Quality Evaluation Of Products Research Articles

Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT-IR spectroscopy.

Recently, the application of Fourier transform infrared (FT-IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT-IR spectra were acquired in the spectral range 1000-8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R 2= .84, RMSE=0.29), acidity (R 2= .71, RMSE=0.0004), phenol (R 2= .35, RMSE=0.19), total anthocyanin (R 2= .93, RMSE=5.85), and browning (R 2= .89, RMSE=0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R 2= .98, RMSE=0.003) and pH (R 2= .99, RMSE=0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT-IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.

Determination of the quality of tea from different picking periods: An adaptive pooling attention mechanism coupled with an electronic nose

An efficient nondestructive testing method is important to inspect the quality of agricultural products. The material content of tea differs across different picking periods, leading to variation in the quality. Traditional methods for evaluating the quality of tea are relatively complex, in-efficient, and generally destructive. This prompted our work, which led to the development of nondestructive testing technology comprising an electronic nose (e-nose) combined with an effective data analysis method, to determine the quality of tea from different picking periods. First, the e-nose system was used to acquire information about the volatile components of tea from different picking periods. Second, an adaptive pooling attention mechanism (APAM) is proposed based on the data characteristics of the e-nose system. By adapting the attention to the essential information that affects the classification performance, the feature-learning ability of the gas information is enhanced. Finally, the proposed APAM was combined with a classic convolutional neural network (CNN) to identify the gas information of tea picked during different picking periods. The method was evaluated in comparison with other gas recognition methods to prove its effectiveness. The results show that APAM can effectively realize the adaptive attention to the deep gas information. The MobileNetV1 +APAM has accuracy of 97.62%, precision of 97.57%, recall of 97.71%, and F1-score of 97.64%, confirming the capability of the method to successfully distinguish among the gas information of tea gathered in different picking periods. In summary, integrating APAM with a classic CNN combined with e-nose technology offers an effective nondestructive testing method for agricultural production and tea quality evaluation.

Development of a Portable Near-Infrared Spectroscopy Tool for Detecting Freshness of Commercial Packaged Pork.

Real-time monitoring of meat quality requires fast, accurate, low-cost, and non-destructive analytical methods that can be used throughout the entire production chain, including the packaged product. The aim of this work was to evaluate the potential of a portable near-infrared (NIR) spectroscopy tool for the on-site detection of freshness of pork loin fillets in modified atmosphere packaging (MAP) stored on display counters. Pork loin slices were sealed in MAP trays under two proportions of O2/CO2/N2: High-Ox-MAP (30/40/30) and Low-Ox-MAP (5/20/75). Changes in pH, color, thiobarbituric acid reactive substances (TBARS), Warner−Bratzler shear force (WBSF), and microbiology (total viable counts, Enteriobacteriaceae, and lactic acid bacteria) were monitored over 15 days post-mortem at 4 °C. VIS-NIR spectra were collected from pork fillets before (through the film cover) and after opening the trays (directly on the meat surface) with a portable LABSPEC 5000 NIR system in diffuse reflectance mode (350−2500 nm). Quantitative NIR models by partial least squares regression (PLSR) showed a promising prediction ability for meat color (L*, a*, C*, and h*) and microbiological variables (R2VAL > 0.72 and RPDVAL > 2). In addition, qualitative models using PLS discriminant analysis obtained good accuracy (over 90%) for classifying pork samples as fresh (acceptable for consumption) or spoiled (not acceptable) based on their microbiological counts. VIS-NIR spectroscopy allows rapid evaluation of product quality and shelf life and could be used for on-site control of pork quality.

Evaluation of cosmetic creams containing Black cumin (Nigella sativa)-Lemon balm (Melissa officinalis L.)-Aloe vera (Aloe barbadensis miller) essences by modeling with Box Behnken method in Design Expert

In this investigation regarding medicinal and aromatic plants, an experimental study was conducted on the effectiveness of black cumin, lemon balm and Aloe vera essences in cosmetic creams, which were selected for their beneficial biological activities such as anti-inflammatory, moisturizing etc. In line with this study stability test, which was important in the quality evaluation of cosmetic products, was carried out. Cream samples containing different concentrations of black cumin and lemon balm essences were kept at under 3 different temperature conditions as 0 °C, 25 °C and 40 °C and the necessary values were recorded. In this direction, the effects of the temperature on the physical and chemical properties of the cosmetic creams were assessed. Homogenization and colors as physical properties and pH value of the herbal cream samples were examined. It was observed that the pH value, which was an important parameter for skin care products, was 6.5 under the optimum experimental condition and it was an acceptable value for skin creams according to the literature. In the second part of the experimental study, the effect of the essence obtained from the Aloe vera plant on the samples was tested with the optimum condition selected from 17 different experimental sets by using the Design Expert optimization application. In addition, from the production of cosmetic products; it was necessary to pay attention to the environmental conditions stipulated in storage, transportation, and sales points. As a result of the tests, it was seen that the medicinal and aromatic plants used could be evaluated in the field of cosmetics and successful results could be obtained.

Re-circulating indoor vertical farm: Technicalities of an automated duckweed biomass production system and protein feed product quality evaluation

Duckweeds are fast-growing and nutritious plants, which are gaining increased attention in different fields of application. Especially for animal nutrition, alternative protein sources are needed to substitute soybean meal. The current bottleneck is the standardized production of biomass, which yields stable quantities of a defined product quality. To solve this problem, an indoor vertical farm (IVF) for duckweed biomass production was developed. It consists of nine vertically stacked basins with a total production area of 25.5 m2. The nutrient solution, a modified N-medium, re-circulated within the IVF with a maximum flow rate of 10 L min−1. Nutrients were automatically added based on electrical conductivity. In contrast, ammonium was continuously supplied. A water temperature of 23 °C and a light intensity of 105 μmol m−2 s−1 with a photoperiod of 12:12 h were applied. During a 40-day production phase, a total of 35.6 kg of fresh duckweed biomass (equals 2.1 kg of dried product) was harvested from the IVF. On average, 0.9 kg day−1 of fresh biomass was produced. The dried product contained 32% crude protein (CP) and high levels of proteinogenic amino acids (e.g. lysine: 5.42 g, threonine: 3.85 g and leucine: 7.59 g/100 g CP). Biomass of this quality could be used as a protein feed alternative to soybean meal. The described IVF represents a modular model system for duckweed biomass production in a controlled environment and further innovations and upscaling processes.

Reliability optimization of flexible test system based on pyro-mechanical device products production driven

The quality evaluation of aerospace pyro-mechanical device products(PDPs) is very important to the safety of aerospace engineering. Aerospace PDPs are small-lot batch, multiple batches and one-off products which rely on the analysis of ignition test with limited samples. To accommodate multiple batches and small-lot batch of aerospace PDPs, the flexible test system(FTS) switches between different batches of products by the selection of instruments and channels. The conventional test mode is driven by the completion time of batch products, which usually leads to frequent switching of instruments and channels and affects the reliability of test system. Therefore, the FTS is configured according to the completion of production missions within a certain time period in the future. This paper considers the FTS of aerospace PDPs as a phased mission system, and designs the corresponding dynamic Bayesian network(DBN) model. A test of one batch is taken as a phase of phased mission system. In order to improve efficiency and optimize reliability of the aerospace PDPs FTS, the receding horizon optimization problem is constructed. Numerical experiments show that the FTS with phased mission model can optimize the test sequence during the test process, which improves efficiency and reliability of test system.

Statistical genetics concepts in biomass-based materials engineering.

With the rise of biomass-based materials such as nanocellulose, there is a growing need to develop statistical methods capable of leveraging inter-dependent experimental data to improve material design, product development, and process optimisation. Statistical approaches are essential given the multifaceted nature of variability in lignocellulosic biomass, which includes a range of different biomass feedstock types, a combinative arrangement of different biomass processing routes, and an array of different product formats depending on the focal application. To account for this large degree of variability and to extract meaningful patterns from research studies, there is a requirement to generate larger datasets of biomass-derived material properties through well-designed experimental systems that enable statistical analysis. To drive this trend, this article proposes the cross-disciplinary utilisation of statistical modelling approaches commonly applied within the field of statistical genetics to evaluate data generated in the field of biomass-based material research and development. The concepts of variance partitioning, heritability, hierarchical clustering, and selection gradients have been explained in their native context of statistical genetics and subsequently applied across the disciplinary boundary to evaluate relationships within a model experimental study involving the production of sorghum-derived cellulose nanofibres and their subsequent fabrication into nanopaper material. Variance partitioning and heritability calculates the relative influence of biomass vs. processing factors on material performance, while hierarchical clustering highlights the obscured similarity between experimental samples or characterisation metrics, and selection gradients elucidate the relationships between characterisation metrics and material quality. Ultimately, these statistical modelling approaches provide more depth to the investigation of biomass-processing-structure-property-performance relationships through outlining a framework for product characterisation, quality evaluation, and data visualisation, not only applicable to nanocellulose production but for all biomass-based materials and products.

A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks

As consumers care more and more about product quality, it is important to mine the deep correlations between production and manufacturing parameters and the evaluation of product quality through the analysis of industrial big data. The existing research of product quality prediction faces several major problems: the lack of diverse quality features, the poor tractability of abnormal parameters, the strong nonlinearity of parameters, the obvious sequential property of data, and the severe time lag of data. To solve these problems, this paper explores the quality prediction and control of multistage MP process (MPMP) based on big data analysis. Firstly, the prediction strategy and flow were specified for MPMP product quality prediction, and the features were extracted from MPMP product quality. After that, the MPMP product quality features were described in multiple dimensions, the attention mechanism was introduced to the prediction process. In addition, the recurrent neural network was improved, and an MPMP product quality prediction model was established on bidirectional long short-term memory (BiLSTM) network. Our model was compared with AdaBoost and XGBoost through experiments. The effectiveness of our model was demonstrated by the results of the appearance quality PQ1, and the area under the curve (AUC) for each process parameter. In general, our model is superior to other algorithms in the accuracy, mean accuracy, and precision of product quality prediction.

An Extended Security Measurement Framework for Open-Source Enterprise Resource Planning Software Security

Modern organizations are adopting new ways of measuring their level of security for compliance and justification of security investments. The highly interconnected environment has seen organizations generate lots of personal information and sensitive organizational data. Easiness in automation provided by open-source enterprise resource planning (ERP) software has accelerated its acceptability. The study aimed at developing a security measurement framework for open-source ERP software. The motivation was twofold: paradigm shift towards open-source ERP software and the need for justified investment on information security. Product quality evaluation method based on ISO 25010 framework guided the selection of attributes and factors. A security measurement framework with security posture at the highest level, attributes and factors was developed presenting a mechanism for assessing organization’s level of security. Security posture promotes customers’ confidence and gives management means to leverage resources for information security investment. The future work includes definition of metrics based on the framework.

Evaluation of product quality of chicken sausages with added cinnamon ( Cinnamomum cassia ) and mushroom ( Pleurotus sajur‐caju ) powders at various concentrations

The objective of this study was to investigate the product quality and shelf-life effects of chicken sausages containing cinnamon (Cinnamomum cassia) and oyster mushroom (Pleurotus sajor-caju) during refrigerated storage for 35 days. They were added into chicken sausages at the combination of 0.1% or 0.2% designated C1M1, C1M2, C2M1, and C2M2. Cinnamon and mushroom powders reduced cooking loss (%) (p < .05), but increased the redness and yellowness values, and expressed moisture (%) (p < .05). Textural profile analyses were decreased as the level of oyster mushroom powder increased (p < .05). The thiobarbituric acid reactive substances (TBARS) and volatile basic nitrogen (mg%, VBN) of all treatments were lower than those of the control (p < .05), and total bacterial counts decreased with increased cinnamon powder. In conclusion, the cinnamon and oyster mushroom powder reduced TBA, VBN, and microbial counts of chicken sausages, resulting in the extension of shelf-life. Practical applications This study applied functional ingredients to a meat product, chicken sausages. Functional ingredients from natural resources might exert useful natural antioxidant and antimicrobial activities. The addition of cinnamon and oyster mushroom powders retarded lipid oxidation and microbial counts during refrigerated storage, prolonging the shelf-life of these meat products. Thus, these results will be useful for the meat industry for the extension of the shelf-life of meat products.

Determination of free amino acids in Eriocheir sinensis by ultra-high performance liquid chromatography-high resolution mass spectrometry

游离氨基酸不仅是一类重要的营养物质,更与中华绒螯蟹独特的滋味和香味密切相关。对游离氨基酸含量进行测定,能为中华绒螯蟹产品的品质评价和风味研究提供有价值的信息。该工作通过优选提取溶剂、优化仪器测定参数,最终使用5%(v/v)高氯酸水溶液提取目标物,采用XDB-C18色谱柱(100 mm×4.6 mm, 1.7 μm),以0.1%(v/v)甲酸水溶液-乙腈为流动相进行梯度洗脱,在电喷雾正离子电离、选择离子扫描模式下进行检测,外标法定量,建立了超高效液相色谱-高分辨质谱(UHPLC-HRMS)测定中华绒螯蟹中17种游离氨基酸含量的方法。结果表明,17种氨基酸在10.0~200.0 mg/L范围内线性关系良好,相关系数(R2)≥0.9990,仪器检出限为0.3 mg/L,仪器定量限为1.0 mg/L。在中华绒螯蟹可食部分中按三水平进行加标回收试验,各目标物的回收率在78.4%~105.3%之间。通过对仪器和方法精密度进行评估,发现17种氨基酸测定仪器重复性的RSD≤4.2%,方法重复性试验的RSD≤5.2%,再现性试验的RSD≤11.4%,精密度数据均在合理范围内。利用该研究建立的检测方法对20个中华绒螯蟹样本进行了实际样品检测,总结了17种游离氨基酸在蟹肉、蟹膏、蟹黄中的分布特点,验证了该方法是一种前处理简便、选择性好、准确度高的检测技术。

Production of nickel-rich LiNi0.89Co0.08Al0.03O2 cathode material for high capacity NCA/graphite secondary battery fabrication

Abstract Li-ion secondary battery is highly recommended as a power source to highly advanced battery electric vehicles. Among various types, the lithium nickel cobalt aluminum oxide (NCA) battery is considered suitable for high energy and power application. In this study, the NCA cathode material LiNi0.89Co0.08Al0.03O2 was produced via the oxalate co-precipitation technique to reduce the overall production cost and process complexity. Oxalic acid and a small amount of sodium hydroxide were used as the precipitant and pH regulator, respectively. Homogenous and loose metal oxalate precipitate formation was confirmed by X-ray diffraction (XRD), scanning electron microscopy, and Fourier-transform infrared spectroscopy analysis. XRD patterns of the as-obtained micron-sized NCA showed a well-layered hexagonal structure. The electrochemical properties of the cathode in the full cell were thoroughly examined. The specific discharge capacity of the as-obtained NCA in NCA/LiPF6/graphite at a current rate of 20 mA/g was 142 mAh/g. The as-prepared NCA sample had capacity retention of 80% after being charged and discharged at 0.1 A/g for 101 cycles. Scaling up of NCA production process to 2 kg per batch was conducted and evaluation of NCA product quality was performed by material characterization. Based on the overall results and considering the overall process, such an approach is expected to be developed and improved for future large-scale production purposes.

Electrical properties measurement: A nondestructive method to determine the quality of bread doughs during fermentation

The quality evaluation of food products using electrical measurements has gathered the attention of food scientists because of its simplicity and being nondestructive. In this study, the electrical resistance variation during the fermentation process of three types of Iranian local bread doughs, namely, Taftan, Lavash, and Sangak, was measured at two electrical frequencies of 120 and 1000 Hz and statistically evaluated using the D-optimal design. The results showed that there is a meaningful correlation between the electrical resistance and the type of dough and fermentation time. The dough samples had different amounts of some ingredients, e.g., yeast and sourdough, which resulted in different levels of microorganism activity. Moreover, the effects of frequency on the sample response were not significant during the experiments. Furthermore, having the type of bread dough, fermentation time, and electrical frequency, the artificial neural network (ANN) could predict the electrical resistance of the dough samples with an MSE of 0.007. Food scientists who work on the bread dough quality can use the apparatus introduced in this study to determine the quality of bread doughs during the fermentation process based on their electrical properties. Increasing the quality of bread dough leads to better durability, longer freshness and better digestion.

PENGARUH KUALITAS PRODUK, KUALITAS PELAYANAN, DAN LOKASI TERHADAP KEPUASAAN KONSUMEN CAFÉ KOPI SUSU BAPER DI KOTA METRO

The increase in coffee consumption makes coffee shops more widespread, the intense competition between coffee shops makes coffee shop entrepreneurs have to continue to innovate and pay attention to consumer satisfaction. This study aims to analyze consumer evaluation of product quality, service quality, and level of customer satisfaction, also the effect of product quality, service quality, and location on consumer satisfaction. This study used a survey method. The sampling technique in this study used the purposive sampling method. The sample collection method used accidental sampling, which means that all consumers who have ever purchased products at Café Kopi Susu Baper. The number of samples in this study was 45 people. The research location was determined deliberately at Café Kopi Susu Baper because Café Kopi Susu Baper was one of the cafes in Metro City with private property rights and has been in existence for two years. Data collection from February to March 2021. Data analysis used qualitative descriptive analysis and multiple linear regression. The result showed that the product quality variable was included in the high category with an index value of 187,66. The service quality variable was included in the high category with an index value of 191,98. The consumer satisfaction variable was in the high category with an index value of 200,67. Product quality, service quality, and location variables have a positive and significant effect on customer satisfaction. Based on the coefficient of partial determination shows that service quality was a variable that has a dominant influence on customer satisfaction.

Analysis and Research on the Marketing Strategy of Agricultural Products Based on Artificial Intelligence

With the gradual development of artificial intelligence (AI), the traditional production, marketing, and management methods for agricultural products have undergone dramatic changes, necessitating a greater optimization of these methods. Agricultural product operators have begun incorporating AI technology into product production, marketing, and distribution processes. This article examines the current state of agricultural product management and then investigates the integration of production, marketing, and distribution using artificial intelligence. In addition, given the limitations of conventional methods for classifying agricultural products, this article presents a classification model that combines factor analysis with an enhanced support vector machine (SVM) based on genetic algorithms (GAs). The results of the experiments indicate that the improved method is capable of distinguishing agricultural product quality categories rapidly and precisely, significantly improving the classification accuracy of agricultural product quality, and being broadly applicable to the evaluation of agricultural product quality.

Intelligent pH-sensing film based on polyvinyl alcohol/cellulose nanocrystal with purple cabbage anthocyanins for visually monitoring shrimp freshness

We aimed to prepare a new pH-sensing film based on the immobilization of purple cabbage anthocyanins (PCA) into Polyvinyl alcohol (PVA) reinforced by cellulose nanocrystals (CNC). FT-IR, XRD and TGA were used to assess the intermolecular interactions and thermo-stability of films. The addition of CNC and PCA resulted in an enhancement in UV–vis barrier, mechanical properties and moisture resistance. Inclusion of PCA imparted intelligent properties to the films. PCA-loaded films displayed strong visually detectable colorimetric responses to pH (2−13) and volatile ammonia. When applied to monitor shrimp freshness at 4 °C, PVA/CNC films containing 0.6 % PCA exhibited conspicuous color fluctuations from purple to gray blue upon deterioration. As a result, PVA/CNC-PCA colorimetric films were considered as intelligent packaging labels with significant mechanical, water vapor barrier properties and pH-sensing qualities for visual quality evaluation of fresh seafood products.

Design of product quality scales for conveying information by infomediaries

Infomediaries, such as movie critics, often evaluate product quality using discrete grades, such as thumbs-up and thumbs-down. Their reports are used by potential consumers to decide whether to purchase the product. Our aim is to examine how the grading can be improved by answering research questions such as: What is the quality cutoff for each grade? How many grades should the grading scale use? How should grading be done for other goals than informativeness, such as maximizing reviewer’s traffic? How should grading be done to encourage producers to improve quality? Using an analytical model, we show that optimal grade cutoffs depend on the distributions of product quality and consumer taste, and the resulting scale may have unequal intervals. As few as five grades can be near-optimal for consumer welfare and grades should be made more difficult to achieve when producers can invest in quality improvement. These results should help improve the design of scales for reporting quality.

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Consumer demand for high-quality healthy food is increasing; therefore, meat processors require the means toassess their products rapidly, accurately, and inexpensively. Traditional methods for quality assessments are time-consum-ing, expensive, and invasive and have potential to negatively impact the environment. Consequently, emphasis has been puton finding nondestructive, fast, and accurate technologies for product composition and quality evaluation. Research in thisarea is advancing rapidly through recent developments in the areas of portability, accuracy, and machine learning.Therefore, the present review critically evaluates and summarizes developments of popular noninvasive technologies(i.e., from imaging to spectroscopic sensing technologies) for estimating beef, pork, and lamb composition and quality,which will hopefully assist in the implementation of these technologies for rapid evaluation/real-time grading of livestockproducts in the near future.

Fuzzy based poultry production quality evaluation

Poultry production is agriculture-based subsidiary business for farmers. Different factors like any kind of disease symptoms, toxic agents, viral infections and many more factors affect the quality of poultry production. The study reveals the systematic framework for analysis of quality of poultry production. This framework involves fuzzy based method for classification, prioritization of poultry quality decisions against measures to define and validate the novel method and results in data mining. The researcher aims to value work of fiction of poultry production quality evaluation in data mining.

Product quality evaluation by confidence intervals of process yield index

Statistical techniques have a beneficial effect on measuring process variability, analyzing the variability concerning product requirements, and eliminating the variability in product manufacturing. Process capability indices (PCIs) are not only easy to understand but also able to be directly employed by the manufacturing industry. The process yield index offers accurate measurement of the process yield, and it is a function of two unilateral six sigma quality indices. This paper initiates to develop the confidence intervals of the process yield index by using joint confidence regions of two unilateral six sigma quality indices for all quality characteristics of a product. Then integrate these joint confidence regions to find the confidence intervals of the product yield index. All manufacturing industries can use these confidence intervals to make statistical inferences to assess whether the process capability of the product and all quality characteristics has reached the required level, and to grasp the opportunities for improvement. An illustrated example on driver integrated circuit of micro hard disk is provided.