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...

The Analysis and Detection Research of Different Coal Samples Based on near Infrared Spectroscopy

2013 ◽  
Vol 634-638 ◽  
pp. 1651-1654
Author(s):  
Peng Qiang Chen ◽  
Hui Shan Lu ◽  
Hong Wei Yan ◽  
Qiang Gao
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Quantitative Model ◽  
Quantitative Detection ◽  
Processing Methods ◽  
Qualitative And Quantitative

The present study is concerning qualitative and quantitative detection of different coal samples based on near infrared spectroscopy. Firstly, near infrared spectroscopy combined with discriminant analysis (DA) was used for the coal cinder characteristics in different spectral pre-processing methods, at the same time it will establish the quantitative of the total moisture. The result indicated that 97.78% recognition ratio for calibration and 93.33% recognition ratio for validation were achieved by DA for the cinder characteristics. The PLS quantitative model of the total moisture of the establishment of the original spectral was the best, the correlation coefficients of calibration and prediction respectively were 0.981 and 0.641, RMSEC, RMSEP and RMSECV respectively were 0.859, 2.51 and 3.44.

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.

scholarly journals Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Automated Recognition ◽  
Textile Materials ◽  
Input Material ◽  
Adequate Quality ◽  
Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Measurement of the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion Based on near Infrared Spectroscopy

2011 ◽  
Vol 301-303 ◽  
pp. 1093-1097 ◽  
Author(s):  
Shi Rong Ai ◽  
Rui Mei Wu ◽  
Lin Yuan Yan ◽  
Yan Hong Wu
Keyword(s):  
Amino Acids ◽  
Infrared Spectroscopy ◽  
Correlation Coefficient ◽  
Green Tea ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tea Polyphenols ◽  
Strong Absorption ◽  
Tea Infusion

This study attempted the feasibility to determine the ratio of tea polyphenols to amino acids in green tea infusion using near infrared (NIR) spectroscopy combined with synergy interval PLS (siPLS) algorithms. First, SNV was used to preprocess the original spectra of tea infusion; then, siPLS was used to select the efficient spectra regions from the preprocessed spectra. Experimental results showed that the spectra regions [7 8 18] were selected, which were out of the strong absorption of H2O. The optimal PLS model was developed with the selected regions when 6 PCs components were contained. The RMSEP value was equal to 0.316 and the correlation coefficient (R) was equal to 0.8727 in prediction set. The results demonstrated that NIR can be successfully used to determinate the ration of tea polyphenols to amino acids in green tea infusion.

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.

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

2019 ◽  
Vol 27 (4) ◽  
pp. 286-292
Author(s):  
Chongchong She ◽  
Min Li ◽  
Yunhui Hou ◽  
Lizhen Chen ◽  
Jianlong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Quality Parameter ◽  
Solidification Point ◽  
Fast Analysis ◽  
Online Application

The solidification point is a key quality parameter for 2,4,6-trinitrotoluene (TNT). The traditional solidification point measurement method of TNT is complicated, dangerous, not environmentally friendly and time-consuming. Near infrared spectroscopy (NIR) analysis technology has been applied successfully in the chemical, petroleum, food, and agriculture sectors owing to its characteristics of fast analysis, no damage to the sample and online application. The purpose of this study was to study near infrared spectroscopy combined with chemometric methods to develop a fast and accurate quantitative analysis method for the solidification point of TNT. The model constructed using PLS regression was successful in predicting the solidification point of TNT ([Formula: see text] = 0.999, RMSECV = 0.19, RPDCa = 33.5, [Formula: see text] = 0.19, [Formula: see text] = 0.999). Principal component analysis shows that the model could identify samples from different reactors. The results clearly demonstrate that the solidification point can be measured in a short time by NIR spectroscopy without any pretreatment for the sample and skilled laboratory personnel.

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