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Journal articles on the topic 'Sensory evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 24 August 2024

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1

Kim, Yong-Sung, and Yong-Suk Kim. "Biometrics Analysis and Evaluation on KoreanMakgeolliUsing Brainwaves and Taste Biological Sensor System." BioMed Research International 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/918631.

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There are several methods available in measuring food taste. The sensory evaluation, for instance, is a typical method for panels to test of taste and recognize smell with their nose by measuring the degree of taste characteristic, intensity, and pleasure. There are many issues entailed in the traditional sensory evaluation method such as forming a panel and evaluation cost; moreover, it is only localized in particular areas. Accordingly, this paper aimed to select food in one particular area, and compare and review the content between sensory evaluations using a taste biological sensor, as well as presenting an analysis of brainwaves using EEG and finally a proposal of a new method for sensory evaluation. In this paper, the researchers have conducted a sensory evaluation whereas a maximum of nine points were accumulated by purchasing eight types of rice wine. These eight types ofMakgeolliwere generalized by generating multidimensional data with the use of TS-5000z, thus learning mapping points and scaling them. The contribution of this paper, therefore, is to overcome the disadvantages of the sensory evaluation with the usage of the suggested taste biological sensor system.

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2

Han, Xiao, Hongwei Yan, Baojian Liu, and Wen Liu. "Emotional Feeling Evaluation Model in Underwater Environment Based on Wearable Sensor." Mathematical Problems in Engineering 2022 (March 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/2104465.

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Underwater sensor network technologies, as well as devices, are developing rapidly, and underwater IoT devices have been widely used in energy surveys, environmental indicator detection, military surveillance, and disaster event monitoring. The transmission of massive amounts of underwater data to the cloud for processing and analysis has become the dominant processing paradigm, and cloud computing has become a dominant computing paradigm. The preparation strategy of elastomer-coated hydrogel optical fibers for stable optical sensing proposed in this work opens up a new method and approach for developing low-cost and highly sensitive water flow sensors while analyzing the design of wearable smart devices to assess underwater environmental emotion perception evaluation schemes. In this paper, we propose a sensory data acquisition technique for event coverage detection of underwater environmental emotions, observing that an event may correspond to deviations from the normal sensory range of sensory data from multiple adjacent sensor nodes. Distributed edge computing is introduced to assume part of the cloud computing pressure, and an edge prediction-based data acquisition and sensing scheme for underwater sensor networks is proposed to realize the conversion of the acoustic communication transmission part of underwater data into data prediction transmission, thus reducing the energy consumption caused by acoustic communication. The model established in this paper effectively reduces sensor energy consumption while ensuring accurate data transmission and can respond to the underlying demand promptly, which is significantly better than the already existing schemes.

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3

Fujioka, Kouki. "Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study." Sensors 21, no. 24 (December 15, 2021): 8368. http://dx.doi.org/10.3390/s21248368.

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Cheese aroma is known to affect consumer preference. One of the methods to measure cheese aroma is the use of an electronic nose (e-nose), which has been used to classify cheese types, production areas, and cheese ages. However, few studies have directly compared the aroma intensity scores derived from sensory evaluations with the values of metal oxide semiconductor sensors that can easily measure the aroma intensity. This pilot study aimed to investigate the relationship between sensory evaluation scores and e-nose values with respect to cheese aroma. Five types of processed cheese (two types of normal processed cheese, one type containing aged cheese, and two types containing blue cheese), and one type of natural cheese were used as samples. The sensor values obtained using the electronic nose, which measured sample aroma non-destructively, and five sensory evaluation scores related to aroma (aroma intensity before intake, during mastication, and after swallowing; taste intensity during mastication; and remaining flavor after swallowing (lasting flavor)) determined by six panelists, were compared. The e-nose values of many of the tested cheese types were significantly different, whereas the sensory scores of the one or two types of processed cheese containing blue cheese and those of the natural cheese were significantly different. Significant correlations were observed between the means of e-nose values and the medians of aroma intensity scores derived from the sensory evaluation testing before intake, during mastication, and after swallowing. In particular, the aroma intensity score during mastication was found to have a linear relationship with the e-nose values (Pearson’s R = 0.983). In conclusion, the e-nose values correlated with the sensory scores with respect to cheese aroma intensity and could be helpful in predicting them.

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Nuyen Ti Tu, Hong, H. Valentová, J. Velíšek, J. Čepička, J. Pokorný, and F. Pudil. "Evaluation of beer quality by sensory analysis." Czech Journal of Food Sciences 18, No. 4 (January 1, 2000): 137–42. http://dx.doi.org/10.17221/8332-cjfs.

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The flavour of pasteurised and stored beer was evaluated by a panel of 12 assessors. The sensory analysis consisted of combined hedonic evaluation and sensory profiling using unstructured graphical scales and 31 descriptors. The obtained results were evaluated by multivariate statistical methods (principal components analysis and linear discrimination analysis) which allow visualisation of relations between individual descriptors and samples and enable classification of samples to groups according to pasteurisation intensity and storage time.

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5

Muir, D. D. "Sensory Evaluation Techniques." International Journal of Dairy Technology 60, no. 4 (November 2007): 305. http://dx.doi.org/10.1111/j.1471-0307.2007.00330.x.

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6

Najeh Nemah, Mohammed, Cheng Yee Low, Pauline Ong, and Noor Ayuni Che Zakaria. "Development and Evaluation of a Spot Sensor Glove for the Tactile Prosthetic Hand." International Journal of Engineering & Technology 7, no. 4.26 (November 30, 2018): 63–69. http://dx.doi.org/10.14419/ijet.v7i4.26.22139.

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A tactile glove sensory system of the haptic feedback stimulation system for the upper limb prostheses was developed in this work to enable the patients of the upper limb amputation to recover the sense of touch and slippage. The system features six of a spot piezoresistive force sensors of type Quantum tunnelling compositesÂ (QTC) with 10 mm diameter, in order to measure the contact pressure between the hand and the objects. Five sensors were distributed on each fingertip and an extra sensor was mounted on the handâ€™s palm to cover all the critical point and increase the probability of detecting the contact pressure. The tactile glove was fabricated from the plastic glove equipping with a rigid foundation under each pressure sensor. The computer system was programmed to select the instant greatest signal from the six sensorsâ€™ signals; in order to create a critical output signal that can be provided to the haptic feedback stimulator. The touch and the slippage detection experimental tests have been done to examine the functionality of the tactile sensory glove for detecting the touch, start of touch, end of touch grasp, and slippage. The testing results showed that the amputees were able to recover the sensation of the contact pressure using a spot sensor tactile glove developed in this work.Â Â

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7

Zhang, Dong Ling, Xiao Mei Xu, Yang Liu, and Peng Fei Mu. "Research on the Sensory Quality Evaluation of Agricultural Food Based on Linguistic Information." Advanced Materials Research 424-425 (January 2012): 560–63. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.560.

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It presents a proposed method for the development of accredited sensory evaluation methods for agricultural food, and shows the application of the linguistic 2-tuple representation model and its advantages. It involved several steps: gathering of sensory information from experts; computing a collective evaluation for each feature; computing collective evaluations for each object; sensory evaluation and quality certification with global evaluation result.

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8

Bílek, Jiří, Ondřej Bílek, Petr Maršolek, and Pavel Buček. "Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation." Environments 8, no. 11 (October 27, 2021): 114. http://dx.doi.org/10.3390/environments8110114.

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Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

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9

Zhang, Dong Ling, Xiu Zhi Zhu, Lian Qun Xing, and Xue Yang. "A Determination of the Engineering Characteristics in QFD Based on Sensory Quality Evaluation." Advanced Materials Research 711 (June 2013): 713–18. http://dx.doi.org/10.4028/www.scientific.net/amr.711.713.

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Quality function deployment is an important product development method, dedicated to translating client requirements into activities to develop products and services. The determination of customer need and engineering characteristics include sensory quality evaluation. This paper presents a proposed method for the sensory evaluation methods in QFD planning system, and shows the application of the linguistic information processing model and its advantages. It involved several steps: gathering of sensory information from experts; computing a collective evaluation for each feature; computing collective evaluations for each object; dynamic aggregation; sensory evaluation and quality certification with global evaluation result.

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10

Duman, Elifcan, Can Altınelataman, and Adnan Tokaç. "The role and importance of photonic sensors in seafood safety applications." Ege Journal of Fisheries and Aquatic Sciences 37, no. 3 (September 15, 2020): 319–24. http://dx.doi.org/10.12714/egejfas.37.3.16.

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Microbiological, chemical, sensory analyses known as traditional methods are used for determination of fish quality including many concepts such as microbiological quality, sensory quality, nutritional properties, product specific properties, freshness, species-specific physical properties. With the developing technology; these time-consuming and error-free analyzes have been replaced by sensor technology, which is very suitable for quality measurements in order to achieve the expected speed and high standard and to be open to improvement. In this study, optical sensors and their applications are emphasized and a general evaluation is made about the usability of seafood processing technology in terms of food safety.

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11

Zhang, Dong Ling. "A Dynamic Sensory Quality Evaluation Model Based on Panel Data and Linguistic Information Processing." Applied Mechanics and Materials 44-47 (December 2010): 3741–45. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3741.

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The sensory quality evaluation processes are widely used for many industrial products, which is evaluated according to the knowledge acquired via human senses (such as sight, taste, touch, smell). Because the evaluation information acquired by our senses is involving uncertainty, vagueness and imprecision, and the sensory quality information of products always has the panel data characteristic, so the sensory quality evaluation and certification is a multi-feature group decision-making problem based on panel linguistic information processing. This paper presents a proposed method for the development of accredited sensory evaluation methods, and shows the application of the linguistic 2-tuple representation model and its advantages. It involved several steps: gathering of sensory information from experts; computing a collective evaluation for each feature; computing collective evaluations for each object; dynamic aggregation; sensory evaluation and quality certification with global evaluation result.

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12

Santo, Edson Francisco do Espírito, Julia Eudoxia Decaris Rolim, Ytaiara Lima Pereira, Anne Caroline Dantas Tavares de Oliveira, and Washington Clementino Amorim Júnior. "Tambaqui's sausage: sensory evaluation." South Florida Journal of Development 1, no. 4 (2020): 186–93. http://dx.doi.org/10.46932/sfjdv1n4-001.

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13

Meshram, B. D. "Sensory evaluation of commercialDahi." Asian Journal of Dairy and Food Research 33, no. 3 (2014): 175. http://dx.doi.org/10.5958/0976-0563.2014.00597.1.

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14

Ronsheim, John, Edward Roessler, Maynard A. Amerine, D. Muscatine, M. A. Amerine, and B. Thompson. "Wines: Their Sensory Evaluation." Antioch Review 43, no. 2 (1985): 248. http://dx.doi.org/10.2307/4611476.

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15

ISHIWAKI, Tomohiro. "Sensory Evaluation of Coffee." Journal of Japan Association on Odor Environment 38, no. 5 (2007): 368–74. http://dx.doi.org/10.2171/jao.38.368.

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16

SAITO, Sachiko. "Sensory evaluation of odeur." JOURNAL OF THE BREWING SOCIETY OF JAPAN 84, no. 5 (1989): 280–86. http://dx.doi.org/10.6013/jbrewsocjapan1988.84.280.

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17

ISHIBASHI, Tokuo. "Sensory Evaluation of Beer." JOURNAL OF THE BREWING SOCIETY OF JAPAN 84, no. 6 (1989): 360–66. http://dx.doi.org/10.6013/jbrewsocjapan1988.84.360.

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18

YOSHIKAWA, Seiji. "Sensory Evaluation of Food." JOURNAL OF THE BREWING SOCIETY OF JAPAN 84, no. 10 (1989): 664–68. http://dx.doi.org/10.6013/jbrewsocjapan1988.84.664.

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19

FURUSHO, Shigeki. "Sensory Evaluation of Beer." JOURNAL OF THE BREWING SOCIETY OF JAPAN 97, no. 3 (2002): 178–87. http://dx.doi.org/10.6013/jbrewsocjapan1988.97.178.

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20

Kappel, F., E. Hogue, and B. Fisher-Fleming. "SENSORY EVALUATION OF PEARS." Acta Horticulturae, no. 367 (June 1994): 439. http://dx.doi.org/10.17660/actahortic.1994.367.71.

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21

NORO, Kageyu. "Sensory evaluation and ergonomics." Japanese journal of ergonomics 22, no. 3 (1986): 115–19. http://dx.doi.org/10.5100/jje.22.115.

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22

NOGUCHI, Yosuke. "Sensory evaluation and industry." Japanese journal of ergonomics 22, no. 3 (1986): 127–36. http://dx.doi.org/10.5100/jje.22.127.

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23

TAKASHIMA, Yasuhiro. "Aroma and Sensory Evaluation." food preservation science 24, no. 3 (1998): 201–9. http://dx.doi.org/10.5891/jafps.24.201.

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24

TAKASHIMA, Yasuhiro. "Odor and Sensory Evaluation." Japanese Journal of Sensory Evaluation 1, no. 1 (1997): 10–17. http://dx.doi.org/10.9763/jjsse.1.10.

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25

Cione, Joseph A., and John Cozzarelli. "Neurometer Diagnostic Sensory Evaluation." Foot & Ankle Specialist 2, no. 2 (February 12, 2009): 95–97. http://dx.doi.org/10.1177/1938640009331632.

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26

Wolins, Leroy, Maynard A. Amerine, and Edward B. Roessler. "Wines: Their Sensory Evaluation." Journal of the American Statistical Association 80, no. 390 (June 1985): 484. http://dx.doi.org/10.2307/2287929.

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27

SAKATA, Yoshitaro. "Sensory Evaluation of Color." Journal of The Surface Finishing Society of Japan 74, no. 11 (November 1, 2023): 575–78. http://dx.doi.org/10.4139/sfj.74.575.

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28

Nakamoto, Hiroyuki, Ryoga Nishimura, and Futoshi Kobayashi. "Effects of sensory combination on crispness and prediction of sensory evaluation value by Gaussian process regression." PLOS ONE 19, no. 2 (February 8, 2024): e0297620. http://dx.doi.org/10.1371/journal.pone.0297620.

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Crispness contributes to the pleasantness and enjoyment of eating foods and is popular with people of wide ages in many countries. Hence, a quantitative evaluation method for crispness is required for food companies developing new food products. In this study, the effects of different sensory combinations on crispness were investigated through sensory evaluation, and a Gaussian process regression model was used to predict the evaluation values of crispness. First, four crispness descriptors in Japanese were selected, and sensory evaluations were conducted with ten participants using commercially available snack foods under three different sensory combinations of force, vibration, and sound to confirm the effects of the three senses. An instrumental system also measured force, vibration, and sound for snack foods under the same conditions. The Gaussian process regression model determined the relationship between the sensory and measurement data and predicted the sensory evaluation values from the measurement data. Cross-validation verified that the Gaussian process regression model accurately predicted the food texture evaluation values from the measurement data even in conditions with different sensory components.

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29

Henderson, Jeffrey, Joan Condell, James Connolly, Daniel Kelly, and Kevin Curran. "Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion." Sensors 21, no. 5 (February 24, 2021): 1555. http://dx.doi.org/10.3390/s21051555.

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Capturing hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.

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Vilela, Alice, Eunice Bacelar, Teresa Pinto, Rosário Anjos, Elisete Correia, Berta Gonçalves, and Fernanda Cosme. "Beverage and Food Fragrance Biotechnology, Novel Applications, Sensory and Sensor Techniques: An Overview." Foods 8, no. 12 (December 5, 2019): 643. http://dx.doi.org/10.3390/foods8120643.

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Flavours and fragrances are especially important for the beverage and food industries. Biosynthesis or extraction are the two main ways to obtain these important compounds that have many different chemical structures. Consequently, the search for new compounds is challenging for academic and industrial investigation. This overview aims to present the current state of art of beverage fragrance biotechnology, including recent advances in sensory and sensor methodologies and statistical techniques for data analysis. An overview of all the recent findings in beverage and food fragrance biotechnology, including those obtained from natural sources by extraction processes (natural plants as an important source of flavours) or using enzymatic precursor (hydrolytic enzymes), and those obtained by de novo synthesis (microorganisms’ respiration/fermentation of simple substrates such as glucose and sucrose), are reviewed. Recent advances have been made in what concerns “beverage fragrances construction” as also in their application products. Moreover, novel sensory and sensor methodologies, primarily used for fragrances quality evaluation, have been developed, as have statistical techniques for sensory and sensors data treatments, allowing a rapid and objective analysis.

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31

Moreno, Raymund B., and Nick John B. Solar. "Development and Sensory Evaluation of Peanut-Pine Butterscotch Bar." Indian Journal of Science and Technology 15, no. 3 (January 17, 2022): 107–14. http://dx.doi.org/10.17485/ijst/v15i3.2089.

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Dr. N.Sabitha, Dr N. Sabitha, and Yamunadevi Puraikalan. "Development and Sensory Evaluation of Sunflower Seed Fortified Cookies." International Journal of Scientific Research 3, no. 2 (June 1, 2012): 214–15. http://dx.doi.org/10.15373/22778179/feb2014/70.

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33

Nguyen Thi Thu, Huong, H. Valentová, J. Velíšek, J. Čepička, J. Pokorný, and F. Pudil. "Evaluation of beer by sensory and gas chromatography analysis." Czech Journal of Food Sciences 18, No. 6 (January 1, 2000): 245–49. http://dx.doi.org/10.17221/8349-cjfs.

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Albert, Anisha, and Yamunadevi Puraikalan. "Development and Sensory Evaluation of Cookies Using Grape Seed." Global Journal For Research Analysis 3, no. 8 (June 15, 2012): 115–16. http://dx.doi.org/10.15373/22778160/august2014/37.

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35

Gao, Li-Bing, Uchechukwu Edna Obianwuna, Hai-Jun Zhang, Kai Qiu, Shu-Geng Wu, Guang-Hai Qi, and Jing Wang. "A Comparison between the Egg Yolk Flavor of Indigenous 2 Breeds and Commercial Laying Hens Based on Sensory Evaluation, Artificial Sensors, and GC-MS." Foods 11, no. 24 (December 13, 2022): 4027. http://dx.doi.org/10.3390/foods11244027.

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The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (n = 135), Xueyu White (n = 135), and Xinyang Blue (n = 135)) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milky flavor, moisture, and compactness scores (p < 0.05) of egg yolk from Xueyu White and Xinyang Blue were significantly higher than that of Hy-Line Brown. The aroma preference scores of Xinyang Blue (p < 0.05) were significantly higher, compared to Hy-Line Brown and Xueyu White. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher foe egg yolks of Hy-Line Brown (p < 0.05), compared to that of Xueyu White and Xinyang Blue. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of Xueyu White (p < 0.05), compared to that of Hy-Line Brown and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (p < 0.05) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.

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Ito, Fumiya, and Kenjiro Takemura. "A Model for Estimating Tactile Sensation by Machine Learning Based on Vibration Information Obtained while Touching an Object." Sensors 21, no. 23 (November 23, 2021): 7772. http://dx.doi.org/10.3390/s21237772.

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The tactile sensation is an important indicator of the added value of a product, and it is thus important to be able to evaluate this sensation quantitatively. Sensory evaluation is generally used to quantitatively evaluate the tactile sensation of an object. However, statistical evaluation of the tactile sensation requires many participants and is, thus, time-consuming and costly. Therefore, tactile sensing technology, as opposed to sensory evaluation, is attracting attention. In establishing tactile sensing technology, it is necessary to estimate the tactile sensation of an object from information obtained by a tactile sensor. In this research, we developed a tactile sensor made of two-layer silicone rubber with two strain gauges in each layer and obtained vibration information as the sensor traced an object. We then extracted features from the vibration information using deep autoencoders, following the nature of feature extraction by neural firing due to vibrations perceived within human fingers. We also conducted sensory evaluation to obtain tactile scores for different words from participants. We finally developed a tactile sensation estimation model for each of the seven samples and evaluated the accuracy of estimating the tactile sensation of unknown samples. We demonstrated that the developed model can properly estimate the tactile sensation for at least four of the seven samples.

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37

Yamagata, Yoshie, and Yoshiaki Sugawara. "Sensory Evaluation Spectrum Method as a Descriptive Sensory Analysis." Psychology 05, no. 14 (2014): 1591–60. http://dx.doi.org/10.4236/psych.2014.514170.

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38

Tadesse, Melkie Getnet, R. Harpa, Y. Chen, L. Wang, V. Nierstrasz, and C. Loghin. "Assessing the comfort of functional fabrics for smart clothing using subjective evaluation." Journal of Industrial Textiles 48, no. 8 (March 15, 2018): 1310–26. http://dx.doi.org/10.1177/1528083718764906.

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Sensory investigations of the functional textiles could be an alternative for the quality inspection and control of the products. The purpose of this research is to use subjective evaluation technique for assessing the tactile comfort of some functional textile fabrics based on AATCC Evaluation procedure 5-2011. Blind subjective evaluations and visual subjective evaluations were performed for sensory investigation. Ten fabric-skin-contact and comfort-related sensory properties were used to evaluate the handle of the functional textile fabrics. The reliability of the sensorial data obtained by subjective tests was evaluated using statistical data analysis techniques. A minimum and maximum consensus distances recorded were 0.58 and 1.61, respectively, using a descriptive sensory panel analysis and proves the consistency and similar sensorial perception between panelists. The Pearson correlation coefficient between panelists was up to 96% and hence a strong agreement between the panelist’s judgment. The results allowed to consider the subjective evaluation using a panel of experts could be validated in the case of functional fabrics. For functional textiles, additional visual subjective evaluation should be considered to have a similar human perception in addition to blind subjective evaluation.

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Fatima, Rimsha, Muhammad Hassan Khan, Muhammad Adeel Nisar, Rafał Doniec, Muhammad Shahid Farid, and Marcin Grzegorzek. "A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data." Sensors 24, no. 1 (December 22, 2023): 75. http://dx.doi.org/10.3390/s24010075.

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This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

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Fregonezi-Nery, Marlene Maria, Sandra Helena Prudencio-Ferreira, Érika Maria Rosa Kedor, Marcela Maria Baracat, and Francis Fregonezi Brinholi. "Sensory evaluation of albendazole suspensions." Brazilian Archives of Biology and Technology 45, no. 4 (December 2002): 457–63. http://dx.doi.org/10.1590/s1516-89132002000600009.

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Sensory analysis was used in the albendazole suspension stability study. Three formulations were prepared and stored for 1, 3, and 6 months at 4 ±1, 26 ±1, 37, 50 and 65 ºC. Samples were evaluated through the difference from control sensory test using 24 trained judges in individual cabins. Although albendazole content was not altered in the conditions studied, sensory test showed differences between control and stored samples, except for one of the formulations stored under refrigeration for one month. These results have shown that the sensory evaluation is an important tool for quality control of pharmaceutical preparations, in association with chemical, physicochemical and microbiological tests.

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Juyal, Dhruv. "Sensory evaluation of kinnow powder." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 11, no. 1 (April 15, 2018): 90–94. http://dx.doi.org/10.15740/has/ijae/11.1/90-94.

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Masuyama, Eitarow. "Kansei and Sensory Evaluation Technique." Japanese journal of ergonomics 29, Supplement (1993): 98–99. http://dx.doi.org/10.5100/jje.29.supplement_98.

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JINGU, Hideo. "Sensory evaluation of computer software." Japanese journal of ergonomics 30, Supplement (1994): 32–33. http://dx.doi.org/10.5100/jje.30.supplement_32.

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Fernandes, S. A. A., de L. S. Ramos, E. M. Ramos, D. V. S. Veras, F. R. Pinheiro, Lage Neto, L. A. Requião, T. S. Amui, and J. C. S. Carneiro. "Sensory evaluation of buffalo butter." Italian Journal of Animal Science 6, sup2 (January 2007): 1140–42. http://dx.doi.org/10.4081/ijas.2007.s2.1140.

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D'Antuono, L. F., S. Elementi, and R. Neri. "Sensory evaluation of carrot germplasm." Acta Horticulturae, no. 1153 (March 2017): 117–24. http://dx.doi.org/10.17660/actahortic.2017.1153.17.

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GOODALL, ELIZABETH, JOHN CORBETT, GLYNIS MURPHY, and MARIA CALLIAS. "Sensory reinforcement table-an evaluation*." Journal of the British Institute of Mental Handicap (APEX) 10, no. 2 (August 26, 2009): 52–55. http://dx.doi.org/10.1111/j.1468-3156.1982.tb00030.x.

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KENGNI, E., Z. TCHOUNDJEU, F. M. TCHOUANGUEP, and C. M. F. MBOFUNG. "SENSORY EVALUATION OFDACRYODES EDULISFRUIT TYPES." Forests, Trees and Livelihoods 11, no. 1 (January 2001): 57–66. http://dx.doi.org/10.1080/14728028.2001.9752371.

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McBRIDE, R. L. "Range bias in sensory evaluation." International Journal of Food Science & Technology 17, no. 3 (June 28, 2007): 405–10. http://dx.doi.org/10.1111/j.1365-2621.1982.tb00195.x.

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Malek, D. M., J. H. Munroe, D. J. Schmitt, and B. Korth. "Statistical Evaluation of Sensory Judges." Journal of the American Society of Brewing Chemists 44, no. 1 (January 1986): 23–27. http://dx.doi.org/10.1094/asbcj-44-0023.

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HORI, Mitsuyo, Keiko HORIGUTI, and Shigeru SAWAYAMA. "Sensory evaluation of commercial yogurt." Japanese Journal of Sensory Evaluation 14, no. 1-2 (2010): 40–45. http://dx.doi.org/10.9763/jjsse.14.40.

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