Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review

Peng, Wei; Beggio, Giovanni; Pivato, Alberto; Zhang, Hua; Lü, Fan; He, Pinjing

doi:10.1016/j.rser.2022.112608

Cited by 21 publications

(10 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This expansion of the databases would enable testing for improvement of the correlation and RPD values. Although NIRS has been used extensively to predict methane production in biogas production [ 42 ] and in rumen batch culture fermentation [ 28 ], to the best of our knowledge this is the first time to have calibrations between feed NIR spectra and CH 4 yields measured from animal experiments. The calibrations could be improved by using alternative algorithms [ 43 ] and combining other techniques, such as terahertz time-domain spectroscopy [ 44 ], in addition to the expansion of the database.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

Sun

Pacheco

Taylor

et al. 2022

Animals

View full text Add to dashboard Cite

Feed chemical composition is associated with methane (CH4) formation in the rumen, and thus CH4 yields (Ym; CH4 emitted from per unit of dry matter intake) could be predicted using near-infrared reflectance spectroscopy (NIRS) of feeds fed to ruminants. Two databases of NIRS data were compiled from feeds used in experiments in which CH4 yields had been quantified in respiration chambers. Each record in the databases represented a batch of feed offered to a group of experimental animals and the mean CH4 yield for the group. A near-infrared reflectance spectrum was obtained from each feed, and these spectra were used to generate a predictive equation for Ym. The predictive model generated from brassica crops and pasture fed at a similar feeding level (n = 40 records) explained 53% of the variation in Ym and had a reasonably good agreement (concordance correlation coefficient of 0.77). The predictive ability of the NIRS calibration could be useful for screening purposes, particularly for predicting the potential Ym of multiple feeds or feed samples, rather than measuring Ym in animal experiments at high expenses. It is recommended that the databases for NIRS calibrations are expanded by collecting feed information from future experiments in which methane emissions are measured, using alternative algorithms and combining other techniques, such as terahertz time-domain spectroscopy.

show abstract

Section: Resultsmentioning

confidence: 99%

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

Sun

Pacheco

Taylor

et al. 2022

Animals

View full text Add to dashboard Cite

show abstract

“…La utilización de sensores con resoluciones espaciales de mayor detalle podría aportar datos significativos sobre la morfología de la vegetación y su contenido en nitrógeno (Yuping et al, 2009). Por lo tanto, en estudios con imposibilidad de obtener imágenes de mayor resolución espacial, o imágenes hiperespectrales (Lim et al, 2020;Peng et al, 2022), sería conveniente realizar análisis de campo con el fin de mejorar la identificación de vertidos de purines con una menor incertidumbre, y reducir los errores en la valoración de la contaminación en niveles inferiores (Witheetrirong et al, 2011;Ben et al, 2021). También son de gran ayuda, la utilización de técnicas geofísicas como la tomografía eléctrica, que permitiría interpretar mejor los procesos de lixiviación de los purines en el subsuelo (Capa-Camacho et al, 2022).…”

Section: Limitaciones Del Estudiounclassified

Análisis de la reflectividad espectral del suelo en la contaminación por purines procedente de la ganadería intensiva

Valero

Oudenhove

Pérez‐Cutillas

2023

ETFVI

View full text Add to dashboard Cite

El sector porcino en España representa el 14% de la producción total agraria, siendo un sector de gran importancia en la economía nacional. Pero las explotaciones de ganadería intensiva suponen un riesgo para la salud pública y el medio ambiente. Las pérdidas de nitratos por lixiviación provenientes del vertido de purines a las aguas superficiales y subterráneas se han convertido en un grave problema medioambiental en la actualidad. En este trabajo se analizaron mediante técnicas de teledetección un conjunto de imágenes del satélite Sentinel-2 para analizar la reflectividad de los vertidos de purines. Se emplearon imágenes de periodos estivales e invernales entre los años 2015 y 2022 para caracterizar la respuesta espectral de dos parcelas, una de vertidos de purines y otra de control. Los resultados obtenidos mostraron cambios significativos en la humedad y cobertura del suelo, que podrían estar relacionadas al vertido de purines. Sin embargo, estos datos no están exentos de incertidumbre, los cuales podrían estar influenciados por las diferencias en la estructura, textura y composición de los suelos, por los fenómenos climáticos, así como por las características de las imágenes de satélite utilizadas (frecuencia de captura y resolución espacial-espectral del sensor). A pesar de estas limitaciones, se ha podido observar un mayor rango espectral en la parcela de vertidos durante los meses de verano respecto al invierno debido a las diferencias de humedad captadas en el espectro de infrarrojo medio (SWIR).

show abstract

“…These techniques also have the advantages of high accuracy, high practicability, and nondestructive detection. HSI has received increasing attention in recent years as it offers a fast and nondestructive measurement for finding objects, identifying materials, and detecting compositions based on rich spatial spectrum information . Additionally, with the rapid advancement of algorithms and hardware architectures, automatic recognition methods based on machine learning have been widely used .…”

Section: Introductionmentioning

confidence: 99%

“…HSI has received increasing attention in recent years as it offers a fast and nondestructive measurement for finding objects, identifying materials, and detecting compositions based on rich spatial spectrum information. 8 Additionally, with the rapid advancement of algorithms and hardware architectures, automatic recognition methods based on machine learning have been widely used. 9 Therefore, a novel method that combines HSI and machine learning is likely to be promising for waste identification, and further research on this is urgently required.…”

Section: Introductionmentioning

confidence: 99%

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Lan

et al. 2023

ACS EST Eng.

Self Cite

View full text Add to dashboard Cite

Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixelwise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.

show abstract

Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review

Cited by 21 publications

References 77 publications

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

Análisis de la reflectividad espectral del suelo en la contaminación por purines procedente de la ganadería intensiva

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Contact Info

Product

Resources

About