Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy

2020 ◽  
Vol 147 ◽  
pp. 106566 ◽  
Author(s):  
Manuela Mancini ◽  
Alex Mircoli ◽  
Domenico Potena ◽  
Claudia Diamantini ◽  
Daniele Duca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Machine Learning Techniques ◽  
Pellet Quality ◽  
Learning Techniques

Discrimination of “Louros” wood from the Brazilian Amazon by near-infrared spectroscopy and machine learning techniques

2021 ◽  
Author(s):  
Joielan Xipaia dos Santos ◽  
Helena Cristina Vieira ◽  
Deivison Venicio Souza ◽  
Marlon Costa de Menezes ◽  
Graciela Inés Bolzon de Muñiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brazilian Amazon ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

2021 ◽  
Vol 8 ◽  
Author(s):  
Lei Feng ◽  
Baohua Wu ◽  
Susu Zhu ◽  
Yong He ◽  
Chu Zhang
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Food Quality ◽  
Near Infrared ◽  
Imaging Techniques ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
Food Fraud ◽  
Learning Techniques

Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

scholarly journals Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions

2020 ◽  
Author(s):  
Oselyne Ong ◽  
Elise Kho ◽  
Pedro Esperança ◽  
Chris Freebairn ◽  
Floyd Dowell ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Field Application ◽  
Machine Learning Techniques ◽  
Island Population ◽  
Natural Environments ◽  
Pls Regression ◽  
Field Methods ◽  
The Impact

Abstract Background: Practical, field-ready age-grading tools for mosquito vectors of disease are urgently needed because of the impact that daily survival has on vectorial capacity. Previous studies have shown that near-infrared spectroscopy (NIRS), in combination with chemometrics and predictive modeling, can forecast the age of laboratory-reared mosquitoes with moderate to high accuracy. It remains unclear whether the technique has utility for identifying shifts in the age structure of wild-caught mosquitoes. Here we investigate whether models derived from the laboratory strain of mosquitoes can be used to predict the age of mosquitoes grown from pupae collected in the field. Methods: NIR spectra from adult female Aedes albopictus mosquitoes reared in the laboratory (2, 5, 8, 12 and 15 days old) were compared to spectra from mosquitoes emerging from wild-caught pupae (1, 7 and 14 days old). Different partial least squares (PLS) regression methods trained on spectra from laboratory mosquitoes were evaluated on their ability to predict the age of mosquitoes from more natural environments. Results: Models trained on spectra from laboratory-reared material were able to predict the age of other laboratory-reared mosquitoes with moderate accuracy and successfully differentiated all day 2 and 15 mosquitoes. Models derived with laboratory mosquitoes could not differentiate between field-derived age groups, with age predictions relatively indistinguishable for day 1-14. Pre-processing of spectral data and improving the PLS regression framework to avoid overfitting can increase accuracy, but predictions of mosquitoes reared in different environments remained poor. Principle component analysis confirms substantial spectral variations between laboratory and field-derived mosquitoes despite both originating from the same island population. Conclusions: Models trained on laboratory mosquitoes were able to predict ages of laboratory mosquitoes with good sensitivity and specificity though they were unable to predict age of field-derived mosquitoes. This study suggests that laboratory-reared mosquitoes do not capture enough environmental variation to accurately predict the age of the same species reared under different conditions. Further research is needed to explore alternative pre-processing methods and machine learning techniques, and to understand factors that affect absorbance in mosquitoes before field application using NIRS.

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