scholarly journals Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 117
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes ◽  
Carmen Hernandez-Brenes
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Near Infrared ◽  
Low Cost ◽  
Regularization Algorithm ◽  
Quality Product ◽  
Brewing Process ◽  
Artificial Neural Network Ann

Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification machine learning (ML) modelling. Four different ML models were developed; Models 1 (M1) and M2 based on NIR (100 inputs from 1596–2396 nm) and M3 and M4 based on the e-nose (nine sensor readings as inputs) and 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neuron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 98.9%, M2 = 98.3%, M3 = 96.8%, and M4 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.

scholarly journals Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5948
Author(s):  
Sigfredo Fuentes ◽  
Eden Tongson ◽  
Ranjith R. Unnithan ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Management Practices ◽  
Near Infrared ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Plant Interactions ◽  
Aphid Infestation

Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.

Fusion of a Low-cost Electronic Nose and Near Infrared Spectroscopy for Qualitative and Quantitative Detection of Beef Adulterated with Duck

2022 ◽  
Author(s):  
FangKai Han ◽  
Xingyi Huang ◽  
Joshua Harington Aheto ◽  
Xiaorui Zhang ◽  
Marwan M.A. Rashed
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Near Infrared ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Quantitative Detection ◽  
Qualitative And Quantitative ◽  
Convenient Technique ◽  
Colorimetric Sensors

A low-cost electronic nose (E-nose) based on colorimetric sensors fused with near-infrared (NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total...

scholarly journals Fast Determination of the Rubber Content in Taraxacum Kok-Saghyz Fresh Biomass Using Portable Near Infrared Spectroscopy and Pyrolysis-Gas Chromatography

2021 ◽  
Author(s):  
Ying Chen ◽  
Dong Yiyang ◽  
Xiang Ma ◽  
Jiaru Li ◽  
Minmin Guo ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Infrared Spectroscopy ◽  
Natural Rubber ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Low Cost ◽  
Rubber Content ◽  
Pyrolysis Gas ◽  
Pyrolysis Gas Chromatography

Abstract Background: Taraxacum kok-saghyz (TKS), a plant native to the Tianshan valley on the border between China and Kazakhstan and inherently rich in natural rubber, inulin and other bioactive ingredients, is an important industrial crop. TKS rubber is a good substitute for natural rubber. TKS's breeding work necessitates the need to screen high-yielding varieties, hence rapid determination of rubber content is essential for the screening. Conventional analytical methods cannot meet actual needs in terms of real-time testing and economic cost. Near-infrared spectroscopy analysis technology, which has developed rapidly in the field of industrial process analysis in recent years, is a green detection technology with obvious merits of fast measurement speed, low cost and no sample loss. This research aims to develop a portable non-destructive near-infrared spectroscopic detection scheme to evaluate the content of natural rubber in TKS fresh roots. Pyrolysis gas chromatography (PyGC), was chosen as the reference method for the development of NIR prediction model. Results: 208 TKS fresh root samples were collected from the Inner Mongolia Autonomous Region of China. Near-infrared spectra were acquired for all samples. Randomly two-thirds of them were selected as the calibration set, the remaining one-third as the verification set, and the partial least squares method was successfully used to establish a good NIR prediction model at 1080-1800nm with a performance to deviation ratio (RPD) of 5.54 and coefficient of determination (R2) of 0.95. Conclusions: This study showed that portable near-infrared spectroscopy could be used with ease for large-scale screening of TKS plants in farmland, and could greatly facilitate TKS germplasm preservation, high-yield cultivation, environment-friendly, high-efficiency and low-cost rubber extraction, and comprehensive advancement of the dandelion rubber industry thereof.

ANALYSIS OF PEACHES RIPENESS BY AN ELECTRONIC NOSE AND NEAR-INFRARED SPECTROSCOPY

2002 ◽  
Author(s):  
S. CAPONE ◽  
C. DISTANTE ◽  
M. EPIFANI ◽  
R. RELLA ◽  
P. SICILIANO ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Near Infrared
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