Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage

Dizaji, Hassan Zaki; Adibzadeh, Abdullah; Nategh, Nahid Aghili

doi:10.1007/s13197-020-04879-4

Cited by 5 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With these models, they were able to estimate the drying time of mint leaves with 93% accuracy. Zaki Dizaji, et al [ 32 ] used the electronic nose technique and the PLS, PCR, and MLR models to predict the quality of sugarcane syrup based on the purity and the percentage of refined sugar. They stated that the accuracy of the PLS method for this purpose was 77 and 71%, respectively; they also reported that the accuracy of the PCR method was 72% and 68%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

et al. 2022

View full text Add to dashboard Cite

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The difference in responses of consumers may present differentiated preference patterns [26]. While, in classic consumer science, liking is measured with the 9-point hedonic scale [27,28], principal component analysis (PCA) is widely used as a multivariate analytical, statistical technique that can be applied to quantitative descriptive analysis for sensory evaluation of various food and dairy products [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Developing an Internal Preference Map for Assessing Cow's and Camel's Milk Enriched with Date Syrup

Elamshity,

Alhamdan

2023

Preprint

View full text Add to dashboard Cite

There is a need to develop enriched milk drinks with healthy and natural additives. A consumer test was conducted to compare milk (cow and camel) flavored with date syrup (Khlass and Sukkary varieties) at different percentage. The objective of the present work was to study consumers liking patterns for flavored milk drinks from dates syrup (from Khlass and Sukkary dates), at different percentages, added to milk (cow and camel) utilizing preference mapping analysis. A total of sixteen trained panelists took part in the test. The obtained data were used to develop an internal preference map for these samples. Principal component analysis (PCA) and Agglomerative hierarchical clustering (AHC) algorithms were used to construct the preference map. The first component signifies overall acceptance of taste and color, while the second focuses on texture and flavor. Consumers were grouped into five clusters based on preferences. Cluster two favored both components positively, while cluster four leaned negatively on both. A factorial ANOVA was performed and the results showed a significant effect of the main factors as well as the interactions. Panelists preferred cow's milk over Camel’s milk in all aspects. For taste, cow’s milk with 20% Khlass syrup or 10% Sukkary syrup was favored, while in flavor, it was cow’s milk with 5% Khlass syrup and Camel’s milk with 10% Sukkary syrup. Texture-wise, Camel’s milk with 5% Khlass syrup and 15% Sukkary syrup, along with cow’s milk with 10% Khlass syrup, received top marks. In color evaluation, cow’s milk with 15% Sukkary syrup was the preferred choice. Finally, for overall color score, cow’s milk with 10% Sukkary syrup led, followed by cow’s milk with 5% Sukkary syrup. This research suggests that dairy companies may have more impact if they use date syrup as a natural and healthy flavor. The preference map includes two main dimensions; the first represents overall acceptance, taste, and color while the second is related to flavor and texture attributes. The preference map can be used in product optimization and development.

show abstract

“…and effective method, allowing for immediate monitoring and analysis. Nevertheless, in order to fully exploit the capabilities of e-nose technology, it is crucial to have uninterrupted connectivity and reliable data transmission [4], [5]…”

mentioning

confidence: 99%

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Ayad A. Al-Ani

2024

jes

View full text Add to dashboard Cite

Electronic Noses (e-noses) are advanced sensors that are being increasingly used in the Internet of Things (IoT) field. They have various applications in environmental monitoring and medical diagnostics. Optimal transmission of odor-related data is essential for fully harnessing the capabilities of these electronic noses in various network environments. This research paper examines the issue of transmitting odor data in Internet of Things (IoT) applications by assessing recent progress in communication and network technologies. A thorough analysis of various network alternatives, such as Ethernet, cellular (4G/5G), Wi-Fi, Bluetooth, Zigbee, and LoRa, is provided, accompanied by an elaborate table displaying precise technical specifications. The analysis of LoRa, a Low-Power Wide-Area Network (LPWAN) technology, encompasses an investigation into its architecture, modulation method, device classifications, and protocols such as LoRaWAN. The analysis of Edge Computing and cloud-based approaches for managing odor data in IoT culminates in a tabular presentation that succinctly outlines their advantages and disadvantages. This study provides valuable information to enhance the efficiency of transmitting odor data and effectively utilize electronic nose technology in different emerging applications in the Internet of Things (IoT) field.

show abstract

Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage

Cited by 5 publications

References 24 publications

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Developing an Internal Preference Map for Assessing Cow's and Camel's Milk Enriched with Date Syrup

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Contact Info

Product

Resources

About