Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical grain properties in rice

Barnaby, Jinyoung Y.; Huggins, Trevis D.; Lee, Hoonsoo; McClung, Anna M.; Pinson, Shannon R. M.; Oh, Mirae; Bauchan, Gary R.; Tarpley, Lee; Lee, Kangjin; Kim, Moon S.; Edwards, Jeremy D.

doi:10.1038/s41598-020-65999-7

Cited by 35 publications

(26 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Barnaby et al correlated the grain chalk of rice to the genomic regions of NIR spectra. These spectral regions can be applied in the automation of grain chalk quantification or for other grain products as well [101].…”

Section: Practical Applications Of Nir Spectroscopy and Chemometrics 21 Nir Spectroscopy In Rice Analysis: Identification And Classificatmentioning

confidence: 99%

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Sampaio¹,

Brites²

2022

Integrative Advances in Rice Research

View full text Add to dashboard Cite

Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants.

show abstract

Section: Practical Applications Of Nir Spectroscopy and Chemometrics 21 Nir Spectroscopy In Rice Analysis: Identification And Classificatmentioning

confidence: 99%

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Sampaio¹,

Brites²

2022

Integrative Advances in Rice Research

View full text Add to dashboard Cite

show abstract

“…Lighting conditions may vary between the samples and even within the samples across the scan line. A common way to calculate this effect is to convert measured raw spectra to reflectance spectra by the following formula: 29,35 GLS is used to achieve an efficiency by transforming the variance covariance matrix into a homoscedastic one. 36 It works as a filter that calculates the differences between the samples.…”

Section: Data Handling and Softwarementioning

confidence: 99%

Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

Ktash

Hauler

Ostertag

et al. 2020

J. Spectral Imaging

View full text Add to dashboard Cite

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

show abstract

“…They determined the NIR range where two genotypes of rice display major differences which allows high accuracy sorting of rice based on these characteristics. Barnaby et al [118] correlated the grain chalk of rice to the genomic regions of NIR spectra. These spectral regions can be applied in the automation of grain chalk quantification and potentially for other grain products as well.…”

Section: Grains (Rice Cereal) and Potatoesmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

View full text Add to dashboard Cite

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

show abstract

Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical grain properties in rice

Cited by 35 publications

References 69 publications

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Contact Info

Product

Resources

About