Determination of oil content in whole corn (Zea mays L.) seeds by means of near infrared reflectance spectroscopy

Fassio, A.; Restaino, E.; Cozzolino, Daniel

doi:10.1016/j.compag.2014.11.015

Cited by 23 publications

(13 citation statements)

References 31 publications

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“…Fassio et al. established a model to determine the crude fat content of corn and obtained an R 2 value of .900 in external validation of the model (Fassio, Restaino, & Cozzolino, ). Xiaodong Mao et al.…”

Section: Resultsmentioning

confidence: 99%

Determination of soybean routine quality parameters using near‐infrared spectroscopy

ZhenYing

Chen

et al. 2018

Food Science & Nutrition

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Large differences in quality existed between soybean samples. In order to rapidly detect soybean quality between samples from different areas, we have developed near‐infrared spectroscopy (NIRS) models for the moisture, crude fat, and protein content of soybeans, based on 360 soybean samples collected from different areas. Compared with whole kernels, soybean powder with particle sizes of 60 mesh was more suitable for modeling of moisture, crude fat, and protein content. To increase the reproducibility of the prediction model, uniform particle sizes of soybeans were prepared by grinding and sieving soybeans with different sizes and colors. Modeling analysis showed that the internal cross‐validation correlation coefficients (R cv) for the moisture, crude fat, and protein content of soybeans were .965, .941, and .949, respectively, and the determination coefficients (R 2) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters and provided reference data for the analysis of soybean quality using FT‐NIRS.

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Section: Resultsmentioning

confidence: 99%

Determination of soybean routine quality parameters using near‐infrared spectroscopy

ZhenYing

Chen

et al. 2018

Food Science & Nutrition

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“…Fassio et al. evaluated the potential of NIRS to determine oil contents in whole corn ( Zea mays L.) seeds. Grunvald et al.…”

Section: Near‐infrared Spectroscopymentioning

confidence: 99%

“…Laroussi-Mezghani et al [69] characterized six Tunisian olive oils (Chemchali, Chemlalisfax, Chetoui, Oueslati, Zalmati, and Zarrazi); FA compositions and NIR spectra associated with chemometric treatment were determined to predict the varietal origin of Tunisian olive oils. Fassio et al [70] evaluated the potential of NIRS to determine oil contents in whole corn (Zea mays L.) seeds. Grunvald et al [71] separated sunflower genotypes with different contents of oleic and linoleic acids through discriminant analysis on NIRS.…”

Section: Near-infrared Spectroscopymentioning

confidence: 99%

Comparison of critical methods developed for fatty acid analysis: A review

Zhang

et al. 2016

J of Separation Science

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Fatty acids are important nutritional substances and metabolites in living organisms. These acids are abundant in Chinese herbs, such as Brucea javanica, Notopterygium forbesii, Isatis tinctoria, Astragalus membranaceus, and Aconitum szechenyianum. This review illustrates the types of fatty acids and their significant roles in the human body. Many analytical methods are used for the qualitative and quantitative evaluation of fatty acids. Some of the methods used to analyze fatty acids in more than 30 kinds of plants, drugs, and other samples are presented in this paper. These analytical methods include gas chromatography, liquid chromatography, near-infrared spectroscopy, and NMR spectroscopy. The advantages and disadvantages of these techniques are described and compared. This review provides a valuable reference for establishing methods for fatty acid determination.

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“…Scientific records of NIRS applications in seeds are sparse with much innovative implementation of this technique being in laboratory and industrial applications. Due to their advantages, near infrared spectroscopy (NIRS) techniques have been widely utilized for the quality analysis of crop seeds and kernels including quantitative analyses of oil content [ 28 ], protein [ 29 , 30 ], water [ 31 ], sugar, starch and carbohydrate content [ 8 , 32 , 33 ], viability and vigor detection [ 15 , 34 , 35 , 36 ], purity or variety discrimination [ 37 ], detection of disease and insect infestation [ 38 ], detection of physical damage [ 15 , 17 ] and other applications [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

ElMasry

Mandour

Al-Rejaie

et al. 2019

Sensors

137

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As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

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Determination of oil content in whole corn (Zea mays L.) seeds by means of near infrared reflectance spectroscopy

Cited by 23 publications

References 31 publications

Determination of soybean routine quality parameters using near‐infrared spectroscopy

Determination of soybean routine quality parameters using near‐infrared spectroscopy

Comparison of critical methods developed for fatty acid analysis: A review

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

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