Predicting rapeseed oil content with near-infrared spectroscopy

Rossato, Roberta; Prete, Cássio Egídio Cavenaghi; Castro, C. de; Tomm, G. O.; Leite, R. S.; Mandarino, J. M. G.; Araújo, Pedro Mário de; Carvalho, C. G. P. de

doi:10.1590/s0100-204x2013001200010

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…For each model, the validation set shows statistics very close to the calibration set, illustrating robustness and absence of overfitting of the models. Concerning oil content, the model described in this paper for Arabidopsis display better performance than the ones described for rapeseed (Tkachuk, 1981; Hom et al, 2007; Rossato et al, 2013). Interestingly, the models were developed on entire seeds without destruction neither any special sample preparation, which is a great advantage compared to calibration developed on powder or oil for example (Khamchum et al, 2013) as it’s faster and allow the seeds to be used for other applications.…”

Section: Discussionmentioning

confidence: 71%

“…In recent decades, NIRS has been widely used as a fast and reliable method for qualitative and quantitative analysis in many fields (Font et al, 2006) and International Standards Committees have formally accepted methods using NIRS for analysis of many compounds (Batten, 1998). Regarding Brassica seeds, many authors have reported NIRS models for different components, such as glucosinolates (Velasco and Becker, 1998; Font et al, 2004), fiber (Font et al, 2003), protein and oil contents (Tkachuk, 1981; Font et al, 2002a,b; Rossato et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

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Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy

Jasinski¹,

Lécureuil²,

Durandet³

et al. 2016

Front. Plant Sci.

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Seed storage compounds are of crucial importance for human diet, feed and industrial uses. In oleo-proteaginous species like rapeseed, seed oil and protein are the qualitative determinants that conferred economic value to the harvested seed. To date, although the biosynthesis pathways of oil and storage protein are rather well-known, the factors that determine how these types of reserves are partitioned in seeds have to be identified. With the aim of implementing a quantitative genetics approach, requiring phenotyping of 100s of plants, our first objective was to establish near-infrared reflectance spectroscopic (NIRS) predictive equations in order to estimate oil, protein, carbon, and nitrogen content in Arabidopsis seed with high-throughput level. Our results demonstrated that NIRS is a powerful non-destructive, high-throughput method to assess the content of these four major components studied in Arabidopsis seed. With this tool in hand, we analyzed Arabidopsis natural variation for these four components and illustrated that they all displayed a wide range of variation. Finally, NIRS was used in order to map QTL for these four traits using seeds from the Arabidopsis thaliana Ct-1 × Col-0 recombinant inbred line population. Some QTL co-localized with QTL previously identified, but others mapped to chromosomal regions never identified so far for such traits. This paper illustrates the usefulness of NIRS predictive equations to perform accurate high-throughput phenotyping of Arabidopsis seed content, opening new perspectives in gene identification following QTL mapping and genome wide association studies.

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Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy

Jasinski¹,

Lécureuil²,

Durandet³

et al. 2016

Front. Plant Sci.

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“…[20][21][22] Modern instruments have incorporated Fourier-Transformation (FT) to NIR instruments (or FTNIR) to reduce the signal to noise ratio. 14,19,23 The use of FTNIR spectroscopy has been reported for the prediction of oil content, fatty acids, moisture, and protein in different plant species such as Brassica napus, [24][25][26] Camelina sativa, 27 Carthamus tinctorius, 23 Olea europaea, 10 Glycine max (soyabeans), 28 Paeonia sect Moutan, 13 Sesamum indicum, 29 Silybum marianum, 7 and Theobroma cacao 30 and Zea mays. 4 However, no studies have been conducted or reported on the utilization of FTNIR spectroscopy to analyse beauty leaf tree samples.…”

Section: Introductionmentioning

confidence: 99%

Predicting oil content of Australian beauty leaf tree kernel samples using near infrared spectroscopy combined with chemometrics

Sreekumar,

Ashwath,

Cozzolino

et al. 2024

Journal of Near Infrared Spectroscopy

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This study was conducted to evaluate the ability of near infrared (NIR) spectroscopy to estimate oil content, and per cent of cake, resin and residue in beauty leaf tree ( Calophyllum inophyllum L.) kernel samples. Fruits were collected from various geographical locations of tropical Australia (from Rockhampton to Darwin) and air dried before the kernels were manually separated from the fruits. Kernel samples were oven dried, crushed (5–10 mm) and their NIR spectra collected using a Fourier transform (FT) NIR instrument where the same batch of kernels were used to extract oil using a screw press. Calibration models between the NIR spectra and reference data were developed using partial least squares (PLS) regression. The cross-validation statistics including the coefficient of determination (r2) and standard error in cross validation (SECV) were 0.83 (SECV: 2.39%) for oil content, 0.89 (SECV: 2.81%) for cake, 0.88 (SECV: 1.92%) for resin and 0.79 (SECV: 2.15%) for residue, respectively. This research showed that NIR spectroscopy can be used as an alternative, faster and low-cost technique to predict oil content, per cent of cake, resins and residues in various genotypes of beauty leaf tree. Further studies should be carried out to increase the sample size and chemical variation, as well as to evaluate different methods of oil extraction (e.g., solvent extraction) to improve the reliability of the calibration models.

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“…Seed research has been successful in evaluating quality, such as soybeans, cotton and tomato (Bazoni et al, 2017;Gaitán-Jurado et al, 2008;Huang et al, 2013;Shrestha et al, 2016), viability in maize and spinach (Ambrose et al, 2016;Shetty et al, 2012), composition and prediction of phytic acid in Vigna radiata (Pande and Mishra, 2015), nitrogen content in Vigna unguiculata (Ishikawa et al, 2017), oil composition in sunflower and canola (Grunvald et al, 2014;Rossato et al, 2013), as well as the classification of genotypes in cotton, barley, castor beans and maize (Cui et al, 2012;Jia et al, 2015;Ringsted et al, 2016;Santos et al, 2014;Soares et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Use of near infrared spectroscopy in cotton seeds physiological quality evaluation

et al. 2020

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This study aimed to evaluate the near-infrared spectroscopy potential in analyzing the quality of cottonseed regarding different physiological quality levels, noting the need for faster techniques and tools to aid decision making. It was used eight samples of cottonseed with and without lint, presenting different physiological quality. The “high” (lots 1, 4, 5, 6 and 7) and “low” (lots 2, 3 and 8) vigor levels were defined based on vigor tests carried out and on the Normative Instruction 45/2013. The near infrared spectroscopy spectra was obtained from four types of sample preparations: whole seeds, cut in a half, without tegument and grounded seeds. Using the spectra and the grouping of lots in high and low vigor, cross validation models were optimized, built using the PLS - DA method, making it possible to predict seed classes. Grounded seeds were the best type of sample preparation, with 95% of correct predictions for high vigor seeds and 100% of low vigor (both for seeds with lint) and with 100% correct predictions for high vigor seeds and 91.7% low vigor (without lint).

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Predicting rapeseed oil content with near-infrared spectroscopy

Cited by 7 publications

References 12 publications

Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy

Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy

Predicting oil content of Australian beauty leaf tree kernel samples using near infrared spectroscopy combined with chemometrics

Use of near infrared spectroscopy in cotton seeds physiological quality evaluation

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