THz imaging and spectroscopy system (TeraView, Ltd., UK) was used in reflection mode.........