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1
Lee, Brian K., Emily J. Mayhew, Benjamin Sanchez-Lengeling, Jennifer N. Wei, Wesley W. Qian, Kelsie A. Little, Matthew Andres, et al. "A principal odor map unifies diverse tasks in olfactory perception." Science 381, no. 6661 (September 2023): 999–1006. http://dx.doi.org/10.1126/science.ade4401.
Повний текст джерелаАнотація:
Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.
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Kang, Jeong-Hee, JiHyeon Song, Sung Soo Yoo, Bong-Jae Lee, and Hyon Wook Ji. "Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)." Atmosphere 11, no. 8 (July 24, 2020): 784. http://dx.doi.org/10.3390/atmos11080784.
Повний текст джерелаАнотація:
The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.
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Qiu, Shanshan, Pingzhi Hou, Jingang Huang, Wei Han, and Zhiwei Kang. "The Monitoring of Black-Odor River by Electronic Nose with Chemometrics for pH, COD, TN, and TP." Chemosensors 9, no. 7 (July 5, 2021): 168. http://dx.doi.org/10.3390/chemosensors9070168.
Повний текст джерелаАнотація:
Black-odor rivers are polluted urban rivers that often are black in color and emit a foul odor. They are a severe problem in aquatic systems because they can negatively impact the living conditions of residents and the functioning of ecosystems and local economies. Therefore, it is crucial to identify ways to mitigate the water quality parameters that characterize black-odor rivers. In this study, we tested the efficacy of an electronic nose (E-nose), which was inexpensive, fast, and easy to operate, for qualitative recognition analysis and quantitative parameter prediction of samples collected from the Yueliang River in Huzhou City. The E-nose sensors were cross-sensitive to the volatile compounds in black-odor water. The device recognized the samples from different river sites with 100% accuracy based on linear discriminant analysis. For water quality parameter predictions, partial least squares regression models based on E-nose signals were established, and the coefficients between the actual water quality parameters (pH, chemical oxygen demand, total nitrogen content, and total phosphorous content) and the predicted values were very high (R2 > 0.90) both in the training and testing sets. These results indicate that E-nose technology can be a fast, easy-to-build, and cost-effective detection system for black-odor river monitoring.
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Tolba, Ahmed, Nihal N. Mostafa, Ali Wagdy Mohamed, and Karam M. Sallam. "Hybrid Deep Learning Approach for Milk Quality Prediction." Precision Livestock 1 (January 9, 2024): 1–13. http://dx.doi.org/10.61356/j.pl.2024.1199.
Повний текст джерелаАнотація:
Milk quality prediction is considered a vital research area due to increase the need for obtain sustainable development goals. This study aims to predict milk quality by integrate gated recurrent units (GRUs) and residual network (ResNet). Our model was evaluated on milk quality prediction dataset with seven unique feature such as pH, temperature, taste, odor, fat, turbidity, and color. The prediction output is classified with high (Goog), Low (Bad), and Medium (Moderate) classes. Our model shows superior results with comparison with multi-layer perceptron (MLP), random forest (RF) and support vector machine (SVM). In terms of accuracy, precision, recall, and F1-score, 0.996, 0.992, 0.992, 0.992.
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CLIFF, MARGARET, KAREEN STANICH, JUDITH MORAN TRUJILLO, PETER TOIVONEN, and CHARLES F. FORNEY. "DETERMINATION AND PREDICTION OF ODOR THRESHOLDS FOR ODOR ACTIVE VOLATILES IN A NEUTRAL APPLE JUICE MATRIX." Journal of Food Quality 34, no. 3 (June 2011): 177–86. http://dx.doi.org/10.1111/j.1745-4557.2011.00383.x.
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Dong, Bo, Shihu Shu, and Dengxin Li. "Optimization of Secondary Chlorination in Water Distribution Systems for Enhanced Disinfection and Reduced Chlorine Odor Using Deep Belief Network and NSGA-II." Water 16, no. 18 (September 19, 2024): 2666. http://dx.doi.org/10.3390/w16182666.
Повний текст джерелаАнотація:
This research explores the strategic optimization of secondary chlorination in water distribution systems (WDSs), in order to enhance the efficiency of disinfection while mitigating odor and operational costs and promoting sustainability in water quality management. The methodology integrates EPANET simulations for water hydraulic and quality modeling with a deep belief network (DBN) within the deep learning framework for accurate chloric odor prediction. Utilizing the non-dominated sorting genetic algorithm-II (NSGA-II), this methodology systematically balances the objectives of chloride dosage and chloramine formation. It combines a chloric odor intensity assessment, a multi-component kinetic model, and dual-objective optimization to conduct a comparative analysis of case studies on secondary chlorination strategies. The optimal configuration with five secondary chlorination stations reduced chloric odor intensity to 1.20 at a cost of USD 40,020.77 per year in Network A while, with eight stations, chloric odor intensity was reduced to 0.88 at a cost of USD 71,405.38 per year in Network B. The results demonstrate a balanced trade-off between odor intensity and operational cost on one hand and sustainability on the other hand, highlighting the importance of precise chlorine management to improve both the sensory and safety qualities of drinking water while ensuring the sustainable use and management of water resources.
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Wang, Yu, Qilong Zhao, Mingyuan Ma, and Jin Xu. "Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine." Applied Sciences 12, no. 17 (August 31, 2022): 8777. http://dx.doi.org/10.3390/app12178777.
Повний текст джерелаАнотація:
Understanding the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception, i.e., the structure–odor relationship, remains a decades-old, challenging task. However, the differences among the molecular structure graphs of different molecules are subtle and complex, and the molecular feature descriptors are numerous, with complex interactions that cause multiple odor perceptions. In this paper, we propose to decompose the features of the molecular structure graph into feature vectors corresponding to each odor perception descriptor to effectively explore higher-order semantic interactions between odor molecules and odor perception descriptors. We propose an olfactory perception prediction model noted as HGAFMN, which utilizes a hypergraph neural network with the olfactory lateral inhibition-inspired attention mechanism to learn the molecular structure feature from the odor molecular structure graph. Furthermore, existing methods cannot effectively extract interactive features in the large number of molecular feature descriptors, which have complex relations. To solve this problem, we add an attentional factorization mechanism to the deep neural network module and obtain a molecular descriptive feature through the deep feature combination based on the attention mechanism. Our proposed HGAFMN has achieved good results in extensive experiments and will help product design and quality assessment in the food, beverage, and fragrance industries.
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Ira Mae Gallo Caray, King Paulo Ramos Ditchon, and Edwin Remeroso Arboleda. "Smart coffee aromas: A literature review on electronic nose technologies for quality assessment." World Journal of Advanced Research and Reviews 21, no. 2 (February 28, 2023): 506–14. http://dx.doi.org/10.30574/wjarr.2024.21.2.0407.
Повний текст джерелаАнотація:
One of the most important aspects of coffee's sensory experience is its scent, which is affected by roasting, microbial contamination, and place of origin. Electronic noses (ENs), which provide quick and precise identification of intricate odor patterns linked to microbial activity, have become highly effective instruments for evaluating coffee odors. This study examines the most recent uses of EN technology for evaluating coffee scent and emphasizes how important it is for assuring the safety and quality of the final product. The integration of ENs with artificial neural networks (ANNs) improves their precision in fragrance profile prediction and classification. Moreover, real-time smell profile monitoring is made possible by automated systems that use ENs, which helps with quality control in the coffee manufacturing process. Beyond the coffee business, EN technology is used in many other areas, including food safety, medical diagnostics, and environmental monitoring. Through innovation in fragrance analysis and quality control, this multidisciplinary approach promotes customer confidence and product integrity across industries.
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Perrot, N. Mejean, Alice Roche, Alberto Tonda, Evelyne Lutton, and Thierry Thomas-Danguin. "Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise." Mathematical Biosciences and Engineering 20, no. 12 (2023): 20528–52. http://dx.doi.org/10.3934/mbe.2023908.
Повний текст джерелаАнотація:
<abstract><p>Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-concept study, we seek to develop an integrative model that combines expert knowledge, fuzzy logic, and machine learning to predict the quantitative odor description of complex mixtures of odorants. The model output is the intensity of relevant odor sensory attributes calculated on the basis of the content in odor-active comounds. The core of the model is the mathematically formalized knowledge of four senior flavorists, which provided a set of optimized rules describing the sensory-relevant combinations of odor qualities the experts have in mind to elaborate the target odor sensory attributes. The model first queries analytical and sensory databases in order to standardize, homogenize, and quantitatively code the odor descriptors of the odorants. Then the standardized odor descriptors are translated into a limited number of odor qualities used by the experts thanks to an ontology. A third step consists of aggregating all the information in terms of odor qualities across all the odorants found in a given product. The final step is a set of knowledge-based fuzzy membership functions representing the flavorist expertise and ensuring the prediction of the intensity of the target odor sensory descriptors on the basis of the products' aggregated odor qualities; several methods of optimization of the fuzzy membership functions have been tested. Finally, the model was applied to predict the odor profile of 16 red wines from two grape varieties for which the content in odorants was available. The results showed that the model can predict the perceptual outcome of food odor with a certain level of accuracy, and may also provide insights into combinations of odorants not mentioned by the experts.</p></abstract>
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Wang, Yangfeng, Xinyi Jin, Lin Yang, Xiang He, and Xiang Wang. "Predictive Modeling Analysis for the Quality Indicators of Matsutake Mushrooms in Different Transport Environments." Foods 12, no. 18 (September 8, 2023): 3372. http://dx.doi.org/10.3390/foods12183372.
Повний текст джерелаАнотація:
Matsutake mushrooms, known for their high value, present challenges due to their seasonal availability, difficulties in harvesting, and short shelf life, making it crucial to extend their post-harvest preservation period. In this study, we developed three quality predictive models of Matsutake mushrooms using three different methods. The quality changes of Matsutake mushrooms were experimentally analyzed under two cases (case A: Temperature control and sealing measures; case B: Alteration of gas composition) with various parameters including the hardness, color, odor, pH, soluble solids content (SSC), and moisture content (MC) collected as indicators of quality changes throughout the storage period. Prediction models for Matsutake mushroom quality were developed using three different methods based on the collected data: multiple linear regression (MLR), support vector regression (SVR), and an artificial neural network (ANN). The comparative results reveal that the ANN outperforms MLR and SVR as the optimal model for predicting Matsutake mushroom quality indicators. To further enhance the ANN model’s performance, optimization techniques such as the Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm techniques were employed. The optimized ANN model achieved impressive results, with an R-Square value of 0.988 and an MSE of 0.099 under case A, and an R-Square of 0.981 and an MSE of 0.164 under case B. These findings provide valuable insights for the development of new preservation methods, contributing to the assurance of a high-quality supply of Matsutake mushrooms in the market.
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Fu, Zetian, Shuang Zhao, Xiaoshuan Zhang, Martin Polovka, and Xiang Wang. "Quality Characteristics Analysis and Remaining Shelf Life Prediction of Fresh Tibetan Tricholoma matsutake under Modified Atmosphere Packaging in Cold Chain." Foods 8, no. 4 (April 22, 2019): 136. http://dx.doi.org/10.3390/foods8040136.
Повний текст джерелаАнотація:
Tricholoma matsutake (T. matsutake) growing in Tibet is very popular for its high economic and medicinal value, but fresh T. matsutake has an extremely short shelf life. The shelf life of T. matsutake is complex, influenced by product characteristics, surrounding environmental conditions, and spoilage development. The objective of this work was to study the quality characteristics of fresh T. matsutake during its shelf life period in modified atmosphere packaging (MAP) conditions and establish its remaining shelf life prediction models in a cold chain. In this study, we measured and analyzed quality indicators of fresh T. matsutake, including hardness (cap, stipe), color, odor of sensory characteristics, pH, soluble solids content (SSC), and moisture content (MC) of physical and chemical characteristics under the temperature condition of 4 °C and relative humidity (RH) of 90%. The sensory evaluation results showed that the odor indicator in sensory characteristics was more sensitive to the freshness of T. matsutake. The changes of pH, SSC, and MC were divided into three periods to analyze the physiological changes of T. matsutake. The cap spread process could affect the changes of pH, SSC, and MC in period S1, and they changed gradually in period S2. In the period S3, they changed complicatedly because of deterioration. The remaining shelf life prediction model of T. matsutake was established by the back propagation (BP) neural network method to quantify the relationship between the quality indicators and the remaining shelf life. The shelf life characteristics are complex, which were optimized by correlation analysis. Significant benefits of this work are anticipated on the transportation and preservation of fresh T. matsutake to the market and the reduction of its losses in the postharvest chain.
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Xu, Zhonghua, Changguo Dai, Jing Wang, Lejun Liu, and Lei Jiang. "Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network." Advances in Civil Engineering 2021 (November 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/3918524.
Повний текст джерелаАнотація:
In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions.
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Torrieri, E., F. Russo, R. Di Monaco, S. Cavella, F. Villani, and F. Masi. "Shelf Life Prediction of Fresh Italian Pork Sausage Modified Atmosphere Packed." Food Science and Technology International 17, no. 3 (June 2011): 223–32. http://dx.doi.org/10.1177/1082013210382328.
Повний текст джерелаАнотація:
The shelf life of fresh Italian pork sausages packed in modified atmosphere was studied. Samples were packed using different levels of oxygen (high and low) with different levels of carbon dioxide (high-low) in the atmospheres headspace and were stored at 4 °C for 9 days. Microbial, physiochemical and sensory parameters were analyzed during storage. A consumer test was performed to determine the critical acceptability levels. Sensory data were mathematically modelled to estimate product shelf life. A first-order kinetic model and a Weibull-type model aptly described, respectively, the changes in fresh pork sausage odor and color over storage time. These models may be used to predict the sensory shelf life of fresh pork sausage. Results showed that 20% O2 and 70% CO2 extend fresh pork sausage shelf life to 9 days at 4 °C. The microbial quality of the samples at the critical sensory level of acceptability was within the range of microbial acceptability.
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Xue, Xiaoyu, Haiqing Tian, Kai Zhao, Yang Yu, Ziqing Xiao, Chunxiang Zhuo, and Jianying Sun. "Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging." Agriculture 14, no. 9 (September 22, 2024): 1653. http://dx.doi.org/10.3390/agriculture14091653.
Повний текст джерелаАнотація:
Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient (RP2), root mean square error of prediction (RMSEP), and a ratio of performance to deviation (RPD) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing.
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Vasconcelos, Lia, Luís G. Dias, Ana Leite, Iasmin Ferreira, Etelvina Pereira, Evandro Bona, Javier Mateo, Sandra Rodrigues, and Alfredo Teixeira. "Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?" Foods 12, no. 23 (December 1, 2023): 4335. http://dx.doi.org/10.3390/foods12234335.
Повний текст джерелаАнотація:
This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.
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Suryanti, Suryanti. "Shelf life assessment of fishery product. By: Suryanti and Theresia Dwi Suryaningrum." Squalen Bulletin of Marine and Fisheries Postharvest and Biotechnology 5, no. 1 (May 1, 2010): 8. http://dx.doi.org/10.15578/squalen.v5i1.41.
Повний текст джерелаАнотація:
The processing of fishery product is a preservation way to maintain its shelf life. The shelf life offishery product is very crucial, since it is an important source of animal protein which is easilydegraded by microbial activity and enzymatic reaction. Furthermore, fish also contains highunsaturated fatty acid which is easy to oxidize and produce rancid odor. The shelf life prediction offishery product is usually studied using conventional(ESS) and accelerated method (ASLT) Arheniusmodel. The conventional method predicts the shelf life of food product at the normal condition andobserve the parameter of quality degradation until the expired quality is reached. ASLT methodArhenius model predicts the shelf life of food product with accelerate the quality degradationbecause of effect temperature. The conventional method can be applied for wet and semi-wetproducts, such as fish fillet and fish burger, where as Arhenius model can be applied for wet, semiwet and dry products, such as frozen shrimp and dendeng fish.
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Anas, D. F., I. Jaya, and Nurjanah. "Design and implementation of fish freshness detection algorithm using deep learning." IOP Conference Series: Earth and Environmental Science 944, no. 1 (December 1, 2021): 012007. http://dx.doi.org/10.1088/1755-1315/944/1/012007.
Повний текст джерелаАнотація:
Abstract Organoleptic assessment of fresh fish includes specifications for the quality of the eyes, gills, mucus, odor, texture and flesh (color and appearance). However, not everyone has knowledge about it. This research uses the tiny yolov2 to facilitate the determination of fish freshness levels (good quality, medium quality, poor quality) correctly and fast. There are a few stages in this research, included organoleptic test accompanied by taking fish eye image dataset every hour, processing organoleptic test data labeling, training, and validation. There are three types of fish used, consists of Rastrelliger, Euthynnus affinis, and Chanos chanos. Detection of fish freshness level for three species was successfully carried out with the result of average precision is 72.9%, average recall is 57.5%, and accuracy is 57.5%. The factors that affect the prediction results in this study is the collection of datasets before the training process is carried out consisting of fish samples obtained from traditional markets, which are considered inadequate so that it affects the organoleptic test process itself, the organoleptic test that was carried out as a reference for image sorting was considered inaccurate because it used less than 30 untrained panelists and dataset imbalance.
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Mello, Renius, Fabiano Nunes Vaz, Paulo Santana Pacheco, Leonir Luiz Pascoal, Rosa Cristina Prestes, Patrícia Barcellos Costa, and Djenifer Kirch Kipper. "Predictive efficiency of distinct color image segmentation methods for measuring intramuscular fat in beef." Ciência Rural 45, no. 10 (October 2015): 1865–71. http://dx.doi.org/10.1590/0103-8478cr20141617.
Повний текст джерелаАнотація:
Intramuscular fat (IMF) influences important quality characteristics of meat, such as flavor, juiciness, palatability, odor and tenderness. Thus, the objective of this study was to apply the following image processing techniques to quantify the IMF in beef: palette; sampling, interval of coordinates; black and white threshold; and discriminant function of colors. Thirty-five samples of beef, with a wide range of IMF, were used. Color images were taken of the meat samples from different muscles, with variability in the IMF content. The IMF of a thin cross-section meat was determined by chemical lipid extraction and was predicted by image analysis. The chemical method was compared with the image analysis. The segmentation procedures were validated by the adjustment of a linear regression equation to the series of values that were observed and predicted, as well as the regression parameters evaluated by the F-test. The predictive power of these approaches was also compared by residual analysis and by the decomposition of the mean square deviations. The results showed that the discriminant function was the best color segmentation method to measure intramuscular fat via digital images, but required adjustments in the prediction pattern.
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Ramu, Kurinjimalar, M. Ramachandran, Vimala Saravanan, Manjula Selvam, and Sowmiya Soundharaj. "Big Data Analytics for Mobility Prediction and Its Classification." Data Analytics and Artificial Intelligence 2, no. 2 (June 1, 2022): 74–81. http://dx.doi.org/10.46632/daai/2/2/2.
Повний текст джерелаАнотація:
Kern forecasting is the detection of which customers may leave a service or cancel a subscription for a service. This is an important forecast for many businesses because gaining new customers will cost more than retaining existing customers. The Retail Banking Seer Forecast is an AI-based model that helps customers assess the likelihood of your bank being blocked. Odor is a good indicator of growth. By comparing and analyzing these two metrics that monitor lost customers, and growth rates, new customers, Kern rates accurately tell you how much your business is growing over time. If growth is greater than recession, you can say that your business is growing. With increasing skill requirements and requirements for quality of experience, mobility forecasting has become widely used for mobile communication and has become one of the key processors that use historical transport information to predict the future locations of traffic users. Predictive maintenance refers to the use of data-driven, efficient maintenance methods, via designed to analyze the Status and maintenance of equipment When Predict what needs to be done. Forecasting maintenance is a type of maintenance that directly monitors the health, condition and performance of an asset in real time. Predictive maintenance is aimed at minimizing costly and unexpected breakdowns and gives the manufacturer the opportunity to plan maintenance around their own production schedule. Some examples of the use of forecast maintenance and forecast maintenance sensors include vibration analysis, oil analysis, Includes thermal imaging and equipment monitoring. This approach guarantees cost of savings in routine or time-based preventive maintenance, due to the tasks are only done when guaranteed. Predictive maintenance techniques to determine the condition of equipment in service Designed to help and evaluate when maintenance should be done. Big Data Analytics is structured, semi-structured, Is the use of advanced analytical techniques against very large, diverse datasets. and an unstructured data of various sizes ranging from different sources to terabytes to zeta bytes. Choosing a career in the field of big data and Analytics can be an exciting career endeavor, and it can be the type of role you are trying to find. Machine learning (ML) is a prediction that considers large-scale multidimensional data from a variety of sources allows you to create models. Several studies have been conducted on the use of ML algorithms to predict road traffic. Traffic forecast is floating car data and traffic flow, average traffic speed and based on historical traffic data such as traffic events The task is to predict real-time traffic information.
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Mai, Nga T. T., Akin Y. Olanrewaju, and Luan V. Le. "Development of a Quality Index Method Scheme for Sensory Assessment of Chilled Yellowfin Tuna." Current Nutrition & Food Science 18, no. 2 (February 2022): 210–19. http://dx.doi.org/10.2174/1573401317666211007143709.
Повний текст джерелаАнотація:
Background: Quality monitoring and/or assessment are parts of a freshness/quality control system, which is of utmost importance for fresh seafood, especially Scombridae fish. The quality index method (QIM) is a simple, convenient, unique, and reliable tool to determine the sensory status and estimate the remaining shelf life of aqua products. Objective: This study aimed to develop a QIM scheme for chilled stored yellowfin tuna and apply the protocol in the fish quality evaluation and storage time estimation. Method: Eight gutted yellowfin tuna of 20, 30, and 40 kg up were used in the study. Five panelists participated in the QIM development, training and application. Control and/or validation analyses were sensory assessment by a control sheet, total volatile basic nitrogen (TVB-N) quantification, and total viable count (TVC) determination. Chilled storage of tuna was performed in liquid ice and traditional crushed block ice. Partial least square regression (PLS-R) was conducted on quality index (QI) dataset over storage time to find the regression line and prediction accuracy. Results: The established QIM protocol for gutted yellowfin tuna comprised 6 attributes (namely, color of whole fish, odor of whole fish and flesh, eyes, appearance of whole fish, flesh color and flesh texture) and a maximal QI of 15. The PLS-R showed that QI could be used to estimate the remaining time with a precision of ± 2.0 and 1.4 days for fish stored in slurry ice and crushed ice, respectively. The TVB-N content in the fish flesh maintained below the acceptable level of 25 mg N/100 g throughout the storage period, which made the parameter impractical to detect the fish shelf life. The TVC overreached the allowable level of 107 CFU/g around the time of fish rejection by the sensory method. Conclusion: The developed QIM scheme for yellowfin tuna showed to be more advantageous in detecting fish quality changes compared to the control sensory method and could be used to estimate the fish's remaining shelf life.
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Li, Huanhuan, Xin Zhang, Xiaojuan Gao, Xiaoxuan Shi, Shuang Chen, Yan Xu, and Ke Tang. "Comparison of the Aroma-Active Compounds and Sensory Characteristics of Different Grades of Light-Flavor Baijiu." Foods 12, no. 6 (March 14, 2023): 1238. http://dx.doi.org/10.3390/foods12061238.
Повний текст джерелаАнотація:
This study comprehensively characterized and compared the aroma differences between four different grades of Fenjiu (FJ, the most representative light-flavor Baijiu). Aroma-active compounds were analyzed by liquid-liquid extraction (LLE) coupled with gas chromatography-olfactometry-mass spectrometry (GC-O-MS). A total of 88 aroma-active compounds were identified, and 70 of them were quantified. The results showed that a majority of aroma compounds in high-grade FJ had higher aroma intensities and concentrations. Among these compounds, there were 28 compounds with odor activity values (OAVs) greater than one in all four wines, which indicated that they might contribute to the characteristic aroma of FJ. Temporal dominance of sensation (TDS) and quantitative descriptive analysis (QDA) were used to characterize the sensory differences. The results suggested that high-grade FJ had a rich, pleasant and lasting retronasal aroma perception and exhibited pleasant orthonasal aroma of floral, fruity, sweet and grassy. Partial least squares regression (PLSR) analysis effectively distinguished four kinds of FJ and revealed associations between the orthonasal aroma attributes and the aroma compounds with OAVs >1. There were 15 compounds with variable importance in projection (VIP) values >1, and they were considered potential aroma markers for quality prediction.
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Hussain, Ayaz, Umar Draz, Tariq Ali, Saman Tariq, Muhammad Irfan, Adam Glowacz, Jose Alfonso Antonino Daviu, Sana Yasin, and Saifur Rahman. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach." Energies 13, no. 15 (August 1, 2020): 3930. http://dx.doi.org/10.3390/en13153930.
Повний текст джерелаАнотація:
Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.
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Bedrníček, Jan, Ivana Laknerová, František Lorenc, Priscila Probio de Moraes, Markéta Jarošová, Eva Samková, Jan Tříska, Naděžda Vrchotová, Jaromír Kadlec, and Pavel Smetana. "The Use of a Thermal Process to Produce Black Garlic: Differences in the Physicochemical and Sensory Characteristics Using Seven Varieties of Fresh Garlic." Foods 10, no. 11 (November 5, 2021): 2703. http://dx.doi.org/10.3390/foods10112703.
Повний текст джерелаАнотація:
Black garlic (BG) is a product originating from fresh garlic (FG) and substantially differs in many aspects from FG due to the process called ageing. During this thermal process, the health-promoting properties of FG are enhanced, and the sensory traits are altered. However, very little is known about how the physicochemical properties of different FG varieties affect these properties of BG. Thus, the aim of this study was to investigate the influence of seven FG varieties subjected to the thermal process on the physicochemical parameters of BG. To prepare the BG samples, a fifteen-day ageing process involving a temperature gradient ranging from 30 to 82 °C was used. It was found that the antioxidant activity, the total polyphenol content, and the total soluble solids increased during ageing, while the pH level, moisture content, and lightness decreased in all the garlic varieties. The varieties of garlic differed in the studied traits significantly, both before (FG) and after ageing (BG). In the sensory analysis, significant differences between the BG varieties were observed only in the pleasantness of texture, while the remaining sensory descriptors (pleasantness of color, odor, taste and intensity of the garlic aroma, and overall acceptability) were not affected by variety. The correlations suggest that most of the FG’s studied parameters in this study do not correlate with the properties of BG and cannot be used for the prediction of the quality of BG. Additionally, HPLC-MS/MS analysis revealed substantial changes in the composition of low molecular compounds.
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Coppola, D. M., C. T. Waggener, S. M. Radwani, and D. A. Brooks. "An electroolfactogram study of odor response patterns from the mouse olfactory epithelium with reference to receptor zones and odor sorptiveness." Journal of Neurophysiology 109, no. 8 (April 15, 2013): 2179–91. http://dx.doi.org/10.1152/jn.00769.2012.
Повний текст джерелаАнотація:
Olfactory sensory neuron (OSN) responses to odors, measured at the population level, tend to be spatially heterogeneous in the vertebrates that have been studied. These response patterns vary between odors but are similar across subjects for a given stimulus. However, few species have been studied making functional interpretation of these patterns problematic. One proximate explanation for the spatial heterogeneity of odor responses comes from evidence that olfactory receptor (OR) genes in rodents are expressed in OSN populations that are spatially restricted to a few zones in the olfactory epithelium (OE). A long-standing functional explanation for response anisotropy in the OE posits that it is the signature of a supplementary mechanism for quality coding, based on the sorptive properties of odor molecules. These theories are difficult to assess because most mapping studies have utilized few odors, provided little replication, or involved but a single species (rat). In fact, to our knowledge, a detailed olfactory response “map” has not been reported for mouse, the species used in most studies of gene localization. Here we report the results of a study of mouse OE response patterns using the electroolfactogram (EOG). We focused on the medial aspect of olfactory turbinates that are accessible in the midsagittal section. This limited approach still allowed us to test predictions derived from the zonal distribution of OSN types and the sorption hypothesis. In 3 separate experiments, 290 mice were used to record EOGs from a set of standard locations along each of 4 endoturbinates utilizing 11 different odors resulting in over 4,400 separate recordings. Our results confirmed a marked spatial heterogeneity in odor responses that varied with odor, as seen in other species. However, no discontinuities were found in the odor-specific response patterns across the OE as might have been predicted given the existence of classical receptor zones nor did we find clear support for the hypothesis that OE response patterns, presumably a reflection of OSN distribution, have been shaped through natural selection by the relative sorptive properties of odors. We propose that receptor zones may be an epiphenomenon of a contingent evolutionary process. In this formulation, constraints on developmental programs for distributing OSN classes within the OE may be minimally related to the odor ligands of specific class members. Further, we propose that odor sorptiveness, which appears to be correlated with the inherent response patterns in the OE of larger species, may be of minimal effect in mice owing to scaling issues.
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Zhang, Yan, Weihua Yang, Günther Schauberger, Jianzhuang Wang, Jing Geng, Gen Wang, and Jie Meng. "Determination of Dose–Response Relationship to Derive Odor Impact Criteria for a Wastewater Treatment Plant." Atmosphere 12, no. 3 (March 12, 2021): 371. http://dx.doi.org/10.3390/atmos12030371.
Повний текст джерелаАнотація:
Municipal wastewater treatment plants (WWTPs) inside cities have been the major complained sources of odor pollution in China, whereas there is little knowledge about the dose–response relationship to describe the resident complaints caused by odor exposure. This study explored a dose–response relationship between the modelled exposure and the annoyance surveyed by questionnaires. Firstly, the time series of odor concentrations were preliminarily simulated by a dispersion model. Secondly, the perception-related odor exposures were further calculated by combining with the peak to mean factors (constant value 4 (Germany) and 2.3 (Italy)), different time periods of “a whole year”, “summer”, and “nighttime of summer”, and two approaches of odor impact criterion (OIC) (“odor-hour” and “odor concentration”). Thirdly, binomial logistic regression models were used to compare kinds of perception-related odor exposures and odor annoyance by odds ratio, goodness of fit and predictive ability. All perception-related odor exposures were positively associated with odor annoyance. The best goodness of fit was found when using “nighttime of summer” in predicting odor-annoyance responses, which highlights the importance of the time of the day and the time of the year weighting. The best predictive performance for odor perception was determined when the OIC was 4 ou/m3 at the 99th percentile for the odor exposure over time periods of nighttime of summer. The study of dose–response relationship could be useful for the odor management and control of WWTP to maximize the satisfaction of air quality for the residents inside city.
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Gostelow, P., S. A. Parsons, and M. Lovell. "Integrated odour modelling for sewage treatment works." Water Science and Technology 50, no. 4 (August 1, 2004): 169–76. http://dx.doi.org/10.2166/wst.2004.0253.
Повний текст джерелаАнотація:
Odours from sewage treatment works are a significant source of environmental annoyance. There is a need for tools to assess the degree of annoyance caused, and to assess strategies for mitigation of the problem. This is the role of odour modelling. Four main stages are important in the development of an odour problem. Firstly, the odorous molecules must be formed in the liquid phase. They must then transfer from the liquid to the gaseous phase. They are then transported through the atmosphere to the population surrounding the odour source, and are then perceived and assessed by that population. Odour modelling as currently practised tends to concentrate on the transportation of odorants through the atmosphere, with the other areas receiving less attention. Instead, odour modelling should consider each stage in an integrated manner. This paper describes the development of integrated odour models for annoyance prediction. The models describe the liquid-phase transformations and emission of hydrogen sulphide from sewage treatment processes. Model output is in a form suitable for integration with dispersion models, the predictions of which can in turn be used to indicate the probability of annoyance. The models have been applied to both hypothetical and real sewage treatment works cases. Simulation results have highlighted the potential variability of emission rates from sewage treatment works, resulting from flow, quality and meteorological variations. Emission rate variations can have significant effects on annoyance predictions, which is an important finding, as they are usually considered to be fixed and only meteorological variations are considered in predicting the odour footprint. Areas for further development of integrated odour modelling are discussed, in particular the search for improved links between analytical and sensory measurements, and a better understanding of dose/response relationships for odour annoyance.
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Wise, Kimber, Nicholas Phan, Jamie Selby-Pham, Tomer Simovich, and Harsharn Gill. "Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour." PLOS ONE 18, no. 4 (April 25, 2023): e0284842. http://dx.doi.org/10.1371/journal.pone.0284842.
Повний текст джерелаАнотація:
Cannabis flower odour is an important aspect of product quality as it impacts the sensory experience when administered, which can affect therapeutic outcomes in paediatric patient populations who may reject unpalatable products. However, the cannabis industry has a reputation for having products with inconsistent odour descriptions and misattributed strain names due to the costly and laborious nature of sensory testing. Herein, we evaluate the potential of using odour vector modelling for predicting the odour intensity of cannabis products. Odour vector modelling is proposed as a process for transforming routinely produced volatile profiles into odour intensity (OI) profiles which are hypothesised to be more informative to the overall product odour (sensory descriptor; SD). However, the calculation of OI requires compound odour detection thresholds (ODT), which are not available for many of the compounds present in natural volatile profiles. Accordingly, to apply the odour vector modelling process to cannabis, a QSPR statistical model was first produced to predict ODT from physicochemical properties. The model presented herein was produced by polynomial regression with 10-fold cross-validation from 1,274 median ODT values to produce a model with R2 = 0.6892 and a 10-fold R2 = 0.6484. This model was then applied to terpenes which lacked experimentally determined ODT values to facilitate vector modelling of cannabis OI profiles. Logistic regression and k-means unsupervised cluster analysis was applied to both the raw terpene data and the transformed OI profiles to predict the SD of 265 cannabis samples and the accuracy of the predictions across the two datasets was compared. Out of the 13 SD categories modelled, OI profiles performed equally well or better than the volatile profiles for 11 of the SD, and across all SD the OI data was on average 21.9% more accurate (p = 0.031). The work herein is the first example of the application of odour vector modelling to complex volatile profiles of natural products and demonstrates the utility of OI profiles for the prediction of cannabis odour. These findings advance both the understanding of the odour modelling process which has previously only been applied to simple mixtures, and the cannabis industry which can utilise this process for more accurate prediction of cannabis odour and thereby reduce unpleasant patient experiences.
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Zarra, Tiziano, Vincenzo Belgiorno, and Vincenzo Naddeo. "Environmental Odour Nuisance Assessment in Urbanized Area: Analysis and Comparison of Different and Integrated Approaches." Atmosphere 12, no. 6 (May 28, 2021): 690. http://dx.doi.org/10.3390/atmos12060690.
Повний текст джерелаАнотація:
Prolonged exposure to odour emissions causes annoyance which leads to nuisance and consequently to complaints. Different methodologies exist in the literature to evaluate odour impacts, but not all are suitable to assess environmental odour nuisance. Information about their applicability criteria and comparison, is scarce and referred to short time analysis. The research presents and discusses the application of different methods to characterize and assess odour nuisance around an industrial plant localized in a sensitive area. Experimental activities are carried out through a long-time analysis programme. Field inspections and predictive methods are investigated and compared. A modification of the traditional dispersion modelling approach is proposed in order to adapt its application for the prediction of the odour nuisance. The offensiveness and location factors are identified as key parameters in the quantification of the perceived nuisance. The integrated dispersion modelling multi-level approach is highlighted as the most suitable for defining the plant strategies. The paper provides useful information to characterize environmental odour problems and identify appropriate solutions for an effective management of odorous sources, with the aim of reducing complaints, restoring the proper relationship between odorous plants and the surrounding communities and increasing the overall quality of the environment.
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Parsons, S. A., N. Smith, P. Gostelow, and J. Wishart. "Hydrogen sulphide dispersion modelling - urban and rural case studies." Water Science and Technology 41, no. 6 (March 1, 2000): 117–26. http://dx.doi.org/10.2166/wst.2000.0100.
Повний текст джерелаАнотація:
Sewage treatment works are subject to a range of parameters governing the quality of effluent and sludge produced. An additional product from treatment plants is odorous air. The causes, source, formation and measurement of odour are widely reported and reasonably understood. An important factor in the design and management of works is the prediction of such odours. The importance of this work is explained by the possibility of future legislation controlling odour at wastewater plants. Odour dispersion modelling involves the on-site measurement or prediction of the emission rate of an odorous compound, oftenhydrogen sulphide, and the subsequent prediction of the atmospheric concentrations of that compound downwind of the source. This paper used the USEPA models SCREEN3 and ISCST to determine hydrogen sulphide contour concentrations emitted from unit processes at two different sewage treatment works in the United Kingdom. Results indicated that the first site, locatedin an urban catchment, emitted hydrogen sulphide at varying rates. The predicted downwind concentrations using “urban” dispersion coefficients correlated well with measured concentrations. At the second site, emission rates were less variable. Results from the second site produced the best correlation using “rural” dispersion coefficients. Results from both sites suggest that the definition of the surrounding land use is critical in predicting odour dispersion. The problem of determining land use is highlighted and the importance of correct meteorology is stressed. Both sites were predicted to be capable of producing hydrogen sulphide concentrations at a detectable level outside the site boundary. Odour complaints were therefore anticipated. The operational performance of a unit treatment operation is proposed as a major influence on hydrogen sulphide emission. The idea of a large database of expected emission rates from individual unit treatment processes is proposed as an input for dispersion modelling and as an aid to future design.
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Barea-Sepúlveda, Marta, José Luis P. Calle, Marta Ferreiro-González, and Miguel Palma. "Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level." Foods 13, no. 9 (April 27, 2024): 1352. http://dx.doi.org/10.3390/foods13091352.
Повний текст джерелаАнотація:
The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box–Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%. Analytical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), efficiently categorized samples by odor intensity. The Gaussian support vector machine (SVM), random forest, partial least squares regression, and support vector regression (SVR) algorithms were evaluated for their efficacy in odor grade classification and quantification. Gaussian SVM emerged as superior in classification tasks, achieving 100% accuracy, while Gaussian SVR excelled in quantifying odor levels, with a coefficient of determination (R2) of 0.9667 and a root mean square error (RMSE) of 6.789. This approach offers a fast, reliable, robust, objective, and reproducible alternative to the current ASTM sensory panel assessments, leveraging the analytical capabilities of HS-GC/MS and the predictive power of ML for quality control in the petrochemical sector’s food-grade paraffin waxes.
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Heijman, S. G. J., W. Siegers, R. Sterk, and R. Hopman. "Prediction of breakthrough of pesticides in GAC-filters and breakthrough of colour in ion-exchange-filters." Water Supply 2, no. 1 (January 1, 2002): 103–8. http://dx.doi.org/10.2166/ws.2002.0013.
Повний текст джерелаАнотація:
Adsorption columns are widely used in drinking water treatment to improve a number of water quality parameters. Granular activated carbon filtration (GAC) can be used to decrease the concentration of DOC, colour, taste, odour and pesticides. Columns filled with ion-exchange resins are used to remove colour, nitrate and DOC. The regeneration frequency of these types of filters depends strongly on the natural water quality. Especially the DOC-concentration and DOC-composition determines the efficiency of the processes. Because pilot-plant experiments with realistic contact-times will last for more than a year (for GAC) there is a need for a prediction of breakthrough based on shorter and less expensive laboratory experiments. The available models are not accurate enough because the exact parameters are not available. In batch experiments with natural water, with realistic (low) pesticide concentrations and the full grain size of the adsorbent the adsorption parameters are measured in an independent experiment. With the parameters obtained with these experiments an accurate prediction of the breakthrough curve is possible. With the same parameters predictions of breakthrough curves are calculated under different process conditions. The possibilities of process optimisation can reduce the investment costs for new full-scale plants.
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Ahn, Jung Min, Jungwook Kim, Lan Joo Park, Jihye Jeon, Jaehun Jong, Joong-Hyuk Min, and Taegu Kang. "Predicting Cyanobacterial Harmful Algal Blooms (CyanoHABs) in a Regulated River Using a Revised EFDC Model." Water 13, no. 4 (February 8, 2021): 439. http://dx.doi.org/10.3390/w13040439.
Повний текст джерелаАнотація:
Cyanobacterial Harmful Algal Blooms (CyanoHABs) produce toxins and odors in public water bodies and drinking water. Current process-based models predict algal blooms by modeling chlorophyll-a concentrations. However, chlorophyll-a concentrations represent all algae and hence, a method for predicting the proportion of harmful cyanobacteria is required. We proposed a technique to predict harmful cyanobacteria concentrations based on the source codes of the Environmental Fluid Dynamics Code from the National Institute of Environmental Research. A graphical user interface was developed to generate information about general water quality and algae which was subsequently used in the model to predict harmful cyanobacteria concentrations. Predictive modeling was performed for the Hapcheon-Changnyeong Weir–Changnyeong-Haman Weir section of the Nakdong River, South Korea, from May to October 2019, the season in which CyanoHABs predominantly occur. To evaluate the success rate of the proposed model, a detailed five-step classification of harmful cyanobacteria levels was proposed. The modeling results demonstrated high prediction accuracy (62%) for harmful cyanobacteria. For the management of CyanoHABs, rather than chlorophyll-a, harmful cyanobacteria should be used as the index, to allow for a direct inference of their cell densities (cells/mL). The proposed method may help improve the existing Harmful Algae Alert System in South Korea.
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Chong, Suna, Heesuk Lee, and Kwang-Guk An. "Predicting Taste and Odor Compounds in a Shallow Reservoir Using a Three–Dimensional Hydrodynamic Ecological Model." Water 10, no. 10 (October 9, 2018): 1396. http://dx.doi.org/10.3390/w10101396.
Повний текст джерелаАнотація:
The objective of this study was to establish a technique to predict the occurrence of algal bloom and the algal-derived taste and odor compounds 2-methylisoborneol (2-MIB) and geosmin using a three-dimensional (3D) model that could reflect the complex physical properties of a shallow reservoir. Water quality, phytoplankton, and taste and odor compounds monitoring was conducted at the Jinyang Reservoir in 2016. In June, there was a potential for a high concentration of 2-MIB (maximum 80 ng/L) to occur owing to the appearance of Pseudanabaena sp.; additionally, from July to August, there was potential for a high concentration of geosmin (maximum 108 ng/L) to occur, because of the presence of Anabaena sp. A 3D hydrodynamic model was coupled with an ecological model to predict cyanobacteria bloom and the presence of taste and odor compounds. Cyanobacteria producing either 2-MIB or geosmin were distinguished to enhance the accuracy of the modeled predictions. The results showed that the simulations of taste and odor compounds spatial distribution and occurrence time were realistic; however, the concentration of geosmin was overestimated when Microcystis sp. was blooming. The model can be used as a management tool to predict the occurrence of algal taste and odor compounds in reservoir systems and to inform decision-making processes concerning dam operation and water treatment.
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Giaffar, Hamza, Sergey Shuvaev, Dmitry Rinberg, and Alexei A. Koulakov. "The primacy model and the structure of olfactory space." PLOS Computational Biology 20, no. 9 (September 10, 2024): e1012379. http://dx.doi.org/10.1371/journal.pcbi.1012379.
Повний текст джерелаАнотація:
Understanding sensory processing relies on the establishment of a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.
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Cui, Yang, Yuebao Yao, Ruiqi Yang, Yashun Wang, Jingni Liang, Shaoqin Ouyang, Shulin Yu, Huiqin Zou, and Yonghong Yan. "Detection of Mildewed Nutmeg Internal Quality during Storage Using an Electronic Nose Combined with Chemical Profile Analysis." Molecules 28, no. 16 (August 14, 2023): 6051. http://dx.doi.org/10.3390/molecules28166051.
Повний текст джерелаАнотація:
Internal mildewed nutmeg is difficult to perceive without cutting the nutmeg open and examining it carefully, which poses a significant risk to public health. At present, macroscopic identification and chromatographic analysis are applied to determine whether nutmeg is moldy or not. However, the former relies on a human panel, with the disadvantages of subjectivity and empirical dependence, whilst the latter is generally time-consuming and requires organic solvents. Therefore, it is urgent to develop a rapid and feasible approach for evaluating the quality and predicting mildew in nutmeg. In this study, the quality and odor characteristics of five groups of nutmeg samples with different degrees of mildew were analyzed by using the responses of an electronic nose combined with chemical profiling. The main physicochemical indicators, such as the levels of α-pinene, β-pinene, elemicin, and dehydro-di-isoeugenol, were determined. The results revealed that the contents of α-pinene, β-pinene, and elemicin changed significantly with the extension of storage time. Through the use of an electronic nose and HS–GC–MS technology to assess the overall odor characteristics of nutmeg samples, it was found that the production of volatile organic compounds (VOCs) such as ammonia/organic amines, carbon monoxide, ethanol, and hydrogen sulfide, as well as changes in the terpene and phenylpropene components of the nutmeg itself, may be the material basis for the changes in odor. The accuracy of the qualitative classification model for the degree of mildew in nutmeg was higher than 90% according to the electronic nose data combined with different machine learning algorithms. Quantitative models were established for predicting the contents of the chemical components, and models based on a BP neural network (BPNN), the support vector machine (SVM), and the random forest algorithm (RF) all showed good performance in predicting the concentrations of these chemical components, except for dehydro-di-isoeugenol. The BPNN performed effectively in predicting the storage time of nutmeg on the basis of the E-nose’s responses, with an RMSE and R2 of 0.268 and 0.996 for the training set, and 0.317 and 0.993 for the testing set, respectively. The results demonstrated that the responses of the electronic nose (E-nose) had a high correlation with the internal quality of nutmeg. This work proposes a quick and non-destructive evaluation method for the quality of nutmeg, which has high accuracy in discriminating between different degrees of mold in nutmeg and is conducive to early detection and warning of moldy phenomena.
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Manzocco, Lara, Giulia Romano, Sonia Calligaris, and Maria Cristina Nicoli. "Modeling the Effect of the Oxidation Status of the Ingredient Oil on Stability and Shelf Life of Low-Moisture Bakery Products: The Case Study of Crackers." Foods 9, no. 6 (June 5, 2020): 749. http://dx.doi.org/10.3390/foods9060749.
Повний текст джерелаАнотація:
In packed low-moisture foods such as crackers, oxidation is generally the main cause of quality depletion during storage. It is commonly believed, but scarcely investigated, that product shelf life depends on the oxidative status of the lipid ingredients. In this study, the influence of oxidation degree of the ingredient sunflower oil on cracker oxidative stability and hence shelf life was investigated. To this aim, oil with increasing peroxide values (PVs) (5, 11, and 25 mEqO2/kgoil) was used to prepare crackers. Just after production, crackers presented similar peroxide and rancid odor intensity, probably due to the interactive pathways of oxidative and Maillard reactions. Crackers were packed and analyzed for PV and rancid odor during storage at 20, 40, and 60 °C. Rancid odor well discriminated cracker oxidative status. Relevant oxidation rates were used to develop a shelf life predictive model based on the peroxide value of the ingredient oil. It was estimated that an oil PV from 5 to 15 mEqO2/kgoil shortens cracker Shelf Life (SL) by 50%, independently of storage temperature. These results demonstrate the critical impact of ingredient quality on product performance on the market.
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Sucker, K., R. Both, and G. Winneke. "Adverse effects of environmental odours: reviewing studies on annoyance responses and symptom reporting." Water Science and Technology 44, no. 9 (November 1, 2001): 43–51. http://dx.doi.org/10.2166/wst.2001.0505.
Повний текст джерелаАнотація:
Air pollution control authorities dealing with odourous emissions from industrial, municipal and agricultural activities are often faced with many complaints from the public. In Germany, the Directive on Odour in ambient air provides a regulation system for the abatement of odour annoyance. Ambient air quality standards have been established based on investigations of the relationship between ambient odour load and community annoyance reaction. This paper describes a tool for the assessment of annoyance reactions, whereby degree of annoyance is correlated with ambient odour load. Systematic exposure response relations have been established for odour annoyance responses and symptom reporting for a variety of industrial sources. However, the precision of annoyance prediction from odour exposure measures rarely exceeds r2 = 0.17 in such studies. This is partly due to the fact that person-related factors, such as age, perceived health or stress coping styles, modify exposure response relations. The contribution of intensity and unpleasantness (hedonic tone) of ambient odours as modifying the annoyance reaction is currently investigated.
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Kaya, Aydin, Ali Seydi Keçeli, Cagatay Catal, and Bedir Tekinerdogan. "Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model." Sensors 20, no. 11 (June 3, 2020): 3173. http://dx.doi.org/10.3390/s20113173.
Повний текст джерелаАнотація:
For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.
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Brown, G. J., and D. F. Fletcher. "CFD Prediction of Odour Dispersion and Plume Visibility for Alumina Refinery Calciner Stacks." Process Safety and Environmental Protection 83, no. 3 (May 2005): 231–41. http://dx.doi.org/10.1205/psep.04007.
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Hachi, T., M. Hachi, H. Essabiri, D. Belghyti, and E. H. Abba. "Impact of bad odors from the wastewater treatment plant on the well-being of the population of M’rirt (Morocco)." IOP Conference Series: Earth and Environmental Science 1090, no. 1 (October 1, 2022): 012016. http://dx.doi.org/10.1088/1755-1315/1090/1/012016.
Повний текст джерелаАнотація:
Abstract In the city of M’rirt, the odour nuisance caused by the wastewater treatment plant (WWTP) has led to numerous complaints from residents whose quality of life has been affected by these odours. Therefore, the objective of our study is to evaluate, in the field, the “real” impact of bad odours emanating from the WWTP with the help of a questionnaire during a period of 6 months. Our study sample composed of 1473 respondents chosen in a simple random way from the two municipalities surrounding the said odour source. The survey results revealed that among the 1473 respondents 71 (n=1040) confirmed “yes” the odour of the WWTP presents a real olfactory annoyance and 29% said “no” and that several factors are involved in its prediction. The statistical analysis of our study confirmed the negative impact of WWTP odours on the well-being of the city’s population, its spatial and temporal distribution with the establishment of a map of the olfactory discomfort in the city.
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Zhang, Yang, Lei Zhang, Yabin Ma, Jinsen Guan, Zhaoxia Liu, and Jihui Liu. "Research on dairy products detection based on machine learning algorithm." MATEC Web of Conferences 355 (2022): 03008. http://dx.doi.org/10.1051/matecconf/202235503008.
Повний текст джерелаАнотація:
In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.
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Kent, P. F., S. L. Youngentob, and P. R. Sheehe. "Odorant-specific spatial patterns in mucosal activity predict perceptual differences among odorants." Journal of Neurophysiology 74, no. 4 (October 1, 1995): 1777–81. http://dx.doi.org/10.1152/jn.1995.74.4.1777.
Повний текст джерелаАнотація:
1. Using operant techniques, rats were trained to differentially report (i.e., identify) the odorants propanol, carvone, citral, propyl acetate, and ethylacetoacetate. After acquisition training, the animals were tested using a 5 x 5 confusion matrix design. The results of the behavioral tests were used to measure the degree of perceptual dissimilarity between any pair of odorants. These dissimilarity measures were then subjected to multidimensional scaling analysis to establish a two-dimensional perceptual odor space for each rat. 2. At the completion of behavioral testing, the fluorescence changes in the dye di-4-ANEPPS were monitored on the rat's nasal septum and medial surface of the turbinates in response to the same odorants. For each mucosal surface a 6.0 x 6.0 mm area was sampled at 100 contiguous sites with a 10 x 10 photodiode array. 3. Formal statistical analysis indicated a highly significant predictive relationship between the relative position of an odorant's mucosal loci of maximal activity or “hot spot” and the relative position of the same odorant in a psychophysically determined perceptual odor space (F = 15.6, P < 0.001). 4. The results of this study suggest for the first time that odorant-induced mucosal activity patterns serve as the substrate for the perception of odorant quality.
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Menyhárt, József, and Ferenc Kalmár. "Investigation of Thermal Comfort Responses with Fuzzy Logic." Energies 12, no. 9 (May 11, 2019): 1792. http://dx.doi.org/10.3390/en12091792.
Повний текст джерелаАнотація:
In order to reduce the energy consumption of buildings a series of new heating, ventilation and air conditioning strategies, methods, and equipment are developed. The architectural trends show that office and educational buildings have large glazed areas, so the thermal comfort is influenced both by internal and external factors and discomfort parameters may affect the overall thermal sensation of occupants. Different studies have shown that the predictive mean vote (PMV)—predictive percentage of dissatisfied (PPD) model poorly evaluates the thermal comfort in real buildings. At the University of Debrecen a new personalized ventilation system (ALTAIR) was developed. A series of measurements were carried out in order to test ALTAIR involving 40 subjects, out of which 20 female (10 young and 10 elderly) and 20 male (10 young and 10 elderly) persons. Based on the responses of subjects related to indoor environment quality, a new comfort index was determined using fuzzy logic. Taking into consideration the responses related to thermal comfort sensation and perception of odor intensity a new the fuzzy comfort index was 5.85 on a scale from 1–10.
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Budarina, Olga A., Svetlana A. Skovronskaya, and Svetlana V. Ivanova. "International experience of air pollution assessment in areas where enterprises with odorous emissions are located (literature review)." Hygiene and sanitation 101, no. 11 (November 30, 2022): 1299–306. http://dx.doi.org/10.47470/0016-9900-2022-101-11-1299-1306.
Повний текст джерелаАнотація:
The article provides a review of literature data on approaches to the ambient air pollution assessment in the areas where enterprises - sources of odour are located. According to the analysis, international practice in the field of odour management in the atmosphere includes a variety of methods of the odour impact assessing. The considered approaches, both predictive and observational (empirical), have their advantages and disadvantages. Thus, atmospheric dispersion modelling is a very valuable predictive tool and plays an important role in assessing ambient odours. However, the models, although based on rigorous quantitative calculations, are a simplification of the real situation. The accuracy of this method is significantly reduced in cases of unpredictable, unplanned or accidental releases. An easier-to-use tool is a qualitative (descriptive) risk-based odour assessment (source-pathway-receptor concept). Empirical approaches (field olfactometry, sniff tests) make it possible to assess odour exposure in given real conditions, while more objective assessment requires long-term studies. The use of instrumental methods is limited by the fact that odours in the air are mainly due to complex multicomponent mixtures of substances with an unknown nature of the combined action, with levels below the detection limits, etc. When developing an odour assessment strategy, it is necessary to select the tools that are most appropriate in each case. According to many authors, to improve the quality and reliability of this assessment in areas where enterprises and other facilities are located, it is advisable to use all available empirical approaches together with modelling, in combination with community surveys and other methods of analyzing the health status of the population. The data obtained as a result of such a comprehensive assessment will make it possible to substantiate measures to reduce air pollution by odorous substances. The literature search was carried out in the English-language text databases PubMed, Scopus, Web of Science and in the scientific electronic library eLIBRARY.ru. (RSCI)
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Mu, Fanglin, Yu Gu, Jie Zhang, and Lei Zhang. "Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques." Sensors 20, no. 15 (July 30, 2020): 4238. http://dx.doi.org/10.3390/s20154238.
Повний текст джерелаАнотація:
In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.
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Walker, J. C. "The performance of the human nose in odour measurement." Water Science and Technology 44, no. 9 (November 1, 2001): 1–7. http://dx.doi.org/10.2166/wst.2001.0496.
Повний текст джерелаАнотація:
Over the last 20 years or so, there has been steadily increasing activity in the area of applied human odour measurement. This has been especially true outside of the United States. Yet, for about 40 years, there has also been decreasing interest and activity, on the part of academic smell researchers, in rigorous quantitative measurement of the functional properties of the human olfactory system. There are some optimistic signs, however, that this situation may be improving. Applied meetings such as this one are reaching out to learn more about basic research in human olfaction and some research groups are venturing out to indoor air quality, environmental health, water quality and other applied areas. In this paper I hope to support and accelerate the increasingly fruitful interactions that are beginning. The paper aims to make four main points. First, some of the most important ways in which the laboratory differs from everyday life will be noted. Keeping these differences in mind lessens the risk that laboratory data will be used uncritically to make predictions of real-world responses to chemical stimuli. Next, the specific benefits that would accrue from more fruitful interactions between basic and applied researchers will be highlighted; this is perhaps best seen by noting problem areas resulting from too little cross-fertilisation. Third, the CEN standard for the measurement of odour thresholds will be discussed in light of what is known concerning both the functional aspects of the human olfactory system and the current state of knowledge concerning best methods for investigating this system. Finally, some recent work we have done that was designed to help characterise human odour responses and demonstrate improved methodology, will be briefly mentioned. The paper concludes with suggestions as to how the scientific basis of applied odour measurement may best be enhanced.
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RUSSELL, SCOTT M. "Capacitance Microbiology as a Means of Determining the Quantity of Spoilage Bacteria on Fish Fillets." Journal of Food Protection 61, no. 7 (July 1, 1998): 844–48. http://dx.doi.org/10.4315/0362-028x-61.7.844.
Повний текст джерелаАнотація:
An experiment was conducted to determine if a method for enumeration of Pseudomonas fluorescens in less than 11 h could be used to predict potential spoilage of fresh fish of four species. In each of three separate replications (Rep), five boneless fillets from each species of fish, including rainbow trout (RT), Atlantic salmon (AS), red grouper (RG), and tilapia (T) were obtained fresh from a retail outlet. For each species, six 25-g samples of fish flesh were asceptically removed from each fillet, placed into a polyethylene bag, and stored at 3°C for 0,1, 2, 3,4, or 5 days. After storage, samples were analyzed for psychrotrophic plate count (PPC), Pseudomonas fluorescens plate counts (PFPC), and P. fluorescens capacitance detection times (PDT) and subjectively evaluated for odor (ODOR). PPC gradually increased on all fish species as storage time increased. In most cases, PFPC decreased slightly and then progressively increased as storage time increased. In Reps 1 and 2, PDT decreased gradually (indicating an increase in bacteria); however, in Rep 3, PDT were erratic and difficult to interpret. Odor increased gradually throughout the storage period for all fish species. Linear correlations (R2 > 0.80) were observed between PPC and day of storage (DAY) for all fish species and Reps except for RT and RG in Rep 3. PFPC correlated (R2 > 0.70) to DAY for all fish except RT in Rep 3 and RG in Rep 2. PDT was negatively correlated to DAY for RT and T in Rep 1 and for all fish in Rep 2. Odor scores were highly correlated (R2 ≥ 0.84) to DAY for all fish tested. PPC and PDT were negatively correlated for RT in Reps 1 and 2, AS in Rep 2, and T in Reps 1 and 2. Because results can be obtained in < 12 h, the capacitance procedure with further refinement may provide an excellent alternative to conducting PPC as a means of predicting potential spoilage of fish such that the fillets of inferior quality (i.e., those that will spoil rapidly) may be sent to distribution outlets that are known to move fish products quickly and are able to sell the fish before it spoils.
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Samrat, Nahidul Hoque, Joel B. Johnson, Simon White, Mani Naiker, and Philip Brown. "A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger." Foods 11, no. 5 (February 23, 2022): 649. http://dx.doi.org/10.3390/foods11050649.
Повний текст джерелаАнотація:
Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.
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Renumarn, Phanida, and Natthaya Choosuk. "Influence of Packaging and Storage Conditions on the Quality and Shelf-life of Chewy Santol (Kraton-Yee) Candies." E3S Web of Conferences 141 (2020): 02002. http://dx.doi.org/10.1051/e3sconf/202014102002.
Повний текст джерелаАнотація:
In the present study, influence of two types packaging (inflated polypropylene (IPP) and laminated aluminium foil (ALU)) and storage conditions (with/without 1 g of silica desiccant packets (SDPs)) on quality and shelf life of chewy santol candies were studied. After storage at 25 degrees Celsius for 30 days, it was found that the combination of ALU with SDPs presented the best treatment to maintain the quality of colour, water activity (aw), moisture content, total acidity, pH value, sensory evaluation i.e. (colour, odour, flavour and overall acceptance). Shelf-life prediction by using accelerated Q10 method based on moisture factors as an indicator of deterioration of the samples during storage. The samples were incubated at 25, 35 and 45°C and sampled every 5 days for estimated on physical, chemical quality and microbiological change. The predicted shelf life of chewy santol candies at 25°C of IPP and ALU packaging with SDPs using Q10 method were 25 and 27 days, respectively. However, the IPP and ALU packaging without SDPs, the products can be stored less than 25 days. SDPs provides a cheaper and easy method to keeping quality of the chewy santol candies. Therefore, the products with SDPs packaging can maintain the product quality during storage and has an acceptable quality to consumers.
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Kaihena, Frida I., Edward G. Tetelepta, and Susan E. Manakane. "Analysis of Clean Water Quality and Quantity for Domestic Needs in Rutong." Jurnal Pendidikan Geografi Unpatti 3, no. 2 (August 10, 2024): 163–75. http://dx.doi.org/10.30598/jpguvol3iss2pp163-175.
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This study evaluates the quality and quantity of clean water available for domestic needs in Rutong Village, South Leitimur District, Ambon City. This study uses a quantitative descriptive research approach to provide a comprehensive overview of the clean water situation in the area. The population of this study comprises the residents of Rutong Village, with samples randomly selected from 10 households using Water Waihula and Water Cabang Dua or Saplaring. Data collection techniques include observation, documentation, and interviews. Quality analysis of clean water involves laboratory tests covering physical, biological, and chemical measurements. Quantity analysis is conducted through water flow rate calculations while predicting water demand utilizing equations based on population and standard water requirements per individual. Test results indicate that the quality of clean water in Rutong Village meets the standards set by Permenkes No. 32 of 2017. All physical, chemical, and biological parameters comply with permissible maximum standards, including odour, taste, TDS, turbidity, temperature, colour, iron, hardness, chloride, zinc, and coliform bacteria. However, despite meeting quality standards, it was found that the quantity of clean water available still needs to meet the community's needs sufficiently. On average, residents can only use 120 litres of water per day, indicating a need for improvement in the clean water supply to adequately meet the community's needs.