Quality Evaluation Technique Research Articles

Novel Techniques for Quality Evaluation of Fish: A Review

ABSTRACT The unique nutritional and chemical composition of fish makes it highly susceptible to autolytic and microbial decomposition arising concerns about the safety of fish and fish products. Conventional techniques of quality evaluation involve destructive approaches, are laborious, tedious and time-consuming. Several novel techniques based on spectroscopy, imaging, molecular markers and sensors provide an accurate, convenient and reliable approach for quality evaluation of fish and fish products. This review focuses on application of these emerging techniques for the evaluation of quality real-time freshness, safety and authenticity of fish and fish products along with their advantages and limitations, highlighting key findings from recent research works.

Analyzing the Quality of Business English Teaching Using Multimedia Data Mining

Data continually act as a substantial role in business and industry for its daily activities to smoothly functional. The data volume is growing with the passage of time and rising of information technology. Using data mining techniques for quality evaluation and business English teaching is essential in the modern world. These technologies are introduced in the classroom, especially in online classes during the COVID-19 pandemic. To analyze the quality of business English teaching, this paper uses multimedia and data mining technologies. Initially, the multimedia data are collected during classes, and the association rule recommendation algorithm using data mining is applied. Based on collaborative filtering algorithms in association rules, indicators for teaching quality evaluation in colleges and universities are set up. Next, the actual teaching data of a university is used. Taking business English as an example, the algorithm that has been built is tested. The application of the algorithm is tested, and the teaching process of College Business English is evaluated. Finally, the conclusion is drawn that data mining technology can describe the behavior of teaching well and evaluate it, and it has the potential of popularization.

The famous Turkish rose essential oil: Characterization and authenticity monitoring by FTIR, Raman and GC–MS techniques combined with chemometrics

There is a necessity for rapid, robust, easy, accurate and cost-effective methodologies for the quality control of essential oils from medicinal and aromatic plants. Rosa damascena essential oil is a high-value natural product with its unique quality properties and economic importance. This research evaluated the capability of Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy and gas chromatography-mass spectrometry (GC–MS) techniques combined with chemometrics for determination of the authenticity of R. damascena essential oil. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully employed with 100% accuracy for discrimination of authentic R. damascena essential oil samples from fraudulent commercial samples. Consistent results were obtained by FTIR, Raman and GC–MS techniques. Two of twenty commercial samples were determined as authentic R. damascena essential oil samples using the three analytical techniques. Findings showed that FTIR and Raman spectroscopy combined with chemometrics could be used as reliable, robust, rapid, accurate and low-cost analytical techniques for quality evaluation of R. damascena essential oil.

Microwave-assisted infrared thermography: A tool for quality assessment of blast furnace feeds

Infrared thermography is a ubiquitous technique for surface non-contact temperature profile analysis and monitoring. The generated thermograms offer a plethora of applications in non-destructive evaluation (NDE) for detecting inhomogeneities in engineering structures. However, the potential of this technique is still in its infancy for quality assessment of minerals and ores. In steel plants, iron ore and coke are the most important raw materials fed into the blast furnace. The alumina content in iron ore and moisture percentage in coke play a significant role in determining ultimate product quality and blast furnace efficiency. Till date, plant operators have relied on time-consuming and cumbersome chemical analysis methods for quality analysis of ores. The present investigation reveals a microwave-assisted infrared thermography based non-invasive technique for fast and accurate quality evaluation of blast furnace feed material, in terms of alumina content in iron ores and moisture content in coke. The estimation of alumina content is based on the difference in dielectric constants of various iron ore constituents. Moisture analysis in coke involves the utilization of well-known black body radiation. The co-relation factor for the alumina content in iron ore is slightly lower than the moisture content in coke. The basic difference occurs owing to inherent limitation of the technique for two different substances. Such type of novel approach is scanty in open domain literature. This non-invasive, real time, precise and fast technique proved its enormous potential during validation for random plant samples and thus promising to improve feed quality and blast furnace efficiency.

Customers’ Perception of Quality and Price Value of Prestigious Cars: A Survey of Customers’ Purchasing Decision in Nigeria

This study was carried out to find out the extent to which quality perception of luxurious cars influenced customer's perceived price value of a preferred car brand in Rivers- Nigeria. A descriptive survey research method was used in this study. The population of this study was 7,303,900 potential and active buyers of luxurious cars drawn from Rivers State- Nigeria. The sample size of 400 respondents was obtained with the use of Taro Yamene's sample size techniques cited in Ani (2012). The purposive based stratified sampling procedure was employed in the study toenable the researcher select the representative sample elements of the population interest. A questionnaire comprising ten (20) item questions was used to determine the extent to which quality perception of luxurious cars influenced customer's perceived price value of a preferred car brand in Rivers State - Nigeria. A test-retest method of the instrument reliability was used and the co-efficient result of ‘R' being .83, was obtained. The data collected were analyzed using the mean score test and Pearson moment correlation coefficient statistics. Results obtained revealed that ‘There is no significant relationship between the durability, performance, economic and innovative quality of prestigious cars and the customer's perceived price value of the preferred car brand in Rivers State'. It was advisable that the ‘Car producing and distribution firms should formulate, ‘Sustainable Price Policy Decision (SPPD), establish Quality Responsibility Performance Unit (QRPU), build Economic Quality Services Solution Center (EQSS-Center)'; and employ Innovative Quality Evaluation Techniques' to oversee issues concerning customers' quality perception of luxurious cars in Rivers State'. The implication of the finding was that if the producing and distribution firms failed to adopt the above recommendations, it might result in customers' dissatisfaction in the firm's brand; and as such, the success of the business operation in Rivers State will be affected.

A new focus detection criterion in holograms of planktonic organisms

Abstract With the increased use of digital holography to observe biological events in three dimensions the need for autofocusing becomes essential. We introduced a modified image quality evaluation technique as a focus criterion in the reconstruction of digital holograms. The method employed was initially proposed by [Wang et al. (2008)] to evaluate image inpainting quality from three aspects: luminance, definition, and gradient similarity. We considered these criteria for automatic focusing, individually and in combination. Our numerical simulation and experimental results obtained from 96 holograms of planktonic organisms show that the combined approach provided more accurate results compared to the individual metrics. We propose the use of such combined criterion to implement autofocusing algorithms for serial holograms, such as from observations of performances of plankton behavior.

Speed of Recalescence as a Measure of Graphite Nucleation in Spheroidal Graphite Cast Iron Castings

The solidification of spheroidal graphite cast iron (SGI) plays an important role in production of sound quality castings. However, owing to the complex solidification behaviour of SGI, prepouring melt quality evaluation technique such as thermal analysis is used to predict quality of liquid metal by analysing the cooling curve pattern. In this study, an attempt has been made to investigate the role of speed of recalescence (SOR) in nucleation of graphite during solidification of SGI. Cooling curve analysis of liquid samples was carried out at different inoculation degrees. Recalescence and SOR data were obtained from thermal analysis, whereas nodule counts were obtained from microstructural examination of respective sample. It was observed that recalescence and nodule counts (nucleation sites) have a statistically significant inverse linear relationship; with the increase in recalescence, the nodule count was found to get reduced. However, the multiple values of nodule count at same recalescence degree were to be understood, which was clearly justified by SOR studies. After analysing the SOR vs nodule count relation, almost each SOR value corresponds to different nodule counts and the relation was statistically more significant. This infers that for an in-depth nodule count estimation, SOR analysis will be more helpful as compared to ∆Tr; hence, it is envisaged that speed of recalescence is true indicator of graphite nucleation as compared to recalescence in SGI.

Examining Four Experimental Paradigms for EEG-Based Sleep Quality Evaluation with Domain Adaptation.

With the quick development of dry electrode electroencephalography (EEG) acquisition technology, EEG-based sleep quality evaluation attracts more attention for its objective and quantitative merits. However, there hasn't been a standard experimental paradigm. This situation hinders the development of sleep quality evaluation method and technique. In this paper, we experimentally examine the performance of four typical experimental paradigms for EEG-based sleep quality evaluation and develop a new EEG dataset recorded by dry-electrode headset. To eliminate individual variation caused by subjects, we evaluate the four experimental paradigms using domain adaptation (DA) methods. Experimental results demonstrate that a relaxing paradigm is more effective than other attention concentration paradigms and achieves the average accuracy of 76.01%. Domain Adversarial Neural Network outperforms other DA methods and obtains 18.69% improvement on accuracy compared with transfer component analysis.

Quality Assessment of Fruits and Vegetables using Bio-impedance based Expert System

World Health Organization (WHO) recommends a daily intake of at least 400 grams of fruits and vegetables, to prevent diet related chronic diseases and micronutrient deficiencies. It is essential to ensure the quality of foods consumed by the population on a daily basis. Quality of fruits and vegetables is governed by nutritional level, appearance, flavor and climate. Quality assessment methods should be environment friendly and should also benefit both consumers and farmers by enhancing taste and increasing yield, respectively. Conventionally employed quality assessment methods like bio-chemical analysis, imaging etc. are destructive, inefficient and time consuming. Bio-tissues are made up of cells with selectively permeable cell membranes and this makes them equivalent to resistive-capacitive network. Such a network impedes an alternating current (AC) excitation signal applied to it. This bio-impedance (BI) is measured through LCR meters, impedance analysers and off-the shelf chip based boards. In this work we have developed an accurate, smart and non destructive bio-impedance based quality evaluation technique. BI is measured as magnitude and phase for 7 days for each variety of fruit and vegetable followed by exhaustive frequency (5 kHz-200 kHz), ripening and rot analysis. BI magnitude increases as number of days advance i.e. with ripening and the phase undergoes considerable decrease with rotting. The system is made smart by incorporating an expert system. 178 samples of bio-impedance data are used to train the expert system and supervised classification is done through Random Forest classifier. Any fruit or vegetable can be classified as ’Good’ or ’Bad’ immediately and accurately with a maximum accuracy of 98.57%.

Optimization using Mahalanobis-Taguchi System for inductor component

The fourth industrial revolution (IR 4.0) is built upon the digital revolution where technology and people are connected. It does not just present modern techniques for supporting aspects within industry but cover as well the sustainability. Thus, it is very important for electric and electronic (E&E) industry to develop a new technique for a better quality evaluation of product. The aim of this work is to apply Mahalanobis-Taguchi System (MTS) in production environment at E&E industry for a better quality evaluation. MTS is used to classify normal and abnormal observations and optimize various parameters in workstation to produce a better quality product. This work found that positive gain through signal to ratio (SNR) indicated the quality of system still in good condition from February with 0.1244 until December with 0.4432 after insignificant variable have been removed. This work also suggested condition of core should be removed since the variable does not contribute to the system along 2018. Meanwhile, some variables are showed inconsistency of significant to the system such as wire condition, epoxy condition, lead part condition and soldering condition. This concluded that MTS is a practical method for classification and optimization in the industry.

Quality evaluation of mango using non-destructive approaches: A review

Mango (Mangifera indica L.) is one of the most popular and nutritionally rich fruits. It is also acknowledged as the king of fruits in India. Quality attributes of mango fruit depends on its appearances such as size, shape, skin colour, flesh colour, flavour, sweetness, and aroma. Over the recent years, non-destructive techniques have been garnering the interest of researchers as potential technologies that can be used for quality assessment of fruits in a part of postharvest processing. The present patterns of non-destructive techniques are more efficient, inexpensive, yield faster and accurate results. This mini review paper focuses on some of the previous applications of non-destructive techniques in quality evaluation of mango, focusing specifically on the non-destructive technique based on quality parameters. The future trend of using non-destructive techniques for quality evaluation is also discussed in this review paper.

Identification of the characteristic components in walnut and anti-inflammatory effect of glansreginin A as an indicator for quality evaluation

Walnut is a nutritious food material, but only a few studies have been conducted on the mechanisms of its functions and the technique for quality evaluation. Therefore, we analyzed the components in aqueous methanol extract of walnut, and characterized 30 components, including three new compounds, glansreginin C, ellagic acid 4-O-(3'-O-galloyl)-β-D-xyloside, and platycaryanin A methyl ester. We analyzed the extracts of other nuts using HPLC and clarified that a characteristic peak corresponding to glansreginin A was mainly observed in walnut. These results suggested that glansreginin A might be an indicator component of the quality of walnut. We then examined whether glansreginin A has neuroprotective effect, using lipopolysaccharide (LPS)-induced inflammatory model mice. The results revealed that oral administration of glansreginin A prevented LPS-induced abnormal behavior and LPS-induced hyper-activation of microglia in the hippocampus. These results suggested that glansreginin A has the ability to exert neuroprotective effect via anti-inflammation in the brain.

A Novel Approach of Image Steganography for Secure Communication Based on LSB Substitution Technique

Steganography aims to hide the messages from unauthorized persons for various purposes, e.g., military correspondence, financial transaction data. Securing the data during transmission is of utmost importance these days. The confidentiality, integrity, and availability of the data are at risk because of the emerging technologies and complexity in software applications, and therefore, there is a need to secure such systems and data. There are various methodologies to deal with security issues when utilizing an open system like the Internet. This research proposes a new technique in steganography within RGB shading space to achieve enhanced security compared with existing systems. We evaluate our approach with the help of diverse image quality evaluation techniques including MSE (Mean Square Error), RMSE (Root Mean Square Error), PSNR (Peak Signal-to-Noise Ratio), MAE (Mean Absolute Error), NCC (Normalized Cross-Correlation) and SSIM (Structural Similarity Index). Our experimental results demonstrate improved strength, intangibility, and security when contrasted with existing techniques and vindicate the effectiveness of this exploration work. The proposed approach achieved a 3.6701% average higher score for PSNR Correlation than the next best existing approach. Moreover, in PSNR with a variable amount of cipher embedded in the same images of the same dimensions, the proposed approach attained a 5.22% better score. Embedding the same size of cipher in images of different size resulted a 3.56% better score.

Application of water quality index, synthetic pollution index, and geospatial tools for the assessment of drinking water quality in the Indus Delta, Pakistan.

Seawater intrusion into the Indus Delta, Pakistan, has spoiled groundwater aquifers in the area. In the delta, the groundwater is widely used by residents for drinking. Considering the gravity of the problem, and concerns expressed by affected communities, the present study was conducted to assess and map the quality of groundwater, based on the physico-chemical properties of 180 samples, using two standard numerical indices, geospatial and statistical techniques. The analysis of water samples revealed that several parameters exceeded the drinking water quality guidelines suggested by the World Health Organization (WHO). The water quality index (WQI) identified that about 1.7%, 1.1%, 27.8%, 42.8%, and 26.6% of the water samples were excellent, good, poor, very poor, and unsuitable for drinking purposes, respectively. However, the synthetic pollution index (SPI) ranked the quality of 2.8%, 2.2%, 23.9%, 41.7%, and 29.4% as suitable, slightly polluted, moderately polluted, highly polluted, and unsuitable, respectively. Though the numerical model's input is different, the proportionate ranking revealed a fair correlation (R2 = 0.75) between the outcomes of both indices. The results of the numerical indices and the interpolated geographical information system (GIS) mapping revealed that the quality of groundwater in most of the delta does not meet WHO guidelines for potable water. Hence, it is recommended that the groundwater of the delta should be properly treated before its use for domestic purposes. The study highlights the significance of using numerical indices and geospatial techniques for water quality evaluation in the Indus Delta and similar deltaic regions throughout the world.

Quality evaluation of sweet potatoes (Ipomoea batatas L.) of different varieties using laser light backscattering imaging technique

Non-destructive quality monitoring for horticultural products by means of optical imaging methods have gained wide interest in recent years. This study sought to investigate the potential use of laser-light backscattering imaging technique (LLBI) for non-invasive quality evaluation of sweet potatoes of different varieties after being harvested and stored at ambient temperature. A total of 360 newly harvested samples from the three varieties of locally-produced sweet potatoes in Malaysia i.e. Keledek Anggun 3, Keledek Jingga, and Keledek Kuning were purchased and stored in an ambient room temperature (28–30 °C) for a period of twenty one (21) days. After each 7-day storage, respective samples were taken out and backscattering images (BSI) were acquired using a charge-coupled device (CCD) camera attached with laser diodes emitting lights at 658 nm and 780 nm wavelengths, respectively. Quality attributes such as moisture content (MC), soluble solid content (SSC), textural properties (hardness, fracturability, adhesiveness), and flesh color properties (L*, a*, b*) as reference quality parameters (QP) were measured right after BSI acquisitions. Multiple Linear Regression (MLR) was applied to correlate and predict the relationship between the extracted backscattering parameters (BP) and the QP. Results revealed that there were significant changes (P < 0.05) in the BP during storage for both wavelengths applied. Among all the QP, the textural properties gave the highest correlation (r> 0.70) with BP, particularly the Keledek Kuning variety. The 658 nm wavelength showed better prediction results than 780 nm with r > 0.85 in measuring the textural properties. Thus, the study demonstrated the feasibility of LLBI as a useful non-destructive technique to evaluate the quality of sweet potatoes and can be further utilized for online optical quality grading.

The utility of multivariate outlier detection techniques for data quality evaluation in large studies: an application within the ONDRI project

BackgroundLarge and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow.MethodsWe illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods – Minimum Covariance Determinant (MCD) and Candès’ Robust Principal Component Analysis (RPCA) – and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification.ResultsOf 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection.ConclusionsManual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex.

Groundwater quality evaluation using the water quality index (WQI), the synthetic pollution index (SPI), and geospatial tools: a case study of Sujawal district, Pakistan

Under changing climate scenario, groundwater aquifers in the coastal areas of Pakistan are under serious threat. Looking at the gravity of the problem, and concerns of the civil society, this study was conducted to evaluate and map the quality of groundwater in the Sujawal district, a coastal area of Pakistan based on the physicochemical analysis of 94 samples using two standard numerical models and geospatial techniques. The WQI model revealed that 2.13%, 6.38%, 55.32%, 22.34%, and 13.83% of water samples were excellent, good, poor, very poor, and unsuitable for drinking purposes. Also, the SPI model identified that 32%, 13.83%, 20.12%, 18.1%, and 15.95% of samples were slightly polluted, moderately polluted, highly polluted, suitable, and unsuitable. Though the model's input is different, the proportionate of ranking revealed a significant correlation (R2=0.78) between the outcomes of both models. Overall, the study revealed that groundwater in most of the areas does not meet WHO guidelines. The prevalence of water-related diseases in the area suggests that groundwater is contaminated and using that water is of high risk for human health. The study highlights the significance of using numerical models and geospatial techniques for water quality evaluation in the coastal areas of the world.

Development of a Low-Cost Multi-Waveband LED Illumination Imaging Technique for Rapid Evaluation of Fresh Meat Quality

Determining the quality of meat has always been essential for the food industry because consumers prefer superior quality meat. Therefore, the food industry requires the development of a rapid and non-destructive method for meat-quality determination. Over the past few years, a number of techniques have been presented for monitoring meat–chemical attributes. However, most previous techniques are quite expensive, destructive, and require complex hardware to operate. Thus, in this work, we demonstrate a low-cost sensing technique (eliminating the expensive equipment and complicated design) for meat–chemical quality detection. The newly developed system was integrated with a low-cost monochrome camera and ordinary light-emitting diode (LED) light sources, with fifteen different wavebands ranging from 458 to 950 nm. The monochrome camera captures images of the meat sample across a spectral range from 458 to 950 nm using a single snapshot method. The chemical values (e.g., moisture, fat, and protein) were also determined using conventional methods. The collected images were combined to produce a multispectral data cube and to extract spectral data. Partial least squares (PLS) and support vector regression (SVR) modeling were used on the extracted spectra and chemical values. The developed models for meat samples displayed accurate chemical-component prediction ( R 2 &gt; 0.80). Our model, based on a monochrome sensor using only fifteen wavebands, provided reasonable results compared with the previously developed expensive spectroscopic techniques. Therefore, this complementary metal-oxide semiconductor (CMOS) based multispectral sensing technique may have the potential to detect meat quality, thereby facilitating a simple, fast, and cost-effective method applicable to small-scale meat-processing industries.

Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of R p = 0.966 and R M S E P = 5.105 % , which is better than those of successive projections algorithm ( R p = 0.932 and R M S E P = 7.329 % ), and the uninformative viable elimination algorithm ( R p = 0.928 and R M S E P = 7.416 % ). The results obtained by BFWA were more stable, with a smaller standard deviation of R p and R M S E P than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.

In‐Process Quality Inspection of Rolling Element Bearings Based on the Measurement of Microelastic Deformation of Outer Ring

Quality inspection is the necessary procedure before bearings leaving manufacturing factories. A testing machine with low shaft speed and light radial load condition is generally used to test the dynamic quality of bearings, which avoids creating any potential damages to testing bearings. However, the signal of defective bearings is easily polluted by very weak noise using the traditional vibration‐based measurement method due to the low shaft speed and light radial load condition specified for nondestructive inspection, which needs complicated and time‐consuming calculation and is not suitable for online inspection. Thus, there are problems about special operating conditions and weak fault severity in quality inspection of bearings, which is quite different from the fault diagnosis of bearings. In this paper, a novel dynamic quality evaluation technique is proposed based on the measurement of Hertz deformations. The measurement system is mainly composed of an eddy current sensor, sensor fixture, and data acquisition platform with less transfer path than the vibration‐based measurement system. The sensor fixture is optimized through numerical simulations to obtain signals with a high signal‐to‐noise ratio. Accurate evaluation of dynamic quality can be implemented reliably with simple signal processing. The proposed method can be used with a rotating speed of 100 rev/min and test load of 100 N, which is remarkably lower than the traditional quality inspection machineries with a rotating speed of around 1000 rev/min and the test load of 400 N. Both simulation and experiment studies have verified the proposed method.