Quality Assessment Of Products Research Articles

Combinative effect of pulsed-light irradiation and solid-state fermentation on ginkgolic acids, ginkgols, ginkgolides, bilobalide, flavonoids, product quality and sensory assessment of Ginkgo biloba dark tea

Pulsed light (PL) is a prospective non-thermal technology that can improve the degradation of ginkgolic acid (GA) and retain the main bioactive compounds in Ginkgo biloba leaves (GBL). However, only using PL hasn't yet achieved the ideal effect of reducing GA. Fermentation of GBL to make ginkgo dark tea (GDT) could decrease GA. Because different microbial strains are used for fermentation, their metabolites and product quality might differ. However, there is no research on the combinative effect of PL irradiation fixation and microbial strain fermentation on main bioactive compounds and sensory assessment of GDT. In this research, first, Bacillus subtilis and Saccharomyces cerevisiae were selected as fermentation strains that can reduce GA from the five microbial strains. Next, the fresh GBL was irradiated by PL for 200 s (fluences of 0.52 J/cm2), followed by B. subtilis, S. cerevisiae, or natural fermentation to make GDT. The results showed that compared with the control (unirradiated and unfermented GBL) and the only PL irradiated GBL, the GA in GDT using PL + B. subtilis fermentation was the lowest, decreasing by 29.74%; PL + natural fermentation reduced by 24.53%. The total flavonoid content increased by 14.64% in GDT using PL + B. subtilis fermentation, whose phenolic and antioxidant levels also increased significantly. Sensory evaluation showed that the color, aroma, and taste of the tea infusion of PL + B. subtilis fermentation had the highest scores. In conclusion, the combined PL irradiation and solid-state fermentation using B. subtilis can effectively reduce GA and increase the main bioactive compounds, thus providing a new technological approach for GDT with lower GA.

Quality Assessment of Skincare Products with Simple Additive Weighting Approach to Detect Illegal Skincare Products

Quality Assessment of Skincare Products with Simple Additive Weighting Approach to Detect Illegal Skincare Products

Formulation and Evaluation of Herbal Face Wash

This study presents the formulation and evaluation of a herbal face wash designed to meet the growing demand for natural and safe skincare products. The formulation process involved selecting a blend of herbal extracts known for their skin-friendly properties, along with appropriate surfactants, thickeners, and preservatives to ensure stability and efficacy. Physicochemical characteristics such as pH, viscosity, and foamability were evaluated to assess product quality. Additionally, sensory analysis was conducted to gauge user preferences regarding fragrance, texture, and overall experience. Microbiological tests were performed to confirm product safety. The results indicated that the formulated herbal face wash exhibited desirable properties, including balanced pH, suitable viscosity, and satisfactory foam formation. Sensory evaluations revealed positive consumer acceptance, with preferences for natural fragrance and smooth texture. Microbial studies confirmed the product's microbial safety for consumer use. Overall, the formulated herbal face wash offers a promising option for individuals seeking natural and effective skincare solutions

Big data analytics for quality variation over work shifts in manufacturing systems

ABSTRACT In the manufacturing industry, mass production enables manufacturers to produce parts with high precision and lower costs. Multiple shifts operate production processes to maximize efficiency. Several researches have been conducted on the impact of shift work on labor’s health and habits. However, there have been few studies on the influence of shift work on the consistency of product quality. This paper provides a methodology to analyze the impact of different work shifts in a real electronic board manufacturing industry. The study uses big data and analytics to assess product quality from data. Non-parametric kernel density estimation is used to approximate the distribution of good products in each work slot. Then several metrics are used to measure the dissimilarity between the estimated densities. The approach can be leveraged in various problems related to process performance and quality. The obtained results show that there is no significant difference in terms of product quality between work shifts. These prove the consistency of the manufacturing processes and the homogeneity of performance across work shifts in the studied factory. Compared to the literature, the paper presents the first quantitative analysis to compare production process performance and product quality over shift works.

An Advanced Quality Assessment and Monitoring of ESA Sentinel-1 SAR Products via the CyCLOPS Infrastructure in the Southeastern Mediterranean Region

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA’s Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure’s reliability.

A Reliable Quantification of Cholesterol and 25-Hydroxycholesterol in Liposomal Adjuvant Formulation by Liquid Chromatography High-Resolution Tandem Mass Spectrometry.

Cholesterol, as one of the major components of liposomes, plays a critical role in modulating membrane bilayer permeability, fluidity, and structural stability. Controlling these quality attributes is essential to maintaining the efficacy and fitness of the liposomes in various applications. However, during the manufacture and storage of liposomes, cholesterol has a propensity to undergo oxidative degradation. Hence, an analytical tool that is capable of determining not only the identity and quantity of cholesterol but also its associated degradants is a prerequisite to effective process control and product quality and safety assessments. In this view, a new liquid chromatography electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) method with parallel reaction monitoring (PRM) was developed and qualified to accurately quantify cholesterol and monitor the formation of 25-hydroxycholesterol degradant in liposomal drug formulations without the use of an isotopic internal standard (IS). The method was qualified according to the FDA Quality Guidance for Industry: Q2(R1). Study results showed that the method presents good specificity for cholesterol and 25-hydroxycholesterol detection in the liposomal matrix, good sensitivity characterized by LOD/LOQ in the nanomolar range, and accuracy within the range of 80 to 120%. The described method enables accurate evaluation of in-process and product release samples of Army Liposome Formulation with QS21 (ALFQ).

Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)

Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of >205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSIλ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSIλ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSIλ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers.

Development of Certified Reference Material Solutions for Phytochemicals in Ginger and Kava.

Dietary supplements derived from botanicals are commonly consumed and investigated in biomedical studies for their potential health benefits. Accurate identification and quantification of key chemical constituents from botanical ingredients is necessary for consistent product preparations and reproducible research results. Manufacturers need quantitative reference materials of the chemical constituents of interest to verify the content of ingredients and products. The rigor and reproducibility of biomedical research is enhanced through thorough characterization of the interventions used in mechanistic, clinical, and safety investigations. Quantitative reference materials enable reliable product quality assessments and reproducible research results. Solution-based certified reference material (CRM) mixes were developed as calibrants for phytochemicals in Ginger and Kava. The Kava CRM contained yangonin, desmethoxyyangonin, dihydrokavain, DL-kavain, methysticin, dihydromethysticin, flavokawain A, flavokawain B, and flavokawain C. The Ginger CRM contained 6-gingerol, 8-gingerol, 10-gingerol, 6-shogaol, 8-shogaol, and 10-shogaol. Each phytochemical was sourced as an isolated compound and assigned a purity factor by a mass balance approach accounting for residual impurities. The solution standard mixes were formulated by gravimetric addition of each phytochemical incorporating the purity factor and diluting with acetonitrile to the target concentrations of 500 µg/mL for the gingerols and shogaols, 250 µg/mL for the kavalactones and 25 µg/mL for the flavokawains. The concentration accuracy of each component in the solution mixes was analytically verified by HPLC-UV assay comparison to an independently prepared calibration solution. Each component in the Ginger and Kava CRMs were within 5% and 7% of the target concentrations, respectively. Homogeneous Kava and Ginger phytochemical solution mixes were produced with accurate constituent concentrations and demonstrated good stability over two years. These solution mixes were launched as commercially available CRMs. These mixes can be used as accurate concentration stock solutions to prepare calibrators and controls for botanical dietary supplement product testing and standardization.

Enhancing agro-biodiversity in chicken: a sensory comparison of broths from German local chicken breeds and their crossbreeds

The poultry meat production landscape has undergone a reduction in chicken breeds, resulting in a reliance on a limited number of varieties. Motivated by the goal of promoting sustainable chicken production and enhancing agro-biodiversity, this study pioneers a comparison between local chicken breeds (LBs) and their crossbreeds (CBs) with modern hybrid lines. Serving as an initial exploration within a larger project, this research acts as a prelude to a comprehensive investigation, aiming to complement the human sensory assessment of product quality. Study I assessed chicken broths prepared from three German LBs Bielefelder (BIE), Altsteirer (ALT), and Ramelsloher (RAM) utilizing a factorial 3×2×2 design that incorporated variations in salt content (unsalted/salted) and cooking time (1 hour/3 hours). The sensory profiles of the LB brothswere largely similar, except for BIE, which exhibited a higher skin odor intensity. Both, increased salt content and longer cooking time intensify sensory perception on most attributes. In Study II, BIE was compared with six CBs, with variations in salt content and cooking time (6×2×2 + 1×2 factorial design). BIE demonstrated higher sensory intensities than the CBs. Those were comparable, with no clear advantages or disadvantages identified from a sensory standpoint. These findings support that crossbreeding with commercial lines is not associated with changes in the sensory profile. It thus represents a strategy for improving the economic viability of local chicken breeds in order to preserve their valuable agro-biodiversity. The provided protocol for evaluating chicken broth from LBs or their CBs aims to offer researchers a standardized foundation for sensory assessments in chicken broth studies.

Methods and indicators for assessing the level of product quality

The article discusses the goals and features of product quality assessment, the methods used for this, and the indicators being verified. How can you understand that a product is of high quality? From the author’s point of view, this can be done using analysis methods that will provide objective information for the consumer about the characteristics of the product, its safety and properties.

Study of color degradation in package prints: Analyzing kinetics with principal component analysis

AbstractColor degradation is a pressing issue in various fields like food and medicine packaging, as well as printed materials, leading to a loss or distortion of vital information. Therefore, understanding how the color of package prints evolves over time is essential for product quality assessment and effective product life cycle management. This study focuses on investigating color changes in prints on blister foils, widely used in the medicine and packaging industries. To assess fading characteristics, samples printed via the gravure process have been exposed to a Xenon Test Chamber (B‐SUN). Spectrophotometric measurements of the prints have been conducted using a spectroradiometer, and the data have been analyzed using principal component analysis (PCA). The fading nature of cyan and black prints aligns with Type IV, while magenta and yellow prints correspond to Type II, as described by Giles. Notably, cyan and black inks demonstrated resistance toward fading at the end of the exposure, with a minor exception. The investigation involved curve fitting, revealing a consistent first‐order kinetic model governing fading rates throughout the exposure time. High R2 values (0.96–0.99) and low root means square error values (0.02–0.08) are observed across all cases. While color fading involves diverse kinetics, the fading rate consistently adheres to a singular first‐order kinetic path. The study highlights that yellow ink is most sensitive to light, followed by magenta ink. These findings offer valuable insights for optimizing packaging materials and ensuring product quality in industries reliant on printed materials. By mitigating color degradation, manufacturers can enhance the longevity and appeal of their products, reinforcing consumer trust and satisfaction. Furthermore, the effective utilization of PCA as a data analysis tool offers a valuable approach for future investigations in color science and related fields.

A novel deep learning model for detection of inconsistency in e-commerce websites

On most e-commerce websites, there are two crucial factors that customers rely on to assess product quality and dependability: customer reviews provided online and related ratings. Reviews offer feedback to customers about the product’s merits, reasons for negative reviews, and feelings of satisfaction or dissatisfaction with the provided service. As for ratings, they express customer opinions about the product’s quality as numerical values from one to five (one or two for the worst opinion, three for the neutral opinion, and four or five for the best opinion). Usually, the customer reviews may be inconsistent with their relevant ratings; the customer may write the worst review despite providing a four- or five-star rating or write the best review with only a one- or two-star rating. Due to this inconsistency, customers may need help to identify relevant information. Therefore, it is required to develop a model that can classify reviews as either positive or negative, depending on the polarity of thoughts, to demonstrate if there is an inconsistency between customer reviews and their actual ratings by comparing them with the ratings resulting from the model. This paper proposes an efficient deep learning (DL) model for classifying customer reviews and assessing whether there is inconsistency. The recommended model’s performance and stability are examined on a large dataset of product reviews from Amazon e-commerce. The experimental findings showed that the proposed model dominates and significantly outperforms its peers regarding prediction accuracy and other performance measures.

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

Sounds too feminine? Blind tastings, phonetic gender scores, and the impact on professional critics

AbstractWe shed light on assessing product quality in blind tastings and their potential (gender) biases. We study how phonetic traits of grape varieties suggest product attributes in the context of professional reviews. This study aims to close this research gap and analyze how product variety and phonetic name traits affect expert ratings. We obtained data on 18,609 wines and their ratings from Wine Enthusiast Magazine between 1997 and 2016, yielding a sample of 31,058 observations. We suppose that the gender of the taster needs to be considered to understand what affects tastings and ratings, as women and men might be attracted differently to masculine or feminine names. This study shows that masculine names receive higher evaluations than feminine ones. This phonetic gender gap is driven by lower ratings for white wines by female reviewers and lower ratings for red wines by male reviewers. In addition, white wines are rated lower overall by both men and women.

The yield of various varieties of lettuce grown in hydroponic conditions

The study is aimed at evaluating the effectiveness of growing various varieties of lettuce using flow hydroponics in protected soil. The following varieties of lettuce were studied: cabbage with an oily leaf (Gauguin, Fairlie, Santoro, Aquino, Casey), semi-rolled with a crispy leaf (Dunstar, Frillis, Extemp, Exact, Skilton), leafy (Lalique, Jokari, Litska, Rosaline, Alzevir, Xerafin). The experiments were conducted in the conditions of the Yelets district of the Lipetsk region in the conditions of the greenhouse of I.A. Bunin YSU. The assessment of product quality helped to identify the main productive varieties recommended for cultivation in protected soil by the method of flow hydroponics. For all-season cultivation: Casey, Gauguin, Dunstar, Alzevir, Jokari, Litska, for the autumn–winter period - Exact, for the spring-summer period – Extemp. Keywords: LETTUCE, VARIETY, YIELD, HYDROPONICS

From vineyard to table: Uncovering wine quality for sales management through machine learning

The literature currently offers limited guidance for retailers on how to use analytics to decipher the relationship between product attributes and quality ratings. Addressing this gap, our study introduces an advanced ensemble learning approach to develop a nuanced framework for assessing product quality. We validated the effectiveness of our framework with a dataset comprising 1,599 red wine samples from Portugal’s Minho region. Our findings show that this model surpasses previous ones in accurately predicting product quality, presenting retailers with a sophisticated tool to transform product data into actionable insights for sales management. Furthermore, our approach yields significant benefits for researchers by identifying latent attributes in extensive data collections, which can inform a deeper understanding of consumer preferences and guide the strategic planning of marketing promotions.

Consumers Preference towards Various Brands of Cold Drinks among Buddhist Tamang Community in Kavresthali, Kathmandu

This topic of study aims at finding out whether the consumers’ expectations of matched their satisfaction level on the basis of quality products provided by cold drinks suppliers and manufactures companies (i.e. Coca-Cola, Pepsi, Sprite, Mountain Dew and Fanta) among Buddhist Tamang Community in Kavresthali, Kathmandu. So, the research is to study the preference of the consumers and their satisfaction level. With a view to remaining competitive leading position and for businesses to grow, product quality and customer preference are key ingredients. The purpose of this study was to describe how consumers perceive product quality whether they are satisfied with product/cold drinks offered by the stated companies. The thrust of this study was to compose an assessment of product quality and consumer satisfaction of the companies. The questionnaire was developed and distributed using a convenience sampling technique among 100 respondents (Individual consumers and Retailers) within and from Buddhist Tamang Community in the Tarakeshwor Municipality 2, Kavresthali, and Kathmandu. From the analysis, it was found that the overall product quality offered by the companies Coca-Cola and Mountain Dew were relatively higher than others. Further, the analysis proved that the dependency of customer satisfaction on the quality of product and value for money were a very high positive correlation. This is why; this has been appeared that the most of the cold drinks suppliers and manufactures companies need to be very much concerned with the satisfaction of consumers by offering quality products with reasonable prices. The researcher makes a few recommendations based on the findings of the study. First of all, the management of the companies should pay attention to improve the product quality and other factors which may lead to customer satisfaction thereby improving its performance and the overall brand image and profitability of the Companies.

Raman spectroscopy and fusion machine learning algorithm: A novel approach to identify dairy fraud

In addressing food safety verification, ensuring the authenticity of food labels remains of utmost importance. With the objective of enhancing label validation and fraud detection, our research introduces an innovative fusion model utilizing Raman spectroscopy technology. This model is uniquely designed to differentiate various dairy products and provide a quantitative assessment of their nutritional components. To achieve this, we integrated four foundational machine learning algorithms: Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and extreme Gradient Boosting (XGBoost). These algorithms individually showcased a classification accuracy exceeding 90% for differentiating dairy brands and achieved a fat prediction Normalized Root Mean Square Error (NRMSE) under 13%. However, when these algorithms were synergistically combined in our fusion model, we observed a remarkable improvement in classification accuracy, reaching 99%. Additionally, for the pivotal task of predicting fat content in dairy samples, the fusion model manifested an unparalleled performance, with metrics indicating R2 = 0.98, Root Mean Square Error (RMSE) = 0.2 g/100 mL, and NRMSE = 6.2%. Notably, while the efficiency of the individual models fluctuated depending on the training objectives, the fusion model consistently outperformed in all scenarios. In essence, our fusion model's adaptability, precision, and generalization capabilities underscore its potential as an invaluable tool in the domain of dairy product quality assessment.

Graphene based T-shaped monopole antenna sensor for food quality evaluation.

Food adulteration has long been considered a major problem. It compromises the quality, safety, and nutritional value of food, posing significant risks to public health. Novel techniques are required to control it. A graphene-based T-shaped monopole antenna sensor was tested for its ability to detect adulteration in liquid foods. Mustard oil was the pure reference sample used for product quality analysis. Olive oil and rice bran oil were adulterants added to the pure sample. It was found that the sensor could be immersed easily in the liquid sample and provided precise results. The graphene-based T-shaped monopole antenna sensor can be used for the quality assessment of liquid food products and is suitable for real-time monitoring. © 2024 Society of Chemical Industry.

Real-Time Monitoring of Dough Quality in a Dough Mixer Based on Current Change

Accurate assessment of dough kneading is pivotal in pasta processing, where both under-kneading and over-kneading can detrimentally impact dough quality. This study proposes an innovative approach utilizing a cost-effective current sensor to ascertain the optimal kneading time for dough. Throughout the kneading process, the dough’s tensile resistance gradually increases, reflecting the evolution of properties such as the gluten network. This leads to a discernible ascending phase in dough quality, evident through an increase in the load current of the mixing machine, succeeded by a subsequent decline beyond a certain threshold. The identification of this peak point enables the achievement of optimal dough consistency, thereby enhancing the overall quality of both the dough and subsequent pasta products. After the final product quality assessment, this novel method promises to be a valuable tool in optimizing pasta processing and ensuring consistent product quality.