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Open Access
Article
Publication date: 30 September 2015

Toru Uehara and Yoko Ishige

This study aims to examine the association of frontal functioning with subclinical bipolar spectrum by a newly developed convenient method. We investigated subclinical…

Abstract

This study aims to examine the association of frontal functioning with subclinical bipolar spectrum by a newly developed convenient method. We investigated subclinical bipolar tendency and frontal lobe activation during word productions using multi-channel near infrared spectroscopy. Participants: 44 healthy university students (mean ages 20.5 years old, and 29 female) gave their written informed consent, and we strictly protected privacy and anonymity was carefully preserved. A 13-items self-report questionnaire (Mood Disorders Questionnaire; MDQ) and a 16-channel near-infrared spectroscopy were used to compare frontal activations between two samples divided by median (4 points) of the total MDQ scores and to analyze correlations between relative changes of cerebral blood volume and bipolarity levels. There was no case suspected as bipolar disorders by MDQ screening (mean 3.4, max 10). Significant differences in lower activations were noted in the right and left pre-frontal cortex (PFC) with higher bipolarity scores using the specific software to analyze the NIRS waveform (P<0.05). Total MDQ were correlated significantly with frontal activation negatively in many channels; therefore, we conducted multiple linear regression to select significant frontal activations using the MDQ as a dependent variable. Stepwise method revealed that activation in left lateral PFC was negatively associated to bipolar tendency, and this regression model was significant (R2=0.10, F=4.5, P=0.04). Differences in frontal functioning suggest that subclinical bipolar tendencies might be related to left lateral PFC activations. It should be confirmed whether the identical pattern can be identified for clinical subjects with bipolar disorders.

Details

Mental Illness, vol. 7 no. 2
Type: Research Article
ISSN: 2036-7465

Keywords

Open Access
Article
Publication date: 19 December 2018

Lei Zhu, Shuguang Li, Yaohua Li, Min Wang, Yanyu Li and Jin Yao

Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is…

Abstract

Purpose

Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions. The purpose of this study is to establish a driver intention prediction model.

Design/methodology/approach

The authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention. The experiment was carried out in a virtual reality environment. During the experiment, the driving simulator recorded the driving data and the functional near-infrared spectroscopy (fNIRS) device recorded the changes in hemoglobin concentration in the cerebral cortex. After the experiment, the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network.

Findings

The research results showed that the accuracy of the model established in this paper was 80.39 per cent. And, the model could identify the driver’s braking intent prior to his braking operation.

Research limitations/implications

The limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic. At the same time, other actions of the driver were not taken into account when establishing the braking intention recognition model. Besides, the verification results obtained in this paper could only reflect the results of a few drivers’ identification of braking intention.

Practical implications

This study can be used as a reference for future research on driving intention through fNIRS, and it also has a positive effect on the research of brain-controlled driving. At the same time, it has developed new frontiers for intention recognition of cooperative driving.

Social implications

This study explores new directions for future brain-controlled driving and wheelchairs.

Originality/value

The driver’s driving intention was predicted through the fNIRS device for the first time.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

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Article
Publication date: 1 September 2005

Christine Connolly

To review the use of nearinfrared (NIR) spectroscopy in the process control of foodstuffs.

Abstract

Purpose

To review the use of nearinfrared (NIR) spectroscopy in the process control of foodstuffs.

Design/methodology/approach

Presents two spectroscopy products used in the production environment: a single‐board system aimed at OEMs, and a complete fibre‐optic spectrometer ready for the end‐user. Gives examples of applications within the food and drink industry.

Findings

Finds that these instruments are fast, effective and inexpensive, and rugged enough for the processing environment.

Originality/value

Draws attention to the potential of NIR spectroscopy outside the confines of the laboratory.

Details

Sensor Review, vol. 25 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 14 June 2013

Ahmad Fairuz Omar

Visible and near infrared spectroscopy have been applied widely in fruits quality assessment especially on the measurement of soluble solids content (SSC) measured in o

Abstract

Purpose

Visible and near infrared spectroscopy have been applied widely in fruits quality assessment especially on the measurement of soluble solids content (SSC) measured in oBrix and acidity measured in pH. Spectroscopy technique has been applied on three botanically different categories of fruits, that is: imported Californian table grape, Mandarin lime and star fruit. The purpose is to examine the ability of spectroscopy technique to quantify internal quality parameters with very narrow variability due to the characteristics of the raw material analyzed. This work also presents comparative study on peak wavelengths that can best be used to calibrate SSC and pH of different types of fruits.

Design/methodology/approach

The effective wavelengths chosen for calibration development are compared with those selected by other researchers in similar experiments. NIR wavelengths 910 nm (C−H band) and 950 nm (O−H band) are the most important wavelengths for the prediction of SSC for all examined fruits while wavelengths 922‐923 nm and 990‐995 nm for pH. Visible wavelength 605, 675 and 654 nm can efficiently improve the SSC and pH prediction for grape, lime and star fruit, respectively.

Findings

The best prediction for SSC has been achieved with R2=0.953 and RMSE=0.182 for grape, R2=0.918 and RMSE=0.109 for lime and R2=0.957 and RMSE=0.354 for star fruit. The best prediction for pH has been achieved with R2=0.763 and RMSE=0.110 for grape, R2=0.841 and RMSE=0.073 for lime and R2=0.862 and RMSE=0.261 for star fruit.

Originality/value

Currently, the spectroscopy research conducted for the measurement of fruits qualities is conducted through wide range spectrometer. However, the peak responses are only located at specific wavelengths. Hence, the selection of wavelengths related to SSC and pH will allow the design of low cost instruments for the prediction of these internal quality parameters.

Details

Sensor Review, vol. 33 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 7 March 2016

Xudong Sun, Mingxing Zhou and Yize Sun

– The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics.

Abstract

Purpose

The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics.

Design/methodology/approach

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively.

Findings

The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics.

Originality/value

It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

Details

International Journal of Clothing Science and Technology, vol. 28 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 13 February 2019

Xudong Sun and Ke Zhu

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate…

Abstract

Purpose

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

Design/methodology/approach

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

Findings

The correlation coefficient of prediction (rp) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

Originality/value

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 31 January 2018

Caspar Krampe, Enrique Strelow, Alexander Haas and Peter Kenning

This study is the first to examine consumer’s neural reaction to different merchandising communication strategies at the point-of-sale (PoS) by applying functional near

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Abstract

Purpose

This study is the first to examine consumer’s neural reaction to different merchandising communication strategies at the point-of-sale (PoS) by applying functional near-infrared spectroscopy (fNIRS). By doing so, the purpose of this study is to extend consumer neuroscience to retail and shopper research.

Design/methodology/approach

Two experiments were conducted in which 36 shoppers were exposed to a realistic grocery shopping scenario while their brain haemodynamics were measured using mobile fNIRS.

Findings

Results revealed that mobile fNIRS appears a valid method to study neural activation of the prefrontal cortex (PFC) in the field of “shopper neuroscience”. More precisely, results demonstrated that the orbitofrontal cortex (OFC) might be crucial for processing and predicting merchandising communication strategy effectiveness.

Research limitations/implications

This research gives evidence that certain regions of the PFC, in particular the OFC and the dorsolateral prefrontal cortex (dlPFC), are crucial to process and evaluate merchandising communication strategies.

Practical implications

The current work opens a promising new avenue for studying and understanding shopper’s behaviour. Mobile fNIRS enables marketing management to collect neural data from shoppers and analyse neural activity associated with real-life settings. Furthermore, based on a better understanding of shoppers’ perceptual processes of communication strategies, marketers can design more effective merchandising communication strategies.

Originality/value

The study is the first to implement the innovative, mobile neuroimaging method of fNIRS to a PoS setting. It, therefore, opens up the promising field of “shopper neuroscience”.

Details

European Journal of Marketing, vol. 52 no. 1/2
Type: Research Article
ISSN: 0309-0566

Keywords

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Article
Publication date: 12 March 2018

Maria Joao Pinho Moreira, Ana Silva, Cristina Saraiva and José Manuel Marques Martins de Almeida

Consumption of game meat is growing when compared to other meats. It is susceptible to adulteration because of its cost and availability. Spectroscopy may lead to rapid…

Abstract

Purpose

Consumption of game meat is growing when compared to other meats. It is susceptible to adulteration because of its cost and availability. Spectroscopy may lead to rapid methodologies for detecting adulteration. The purpose of this study is to detect the adulteration of wild fallow deer (Dama dama) meat with domestic goat (G) (Capra aegagrus hircus) meat, for samples stored for different periods of time using Fourier transform infrared (FTIR) spectroscopy coupled with chemometric.

Design/methodology/approach

Meat was cut and mixed in different percentages, transformed into mini-burgers and stored at 3°C from 12 to 432 h and periodically examined for FTIR, pH and microbial analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to detect adulteration.

Findings

The PCA model, applied to the spectral region from 1,138 to 1,180, 1,314 to 1,477, 1,535 to 1,556 and from 1,728 to 1,759 cm−1, describes the adulteration using four principal components which explained 95 per cent of variance. For the levels of Adulteration A1 (pure meat), A2 (25 and 50 %w/wG) and A3 (75 and 100 %w/wG) for an external set of samples, the correlation coefficients for prediction were 0.979, 0.941 and 0.971, and the room mean square error were 8.58, 12.46 and 9.47 per cent, respectively.

Originality/value

The PLS-DA model predicted the adulteration for an external set of samples with high accuracy. The proposed method has the advantage of allowing rapid results, despite the storage time of the adulterated meat. It was shown that FTIR combined with chemometrics can be used to establish a methodology for the identification of adulteration of game meat, not only for fresh meat but also for meat stored for different periods of time.

Details

Nutrition & Food Science, vol. 48 no. 2
Type: Research Article
ISSN: 0034-6659

Keywords

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Article
Publication date: 12 August 2019

Kyoung Cheon Cha, Minah Suh, Gusang Kwon, Seungeun Yang and Eun Ju Lee

The purpose of this paper is to determine the auditory-sensory characteristics of the digital pop music that is particularly successful on the YouTube website by measuring…

Abstract

Purpose

The purpose of this paper is to determine the auditory-sensory characteristics of the digital pop music that is particularly successful on the YouTube website by measuring young listeners’ brain responses to highly successful pop music noninvasively.

Design/methodology/approach

The authors conducted a functional near-infrared spectroscopy (fNIRS) experiment with 56 young adults (23 females; mean age 24 years) with normal vision and hearing and no record of neurological disease. The authors calculated total blood flow (TBF) and hemodynamic randomness and examined their relationships with online popularity.

Findings

The authors found that TBF to the right medial prefrontal cortex increased more when the young adults heard music that presented acoustic stimulation well above previously defined optimal sensory level. The hemodynamic randomness decreased significantly when the participants listened to music that provided near- or above-OSL stimulation.

Research limitations/implications

Online popularity, recorded as the number of daily hits, was significantly positively related with the TBF and negatively related with hemodynamic randomness.

Practical implications

These findings suggest that a new media marketing strategy may be required that can provide a sufficient level of sensory stimulation to Millennials in order to increase their engagements in various use cases including entertainment, advertising and retail environments.

Social implications

Digital technology has so drastically reduced the costs of sharing and disseminating information, including music, that consumers can now easily use digital platforms to access a wide selection of music at minimal cost. The structure of the current music market reflects the decentralized nature of the online distribution network such that artists from all over the world now have equal access to billions of members of the global music audience.

Originality/value

This study confirms the importance of understanding target customer’s sensory experiences would grow in determining the success of digital contents and marketing.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 32 no. 5
Type: Research Article
ISSN: 1355-5855

Keywords

Open Access
Article
Publication date: 5 January 2022

Alex Mason, Dmytro Romanov, L. Eduardo Cordova-Lopez, Steven Ross and Olga Korostynska

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events…

Abstract

Purpose

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

Design/methodology/approach

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Findings

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

Originality/value

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

Details

Sensor Review, vol. 42 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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